- 137 -
5. MOVING QUALITY MEASUREMENT FORWARD
Annual health care expenditures in the United States exceed 13% of
gross domestic product (GDP), or more than $1.5 trillion (Heffler, Smith
et al. 2002).
Despite a growing number of efforts to measure and report
levels of health care quality resulting from these expenditures, useful
information is neither uniformly nor widely available (Institute of
Medicine 2001; McGlynn and Brook 2001).
However, the information that
is available suggests that millions of Americans fail to receive
adequate quality care (Schuster, McGlynn et al. 1998).
Consequently,
there are large potential gains in welfare associated with improving the
performance of the health care system.
Quality of care measurement can
serve both as a catalyst and a tool for such improvement.
In 1999 Congress mandated that the Agency for Healthcare Research
and Quality (AHRQ) develop and publish an annual national quality report
on health care delivery, with the first report to be published in 2003.
To respond to this mandate, AHRQ commissioned the IOM to conduct a study
of how best to measure the overall quality of health care in the nation.
In its resulting report, Envisioning the National Health Care Quality
Report, the IOM suggested that quality measurement should address four
components of health care – safety, patient centeredness, timeliness,
and effectiveness.
Effectiveness refers to the technical quality of
care – that is, “providing services based on scientific knowledge to all
who could benefit, and refraining from providing services to those not
likely to benefit” (Institute of Medicine 2001).
Among its many
findings and recommendations, the report specifically highlights that
data sources presently available to measure effectiveness are deficient.
Clinically detailed information, such as that found in medical
records, is needed for comprehensive measurement of technical quality.
However, paper medical records are not a long-term solution for quality
measurement (Institute of Medicine, 2001).
The costs associated with
locating and abstracting paper medical records are too large to sustain
quality measurement on a routine, timely, and broad basis.
Recognizing
these constraints, this dissertation analyzes whether we are utilizing
- 138 -
claims data, which are widely available and affordable, to their fullest
capacity for quality measurement.
The findings from this study can be
used to develop a road map to close the gap in information needed to
measure technical quality.
Untapped Potential of Claims Data for Quality Measurement
The analysis in Chapter 3 determined that 186 of the QA Tools
indicators (i.e., about one-third of the indicators) could be
constructed with claims data, albeit some more accurately than others.
This is a significant finding.
As a comparison, HEDIS, the most common
set of measures now used to measure performance, includes only 26
indicators of technical quality.
Thus, the capacity of claims data for
quality measurement is not being fully recognized.
These missed opportunities for measurement with claims data could
offer significant value to the health care system.
Increasing the
breadth of quality measurement has the potential to affect the behavior
of payers, providers, and consumers in ways that would improve the
quality of health care. There may even be a business case for expanding
quality measurement with claims data for some types of care.
In
particular, it could be profitable for health plans or providers to do
so to the extent that improving quality reduces costs (e.g., costs of
treatment related to injuries that result from sub-standard quality,
liability for sub-standard care), increases revenues from qualityconscious payers and consumers, or both.
Additional measures of quality would reduce the risk of payers and
providers shifting resources to a small set of indicators on which they
know they will be measured.
When using the leading indicator approach,
such as represented by HEDIS, there is an opportunity for performance to
look good on a small selection of indicators, while having
unsatisfactory performance on the multitude of care processes not being
assessed (Bodenheimer and Casalino 1999).
By increasing the number of
indicators that are routinely measured from 26 to 186, or even a
substantial subset of the 186 selected on the basis of accuracy, the
chance of this undesirable shifting of resources is minimized.
- 139 -
Consumers must find the information in performance reports salient
for it to affect their behavior (Hibbard and Jewett 1997). By increasing
the number of indicators routinely constructed with claims data,
consumers are more likely to identify with indicators that are relevant
to their health care needs and subsequently use comparative reports to
select health plans and providers that offer the best value.
What can and cannot be Measured with Claims Data?
Using claims data for quality measurement is inherently limited to
services that are reimbursed and by the standardized coding systems used
in the claims files.
Despite these limitations to quality measurement
with claims data, this study found that one or more indicators could be
constructed in 35 of the 37 clinical areas included in QA Tools.39
The
ability to identify whether patients receive the indicated care is
primarily determined by whether the services involved are reimbursed.
This means that claims data can be used to measure quality when billable
modes of care such as admissions, immunizations, laboratory services,
medications, surgery, and visits are being assessed.
In contrast, modes
of care that are not typically billable such whether appropriate
education was provided, history was taken, or physical exam was
performed cannot be measured with claims data.
For most clinical
conditions, the indicated services will include modes of care that can
be assessed with claims data.
Determining which patients satisfy an indicator’s eligibility
criteria is largely affected by whether the standardized codes in claims
data sufficiently describe the clinical characteristics of patients.
Since clinically detailed information such as signs and symptoms,
laboratory results, and the findings of procedures, are not coded in
claims data, for many indicators it is not possible to identify the
patients who would be eligible for the indicated care. Although claims
data can be used to assess a wide variety of clinical conditions and
processes, the depth of measurement is limited.
In the remainder of
this chapter, the value of capturing health information electronically
___________
39 No QA Tools indicators assessing the care for Cesarean Delivery
or Osteoarthritis could be constructed with claims data.
- 140 -
and the types of information of high value to quality measurement are
discussed.
Building a Health Information Infrastructure
There have been many calls to develop and implement electronic data
systems to facilitate quality measurement and satisfy other information
needs in the health care system (Institute of Medicine 1991; The
President's Advisory Commission on Consumer Protection and Quality in
the Health Care Industry 1998; Institute of Medicine 2001; Institute of
Medicine 2001).
However, payment issues, rather than clinical needs,
have driven investment in health information technology.
Therefore,
electronic billing systems are generally much more prevalent and
relatively more standardized than clinical information systems.
Although the benefits of electronic clinical data are difficult to
quantify, evidence has begun to accumulate about the ability of
connected and automated information to improve the efficiency and
effectiveness of clinical care.
The availability of information across
providers and settings, for example, allows access to longitudinal
patient information that can help reduce duplicate tests and avoid
errors associated with decisions based on missing information (Schiff
and Rucker 1998; Schiff, Aggarwal et al. 2000).
Using automated
clinical information in the form of decision support tools and reminder
systems also improves care and reduces errors (Bates, Leape et al. 1998;
Dexter, Perkins et al. 2001), and health plans and providers are
beginning to seriously consider the implementation of electronic medical
records (EMRs).
In 2001, 13% of providers had a fully operational EMRs
in place, and another 53% reported they were either beginning to install
the hardware and software for EMRs or have planned EMR implementation
(National Committee on Vital and Health Statistics 2001).
Only a few private and public sector provider organizations have
made strategic moves toward fully integrated information systems.
For
example, Kaiser Permanente is developing a Web-based system that
includes a nationwide clinical information system, patient communication
with doctors and nurses for advice, online guidelines and protocols for
providers, and all administrative functions.
Intermountain Health Care
- 141 -
has a well-integrated hospital-based information system with clinical
decision-support capabilities (Gardner, Pryor et al. 1999).
The
Department of Veteran Affairs and the Department of Defense’s TRICARE40
program have the ability to measure and improve quality through
continuous feedback and the application of computerized decision support
systems (Institute of Medicine 2002).
Fully integrated health information systems have the potential to
facilitate quality measurement and improve the delivery of efficient and
effective care.
However, a giant leap is not practical for most health
care providers and delivery systems.
A series of incremental steps are
required to make widespread automation of health information a reality.
Substantial effort has gone into addressing two of the greatest
impediments to electronic clinical information -- privacy concerns41 and
data standards.42
There have also been attempts to identify the types
___________
40 TRICARE is the health care delivery system for the US military.
41 Information security technologies, such as encryption,
authentication of both the sender and receiver of data, and audit trails
to detect unauthorized users, are available to protect the privacy of
individually identifiable health information (U.S. General Accounting
Office 1999; Detmer 2000). In addition to the technical capacity to
protect privacy, DHHS finalized rules about the confidentiality of
individually identifiable health information in 2002 (Code of Federal
Regulations 2002).
42 A number of US standard development organizations have developed
standards for the exchange and management of data. Some of these
transaction standards, such as Health Level Seven (HL7) and Digital
Imaging and Communications in Medicine (DICOM), are in widespread use in
the US and other countries. HL7 is a Standards Developing Organization
(SDO) that produces standards, or protocols, for the exchange,
management, and integration of clinical and claims data. HL7 does not
develop software for the exchange of data, but rather develops a
messaging standard that enables disparate health care applications to
exchange key sets of clinical and claims data (http://www.hl7.org;
Accessed 2/18/2002). The DICOM Standards Committee creates and
maintains standards for communicating medical images and their
associated information across disparate health care applications
(http://medical.nema.org/dicom/geninfo/dicom_strategy/Strategy_2002-0809b.htm; Accessed 1/1/2003). Standards for codes that give specific
meanings to the content of these messages have also been developed. As
described in Chapter 3, standardized coding systems such as SNOMED and
LOINC have been developed, but adoption of these standards has been
limited. Although LOINC is in the public domain, using SNOMED codes in
any application requires a licensing fee that might prohibit widespread
adoption.
- 142 -
of data elements that should constitute the core of electronic data
collection efforts in health care (National Committee on Vital and
Health Statistics 1996; Schneider, Riehl et al. 1999).
However, the
relative values of these data elements have not been evaluated.
Therefore, as health care delivery systems and providers look to make
investments in clinical information systems, there is no guide to the
types of information that should be obtained first.
What Additional Data Elements Contribute the Most to Quality
Measurement?
The analyses in this dissertation were framed by RAND’s QA Tools
system, which has been recognized by the IOM as a comprehensive approach
to measuring the quality of care (Institute of Medicine, 2001). The QA
Tools system allowed me to consider a large set of indicators that
represent what we would like to know about the delivery of care without
the bias of what information is typically considered to be unavailable
from claims data.
By identifying the data elements required to assess
quality, this dissertation described what can be measured currently with
electronic data and identified the types of data elements that are
required to improve the capacity for quality measurement.
The analyses from Chapter 3 suggest that about 40% of the
information needed for comprehensive quality measurement is not
available from claims data.
Information that is typically unavailable
includes:
•
Counseling and educational discussion
•
Elements of physical exam performed during an encounter and
findings
•
Laboratory results
•
Patient history
•
Procedure findings
•
Signs and symptoms
These types of missing information should be included in health
information systems.
But, which data elements would have the most
important effects on quality measurement?
The findings in Chapter 3
suggest that the addition of no one type would provide a major
- 143 -
contribution to the capacity for quality measurement.
However, among
the five types of information investigated in Chapter 3 –laboratory
results, procedure findings, vital signs, and signs and symptoms- access
to information about patients’ signs and symptoms would leverage quality
of care measurement more than other types of information, however.
Further, Chapter 4 highlights that using diagnostic criteria to identify
who should receive care is the most significant source of inaccuracy.
These findings suggest that as health care delivery systems and
providers consider their next set of investments in health information
systems, they should consider the electronic storage of problem lists
with standardized clinical codes such as SNOMED.
The capacity to
classify people into clinically meaningful groups would improve our
ability to assess whether appropriate care is being delivered to those
most likely to benefit.
This information would also help providers
better manage their patients’ clinical care and provide information to
implement decision support systems.
The capacity for individual health care delivery systems and
providers to measure their performance is not sufficient.
The power of
quality measurement to inform choice and motivate change comes from
having performance information reported across the entire health care
system.
As investments are made in clinical information systems, the
ability to integrate data across delivery systems will be central to
broad and routine quality measurement.
A coordinating structure to
standardize health information is needed.
A health care information
infrastructure could evolve through a Federal mandate to support the
National Healthcare Quality Report or market pressure from health care
purchasers, such as that used to develop HEDIS.
Regardless, resources
and participation from both the public and private sectors are needed to
move quality measurement forward and thereby improve the quality of care
delivered in the US.
In sum, while widespread quality measurement relying extensively
on medical records would provide more broad-based and accurate
information, the associated costs currently appear prohibitive to most
organizations.
This economic reality provides the key motivation for
this dissertation, which has explored less costly, albeit less
- 144 -
informative, ways to expand the scope of quality measurement.
There are
four broad options over the next several years: (1) the status quo; (2)
expanded use of claims data, possibly augmented with the kinds of
information analyzed in chapter 3; (3) expanded use of medical records;
and (4) expanded use of both claims data and medical records.
The
benefits and costs of various versions of the last three options
relative to the status quo, and relative to each other, are important,
but open, questions.