Hospital Quality Data: HHS Should Specify Steps and Time Frame	 
for Using Information Technology to Collect and Submit Data	 
(25-APR-07, GAO-07-320).					 
                                                                 
Hospitals submit data in electronic form on a series of quality  
measures to the Centers for Medicare & Medicaid Services (CMS)	 
and receive scores on their performance. Increasingly, the	 
clinical information from which hospitals derive the quality data
for CMS is stored in information technology (IT) systems. GAO was
asked to examine (1) hospital processes to collect and submit	 
quality data, (2) the extent to which IT facilitates hospitals'  
collection and submission of quality data, and (3) whether CMS	 
has taken steps to promote the use of IT systems to facilitate	 
the collection and submission of hospital quality data. GAO	 
addressed these issues by conducting case studies of eight	 
hospitals with varying levels of IT development and interviewing 
relevant officials at CMS and the Department of Health and Human 
Services (HHS). 						 
-------------------------Indexing Terms------------------------- 
REPORTNUM:   GAO-07-320 					        
    ACCNO:   A68725						        
  TITLE:     Hospital Quality Data: HHS Should Specify Steps and Time 
Frame for Using Information Technology to Collect and Submit Data
     DATE:   04/25/2007 
  SUBJECT:   Data collection					 
	     Data integrity					 
	     Data transmission					 
	     Electronic data processing 			 
	     Health resources utilization			 
	     Hospitals						 
	     Information management				 
	     Information technology				 
	     Medical records					 
	     Medical information systems			 
	     Health information architecture			 
	     Annual Payment Update Program			 

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GAO-07-320

   

     * [1]Results in Brief
     * [2]Background
     * [3]Hospitals Use Six Basic Steps to Collect and Submit Quality

          * [4]Hospitals Collect and Submit Quality Data by Completing Six
          * [5]Two Most Complex Steps Were Locating Relevant Clinical Infor
          * [6]Clinical Staff Abstract Quality Data at Most Hospitals
          * [7]Adding Quality Measures Required a Proportionate Increase in
          * [8]Hospitals Value and Use Quality Data

     * [9]Existing IT Systems Can Help Hospitals Gather Some Quality D

          * [10]Existing IT Systems Help Abstractors Obtain Information from
          * [11]Full Automation of Quality Data Collection Is Not Imminent

     * [12]CMS Sponsored Studies and Joined Broader HHS Initiatives to

          * [13]CMS Sponsored Studies Examining Use of IT Systems for Collec
          * [14]CMS Has Joined HHS's Efforts to Promote Greater Use of Healt

     * [15]Conclusions
     * [16]Recommendations for Executive Action
     * [17]Agency Comments and Our Evaluation
     * [18]Appendix I: Medicare Quality Measures Required for Full Annu
     * [19]Appendix II: Data Elements Used to Calculate Hospital Perfor
     * [20]Appendix III: Tables on Eight Case Study Hospitals
     * [21]Appendix IV: Scope and Methodology
     * [22]Appendix V: Comments from the Centers for Medicare & Medicai
     * [23]Appendix VI: GAO Contact and Staff Acknowledgments

          * [24]GAO Contact
          * [25]Acknowledgments

               * [26]Order by Mail or Phone

Report to the Committee on Finance, U.S. Senate

United States Government Accountability Office

GAO

April 2007

HOSPITAL QUALITY DATA

HHS Should Specify Steps and Time Frame for Using Information Technology
to Collect and Submit Data

GAO-07-320

Contents

Letter 1

Results in Brief 5
Background 6
Hospitals Use Six Basic Steps to Collect and Submit Quality Data, Two of
Which Involve Complex Abstraction by Hospital Staff 9
Existing IT Systems Can Help Hospitals Gather Some Quality Data but Are
Far from Enabling Automated Abstraction 22
CMS Sponsored Studies and Joined Broader HHS Initiatives to Promote Use of
IT for Quality Data Collection and Submission, but HHS Lacks Detailed
Plans, Milestones, and Time Frame 27
Conclusions 31
Recommendations for Executive Action 32
Agency Comments and Our Evaluation 32
Appendix I Medicare Quality Measures Required for Full Annual Payment
Update 35
Appendix II Data Elements Used to Calculate Hospital Performance on a
Heart Attack Quality Measure 36
Appendix III Tables on Eight Case Study Hospitals 38
Appendix IV Scope and Methodology 45
Appendix V Comments from the Centers for Medicare & Medicaid Services 50
Appendix VI GAO Contact and Staff Acknowledgments 53

Tables

Table 1: Case Study Hospital Characteristics 38
Table 2: How Case Study Hospital Officials Described the Steps Taken to
Complete Quality Data Collection and Submission 40
Table 3: Resources Used for Abstraction and Data Submission at Eight Case
Study Hospitals 42
Table 4: Electronic and Paper Records at Eight Case Study Hospitals 44

Figures

Figure 1: Six Basic Steps for Hospitals Collecting and Submitting Quality
Data 10
Figure 2: Example of the Process for Locating and Assessing Clinical
Information to Determine the Appropriate Value for One Data Element 18
Figure 3: Data Elements Used to Calculate Hospital Performance on the
Heart Attack Quality Measure That Asks Whether a Beta Blocker Was Given
When the Patient Arrived at the Hospital 36

Abbreviations

ACEI angiotensin-converting enzyme inhibitor
AHIC American Health Information Community
AHIMA American Health Information Management Association
AHRQ Agency for Healthcare Research and Quality Alliance
National Alliance for Health Information Technology
AMI acute myocardial infarction
APU Annual Payment Update
ARB angiotensin receptor blocker
CART CMS Abstraction & Reporting Tool
CCHIT Certification Commission for Health Information Technology
CHI Consolidated Healthcare Informatics CMS Centers for Medicare
  & Medicaid Services
CPOE computerized physician order entry
DICOM Digital Imaging Communications in Medicine
DRA Deficit Reduction Act of 2005
FTE full-time equivalent
H&P history and physical
HCAHPS Hospital Consumer Assessment of Healthcare Providers and
  Systems
HHS Department of Health and Human Services
IMSS Healthcare Information and Management Systems Society
HITSP Healthcare Information Technology Standards Panel
ICD-9 International Classification of Diseases, Ninth Revision
IFMC Iowa Foundation for Medical Care
IT information technology
JCAHO Joint Commission on Accreditation of Healthcare Organizations
LOINC Laboratory Logical Observation Identifier Name Codes
LPN licensed practical nurse
LVSD left ventricular systolic dysfunction
MAR medication administration record
MMA Medicare Prescription Drug, Improvement, and Modernization Act
MSA metropolitan statistical area
NCDCP National Council on Prescription Drug Programs
ONC Office of the National Coordinator for Health Information
  Technology
POS provider of services
QIO quality improvement organization
RN registered nurse
SNOMED-CT Systematized Nomenclature of Medicine Clinical Terms

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United States Government Accountability Office
Washington, DC 20548

April 25, 2007

The Honorable Max Baucus
Chairman
The Honorable Charles E. Grassley
Ranking Minority Member
Committee on Finance
United States Senate

The Medicare Prescription Drug, Improvement, and Modernization Act of 2003
(MMA) created a financial incentive for hospitals to submit to the Centers
for Medicare & Medicaid Services (CMS) data that are used to calculate
hospital performance on measures of the quality of care provided.^1 CMS
established the Annual Payment Update (APU) program^2 to implement that
incentive. The APU program requires that participating hospitals submit
these quality data^3 on a quarterly basis in order to avoid a reduction in
their full Medicare payment update each fiscal year.^4 Although the APU
program was originally set to expire in 2007, the Deficit Reduction Act of
2005^5 (DRA) made the APU program permanent. The act also raised the
reduction^6 and required the Secretary of Health and Human Services (HHS)
to increase the number of measures for which hospitals participating in
the APU program would have to provide data in order to receive their full
Medicare payment update.^7 CMS plans to continue expanding the number of
required measures in future years.^8 Furthermore, DRA directed the
Secretary to develop a plan to implement a value-based purchasing program
for Medicare that beginning in fiscal year 2009 would adjust payments to
hospitals based on factors related to the quality of care they provide.
Such pay-for-performance programs are intended to strengthen the financial
incentives for hospitals to invest in quality improvement efforts.

^1See Pub. L. No. 108-173, S 501(b), 117 Stat. 2066, 2289-90.

^2Throughout this report, we refer to CMS's Reporting Hospital Quality
Data for the Annual Payment Update program as the "APU program."

^3Throughout this report, we refer to the data that hospitals submit to
CMS that the agency uses to calculate their performance on its quality
measures as "quality data."

^4Most acute care hospitals (i.e., those paid under the Medicare inpatient
prospective payment system) receive an annual payment update that
increases the standardized payment amount that Medicare pays them per
patient, based on projected increases in hospital operating expenses. For
fiscal year 2007, 3,319 hospitals received their full payment update,
about 95 percent of those eligible to participate in the APU program, and
the remaining 5 percent of eligible hospitals received a reduced annual
payment update. CMS posts on a public Web site the performance scores that
hospitals receive on quality measures derived from the data they submit.

^5See Pub. L. No. 109-171, S 5001(a), 120 Stat. 4, 28-29.

^6The magnitude of the reduction in the annual payment update for
hospitals not submitting the quality data rose from 0.4 percentage points
to 2 percentage points, starting in fiscal year 2007.

Each quality measure consists of a set of standardized data elements,
which define the specific data that hospitals need to submit to CMS.
Hospitals determine a value for each data element of a measure for
patients--Medicare and non-Medicare--who have a medical condition covered
by the APU program, that is, heart attack, heart failure, pneumonia, or
surgery. The values for the data elements consist of numerical data and
other administrative and clinical information that are obtained from the
medical records of the patients.^9 For example, there are 8 required
quality measures for the heart attack condition, one of which is whether a
beta blocker was given to the patient upon arrival at the hospital.^10
This single measure, in turn, consists of 11 data elements, including
administrative data elements, such as the patient's date of arrival at the
hospital, and clinical data elements, such as whether the patient received
a beta blocker within 24 hours after hospital arrival (see app. II). The
values entered for data elements are used to calculate hospital
performance on the 21 quality measures that are in effect as of fiscal
year 2007. For a hospital submitting data on all 21 measures, CMS receives
values for a total of 73 unique data elements. For heart attack measures
alone, the 8 measures utilize 35 of the 73 data elements. (Some data
elements are used in more than 1 measure. See app. I for the number of
data elements required for each measure.) Hospitals submit their quality
data electronically, over the Internet, to a clinical data warehouse
operated by a CMS contractor.

^7Initially, CMS designated 10 required quality measures under the APU
program that applied to patients treated for heart attacks, heart failure,
or pneumonia. In accordance with DRA, the Secretary increased the number
of required quality measures to 21. Nine of the new measures related to
the original three conditions, and 2 related to surgery, a new condition
for the program. (See app. I for the list of measures.)

^8CMS recently announced the addition of three surgery measures for fiscal
year 2008. In addition, to receive their full annual update payment,
hospitals will have to submit to CMS the responses provided by a random
sample of their discharged patients on a specified survey instrument--the
Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS)
survey--which is designed to obtain patient assessments of the care they
received. See 71 Fed. Reg. 67960, 68201-10 (Nov. 24, 2006).

^9Although the term value is often associated with numerical data, we use
it in this report to identify the information that hospitals submit to CMS
for a given data element. Some data elements call for numerical values,
and others call for nonnumerical values, such as Y or N for "yes" or "no."

^10Beta blockers are medications that decrease the rate and force of heart
contractions, which over time improves the heart's pumping ability.

Increasingly, the information in patients' medical records that provides
the basis for hospital quality data submissions may be stored and accessed
in electronic form in information technology (IT) systems. Currently, many
hospitals record and store such clinical information on patients in a
combination of paper and electronic systems. Over time, hospitals have
added new health IT systems to expand the amount of information that is
stored electronically. In 2005, the Secretary of HHS established the
American Health Information Community (AHIC) to advance the adoption of
electronic health records, after the President called in 2004 for the
widespread adoption of interoperable electronic health records within 10
years and appointed a National Coordinator for Health Information
Technology to promote that goal. On August 12, 2005, CMS issued a
regulation for the APU program that stated a goal of facilitating the use
of health IT by hospitals to make it easier for them to collect the
quality data from the medical record and submit them to CMS.^11 In the
preamble to the regulation, CMS said that it intended to begin working
toward modifying its requirements and mechanisms for accepting quality
data to allow hospitals to transfer their data directly from hospital IT
systems without having to first transfer the data into specially formatted
files as is currently required.

Because the vast majority of acute care hospitals treating Medicare
patients choose to submit quality data each quarter to CMS, rather than
accept a reduced annual payment update, you asked us to examine (1) how
hospitals collect and submit quality data for the Medicare hospital
quality measures, (2) the extent to which IT facilitates hospitals'
collection and submission of quality data for the Medicare hospital
quality measures, and (3) whether CMS has taken steps to promote the
development and use of IT systems that could facilitate the collection and
submission of hospital quality data.

^1170 Fed. Reg. 47278, 47420 (Aug. 12, 2005).

To assess how hospitals collect and submit quality data, we conducted case
studies of eight individual acute care hospitals to obtain information
about the processes they used to collect and submit the data.^12 The
hospitals varied on a number of standard hospital characteristics,
including size, urban/rural location, and teaching status (see app. III,
table 1). We visited each case study hospital, and we interviewed the
individuals responsible for collecting and submitting the quality data to
CMS, managers of the hospital's quality department, and hospital
administrators. To assess the extent to which IT facilitates hospitals'
collection and submission of quality data, we selected the case study
hospitals to include both hospitals with relatively well-developed IT
systems that supported electronic patient records and hospitals with
less-developed levels of IT, based on screening interviews done at the
time we selected the case study hospitals.^13 During our site visits, we
also interviewed IT staff involved in the process of collecting and
submitting the quality data. To assess whether CMS has taken steps to
promote the development and use of IT systems that could facilitate the
collection and submission of hospital quality data, we reviewed relevant
federal regulations, reports, and related documents and interviewed CMS
officials and CMS contractors, as well as officials in HHS's Office of the
National Coordinator for Health Information Technology (ONC). Because our
evidence is limited to the eight case studies, it does not offer a basis
for relating any differences we observed among these particular hospitals
to their differences on specific dimensions, such as size or teaching
status. Nor can we generalize from the group of eight as a whole to acute
care hospitals across the country. Where appropriate, we obtained relevant
information about these hospitals from CMS documents and databases;
however, most of our information for these case studies was reported by
hospital officials.^14 Furthermore, although we examined the processes
hospitals used to collect and submit quality data and the role that IT
plays in that process, we did not examine general IT adoption in the
hospital industry. We conducted our work from February 2006 to April 2007
in accordance with generally accepted government auditing standards. For a
complete description of our methodology, see appendix IV.

^12All eight hospitals participated in the APU program and had their
performance scores on the quality measures posted on CMS's Hospital
Compare Web site, www.hospitalcompare.hhs.gov.

^13Available research suggested that only a handful of hospitals had
developed a high level of IT implementation for purposes of documenting
patient care in electronic records, with the large majority having reached
just the initial stages of this process. See D. Garets and M. Davis,
"Electronic Medical Records vs. Electronic Health Records: Yes, There Is a
Difference" (Chicago, Ill.: HIMSS Analytics, LLC, updated Jan. 26, 2006).

^14Information obtained from CMS sources included the projected amount of
Medicare payments that the hospitals would lose if they had not submitted
quality data under the APU program and the number of patients for whom
they submitted data to CMS. See app. IV.

Results in Brief

The case study hospitals we visited used six steps to collect and submit
quality data, two of which (steps 2 and 3) involved complex
abstraction--the process of reviewing and assessing all relevant pieces of
information in a patient's medical record to determine the appropriate
value for each data element. Whether that patient information was recorded
electronically, on paper, or as a mix of both, the six steps were (1)
identify the patients, (2) locate information in their medical records,
(3) determine appropriate values for the data elements, (4) transmit the
quality data to CMS, (5) ensure that the quality data have been accepted
by CMS, and (6) supply copies of selected medical records to CMS to
validate the data. Several factors account for the complexity of the
abstraction process (steps 2 and 3), including the content and
organization of the medical record, the scope of information and clinical
judgment required for the data elements, and frequent changes by CMS in
its data specifications. Due in part to these complexities, most of our
case study hospitals relied on clinical staff to abstract the quality
data. Increases in the number of quality measures required by CMS led to
increased demands on clinical staff resources. Offsetting the demands
placed on clinical staff were the benefits that case study hospitals
reported finding in the quality data. For example, all the hospitals
reported having a process in place to track changes in their performance
over time and provide feedback to clinicians and reports to hospital
administrators and trustees.

Our case studies showed that existing IT systems can help hospitals gather
some quality data but are far from enabling hospitals to automate the
abstraction process. IT systems helped hospital staff abstract information
from patients' medical records, in particular by improving accessibility
to and legibility of the medical record and by enabling hospitals to
incorporate CMS's required data elements into the medical record. The
limitations reported by officials in the case study hospitals included
having a mix of paper and electronic records, which required staff to
check multiple places to get the needed information; the prevalence of
data recorded as unstructured narrative or text, which made locating the
information time-consuming because it was not in a prescribed place in the
record; and the inability of some IT systems to access related data stored
in another IT system in the same hospital, which required hospital staff
to access each IT system separately to obtain related pieces of
information. While hospital officials expected the scope and functionality
of their IT systems to increase over time, they projected that this
process would occur incrementally over a period of years.

CMS has sponsored studies and joined HHS initiatives to examine and
promote the current and potential use of hospital IT systems to facilitate
the collection and submission of quality data, but HHS lacks detailed
plans, including milestones and a time frame against which to track its
progress. CMS sponsored two studies that examined the use of hospital IT
systems for quality data collection and submission. Promoting the use of
health IT for quality data collection is also 1 of 14 objectives that HHS
has identified in its broader effort to encourage the development and
nationwide implementation of interoperable IT in health care. CMS has
joined this broader effort by HHS, as well as the Quality Workgroup that
AHIC created in August 2006 to specify how IT could capture, aggregate,
and report inpatient and outpatient quality data. Through its
representation in AHIC and the Quality Workgroup, CMS has participated in
decisions about the specific focus areas to be examined through contracts
with nongovernmental entities. These contracts currently address the use
of health IT for a range of purposes, which may also include quality data
collection and submission in the near future. However, HHS has identified
no detailed plans, milestones, or time frames for either its broad effort
to encourage IT in health care nationwide or its specific objective to
promote the use of health IT for quality data collection.

To support the expansion of quality measures for the APU program, we
recommend that the Secretary of HHS identify the specific steps that the
department plans to take to promote the use of health IT for the
collection and submission of data for CMS's hospital quality measures and
inform interested parties on those steps and the expected time frame,
including milestones for completing them. In commenting on a draft of this
report on behalf of HHS, CMS expressed its appreciation of our thorough
analysis of the processes that hospitals use to report quality data and
the role that IT systems can play in that reporting, and it concurred with
our two recommendations.

Background

The quality data submitted by hospitals are collected from the medical
records of patients admitted to the hospital. Hospital patient medical
records contain many different types of information, which are organized
into different sections. Frequently found examples of these sections
include

           o the face sheet, which summarizes basic demographic and billing
           data, including diagnostic codes;

           o history and physicals (H&P), which record both patient medical
           history and physician assessments;

           o physician orders, which show what medications, tests, and
           procedures were ordered by a physician;

           o medication administration records (MAR), which show that a
           specific medication was given to a patient, when it was given, and
           the dosage;

           o laboratory reports, radiology reports, and test results, such as
           an echocardiogram reading;

           o progress notes, in which physicians, nurses, and other
           clinicians record information chronologically on patient status
           and response to treatments during the patient's hospital stay;

           o operative reports for surgery patients;

           o physician and nursing notes for patients treated in the
           emergency department; and

           o discharge summaries, in which a physician summarizes the
           patient's hospital stay and records prescriptions and instructions
           to be given to the patient at discharge.

           Hospitals have discretion to determine the structure of their
           patient medical records, as well as to set general policies
           stating what, where, and how specific information should be
           recorded by clinicians. To guide the hospital staff in the
           abstraction process--that is, in finding and properly assessing
           the information in the patient's medical record needed to fill in
           the values for the data elements--CMS and the Joint Commission^15
           have jointly issued a Specifications Manual.^16 It contains
           detailed specifications that define the data elements for which
           the hospital staff need to collect information and determine
           values and the correct interpretation of those data elements. The
           Joint Commission also requires hospitals to submit the same data
           that they submit to CMS for the APU program (and some additional
           data) to receive Joint Commission accreditation.

           In many hospitals, information in a patient's medical record is
           recorded and stored in a combination of paper and electronic
           systems. Patient medical records that clinicians record on paper
           may be stored in a folder in the hospital's medical record
           department and contain all the different forms, reports, and notes
           prepared by different individuals or by different departments
           during the patient's stay. Depending on the length of the
           patient's hospital stay and the complexity of the care, an
           individual patient medical record can amount to hundreds of
           pages.^17 For information stored electronically, clinicians may
           enter information directly into the electronic record themselves,
           as they do for paper records, or they may dictate their notes to
           be transcribed and added to the electronic record later.
           Information may also be recorded on paper and then scanned into
           the patient's electronic record. For example, if a patient is
           transferred from another hospital, the paper documents from the
           transferring hospital may be scanned into the patient's electronic
           record.

           The patient medical information that hospitals store
           electronically, rather than on paper, typically resides in
           multiple health IT systems. One set of IT systems usually handles
           administrative tasks such as patient registration and billing.
           Hospitals acquire other IT systems to record laboratory test
           results, to store digital radiological images, to process
           physician orders for medications, and to record notes written by
           physicians and nurses. Hospitals frequently build their health IT
           capabilities incrementally by adding new health IT systems over
           time.^18 If the systems that hospitals purchase come from
           different companies, they are likely to be based on varying
           standards for how the information is stored and exchanged
           electronically. As a result, even in a single hospital, it can be
           difficult to access from one IT system clinical data stored in a
           different health IT system.

           One of the main objectives of ONC is to overcome the problem of
           multiple health IT systems, within and across health care
           providers, that store and exchange information according to
           varying standards. The mission of ONC is to promote the
           development and nationwide implementation of interoperable health
           IT in both the public and the private sectors in order to reduce
           medical errors, improve quality of care, and enhance the
           efficiency of health care.^19 Health IT is interoperable when
           systems are able to exchange data accurately, effectively,
           securely, and consistently with different IT systems, software
           applications, and networks in such a way that the clinical or
           operational purposes and meaning of the data are preserved and
           unaltered.
		   
		   Hospitals Use Six Basic Steps to Collect and Submit Quality Data,
		   Two of Which Involve Complex Abstraction by Hospital Staff

           The case study hospitals we visited used six steps to collect and
           submit quality data, two of which involved complex
           abstraction--the process of reviewing and assessing all relevant
           pieces of information in a patient's medical record to determine
           the appropriate value for each data element. Factors accounting
           for the complexity of the abstraction process included the content
           and organization of the medical record, the scope of information
           required for the data elements, and frequent changes by CMS in its
           data specifications. Due in part to these complexities, most of
           our case study hospitals relied on clinical staff to abstract the
           quality data. Increases in the number of required quality measures
           led to increased demands on clinical staff resources. However, all
           case study hospitals reported finding benefits in the quality data
           that helped to offset the demands placed on clinical staff.
		   
		   Hospitals Collect and Submit Quality Data by Completing Six Basic
		   Steps

           We found that whether patient information was recorded
           electronically, on paper, or as a mix of both, all the case study
           hospitals collected and submitted their quality data by carrying
           out six sequential steps (see fig. 1). These steps started with
           identifying the patients for whom the hospitals needed to provide
           quality data to CMS and continued through the process of examining
           each patient's medical record, one after the other, to find the
           information needed to determine the appropriate values for each of
           the required data elements for that patient. Then, for each
           patient, those values were entered by computer into an electronic
           form or template listing each of the data elements for that
           condition. These forms were provided by the data vendor with which
           the hospital had contracted to transmit its quality data to CMS.
           The vendors also assisted the hospitals in checking that the data
           were successfully received by CMS. Finally, the hospitals sent
           copies of the medical records of a selected sample of patients to
           a CMS contractor that used those records to validate the accuracy
           of the quality data submitted by the hospital.

           Figure 1: Six Basic Steps for Hospitals Collecting and Submitting
           Quality Data

           Note: Patient information may be obtained from either electronic
           or paper records.

           Specifically, the six steps, which are summarized for each case
           study hospital in appendix III, table 2, were as follows:

           Step 1: Identify patients--The first step was to identify the
           patients for whom the hospitals needed to submit quality data to
           CMS. Staff at three case study hospitals identified these patients
           using information on the patient's principal diagnosis, or
           principal procedure in the case of surgery patients, obtained from
           the hospital's billing data.^20 Five case study hospitals had
           their data vendor use the hospital's billing data to identify the
           eligible patients for them. Every month, all eight hospitals that
           we visited identified patients discharged in the prior month for
           whom quality data should be collected. The hospitals identified
           all patients retrospectively for quality data collection because
           hospitals have to wait until a patient is discharged to determine
           the principal diagnosis.^21

           CMS permits hospitals to reduce their data collection effort by
           providing quality data for a representative sample of patients
           when the total number of patients treated for a particular
           condition exceeds a certain threshold.^22 Five case study
           hospitals drew samples for at least one condition. The data vendor
           performed this task for four of those case study hospitals, and
           assisted the hospital in performing this task for the fifth
           hospital.

           Only one of the case study hospitals reported using nonbilling
           data sources to check the accuracy of the lists of patients
           selected for quality data collection that the hospitals drew from
           their billing data (see app. III, table 3). Several stated that
           they occasionally noted discrepancies, such as patients selected
           for heart attack measures who, upon review of their medical
           record, should not have had that as their principal diagnosis.
           However, the hospital officials we interviewed told us that
           discrepancies of this sort were likely to be minor. Officials at
           three hospitals noted that hospitals generally have periodic
           routine audits conducted of the coding practices of their medical
           records departments, which would include the accuracy of the
           principal diagnoses and procedures.

           Step 2: Locate information in the medical record--Steps 2 and 3
           were in practice closely linked in our case study hospitals.
           Abstractors^23 at the eight case study hospitals examined each
           selected patient's medical record, looking for all of the discrete
           pieces of information that, taken together, would determine what
           they would decide--in step 3--was the correct value for each of
           the data elements. For some data elements, there was a one-to-one
           correspondence between the piece of information in the medical
           record and the value to be entered. Typical examples included a
           patient's date of birth and the name of a medication administered
           to the patient. For other data elements, the abstractors had to
           check for the presence or absence of multiple pieces of
           information in different parts of the medical record to determine
           the correct value for that data element. For example, to determine
           if the patient did, or did not, have a contraindication for
           aspirin, abstractors looked in different parts of the medical
           record for potential contraindications, such as the presence of
           internal bleeding, allergies, or prescriptions for certain other
           medications such as Coumadin.^24

           In order for abstractors to find information in the patient's
           medical record, it had to be recorded properly by the clinicians
           providing the patient's care. Officials at all eight case study
           hospitals described efforts designed to educate physicians and
           nurses about the specific data elements for which they needed to
           provide information in each patient's medical record. The hospital
           officials were particularly concerned that the clinicians not
           undermine the hospital's performance on the quality measures by
           inadequately documenting what they had done and the reasons why.
           For example, one heart failure measure tracks whether a patient
           received each of six specific instructions at the time of
           discharge, but unless information was explicitly recorded in a
           heart failure patient's medical record for each of the six data
           elements, that patient was counted by CMS as one who had not
           received all pertinent discharge instructions and therefore did
           not meet that quality measure.^25 This particular measure was
           cited by officials at several hospitals as one that required a
           higher level of documentation than had previously been the norm at
           their hospital.

           Step 3: Determine appropriate data element values--Once
           abstractors had located all the relevant pieces of information
           pertaining to a given data element, they had to put those pieces
           together to arrive at the appropriate value for the data element.
           The relevance of that information was defined by the detailed
           instructions provided by the hospitals' vendors, as well as the
           Specifications Manual jointly issued by CMS and the Joint
           Commission that serves as the basis for the vendor instructions.
           The Specifications Manual sets out the decision rules for choosing
           among the allowable values for each data element. It also
           identifies which parts of the patient's medical record may or may
           not provide the required information, and often lists specific
           terms or descriptions that, if recorded in the patient's medical
           record, would indicate the appropriate value for a given data
           element. In addition, the Specifications Manual provides
           abstractors with guidance on how to interpret conflicting
           information in the medical record, such as a note from one
           clinician that the patient is not a smoker and a note elsewhere in
           the record from another clinician that the patient does smoke. To
           help keep track of multiple pieces of information, many
           abstractors reported that they first filled in the data element
           values on a paper copy of the abstraction form provided by the
           data vendor. In this way, they could write notes in the margin to
           document how they came to their conclusions.

           Step 4: Transmit data to CMS--In order for the quality data to be
           accepted by the clinical data warehouse, they must pass a battery
           of edit checks that look for missing, invalid, or improperly
           formatted data element entries.^26 All the case study hospitals
           contracted with data vendors to submit their quality data to CMS.
           They did so, in part, because all of the hospitals submitted the
           same data to the Joint Commission, and it requires hospitals to
           submit their quality data through data vendors that meet the Joint
           Commission's requirements. The additional cost to the hospitals to
           have the data vendors also submit their quality data to CMS was
           generally minimal (see app. III, table 3).

           All of the case study hospitals submitted their data to the data
           vendor by filling in values for the required data elements on an
           electronic version of the vendor's abstraction form.^27 Many
           abstractors did this for a batch of patient records at a time,
           working from paper copies of the form that they had filled in
           previously. Some abstractors entered the data online at the same
           time that they reviewed the patient's medical records. In other
           cases, someone other than the abstractor who filled in the paper
           form used the completed form to enter the data on a computer.

           Step 5: Ensure data have been accepted by CMS--The case study
           hospitals varied in the extent to which they actively monitored
           the acceptance of their quality data into CMS's clinical data
           warehouse. After the data vendors submitted the quality data
           electronically, they and the hospitals could download reports from
           the clinical data warehouse indicating whether the submitted data
           had passed the screening edits for proper formatting and valid
           entries. The hospitals could use these reports to detect data
           entry errors and make corrections prior to CMS's data submission
           deadline. Three case study hospitals shared this task with their
           data vendors, three hospitals left it for their data vendors to
           handle, and two hospitals received and responded to reports on
           data edit checks produced by their data vendors, rather than
           reviewing the CMS reports. Approximately 2 months after hospitals
           submitted their quality data, CMS released reports to the
           hospitals showing their performance scores on the quality measures
           before posting the results on its public Web site.

           Step 6: Supply copies of selected medical records--CMS has put in
           place a data validation process to ensure the accuracy of hospital
           quality data submissions. It requires hospitals to supply a CMS
           contractor with paper copies of the complete medical record for
           five patients selected by CMS each quarter.^28 Officials at five
           hospitals noted that they check to make sure that all parts of the
           medical records that they used to abstract the data originally are
           included in the package shipped to the CMS contractor. Most of the
           case study hospitals relied on CMS's data validation to ensure the
           accuracy of their abstractions. However, two hospitals reported
           that they also routinely draw their own sample of cases, which are
           abstracted a second time by a different abstractor in the
           hospital, followed by a comparison of the two sets of results (see
           app. III, table 3).
		   
		   Two Most Complex Steps Were Locating Relevant Clinical Information
		   and Determining Appropriate Values for Data Elements

           The description by hospital officials of the processes they used
           to collect and submit quality data indicated that locating the
           relevant clinical information and determining appropriate values
           for the data elements (steps 2 and 3) were the most complex steps
           of the six identified, due to several factors. These included the
           content and organization of the medical record, the scope of the
           information encompassed by the data elements, and frequent changes
           in data specifications.

           The first complicating factor related to the medical record was
           that the information abstractors needed to determine the correct
           data element values for a given patient was generally located in
           many different sections of the patient's medical record. These
           included documents completed for admission to the hospital,
           emergency department documents, laboratory and test results,
           operating room notes, medication administration records, nursing
           notes, and physician-generated documents such as history and
           physicals, progress notes and consults, orders for medications and
           tests, and discharge summaries. In addition, the abstractors may
           have had to look at documents that came from other providers if
           the patient was transferred to the hospital. Much of the clinical
           information needed was found in the sections of the medical record
           prepared by clinicians. Often the information in question, such as
           contraindications for aspirin or beta blockers, could be found in
           any of a number of places in the medical record where clinicians
           made entries. As a result, abstractors frequently had to read
           through multiple parts of the record to find the information
           needed to determine the correct value for just one data element.
           At two case study hospitals, abstractors said that they routinely
           read each patient's entire medical record.

           Experienced abstractors often knew where they were most likely to
           find particular pieces of information. They nevertheless also had
           to check for potentially contradictory information in different
           parts of the medical record. For example, as noted, patients may
           have provided varying responses about their smoking history to
           different clinicians. If any of these responses indicated that the
           patient had smoked cigarettes in the last 12 months, the patient
           was considered to be a smoker according to CMS's data
           specifications. Another example concerns the possibility that a
           heart attack or heart failure patient may have had multiple
           echocardiogram results recorded in different parts of the medical
           record. Abstractors needed to find all such results in order to
           apply the rules stated in the Specifications Manual for
           identifying which result to use in deciding whether the patient
           had left ventricular systolic dysfunction (LVSD). This data
           element is used for the quality measure assessing whether an
           angiotensin-converting enzyme inhibitor (ACEI) or angiotensin
           receptor blocker (ARB) was prescribed for LVSD at discharge.^29

           The second factor was related to the scope of the information
           required for certain data elements. Some of the data elements that
           the abstractors had to fill in represented a composite of related
           data and clinical judgment applied by the abstractor, not just a
           single discrete piece of information. Such composite data elements
           typically were governed by complicated rules for determining the
           clinical appropriateness of a specific treatment for a given
           patient. For example, the data element for contraindications for
           both ACEIs and ARBs at discharge requires abstractors to check for
           the presence and assess the severity of any of a range of clinical
           conditions that would make the use of either ACEIs or ARBs
           inappropriate for that patient.^30 (See fig. 2.) These conditions
           may appear at any time during the patient's hospital stay and so
           could appear at any of several places in the medical record.
           Abstractors must also look for evidence in the record from a
           physician^31 linking a decision not to prescribe these drugs to
           one or more of those conditions.

^15The Joint Commission (previously the Joint Commission on Accreditation
of Healthcare Organizations or JCAHO) is a private, not-for-profit
organization that accredits approximately 82 percent of hospitals that
participate in Medicare.

^16Current and past versions of the Specifications Manual for National
Hospital Quality Measures are available at www.qualitynet.org.

^17Generally, the records for multiple hospital admissions for the same
patient are stored together, creating an even more voluminous collection
of documents in a single patient record.

^18For example, many hospitals have IT systems to record laboratory test
results electronically, but fewer have added IT systems to record
radiology results electronically. D. Blumenthal et al., Health Information
Technology in the United States: The Information Base for Progress
(Princeton, N.J.: Robert Wood Johnson Foundation, 2006), 3:26.

^19Exec. Order No. 13335, 69 Fed. Reg. 24059 (Apr. 27, 2004).

^20The CMS data specifications list the International Classification of
Diseases, Ninth Revision (ICD-9) diagnostic codes that make a patient
eligible for quality data collection. The other factor determining basic
patient eligibility is age at the time of admission, derived from the
patient's admission date and date of birth, also available from billing
data. For pneumonia patients, secondary diagnoses may also affect
eligibility.

^21Medicare bases its payments for inpatient care on principal diagnosis,
which it defines as the condition established after study to be chiefly
responsible for the admission. Principal diagnosis may also affect
determination of the principal procedure, if several procedures were
performed.

^22CMS's specific sampling requirements vary by medical condition. For
example, hospitals that have more than 78 heart attack patients in a given
quarter can submit quality data for a random sample of those patients, as
long as their sample includes a minimum of 78 patients and applies a
sampling rate of 20 percent up to a maximum required sample of 311.

^23Throughout this report, we use the term abstractor to indicate hospital
staff who are trained to follow a detailed protocol in order to extract
specified information in a consistent fashion from the medical records of
multiple patients.

^24Coumadin is a medication that acts as an anticoagulant. It is used to
prevent and treat harmful blood clots that increase the risk of heart
attack and stroke.

^25These discharge instructions are supposed to cover recommended level of
activity, diet, follow-up care after discharge, medications, weight
monitoring, and what to do if symptoms worsen. The abstractor fills in a
value for six separate data elements, one for each of the six specific
instructions. The value is either a yes, written discharge instructions
addressing the specified activity were provided, or no, instructions
addressing the specified activity were not provided or unable to determine
from medical record documentation. Anything less than a yes on all six
data elements leads to a negative score on this quality measure for that
patient.

^26This process is described in GAO, Hospital Quality Data: CMS Needs More
Rigorous Methods to Ensure Reliability of Publicly Released Data,
[27]GAO-06-54 (Washington, D.C.: Jan. 31, 2006), 14.

^27Abstractors at many hospitals entered the data through an online
connection to the vendor. Other hospitals submitted their quality data in
the form of electronic files.

^28CMS draws a sample of five patient records from among those submitted
by each hospital that provided data on six or more patients to the
clinical data warehouse in a given quarter. CMS then tells each hospital
which patients need to have their records copied, or printed out in the
case of electronic records, and shipped to its contractor. The contractor
then abstracts values for clinical data elements from those records,
following the CMS/Joint Commission Specifications Manual, and the results
are compared--data element by data element--with the values originally
abstracted and submitted by the hospital. If hospitals do not achieve a
match of at least 80 percent of their data element values with those of
the CMS contractor, following the outcome of any appeals, the hospitals
will not receive a full payment update from Medicare for the subsequent
fiscal year. See [28]GAO-06-54 , 14-16.

^29ACEIs and ARBs are two classes of drugs that have been shown in
clinical trials to reduce mortality and morbidity in patients with LVSD.

^30ACEIs or ARBs may be contraindicated if the patient is known to be
allergic to such drugs or suffers from certain medical conditions, such as
moderate to severe aortic stenosis or renal disease.

^31A nurse practitioner or physician assistant may also provide this
documentation in the patient's medical record.

Figure 2: Example of the Process for Locating and Assessing Clinical
Information to Determine the Appropriate Value for One Data Element

Note: In this illustrative case, adapted from CMS training materials, an
abstractor would find that the patient was given an ACEI, Zestril, in the
emergency department (see MAR,1/30), but because of its apparent effect on
the patient's pulse and blood pressure (see Progress Notes, 01/31), it was
not continued during the hospital stay (see Progress Notes, 02/03) and no
ACEI was prescribed at discharge (see Discharge Summary). However, there
is no mention in the patient's record of ARBs or aortic stenosis. The
arrows point to some of the key pieces of information an abstractor would
take note of in determining that the appropriate value for this data
element was "N" for "no."

The third factor is the necessity abstractors at the case study hospitals
faced to adjust to frequent changes in the data specifications set by CMS.
Since CMS first released its detailed data specifications jointly with the
Joint Commission in September 2004, it has issued seven new versions of
the Specifications Manual.^32 Therefore, from fall 2004 through summer
2006, roughly every 3 months hospital abstractors have had to stop and
take note of what had changed in the data specifications and revamp their
quality data collection procedures accordingly. Some of these changes
reflected modifications in the quality measures themselves, such as the
addition of ARBs for treatment of LVSD. Other changes revised or expanded
the guidance provided to abstractors, often in response to questions
submitted by hospitals to CMS. CMS recently changed its schedule for
issuing revisions to its data specifications from every 3 months to every
6 months, but that change had not yet affected the interval between new
revisions issued to hospitals at the time of our case study site visits.

Clinical Staff Abstract Quality Data at Most Hospitals

Case study hospitals typically used registered nurses (RN), often
exclusively, to abstract quality data for the CMS quality measures (see
app. III, table 3). One hospital relied on a highly experienced licensed
practical nurse, and two case study hospitals used a mix of RNs and
nonclinical staff. Officials at one hospital noted that RNs were familiar
with both the nomenclature and the structure of the hospital's medical
records and they could more readily interact with the physicians and
nurses providing the care about documentation issues. Even when using RNs,
all but three of the case study hospitals had each abstractor focus on one
or two medical conditions with which they had expertise.

Four hospitals had tried using nonclinical staff, most often trained as
medical record coders, to abstract the quality data. Officials at one of
these hospitals reported that this approach posed challenges. They said
that it was difficult for nonclinical staff to learn all that they needed
to know to abstract quality data effectively, especially with the constant
changes being made to the data specifications. At the second hospital,
officials reported that using nonclinical staff for abstraction did not
work at all and they switched to using clinically trained staff. At the
third hospital, the chief clinician leading the quality team stated that
the hospital's nonclinical abstractors worked well enough when clinically
trained colleagues were available to answer their questions. Officials at
the fourth hospital cited no concerns about using staff who were not RNs
to abstract quality data, but they subsequently hired an RN to abstract
patient records for two of the four conditions.

^32This tally of revisions only takes account of new versions of the
Specifications Manual. Recently, the release of new versions has been
followed by multiple addendums to the revision, weeks or months later, to
provide further modifications and clarifications.

Case study hospitals drew on a mix of existing and new staff resources to
handle the collection and submission of quality data to CMS. In two
hospitals, new staff had been hired specifically to collect quality data
for the Joint Commission and CMS. In other hospitals, quality data
collection was assigned to staff already employed in the hospital's
quality management department or performing other functions.

Adding Quality Measures Required a Proportionate Increase in Staff Resources

All the case study hospitals found that, over time, they had to increase
the amount of staff resources devoted to abstracting quality data for the
CMS quality measures, most notably as the number of measures on which they
were submitting data expanded. Officials at the case study hospitals
generally reported that the amount of staff time required for abstraction
increased proportionately with the number of conditions for which they
reported quality data. The hospitals had all begun to report most recently
on the surgical quality measures. They found that the staff hours needed
for this new set of quality measures were directly related to the number
of patient records to be abstracted and the number of data elements
collected. In other words, they found no "economies of scale" as they
expanded the scope of quality data abstraction. At the time of our site
visits, four hospitals continued to draw on existing staff resources,
while others had hired additional staff. Hospital officials estimated that
the amount of staff resources devoted to abstracting data for the CMS
quality measures ranged from 0.7 to 2.5 full-time equivalents (FTE) (app.
III, table 3).^33

Hospitals Value and Use Quality Data

Hospital officials reported that the demands that quality data collection
and submission placed on their clinical staff resources were offset by the
benefits that they derived from the resulting information on their
clinical performance. Each one had a process for tracking changes in their
performance over time. Based on those results, they provided feedback to
individual clinicians and reports to hospital administrators and trustees.
Because they perceived feedback to clinicians to be much more effective
when provided as soon as possible, several of the case study hospitals
found ways to calculate their performance on the quality measures
themselves, often on a monthly basis, rather than wait for CMS to report
their results for the quarter.

Officials at all eight case study hospitals pointed to specific changes
they had made in their internal procedures designed to improve their
performance on one or more quality measures. Most of the case study
hospitals developed "standing order sets" for particular diagnoses. Such
order sets provide a mechanism for standardizing both the care provided
and the documentation of that care, in such areas as prescribing beta
blockers and aspirin on arrival and at discharge for heart attack
patients. Another common example involved prompting physicians to
administer pneumococcal vaccinations to pneumonia patients. However, at
most of the case study hospitals, use of many standing order sets was
optional for physicians, and hospital officials reported widely varying
rates of physician use, from close to 100 percent of physicians at one
hospital using its order set for heart attack patients to just a few
physicians using any order sets in another hospital.

Case study hospitals also responded to the information generated from
their quality data by adjusting their treatment protocols, especially for
patients treated in their emergency departments. For example, five
hospitals developed or elaborated on procedural checklists for emergency
department nurses treating pneumonia patients. The objective of these
changes was to more quickly identify pneumonia patients when they arrived
at the emergency department and then expeditiously perform required blood
tests so that the patients would score positively for the quality measure
on receiving antibiotics within 4 hours of arrival at the hospital. Three
hospitals strengthened their procedures to identify smokers and make sure
that they received appropriate counseling.

^33These represent the FTEs devoted specifically to quality data
collection and submission. Hospital officials noted that additional FTEs
were involved in analyzing the hospital's performance on the quality
measures and achieving improvements through changes in clinical process
and educational efforts with the hospital's clinicians.

Hospital officials noted that they provided quality of care data to
entities other than CMS and the Joint Commission, such as state
governments and private insurers, but for the most part they reported that
the CMS quality measures had two advantages. First, the CMS quality
measures enabled hospitals to benchmark their performance against the
performances of virtually every other hospital in the country. Second,
officials at two hospitals noted that the CMS measures were based on
clinical information obtained from patient medical records and therefore
had greater validity as measures of quality of care than measures based
solely on administrative data.^34 Many hospital officials said that they
wished that state governments and other entities collecting quality data
would accept the CMS quality measures instead of requiring related quality
data based on different definitions and patient populations. Hospital
officials in two states reported some movement in that direction.

Existing IT Systems Can Help Hospitals Gather Some Quality Data but Are Far from
Enabling Automated Abstraction

In the case studies, existing IT systems helped hospital abstractors to
complete their work more quickly, but the limitations of those IT systems
meant that trained staff still had to examine the entire patient medical
record and manually abstract the quality data submitted to CMS. IT systems
helped abstractors obtain information from patients' medical records, in
particular by improving their accessibility and legibility, and by
enabling hospitals to incorporate CMS's required data elements into those
medical records. The challenges reported by hospital officials included
having a mix of paper and electronic records, which required abstractors
to check multiple places to get the needed information; the prevalence of
unstructured data, which made locating the information time-consuming
because it was not in a prescribed place in the record; and the presence
of multiple IT systems that did not share data, which required abstractors
to separately access each IT system for related pieces of information that
were in different parts of the medical record. While hospital officials
expected the scope and functionality of their IT systems to increase over
time, they projected that this would occur incrementally over a period of
years.^35

34For example, hospital officials identified several private insurers that
assess quality based on patient outcomes derived from administrative data,
such as hospital billing data.

Existing IT Systems Help Abstractors Obtain Information from Medical Records but
Have Notable Limitations

Hospitals found that their existing IT systems could facilitate the
collection of quality data, but that there were limits on the advantages
that the systems could provide. IT systems, and the electronic records
they support, offered hospitals two key benefits: (1) improving
accessibility to and legibility of the medical record, and (2)
facilitating the incorporation of CMS's required data elements into the
medical record.

Many hospital abstractors noted that existing electronic records helped
quality data collection by improving accessibility and legibility of
patient records. In general, paper records were less accessible than
electronic records because it took time to find them or to have them
transported if hospitals had stored them in a remote location after the
patients were discharged. Also, paper records were more likely to be
missing or in use by someone else. However, in one case study hospital, an
abstractor noted difficulties in gaining access to a computer terminal to
view electronic medical records. Many abstractors noted improvements in
legibility as a fundamental benefit of electronic records. This advantage
applied in particular to the many sections of the medical record that
consisted of handwritten text, including history and physicals, progress
notes, medication administration records, and discharge summaries.

Some hospitals have used their existing IT systems to facilitate the
abstraction of information by designing a number of discrete data fields
that match CMS's data elements. For example, two hospitals incorporated
prompts for pneumococcal vaccination in their electronic medication
ordering system. These prompts not only reminded physicians to order the
vaccination (if the patient was not already vaccinated) but also helped to
insure documentation of the patient's vaccination status. One hospital
developed a special electronic discharge program for heart attack and
heart failure patients that had data elements for the quality measures
built into it. Another hospital built a prompt into its electronically
generated discharge instructions to instruct patients to measure their
weight daily. This enabled the hospital to document more consistently one
of the specific instructions that heart failure patients are supposed to
receive on discharge but that physicians and nurses tended to overlook in
their documentation.

^35For example, one case study hospital began several years ago to use an
IT system to record nursing notes. Hospital officials told us that they
planned to initiate a pilot test of a new component of that IT system that
would provide computerized physician order entry (CPOE) beginning in
October 2006. The officials reported that they would assess their
experience with the pilot test in one hospital unit before deciding how
quickly to expand it to the rest of the hospital. They said that they were
planning ultimately to store all patient medical records in electronic
form but there was no fixed timeline for that objective. The timing would
depend, they said, on the success of their CPOE pilot test.

The limitations that hospital officials reported in using existing IT
systems to collect quality data stemmed from having a mix of paper and
electronic systems; the prevalence of data recorded in IT systems as
unstructured paragraphs of narrative or text, as opposed to discrete data
fields reserved for specific pieces of information; and the inability of
some IT systems to access related data stored on another IT system in the
same hospital. Because all but one of the case study hospitals stored
clinical records in a mix of paper and electronic systems, abstractors
generally had to consult both paper and electronic records to obtain all
needed information. What was recorded on paper and what was recorded
electronically varied from hospital to hospital (see app. III, table 4).
However, admissions and billing data were electronic at all the case study
hospitals. Billing data include principal diagnosis and birth date, which
are among the CMS-required data elements. With regard to clinical data,
all case study hospitals had test results, such as echocardiogram
readings, in an electronic form. In contrast, nurse progress notes were
least likely to be in electronic form at the case study hospitals.
Moreover, it was not uncommon for a hospital to have the same type of
clinical documentation stored partly in electronic form and partly on
paper. For example, five of the eight case study hospitals had a mix of
paper and electronic physician notes, reflecting the differing personal
preferences of the physicians. Discharge summaries and medication
administration records, on the other hand, tended to be either paper or
electronic at a given hospital.

Many of the data in existing IT systems were recorded in unstructured
formats--that is, as paragraphs of narrative or other text, rather than in
data fields designated to contain specific pieces of information--which
created problems in locating the needed information. For example,
physician notes and discharge summaries were often dictated and
transcribed. Abstractors typically read through the entire electronic
document to make sure that they had found all potentially relevant
references, such as for possible contraindications for a beta blocker or
an ACEI. By contrast, some of the data in existing IT systems were in
structured data fields so that specific information could be found in a
prescribed place in the record. One common example was a list of
medication allergies, which abstractors used to quickly check for certain
drug contraindications. However, officials at several hospitals said that
developing and implementing structured data fields were labor intensive,
both in terms of programming and in terms of educating clinical staff in
their use. That is why many of the data stored in electronic records at
the case study hospitals remained in unstructured formats.

Another limitation with existing IT systems was the inability of some
systems to access related data stored on another IT system in the same
hospital. This situation affected six of the eight case study hospitals to
some degree. For example, one hospital had an IT system in the emergency
department and an IT system on the inpatient floors, but the two systems
were independent and the information in one was not linked to the
information in the other. Abstractors had to access each IT system
separately to obtain related pieces of information, which made abstraction
more complicated and time-consuming.

Existing IT systems helped hospital abstractors to complete their work
more quickly, but the limitations of those IT systems meant that, for the
most part, the nature of their work remained the same. Existing IT systems
enabled abstractors at several hospitals to more quickly locate the
clinical information needed to determine the appropriate values for at
least some of the data elements that the hospitals submitted to CMS. Where
hospitals designed a discrete data field in their IT systems to match a
specific CMS data element, abstractors could simply transcribe that value
into the data vendor's abstraction form. However, in all the case study
hospitals there remained a large number of data elements for which there
was no discrete data field in a patient's electronic record that could
provide the required value for that data element. As a result, trained
staff still had to examine the medical record as a whole and manually
abstract the quality data submitted to CMS, whether the information in the
medical record was recorded electronically or on paper.^36

36Some of the data vendors captured values for certain data elements from
the hospital's billing data, such as the patient's birth date and
discharge status, and entered those values in the abstraction form that it
provided to the hospital for that patient. The abstractors were supposed
to make sure those entries were consistent with the information found in
the patient's medical record.

Full Automation of Quality Data Collection Is Not Imminent

All the case study hospitals were working to expand the scope and
functionality of their IT systems, but this expansion was generally
projected to occur incrementally over a period of years. Hospital
officials noted that with wider use of IT systems, the advantages of these
systems--including accessibility, legibility, and the use of discrete data
fields--would apply to a larger proportion of the clinical records that
abstractors have to search. As the case study hospitals continue to bring
more of their clinical documentation into IT systems, and to link separate
systems within their hospital so that data in one system can be accessed
from another, it should reduce the time required to collect quality data.

However, most officials at the case study hospitals viewed full-scale
automation of quality data collection and submission through
implementation of IT systems as, at best, a long-term prospect. They
pointed to a number of challenges that hospitals would have to overcome
before they could use IT systems to achieve full-scale automation of
quality data collection and submission. Primary among these were
overcoming physician reluctance to use IT systems to record clinical
information and the intrinsic complexity of the quality data required by
CMS. One hospital with unusually extensive IT systems had initiated a
pilot project to see how close it could get to fully automating quality
data collection for patients with heart failure. Drawing to the maximum
extent on the data that were amenable to programming, which excluded
unstructured physician notes, the hospital found that it could complete
data collection for approximately 10 percent of cases without additional
manual abstraction. Reflecting on this effort, the hospital official
leading this project noted that at least some of the data elements
required for heart failure patients represented "clinical judgment calls."
An official at another hospital observed that someone had to apply CMS's
complex decision rules to determine the appropriate value for the data
elements. If a hospital wanted to eliminate the need for an abstractor,
who currently makes those decisions retrospectively after weighing
multiple pieces of information in the patient's medical record, the same
complex decisions would have to be made by the patient's physician at the
time of treatment. The official suggested that it was preferable not to
ask physicians to take on that additional task when they should be focused
on making appropriate treatment decisions.

Another barrier to automated quality data collection mentioned by several
hospital officials was the frequency of change in the data specifications.
As noted above, hospitals had to invest considerable staff resources for
programming and staff education to develop structured data fields for the
clinical information required for the data elements. Officials at one
hospital stated that it would be difficult to justify that investment
without knowing how long the data specifications underlying that
structured data field would remain valid.

CMS Sponsored Studies and Joined Broader HHS Initiatives to Promote Use of IT
for Quality Data Collection and Submission, but HHS Lacks Detailed Plans,
Milestones, and Time Frame

CMS has sponsored studies and joined HHS initiatives to examine and
promote the current and potential use of hospital IT systems to facilitate
the collection and submission of quality data, but HHS lacks detailed
plans, including milestones and a time frame against which to track its
progress. CMS sponsored two studies that examined the use of hospital IT
systems for quality data collection and submission. Promoting the use of
health IT for quality data collection is also 1 of 14 objectives that HHS
has identified in its broader effort to encourage the development and
nationwide implementation of interoperable IT in health care. CMS has
joined this broader effort by HHS, as well as the Quality Workgroup that
AHIC created in August 2006 to specify how IT could capture, aggregate,
and report inpatient and outpatient quality data. Through its
representation in AHIC and the Quality Workgroup, CMS has participated in
decisions about the specific focus areas to be examined through contracts
with nongovernmental entities. These contracts currently address the use
of health IT for a range of purposes, which may also include quality data
collection and submission in the near future. However, HHS has identified
no detailed plans, milestones, or time frames for either its broad effort
to encourage IT in health care nationwide or its specific objective to
promote the use of health IT for quality data collection.

CMS Sponsored Studies Examining Use of IT Systems for Collection and Submission
of Quality Data

Over the past several years, CMS sponsored two studies to examine the
current and potential capacity of hospital IT systems to facilitate
quality data collection and submission. These studies identified
challenges to using existing hospital IT systems for quality data
collection and submission, including gaps and inconsistencies in
applicable data standards, as well as in the content of clinical
information recorded in existing IT systems. Data standards create a
uniform vocabulary for electronically recorded information by providing
common definitions and coding conventions for a specified set of medical
terms. Currently, an array of different standards apply to different
aspects of patient care, including drug ordering, digital imaging,
clinical laboratory results, and overall clinical terminology relating to
anatomy, problems, and procedures.^37 The studies also found that existing
IT systems did not record much of the specific clinical information needed
to determine the appropriate data element values that hospitals submit to
CMS. To achieve CMS's goal of enabling hospitals to transmit quality data
directly from their own IT systems to CMS's nationwide clinical database,
the sets of data in the two systems should conform to a common set of data
standards and capture all the data necessary for quality measures.^38 A
key element in the effort to create this congruence is the further
development and implementation of data standards.

In the first study, completed in March 2005, CMS contracted with the
Colorado Foundation for Medical Care to test the potential for directly
downloading values for data elements for CMS's hospital quality measures
using patient data from electronic medical records in three hospitals and
one hospital system.^39 The study found that numerous factors impeded this
process under current conditions, including the lack of certain key types
of information in the hospitals' IT systems, such as emergency department
data, prearrival data, transfer information, and information on medication
contraindications. The study also noted that hospitals differed in how
they coded their data, and that even when they had implemented data
standards, the hospitals had used different versions of the standards or
applied them in different ways.^40 For example, the study found wide
variation in the way that the hospitals recorded drug names and laboratory
results in their IT systems, as none of the hospitals had implemented the
existing data standards in those areas.

^37The following standards apply in these areas: the National Council on
Prescription Drug Programs (NCDCP) for drug ordering, Digital Imaging
Communications in Medicine (DICOM) for radiological and other images,
Laboratory Logical Observation Identifier Name Codes (LOINC) for clinical
laboratory results, and Systematized Nomenclature of Medicine Clinical
Terms (SNOMED-CT) for clinical terminology.

^38This congruence is one component within the broader initiative
announced by the President to promote the adoption of interoperable
electronic health records.

^39They were MedStar Health hospital system in Baltimore, New York
Presbyterian Hospital in New York, Vanderbilt University Medical Center in
Nashville, and Wishard Memorial Hospital in Indianapolis. All had
volunteered to participate in a demonstration project called Connecting
for Health sponsored by the independent, nonprofit eHealth Initiative. See
Colorado Foundation for Medical Care, Analysis of Data from the
"Healthcare Collaborative Network" (HCN) Project, CMS Special Study
SS-CO-08, Final Report (Denver, Colo., Mar. 31, 2005).

^40Most notably, the hospitals used the messaging standard for
transmitting clinical and administrative data--HL7--in different ways,
including their coding for such data fields as admission source and
discharge disposition.

In the second study, which was conducted by the Iowa Foundation for
Medical Care and completed in February 2006, CMS examined the potential to
expand its current data specifications for heart attack, heart failure,
pneumonia, and surgical measures to incorporate the standards adopted by
the federal Consolidated Healthcare Informatics (CHI) initiative.^41
Unlike the first study, which focused on actual patient data in existing
IT systems, this study focused on the relationship of current data
standards to the data specifications for CMS's quality data. It found that
there were inconsistencies in the way that corresponding data elements
were defined in the CMS/Joint Commission Specifications Manual and in the
CHI standards that precluded applying those standards to all of CMS's data
elements. Moreover, it found that some of the data elements are not
addressed in the CHI standards. These results suggested to CMS officials
that the data standards needed to undergo further development before they
could support greater use of health IT to facilitate quality data
collection and submission.

CMS Has Joined HHS's Efforts to Promote Greater Use of Health IT for Quality
Data Collection and Submission, but HHS Lacks Detailed Plans, Milestones, and a
Time Frame to Track Progress

CMS has joined efforts by HHS to promote greater use of health IT in
general and, more recently, in facilitating the use of health IT for
quality data collection and submission. The overall goal of HHS's efforts
in this area, working through AHIC and ONC, is to encourage the
development and nationwide implementation of interoperable health IT in
both the public and the private sectors. To guide those efforts, ONC has
developed a strategic framework that outlines its goals, objectives, and
high-level strategies. One of the 14 objectives involves the collection of
quality information.^42

41The results of the Hospital Data Collection Consolidated Healthcare
Informatics Adaptation Project were summarized in an internal CMS memo
dated March 9, 2006. The CHI initiative is a collaborative agreement among
federal agencies to adopt a common set of health information
interoperability standards encompassing a wide range of clinical domains,
including the data standards referred to in footnote 37. It is a component
of the Federal Health Architecture, which is a partnership of
approximately 20 federal agencies that use health IT.

^42See GAO, Health Information Technology: HHS Is Continuing Efforts to
Define Its National Strategy, [29]GAO-06-1071T (Washington, D.C.: Sept. 1,
2006), 17-18. Other objectives that are in the strategic framework, and
that ONC has initiated specific activities to address, include encouraging
widespread adoption of data standards, promoting consumer use of personal
health information, and expanding health information support in disasters
and crises.

CMS, through its participation in AHIC, has taken part in the selection of
specific focus areas for ONC to pursue in its initial activities to
promote health IT. Those activities have largely taken place through a
series of contracts with a number of nongovernmental entities. ONC has
sought through these contracts to address issues affecting wider use of
health IT, including standards harmonization, the certification of IT
systems, and the development of a Nationwide Health Information Network.
For example, the initial work on standards harmonization, conducted under
contract to ONC by the Healthcare Information Technology Standards Panel
(HITSP), focused on three targeted areas: biosurveillance,^43 sharing
laboratory results across institutions, and patient registration and
medication history. Meanwhile, the Certification Commission for Health
Information Technology (CCHIT) has worked under a separate contract with
ONC to develop and apply certification criteria for electronic health
record products used in physician offices, with some initial work on
certification of electronic health record products for inpatient care as
well.^44

CMS is also represented on the Quality Workgroup that AHIC created in
August 2006 as a first step in promoting the use of health IT for quality
data collection and submission. One of seven workgroups appointed by AHIC,
the Quality Workgroup received a specific charge to specify how health IT
should capture, aggregate, and report inpatient as well as outpatient
quality data. It plans to address this charge by adding activities related
to using IT for quality data collection to the work performed by HITSP and
CCHIT addressing other objectives under their ongoing ONC contracts.
Members of the Quality Workgroup, along with AHIC itself, have recently
begun to consider the specific focus areas to include in the directions
given to HITSP and CCHIT for their activities during the coming year.^45
Early discussions among AHIC members indicated that they would try to
select focus areas that built on the work already completed by ONC's
contractors and that targeted specific improvements in quality data
collection that could also support other priorities for IT development
that AHIC had identified.^46 The focus areas that AHIC selects will, over
time, influence the decisions that HHS makes regarding the resources it
will allocate and the specific steps it will take to overcome the
limitations of existing IT systems for quality data collection and
submission.

^43Biosurveillance generally refers to the automated monitoring of
information sources of potential value in detecting an emerging epidemic,
whether naturally occurring or the result of bioterrorism.

^44CCHIT is a voluntary, private-sector organization set up in July 2004
by three leading health IT industry associations--the American Health
Information Management Association (AHIMA), the Healthcare Information and
Management Systems Society (HIMSS), and the National Alliance for Health
Information Technology (Alliance)--to certify health IT products.

^45As discussed at the AHIC Meeting, Washington, D.C., October 31, 2006.
These discussions resulted in a set of recommendations that the workgroup
presented at AHIC's Meeting on March 13, 2007.

In a previous report and subsequent testimony, we noted that ONC's overall
approach lacked detailed plans and milestones to ensure that the goals
articulated in its strategic framework were met. We pointed out that
without setting milestones and tracking progress toward completing them,
HHS cannot tell if the necessary steps are in place to provide the
building blocks for achieving its overall objectives.^47 HHS concurred
with our recommendation that it establish detailed plans and milestones
for each phase of its health IT strategic framework, but it has not yet
released any such plans, milestones, or a time frame for completion.
Moreover, HHS has not announced any detailed plans or milestones or a time
frame relating to the efforts of the Quality Workgroup to promote the use
of health IT to capture, aggregate, and report inpatient and outpatient
quality data. Without such plans, it will be difficult to assess how much
the focus areas AHIC selects in the near term on its contracted activities
will contribute to enabling the Quality Workgroup to fulfill its charge in
a timely way.

Conclusions

There is widespread agreement on the importance of hospital quality data.
The Congress made the APU program permanent to provide a financial
incentive for hospitals to submit quality data to CMS and directed the
Secretary of HHS to increase the number of measures for which hospitals
would have to provide data. In addition, the hospitals we visited reported
finding value in the quality data they collected and submitted to CMS to
improve care.

Collecting quality data is a complex and labor-intensive process. Hospital
officials told us that as the number of quality measures required by CMS
increased, the number of clinically trained staff required to collect and
submit quality data increased proportionately. They also told us that
increased use of IT facilitates the collection and submission of quality
data and thereby lessens the demand for greater staff resources. The
degree to which existing IT systems can facilitate data collection is,
however, constrained by limitations such as the prevalence of data
recorded as unstructured narrative or text. Overcoming these limitations
would enhance the potential of IT systems to ease the demand on hospital
resources.

^46AHIC has identified priority areas involving consumer empowerment,
biosurveillance, electronic health records, and chronic care.

^47GAO, Health Information Technology: HHS Is Taking Steps to Develop a
National Strategy, [30]GAO-05-628 (Washington, D.C.: May 27, 2005), 3;
[31]GAO-06-1071T , 18.

Promoting the use of health IT for quality data collection is 1 of 14
objectives that HHS has identified in its broader effort to encourage the
development and nationwide implementation of interoperable IT in health
care. The extent to which HHS can overcome the limitations of existing IT
systems and make progress on this objective will depend in part on where
this objective falls on the list of priorities for the broader effort. To
date, HHS has identified no detailed plans, milestones, or time frames for
either the broad effort or the specific objective on promoting the use of
health IT for collecting quality data. Without such plans, HHS cannot
track its progress in promoting the use of health IT for collecting
quality data, making it less likely that HHS will achieve that objective
in a timely way. Our analysis indicates that unless activities to
facilitate greater use of IT for quality data collection and submission
proceed promptly, hospitals may have difficulty collecting and submitting
quality data required for an expanded APU program.

Recommendations for Executive Action

To support the expansion of quality measures for the APU program, we
recommend that the Secretary of HHS take the following actions:

           o identify the specific steps that the department plans to take to
           promote the use of health IT for the collection and submission of
           data for CMS's hospital quality measures; and

           o inform interested parties about those steps and the expected
           time frame, including milestones for completing them.
		   
		   Agency Comments and Our Evaluation

           In commenting on a draft of this report on behalf of HHS, CMS
           expressed its appreciation of our thorough analysis of the
           processes that hospitals use to report quality data and the role
           that IT systems can play in that reporting, and it concurred with
           our two recommendations. (CMS's comments appear in app. V.) With
           respect to the recommendations, CMS stated that it will continue
           to participate in relevant HHS studies and workgroups, and, as
           appropriate, it will inform interested parties regarding progress
           in the implementation of health IT for the collection and
           submission of hospital quality data as specific steps, including
           time frames and milestones, are identified. In addition, as health
           IT is implemented, CMS anticipates that a formal plan will be
           developed that includes training for providers in the use of
           health IT for reporting quality data. CMS also provided technical
           comments that we incorporated where appropriate.

           CMS made two additional comments relating to the information
           provided on our case study hospitals and our discussion of
           patients excluded from the hospital performance assessments. CMS
           suggested that we describe the level of health IT adoption in the
           case study hospitals in table 1 of appendix III; this information
           was already provided in table 4 of appendix III. CMS suggested
           that we highlight the application of patient exclusions in
           adapting health IT for quality data collection and submission. We
           chose not to because our analysis showed that the degree of
           challenge depended on the nature of the information required for a
           given data element. Exclusions based on billing data, such as
           discharge status, pose much less difficulty than other exclusions,
           such as checking for contraindications to ACEIs and ARBs for LVSD,
           which require a wide range of clinical information.

           CMS noted that the AHIC Quality Workgroup had presented its
           initial set of recommendations at AHIC's most recent meeting on
           March 13, 2007, and provided a copy of those recommendations as an
           appendix to its comments. The agency characterized these
           recommendations as first steps, with initial timelines, to address
           the complex issues that affect implementation of health IT for
           quality data collection and submission. Specifically with
           reference to collecting quality data from hospitals as well as
           physicians, the Quality Workgroup recommended the appointment of
           an expert panel that would designate a set of quality measures to
           have priority for standardization of their data elements, which,
           in turn, would enable automation of their collection and
           submission using electronic health records and health information
           exchange. The first recommendations from the expert panel are due
           June 5, 2007. The work of the expert panel is intended to guide
           subsequent efforts by HITSP to fill identified gaps in related
           data standards and by CCHIT to develop criteria for certifying
           electronic health record products. In addition, the Quality
           Workgroup recommended that CMS and the Agency for Healthcare
           Research and Quality (AHRQ) both work to bring together the
           developers of health quality measures and health IT vendors, so
           that development of future health IT systems would take greater
           account of the data requirements of emerging quality measures.
           AHIC approved these recommendations from the Quality Workgroup at
           its March 13 meeting.

           We also sent to each of the eight case study hospitals sections
           from the appendixes pertaining to that hospital. We asked each
           hospital to check that the section accurately described its
           processes for collecting and submitting quality data as well as
           related information on its characteristics and resources.
           Officials from four of the eight hospitals responded and provided
           technical comments that we incorporated where appropriate.

           As arranged with your offices, unless you publicly announce its
           contents earlier, we plan no further distribution of this report
           until 30 days after its issue date. At that time, we will send
           copies of this report to the Secretary of HHS, the Administrator
           of CMS, and other interested parties. We will also make copies
           available to others on request. In addition, the report will be
           available at no charge on GAO's Web site at http://www.gao.gov
           .

           If you or your staffs have any questions about this report, please
           contact me at (202) 512-7101 or [email protected]. Contact points
           for our Offices of Congressional Relations and Public Affairs may
           be found on the last page of this report. GAO staff who made major
           contributions to this report are listed in appendix VI.

           Cynthia A. Bascetta
		   Director, Health Care
		   
		   Appendix I: Medicare Quality Measures Required for Full Annual
		   Payment Update

                                                                    Number of 
                                                                required data 
Condition     Quality measure                                     elements 
Heart attack  Aspirin at hospital arrival^a                             11 
                 Aspirin prescribed at discharge^a                          7 
                 Angiotensin-converting enzyme inhibitor or                   
                 angiotensin receptor blocker for left                        
                 ventricular systolic dysfunction^a                         9 
                 Beta blocker at hospital arrival^a                        11 
                 Beta blocker prescribed at discharge^a                     7 
                 Thrombolytic agent received within 30 minutes                
                 of hospital arrival                                       13 
                 Percutaneous coronary intervention received                  
                 within 120 minutes of hospital arrival                    16 
                 Adult smoking cessation advice/counseling                  7 
Heart failure Left ventricular function assessment^a                     7 
                 Angiotensin-converting enzyme inhibitor or                   
                 angiotensin receptor blocker for left                        
                 ventricular systolic dysfunction^a                        10 
                 Discharge instructions                                    12 
                 Adult smoking cessation advice/counseling                  8 
Pneumonia     Initial antibiotic received within 4 hours of                
                 hospital arrival^a                                        16 
                 Oxygenation assessment^a                                  11 
                 Pneumococcal vaccination status^a                          8 
                 Blood culture performed before first                         
                 antibiotic received in hospital                           19 
                 Adult smoking cessation advice/counseling                  9 
                 Appropriate initial antibiotic selection                  24 
                 Influenza vaccination status                               9 
Surgery       Prophylactic antibiotic received within 1 hour               
                 prior to surgical incision                                14 
                 Prophylactic antibiotics discontinued within                 
                 24 hours after surgery end time                           17 
		   
           Sources: Federal Register, CMS, GAO (analysis).

           Notes: The 21 measures are listed in 71 Fed. Reg. 47870,
           48033-48034, 48045 (Aug. 18, 2006), and we analyzed the
           Specifications Manual for National Hospital Quality Measures,
           version 2.1a, to calculate the number of required data elements
           for each. This set of quality measures is effective for discharges
           from July 2006 on. The Centers for Medicare & Medicaid Services
           (CMS) uses 73 different data elements to calculate hospital
           performance on the 21 measures required for the APU program. The
           total number of unique data elements is less than the sum of the
           data elements used to calculate each measure because some data
           elements are included in the calculation of more than one quality
           measure. In addition, CMS obtains from hospitals approximately 20
           other data elements on each patient, including demographic and
           billing data.

           aOne of the 10 original quality measures.
		   
		   Appendix II: Data Elements Used to Calculate Hospital Performance
		   on a Heart Attack Quality Measure

           Figure 3: Data Elements Used to Calculate Hospital Performance on
           the Heart Attack Quality Measure That Asks Whether a Beta Blocker
           Was Given When the Patient Arrived at the Hospital

           Notes: The boxes represent data elements and the circles and
           rounded rectangles represent values for those elements. In
           addition to the seven data elements shown in the figure (including
           arrival date and discharge date that appear in the same box), an
           eighth data element, comfort measures only, is first applied for
           this quality measure, as well as all the other heart attack, heart
           failure, and pneumonia quality measures, to screen out terminal
           patients receiving palliative care. Three other data
           elements--principal diagnosis, admission date, and birthdate--are
           used to initially identify the patients for whom the heart failure
           quality measures apply in a given quarter.

           aIncluded codes consist of eight different values for admission
           source that represent patients who were admitted from any source
           other than those listed in footnote b, including physician
           referral, skilled nursing facility, and the hospital's emergency
           room.

           bExcluded codes consist of three different values for admission
           source that represent patients who were transferred to this
           hospital from another acute care hospital, from a critical access
           hospital, or within the same hospital with a separate claim.

           cPatients may be excluded from the population used to calculate a
           hospital's performance for a variety of reasons, including
           inappropriateness of beta blockers for their treatment--for
           example, if they have a contraindication for their use--or prior
           treatment in another acute care facility.

           dIncluded codes consist of 13 different values for discharge
           status that represent patients who were discharged to any setting
           other than those listed in footnote e, including home care,
           skilled nursing facility, and hospice.

           eExcluded codes consist of five different values for discharge
           status that represent patients who were discharged to another
           acute care hospital or federal health care facility, left against
           medical advice, or died.

Appendix III: Tables on Eight Case Study Hospitals

Table 1: Case Study Hospital Characteristics

                                                 Case study hospital
              A          B           C      D              E           F          G                      H 
Number of     300-349    500+        50-99      500+       100-149     500+       150-199     500+       
beds                                                                                                     
Urban/rural   Urban      Urban       Rural      Urban      Suburban    Urban      Suburban    Urban      
Major         Yes        Yes         No         Yes        No          Yes        No          Yes        
teaching                                                                                                 
Member of     Yes        Yes         Yes        No         No          No         No          No         
multihospital                                                                                            
system                                                                                                   
Joint         Yes        Yes         Yes        Yes        Yes         Yes        Yes         Yes        
Commission                                                                                               
accredited                                                                                               
Vendor        Yes        Yes         Yes        Yes        Yes         Yes        Yes         Yes        
submits                                                                                                  
quality data                                                                                             
Patients      Monthly    Monthly     Weekly     Monthly    Monthly     Monthly    Monthly     Monthly    
identified                                                                                               
for data                                                                                                 
collection                                                                                               
how often                                                                                                
Abstraction   Vendor's   Vendor's    Vendor's   CART^a     Vendor's    Vendor's   Vendor's    Vendor's   
tool used                                                                                                
Conditions    Heart      Heart       Heart      Heart      Heart       Heart      Heart       Heart      
reported on   attack,    attack,     attack,    attack,    attack,     attack,    attack,     attack,    
              heart      heart       heart      heart      heart       heart      heart       heart      
              failure,   failure,    failure,   failure,   failure,    failure,   failure,    failure,   
              pneumonia, pneumonia,  pneumonia, pneumonia, pneumonia,  pneumonia, pneumonia,  pneumonia, 
              surgery    surgery     surgery    surgery    surgery     surgery    surgery     surgery    
Entities that CMS, Joint CMS, Joint  CMS, Joint CMS, Joint CMS, Joint  CMS, Joint CMS, Joint  CMS, Joint 
receive       Commission Commission, Commission Commission Commission, Commission Commission  Commission 
Annual                   vendor                            vendor                                        
Payment                  database,                         database                                      
Update (APU)             private                                                                         
program data             insurers                                                                        
Entities that Leapfrog^b Leapfrog,   Private    Leapfrog,  Private     Leapfrog,  State       Private    
receive                  state       insurer    private    insurer     private    health      insurer    
different                health                 insurer                insurer    department,            
quality data             department,                                              private                
                         private                                                  insurers               
                         insurers                                                                        
Amount of     $139,000   $608,000    $33,000    $449,000   $57,000     $430,000   $93,000     $123,000   
projected                                                                                                
reduction in                                                                                             
fiscal year                                                                                              
2006 Medicare                                                                                            
payments if                                                                                              
quality data                                                                                             
not                                                                                                      
submitted^c                                                                                              
Amount of     $801,000   $3,250,000  $161,000   $2,298,000 $283,000    $2,451,000 $503,000    $608,000   
projected                                                                                                
reduction in                                                                                             
fiscal year                                                                                              
2007 Medicare                                                                                            
payments if                                                                                              
quality data                                                                                             
not                                                                                                      
submitted^c                                                                                              

Sources: American Hospital Association, GAO, Centers for Medicare &
Medicaid Services (CMS).

aCART, which stands for the CMS Abstraction and Reporting Tool, was
developed by CMS and made available to hospitals at no charge for
collecting and submitting quality data.

bThe Leapfrog Group is a consortium of large private and public health
care purchasers that publicly recognizes hospitals that have implemented
certain specific quality and safety practices, such as computerized
physician order entry.

cThe projected reduction in fiscal year 2006 and fiscal year 2007 Medicare
payments (rounded to the nearest $1,000) represents the amount that the
hospital's revenue from Medicare would have decreased for that fiscal year
had the hospital not submitted quality data under the Annual Payment
Update program. These estimates are based on information on the number and
case mix of Medicare patients served by these hospitals during the
previous period. This is the information that was available to hospital
administrators from CMS at the beginning of the fiscal year. The actual
reduction would ultimately depend on the number and case mix of the
Medicare patients that the hospital actually treated during the course of
that fiscal year. The projected reduction for fiscal year 2007 was
substantially larger because that was the first year in which the higher
rate of reduction mandated by the Deficit Reduction Act of 2005--from 0.4
percentage points to 2.0 percentage points--took effect.

Table 2: How Case Study Hospital Officials Described the Steps Taken to
Complete Quality Data Collection and Submission

                                      Case study hospital
                   A               B              C             D      
      1. Identify  Vendor prepares Vendor         Vendor        Hospital IT
      patients^a   list of         prepares list  prepares list department
                   patients to     of patients    of patients   identifies
                   abstract,       based on       to abstract   patients
                   sampling heart  diagnosis      based on      based on
                   failure,        codes, and     billing data, billing data,
                   pneumonia, and  draws samples  no sampling   no sampling
                   surgery         for heart                           
                                   failure,                            
                                   pneumonia, and                      
                                   surgery                             
      2. Locate    Abstractor      Abstractor     Abstractor    Abstractor
      information  searches        starts search  searches      clicks 
      in the       through         with           through       through
      medical      emergency room  electronic     different     various
      record       and inpatient   discharge      components of electronic
                   electronic and  summary, then  paper record, screens
                   paper records,  other          including     representing
                   checking        electronic     printouts     different
                   multiple forms  records and    from          types of
                   and screens     paper          electronic    records, plus
                   where relevant  documents      records       some scanned
                   information                                  documents,
                   could be found                               for example,
                                                                from other
                                                                providers
      3. Determine Some            Some           Some          Some   
      appropriate  demographic     demographic    demographic   demographic
      data element data            data           data          data   
      values       prepopulated;   prepopulated;  prepopulated; prepopulated;
                   abstractor      other data     other data    most   
                   notes ambiguous elements       elements      abstractors
                   or conflicting  written on     entered       fill in data
                   information on  paper          directly into elements on
                   paper           abstraction    vendor's      paper  
                   abstraction     form           online        abstraction
                   form                           abstraction   form   
                                                  tool                 
      4. Transmit  Data elements   Data elements  Data elements Data elements
      data to CMS  copied from     copied from    entered       copied from
                   paper           paper          directly into paper  
                   abstraction     abstraction    vendor's      abstraction
                   form to         form to        online        form to
                   vendor's online vendor's       abstraction   vendor's
                   form            online form    tool          electronic
                                                                form; data
                                                                manager
                                                                checks data
                                                                and uploads
                                                                file to
                                                                vendor 
      5. Ensure    Performed by    Hospital staff Performed by  Hospital
      data have    vendor          reviews error  vendor        staff reviews
      been                         reports from                 error reports
      accepted by                  clinical data                from vendor
      CMS                          warehouse and                       
                                   corrects                            
                                   errors                              
      6. Supply    Hospital copies Hospital       Hospital      Hospital
      copies of    and ships       copies, checks copies,       copies and
      selected     requested       completeness   checks        ships  
      medical      patient records of, and ships  completeness  requested
      records                      requested      of, and ships patient
                                   patient        requested     records
                                   records        patient              
                                                  records              

                              Case study hospital
E                F                     G                  H                
Hospital         Hospital provides     Hospital creates   Hospital submits 
prepares list of billing data to       list from billing  billing data to  
patients from    vendor; vendor draws  data; vendor       vendor, which    
billing data, no samples and generates provides           identifies       
sampling         list of patients to   instructions to    eligible         
                    abstract              draw sample of     patients and     
                                          pneumonia cases    draws samples    
Abstractor works Abstractor starts     Abstractor starts  Abstractor       
through paper    with electronic       searching through  searches through 
records, such as records (for heart    paper records,     both electronic  
face sheet,      attack and heart      then looks for     and paper        
emergency room   failure)--first       additional         records          
treatment forms, structured records    information in                      
progress notes,  (discharge) and then  electronic records                  
and discharge    free text--and then   (e.g., for                          
summary          examines paper        echocardiogram                      
                    records if needed;    results)                            
                    paper records                                             
                    searched for                                              
                    pneumonia and surgery                                     
Data elements    Some demographic data Some demographic   Some demographic 
entered into     prepopulated;         data prepopulated; data             
computerized     abstractors fill out  other data         prepopulated;    
abstraction form abstraction form,     elements written   other data       
                    some on paper and     on paper           elements written 
                    some online           abstraction form   on paper         
                                                             abstraction form 
Completed        For pneumonia and     Data elements      Data elements    
abstraction      surgery, abstractor   copied from paper  copied from      
forms sent on    enters data online,   abstraction form   paper            
disk to vendor;  for heart attack and  to vendor's online abstraction form 
will change soon heart failure,        form               to vendor's      
to completion of hospital scans paper                     online form      
forms online     abstraction forms and                                     
                    sends electronic file                                     
                    to vendor, which                                          
                    submits data to CMS                                       
Hospital reviews Performed by vendor   Hospital receives  Hospital reviews 
error reports                          error report from  error reports    
from vendor and                        vendor and         from vendor and  
clinical                               clinical data      makes            
warehouse                              warehouse and      corrections;     
                                          makes corrections  vendor deals     
                                                             with clinical    
                                                             data warehouse   
Hospital copies  Hospital copies and   Hospital copies,   Hospital copies, 
and ships        ships requested       checks             checks           
requested        patient records;      completeness of,   completeness of, 
patient records  before shipping       and ships          and ships        
                    hospital flags        requested patient  requested        
                    relevant information  records            patient records  

Source: GAO.

Note: Information summarized from hospital case study interviews.

aThe identifying patients step included both determining all the patients
who met the CMS criteria for inclusion and the application of the CMS
sampling procedures, if applicable. CMS only permitted hospitals to sample
patients for a given condition in a given quarter if the number of
eligible patients met a certain threshold. Otherwise, the hospital was
required to abstract quality data for all patients who met the inclusion
criteria for any one of the four conditions. Hospitals could also choose
not to sample, even if it were permitted under the CMS sampling
procedures.

Table 3: Resources Used for Abstraction and Data Submission at Eight Case
Study Hospitals

                                                    Case study hospital
               A       B            C            D         E         F           G            H    
Qualifications Medical       Registered    All RN    All RN     Licensed  Medical       RN and     All RN     
of abstractors record coders nurse (RN)                         practical records coder LPN^a                 
               and a Master  and                                nurse     and RN with                         
               of Public     nonclinical                        (LPN)     physician                           
               Health                                                     support                             
Number of      3             3             3         9          2         3             3          4          
abstractors                                                                                                   
Estimated full 0.7           <2.0          <1.5      2.5        1.2       1.3           1.2        2.0        
time                                                                                                          
equivalents                                                                                                   
for                                                                                                           
abstraction of                                                                                                
data elements                                                                                                 
Estimated time 60 minutes    10 to 15      20        3 to 120   5 to 60   5 to 60       10 to 30   10 to 90   
to abstract    (average)     minutes       minutes   minutes    minutes   minutes       minutes    minutes    
one chart                                  (average)                                                          
Average number 222           399           86        686        118       252           190        202        
of heart                                                                                                      
attack, heart                                                                                                 
failure, and                                                                                                  
pneumonia                                                                                                     
charts                                                                                                        
abstracted per                                                                                                
quarter^b                                                                                                     
Average number 94^b          218^c         6^c       553^b      105^b     186^b         82^c       61^c       
of surgery                                                                                                    
charts                                                                                                        
abstracted per                                                                                                
quarter                                                                                                       
Data vendor    $8,200        $5,000 to     $3,600    $3,500     $560      $1,800        $12,450    $13,000    
costs for CMS                $7,500                                                                           
quality data                                                                                                  
services per                                                                                                  
year                                                                                                          
Checks for     Some          A few         Relies on Checks     None      Some          None       Checks     
accuracy of    discrepancies discrepancies vendor    only that            discrepancies            patient    
case selection observed      observed      processes data were            observed                 lists for  
against                                    and       submitted                                     heart      
another data                               audits of to CMS for                                    attack     
source                                     medical   all                                           patients   
                                           records   patients                                      against    
                                           coding    on                                            medical    
                                                     original                                      records    
                                                     list to be                                               
                                                     abstracted                                               
Checks for     Hospital      None beyond   Hospital  Only for   Only for  Only for      Not        None       
accuracy of    reabstracts 5 reviews by    redoes 5  cases      cases     cases where   routinely, beyond     
data           percent of    CMS           to 10     where      where     quality       only for   reviews by 
abstraction    cases each    contractor    cases per quality    quality   standard not  startup in CMS        
               quarter                     measure   standard   standard  met           new        contractor 
                                           set every not met    not met                 condition             
                                           quarter                                                            

Sources: GAO, CMS.

aThe LPN was abstracting cases for one condition temporarily until an RN
could be hired to perform the work.

bBased on submissions to the clinical warehouse for four quarters of
discharges from April 2005 through March 2006.

cBased on submissions to the clinical warehouse for one quarter of
discharges from January through March 2006.

Table 4: Electronic and Paper Records at Eight Case Study Hospitals

                                                 Case study hospital
                                            A   B   C   D E   F   G   H   
Admissions                               E   E   E   E E   E   E   E   
Billing                                  E   E   E   E E   E   E   E   
Emergency department                     E&P E&P P   E P   E   P   P   
Medication administration                E   E   P   E P   P   E   P   
Physician orders including prescriptions E&P E&P P   E P   E   P   E   
Nursing notes                            P   P   P   E P   P   E   P   
Laboratory and test results              E   E   E   E E   E   E   E   
Physician notes                          P   E&P P   E E&P E&P E&P E&P 
Discharge summaries and instructions     P   E   P   E P   E&P E   E   
Operating room                           P   E&P E&P E P   E&P E   E   

Source: GAO.

Note: E = electronic, P = paper.

Appendix IV: Scope and Methodology
	
To examine how hospitals collect and submit quality data, and to determine
the extent to which information technology (IT) facilitates those
processes, we conducted case studies of eight individual acute care
hospitals that collect and submit quality data to the Centers for Medicare
& Medicaid Services (CMS). We chose this approach to obtain an in-depth
understanding of these processes as they are currently experienced at the
hospital level. For background information on the requirements that the
hospitals had to satisfy, we reviewed CMS documents relevant to the Annual
Payment Update (APU) program. In particular, we examined multiple
revisions of the Specifications Manual for National Hospital Quality
Measures, which is issued jointly by CMS and the Joint Commission
(formerly the Joint Commission on Accreditation of Healthcare
Organizations).

We structured our selection of hospitals for the eight case studies to
provide a contrast of hospitals with highly sophisticated IT systems and
hospitals with an average level of IT capability. We excluded critical
access hospitals from this selection process because they are not included
in the APU program.^1 The selected hospitals varied on several hospital
characteristics, including urban/rural location, size, teaching status,
and membership in a system that linked multiple hospitals through shared
ownership or other formal arrangements. (See app. III, table 1.)

To select four hospitals with highly sophisticated IT systems, we relied
on recommendations from interviews with a number of experts in the field
of health IT, as well as on a recent review of the research literature on
the costs and benefits of health IT^2 and other published articles. Three
of the four hospitals we chose were among those where much of the
published research has taken place. They were all early adopters of health
IT, and each had implemented internally developed IT systems. The fourth
hospital had more recently acquired and adapted a commercially developed
system. This hospital was distinguished by the extent to which it had
replaced its paper medical records with an integrated system of electronic
patient records. Each of these four case study hospitals was located in a
different metropolitan area.

^1Some critical access hospitals submit quality data to CMS voluntarily,
but this does not affect their Medicare payments.

^2P.G. Shekelle, S.C. Morton, and E.B. Keeler, Costs and Benefits of
Health Information Technology, Evidence Report/Technology Assessment No.
132 (prepared by the Southern California Evidence-based Practice Center
under Contract No. 290-02-0003), Agency for Healthcare Research and
Quality Publication No. 06-E006 (Rockville, Md., April 2006).

We selected the four hospitals with less sophisticated IT systems from the
geographic vicinity of the four hospitals already chosen, thus providing
two case study hospitals from each of four metropolitan areas. We decided
that one should be a rural hospital, using the Medicare definition of
rural, which is located outside of a Metropolitan Statistical Area (MSA).
To determine from which of the four metropolitan areas we should select a
neighboring rural hospital, we analyzed data on Medicare-approved
hospitals drawn from CMS's Provider of Services (POS) file. We identified
the rural hospitals located within 150 miles of each of the first four
hospitals. From among those four sets of rural hospitals, we chose the set
with the largest number of acute care hospitals as the set from which to
choose our rural case study hospital. For each of the remaining three
metropolitan areas, we used the hospitals listed in the POS file as
short-term acute care hospitals located in the same MSAs as the three sets
from which to choose our remaining three hospitals. We excluded hospitals
located in a different state from the first hospital selected for that
metropolitan area, so that all of the hospitals under consideration for
that area would come under the jurisdiction of the same Quality
Improvement Organization (QIO).^3

To select the second case study hospital from among those available in or
near each of the four metropolitan areas, we applied a procedure designed
to produce a straightforward and unbiased selection. We began by recording
the total number of cases for which each of these hospitals had reported
results on CMS's Web site for heart attack, heart failure, and pneumonia
quality measures. We obtained this information from the Web site itself,
running reports for each hospital that showed, for each quality measure,
the number of cases that the hospital's quality performance score was
based on. Since some quality measures apply only to certain patients, we
recorded the largest number of cases listed for any of the quality
measures reported for a given condition. Next we summed the cases for the
three conditions and rank ordered the hospitals in each of the three MSAs,
and the rural hospitals in the fourth metropolitan area, from most to
least total cases submitted. We then made a preliminary selection by
taking the hospital with the median value in each of those lists.^4 By
selecting the hospital with the median number of cases reported, we
attempted to minimize the chances of picking a hospital that would
represent an outlier compared to other hospitals in the selection pool.^5

3QIOs are independent organizations that work under contract to CMS to
monitor quality of care for the Medicare program within a given state and
help providers to improve their clinical practices. CMS has assigned
primary responsibility to the QIOs to inform hospitals about the APU
program's requirements and to provide technical assistance to hospitals in
meeting those requirements.

Before selecting the final four case study hospitals, we checked to make
sure that the hospitals did not happen to have an unusually high level of
IT capabilities with respect to electronic patient records. To do this, we
contacted each of the selected hospitals and obtained a description of its
current IT systems. We compared this description to the stages of
electronic medical record implementation laid out by the Healthcare
Information and Management Systems Society (HIMSS).^6 The HIMSS model
identifies eight stages based on the scope and sophistication of clinical
functions implemented through a hospital's system of electronic medical
records. According to HIMSS, the large majority of hospitals in the United
States are at the lower three stages. Based on the descriptions of these
stages, we determined that none of the prospectively selected hospitals
had IT systems that exceeded the third stage.

We collected information about the processes used to collect and submit
quality data from each of the eight case study hospitals through on-site
interviews with hospital abstractors, quality managers, IT staff, and
hospital administrators. We told these officials that neither they
personally nor their hospitals would be identified by name in our report.
The site visits took place between mid-July and early September 2006 and
ranged in duration from 3 to 8 hours. Our data collection at each hospital
was guided by a protocol that specified a series of topics to cover in our
interviews. These topics included a description of the processes used at
each hospital and the financial and staff resources devoted to quality
data collection and submission. We pretested the protocol at two hospitals
not included in our set of eight case study hospitals.

^4Any ties on median values, which were possible if the list had an even
number of hospitals, were resolved by implementing a decision rule that
alternated between taking the higher number of cases for the first
instance, the lower number for the second instance, and so on.

^5For example, a few hospitals on these lists had submitted results for
only one condition.

^6D. Garets and M. Davis, "Electronic Medical Records vs. Electronic
Health Records: Yes, There Is a Difference" (Chicago, Ill.: HIMSS
Analytics, LLC, updated Jan. 26, 2006).

As part of the protocol, we asked abstractors at each hospital to explain
in detail how they found the information needed to determine the
appropriate values for each of the data elements required for two specific
quality measures: (1) angiotensin-converting enzyme inhibitor (ACEI) or
antiotensin receptor blocker (ARB) for left ventricular systolic
dysfunction (LVSD) for heart failure patients and (2) initial antibiotic
received within 4 hours of hospital arrival for pneumonia patients. We
selected these measures because they covered a number of different types
of data elements, including those involving administration of medications,
determining contraindications, date and time variables, and making
clinical assessments such as whether a patient had LVSD.

To determine the extent to which IT facilitated these processes at the
eight case study hospitals, we included several topics on IT systems in
our site visit protocol. We asked about any IT systems used by the
abstractors in locating relevant clinical information in patient medical
records and the specific advantages and limitations they encountered in
using those systems. We also asked hospital officials to assess the
potential for IT systems to provide higher levels of assistance for
quality data collection and submission over time. If separate IT staff
were involved in the hospital's quality data collection and submission
process, we included them in the interviews.

Where possible, we supplemented the information provided through
interviews with direct observation of the processes used by hospitals to
collect and submit quality data. We asked the case study hospitals to show
us how they performed these processes, and five of the eight hospitals
arranged for us to observe the collection of quality data for all or part
of a patient record. We observed abstractors accessing clinical
information from both paper and electronic records.

We also obtained pertinent information about the case study hospitals from
CMS documents and contractors. The estimated amount of dollars that the
case study hospitals would have lost had they not submitted quality data
to CMS, presented in appendix III, table 1, was calculated from data
provided in documents made available to all hospitals at the start of each
of the fiscal years.^7 Information on the average number of patient charts
abstracted quarterly by each case study hospital, shown in appendix III,
table 3, was drawn from a table showing the number of patients for whom
quality data were submitted to CMS's clinical data warehouse. We obtained
that table from the Iowa Foundation for Medical Care (IFMC), which is the
CMS contractor that operates the clinical data warehouse. The IFMC table
provided this information for all hospitals submitting quality data for
discharges that occurred from April 2005 through March 2006. These were
the most recent data available.

The evidence that we obtained from our eight case study hospitals is
specific to those hospitals. In particular, it does not offer a basis for
relating any differences we observed among these individual hospitals to
their differences on specific dimensions, such as size or teaching status.
Nor can we generalize from the group of eight as a whole to acute care
hospitals across the country. Furthermore, although we examined the
processes hospitals used to collect and submit quality data and the role
that IT plays in that process, we did not examine general IT adoption in
the hospital industry.

To obtain information on whether CMS has taken steps to promote the
development of IT systems to facilitate quality data collection and
submission, we interviewed CMS officials as well as CMS contractors and
reviewed documents including reports on related studies funded by CMS. We
also interviewed officials at the Office of the National Coordinator for
Health Information Technology (ONC) regarding the plans and activities of
the American Health Information Community (AHIC) quality workgroup. In
addition, we downloaded relevant documents from the AHIC Web site,
including meeting agendas, prepared presentations, and meeting minutes for
both AHIC as a whole and its Quality Workgroup.

We conducted our work from February 2006 to April 2007 in accordance with
generally accepted government auditing standards.

^7These included the final rule for Inpatient Prospective Payment System
updates for fiscal years 2006 and 2007, 70 Fed. Reg. 47507 (Aug. 12, 2005)
and 71 Fed. Reg. 48166 (Aug. 18, 2006), and the "Impact file for IPPS FY
2006 Final Rule" and "Impact file for IPPS FY 2007 Final Rule" downloaded
from http://www.cms.hhs.gov/AcuteInpatientPPS/FFD/list.asp#TopOfPage on
October 12, 2006.

Appendix V: Comments from the Centers for Medicare & Medicaid Services 

Appendix VI: GAO Contact and Staff Acknowledgments

GAO Contact

Cynthia A. Bascetta, (202) 512-7101 or [email protected]

Acknowledgments

In addition to the contact named above, Linda T. Kohn, Assistant Director;
Mohammad S. Khan; Eric A. Peterson; Roseanne Price; Jessica C. Smith; and
Teresa F. Tucker made key contributions to this report.

(290520)

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www.gao.gov/cgi-bin/getrpt?GAO-07-320 .

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Highlights of [40]GAO-07-320 , a report to the Committee on Finance, U.S.
Senate

April 2007

HOSPITAL QUALITY DATA

HHS Should Specify Steps and Time Frame for Using Information Technology
to Collect and Submit Data

Hospitals submit data in electronic form on a series of quality measures
to the Centers for Medicare & Medicaid Services (CMS) and receive scores
on their performance. Increasingly, the clinical information from which
hospitals derive the quality data for CMS is stored in information
technology (IT) systems.

GAO was asked to examine (1) hospital processes to collect and submit
quality data, (2) the extent to which IT facilitates hospitals' collection
and submission of quality data, and (3) whether CMS has taken steps to
promote the use of IT systems to facilitate the collection and submission
of hospital quality data. GAO addressed these issues by conducting case
studies of eight hospitals with varying levels of IT development and
interviewing relevant officials at CMS and the Department of Health and
Human Services (HHS).

[41]What GAO Recommends

GAO recommends that the Secretary of HHS identify the specific steps the
department plans to take to promote the use of health IT for the
collection and submission of data for CMS's hospital quality measures and
inform interested parties about those steps, the expected time frame, and
associated milestones. In commenting on a draft of this report on behalf
of HHS, CMS concurred with these recommendations.

The eight case study hospitals used six steps to collect and submit
quality data: (1) identify the patients, (2) locate information in their
medical records, (3) determine appropriate values for the data elements,
(4) transmit the quality data to CMS, (5) ensure that the quality data
have been accepted by CMS, and (6) supply copies of selected medical
records to CMS to validate the data. Several factors account for the
complexity of abstracting all relevant information in a patient's medical
record, including the content and organization of the medical record, the
scope of information and the clinical judgment required for the data
elements, and frequent changes by CMS in its data specifications. Due in
part to these complexities, most of the case study hospitals relied on
clinical staff to abstract the quality data. Increases in the number of
quality measures required by CMS led to increased demands on clinical
staff resources. Offsetting the demands placed on clinical staff were the
benefits that case study hospitals reported finding in the quality data,
such as providing feedback to clinicians and reports to hospital
administrators.

GAO's case studies showed that existing IT systems can help hospitals
gather some quality data but are far from enabling hospitals to automate
the abstraction process. IT systems helped hospital staff to abstract
information from patients' medical records, in particular by improving
accessibility to and legibility of the medical record. The limitations
reported by officials in the case study hospitals included having a mix of
paper and electronic records, which required staff to check multiple
places to get the needed information; the prevalence of data recorded as
unstructured narrative or text, which made locating the information
time-consuming because it was not in a prescribed place in the record; and
the inability of some IT systems to access related data stored in another
IT system in the same hospital, which required staff to access each IT
system separately to obtain related pieces of information. Hospital
officials expected the scope and functionality of their IT systems to
increase over time, but this process will occur over a period of years.

CMS has sponsored studies and joined HHS initiatives to examine and
promote the current and potential use of hospital IT systems to facilitate
the collection and submission of quality data, but HHS lacks detailed
plans, including milestones and a time frame against which to track its
progress. CMS has joined efforts by HHS to promote the use of IT in health
care, including a Quality Workgroup charged with specifying how IT could
capture, aggregate, and report inpatient and outpatient quality data. HHS
plans to expand the use of health IT for quality data collection and
submission through contracts with nongovernmental entities that currently
address the use of health IT for a range of other purposes. However, HHS
has identified no detailed plans, milestones, or time frames for either
its broad effort to encourage IT in health care nationwide or its specific
objective to promote the use of health IT for quality data collection.

References

Visible links
  27. http://www.gao.gov/cgi-bin/getrpt?GAO-06-54
  28. http://www.gao.gov/cgi-bin/getrpt?GAO-06-54
  29. http://www.gao.gov/cgi-bin/getrpt?GAO-06-1071T
  30. http://www.gao.gov/cgi-bin/getrpt?GAO-05-628
  31. http://www.gao.gov/cgi-bin/getrpt?GAO-06-1071T
  40. http://www.gao.gov/cgi-bin/getrpt?GAO-07-320
*** End of document. ***