Download The Potential of Claims Data to Support the Measurement of Health

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2.
CONCEPTUALIZING AND MEASURING QUALITY OF CARE
How can we tell whether health care performance is poor, average or
superior?
To answer this question, standards of performance must be
defined and measured.
This chapter provides a brief review of how
quality is conceptualized by health services researchers and discusses
potential data sources for quality measurement.
Measuring the quality
of care with claims data is emphasized.
WHAT IS QUALITY AND HOW IS IT MEASURED?
Quality of care is a multidimensional concept that includes
technical care, the application of medical science and technology to a
problem, and interpersonal care, the personal interaction between
patient and provider.
Quality is often assessed with measures of
structure, process, and outcome (Donabedian 1980).
Measures of
structure are concerned with descriptive characteristics of the health
care market, provider organizations, professional personnel, and the
individuals needing health care services.
providers do for patients.
Processes reflect what
Outcomes pertain to the effects of care on
the patient’s physical, social, and mental functioning.
The Institute of Medicine (IOM) defines quality as “the degree to
which health care services for individuals and populations increase the
likelihood of desired health outcomes and are consistent with current
professional knowledge” (Lohr 1990).
Health outcomes include the
presence or absence of illness, impairments, or handicaps; a patient’s
physical functioning, emotional health, cognitive functioning, pain and
other symptoms; days lost from work, school or usual activities; and a
patient’s satisfaction, knowledge, or compliance.
Although improved
health outcomes are the ultimate goal of delivering health care,
measures of structure and process are of interest because good structure
increases the likelihood of good process, and good process increases the
likelihood of a good outcome.
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Structural measures evaluate the human, physical and financial
resources that are needed to provide medical care.
For health care
providers, structural variables include demographics and professional
characteristics such as specialty and board certification.
For
institutions, structural variables include the number, size and
geographic distribution of health care providers and hospitals as well
as their access to health care equipment and technologies.
The way in
which health care is financed and how providers are reimbursed are also
structural components of health care.
Accreditation of health care organizations by groups such as the
Joint Commission on Accreditation of Healthcare Organizations (JCAHO)
and the National Committee for Quality Assurance (NCQA) has
traditionally relied heavily upon structural measures.
The Leapfrog
Group, a consortium of health care purchasers, uses structural measures
to encourage quality improvement (Meyer and Massagli 2001).
Their
measures include the presence of computerized physician order entry
systems for medications in hospitals, the rate at which patients are
referred to high volume providers for specific surgical procedures, and
the immediate availability of board-certified critical care specialists
in intensive care units.
Each of these measures has been associated
with improved processes and outcomes (Begg, Cramer et al. 1998; Cebul,
Snow et al. 1998; Wennberg, Lucas et al. 1998; Pronovost, Jenckes et al.
1999; Teich, Merchia et al. 2000).
Process measures evaluate the preventive, diagnostic and
therapeutic interventions received by a patient.
A process measure is a
valid indicator of quality when the indicated care has been shown to
have a direct link to improved outcomes.
The rate at which patients
receive aspirin promptly upon presentation with a heart attack, for
example, is a valid quality measure because early initiation of aspirin
improves survival (ISIS-2 1988).
Another valid measure of quality is
the rate at which diabetics have regular testing of their blood sugar
levels because control of HbA1c levels reduces the risk for many of the
complications associated with diabetes (DCCT 1993; Eastman, Javitt et
al. 1997).
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The type of measure selected—structure, process, or outcome—depends
on the purpose of assessment.
Structural characteristics, for example,
can be used to infer that the context in which care is delivered is
conducive to good care, but are generally too blunt to determine whether
care is good or bad.
For example, there can be wide variation in the
quality of care delivered at the same hospital depending on diagnosis,
procedure, or attending physician (Rosenthal, Shah et al. 1998).
Structural measures are unable to reflect these differences.
The
principal value of process measures is that the link between health care
and outcomes highlight what can be changed in the delivery of care to
improve health outcomes.
Nevertheless, our ability to use process
measures is limited by the strengths and weaknesses of clinical science.
Outcome measures are appealing because they appear to be the most direct
assessment of quality; but good quality may not prevent a bad outcome.
Other factors outside the control of the health care system, such as
patient behavior and environment, also affect outcomes.
For a health
outcome to be a valid quality measure, it must be possible to
differentiate between the influences of the health care system from the
effects of other factors.
Outcome measurement is also problematic
because the time between the delivery of health care services and the
outcome of interest can be quite long.
As a rule, quality measurement
activities with components of structure, process, and outcome allow the
strengths of each approach to compensate for the weaknesses of the
others.
WHAT IS A QUALITY INDICATOR?
There is no standardized vocabulary to describe the measurement of
quality in health care.
A variety of terms such as performance reports,
report cards, provider profiles, recommended care, criteria, standards,
measures, and indicators are used in the discussions of quality
measurement – often with subtle, but not standard, distinctions between
them.
I will use a single term, indicator, to refer to explicit
criteria by which the quality of care can be evaluated.
indicators are:
Examples of
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•
All patients aged 65 and over should have been offered
influenza vaccine annually or have documentation that they
received it elsewhere.
•
Recurrent moderate or severe tension headaches should be
treated with a trial of tricyclic antidepressant agents, if
there are no medical contraindications to use.
•
A urine culture should be obtained for patients who have
dysuria and have had "several" (three or more) infections in
the past year.
When an indicator is used to measure a process for many people, it
can be summarized as a performance rate or score.
The performance rate
is the proportion of people who received the indicated care among those
who should have received it.
I refer to the method by which one would
calculate a performance rate as constructing the indicator.
The
patients who comprise the numerator and denominator of a performance
rate must be identified to construct an indicator.
The denominator is
the number of patients who are eligible for the indicator, that is
people who should receive the indicated care.
The numerator is the
number of eligible patients who received the indicated care, or passed
the indictor. The pieces of information required to determine who
satisfies the eligibility and scoring criteria are referred to as data
elements.
Consider the following indicator:
Men under age 75 with preexisting heart disease who are not on
pharmacological therapy for hyperlipidemia should have total
cholesterol, HDL, and LDL levels documented at least every
five years.
For this indicator, eligible patients are men who are less than 75 years
old, have preexisting heart disease, and are not taking a lipid lowering
medication.
Among those who satisfy the eligibility criteria, the
indicator is passed if the patient had his total cholesterol, HDL, and
LDL levels documented at least once in the last five years. If there
were 500 men who met the eligibility criteria, for example, and 300 of
them had their total cholesterol, HDL, and LDL levels documented in the
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past five years, then the performance rate, or score, for this indicator
would be 60%.
DATA SOURCES FOR QUALITY MEASUREMENT
Data are required to measure the quality of health care.
Data can
come from directly observing the practice of care providers, by asking
providers and patients about their actions and experiences, or by
studying the documentation and other records that are produced as health
care is delivered.
The best choice of a data source depends on
available data sources’ content, accuracy, ease of use, cost, and the
purpose of the quality measurement activity.
If cost were not an issue,
a comprehensive quality measurement system would draw on multiple data
sources so that the strengths of each source could be brought to bear.
Logistical difficulties and limited resources generally make such a
comprehensive approach infeasible.
In this thesis, two data sources for
the widespread measurement of technical quality are compared: medical
records and claims data.
Although patient surveys are a common data
source for quality measurement, they are most often used to study
special issues or populations not routinely captured in medical records
or claims data (McGlynn, Damberg et al. 1998).
Medical records.
Health care professionals and institutions
generally maintain medical records for individuals in a paper format.3
Medical records are rich in clinical information such as patient medical
history, primary complaints, presenting symptoms, results of physical
examinations, clinical assessments and diagnoses, test and lab results,
performed procedures, prescribed treatments, and patient response to
treatments.
In the absence of direct observation, medical records are
frequently considered the gold standard data source to measure technical
___________
3 The concept of an electronic medical record (EMR) has been around
for more than thirty years, but adoption has been slow, and paper
records continue to be the dominant format. Among the barriers to the
implementation of EMR systems are software problems of encoding the
complex clinical information found in the written medical records,
security issues, a dearth of integrated delivery systems, reluctant
providers, and high costs (Retchin and Wenzel 1999).
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quality (Fowles, Fowler et al. 1997; Steinwachs, Stuart et al. 1998).
Unfortunately, medical records are not amenable to large-scale quality
assessment because they are an expensive source of data.
Medical record
review is expensive because it requires trained personnel to abstract
data from medical records in a standard format for analysis.
Further,
each patient can have multiple medical records as a result of receiving
care from different doctors and hospitals; to understand fully the care
patients have received from the different providers across multiple
medical conditions, all of their medical records must be obtained and
abstracted.
The burden associated with data collection from medical
records is even higher because it is increasingly difficult to obtain
medical records for quality studies due to concerns over protecting the
privacy of patients’ health information and the enactment of the Health
Insurance Portability and Accountability Act (HIPAA).4
Claims data.
In contrast to medical records, claims data are
generally contained in electronic files.
Claims data are generated for
billing purposes as a result of a patient’s encounter with the health
care system, including outpatient care, hospital care, and filled
prescriptions.
Enrollment data are also maintained electronically by
health plans to identify the people who are eligible for coverage.
Together, claims and enrollment files may contain information on
demographics, diagnoses, delivered services, and prescriptions (McGlynn,
Damberg et al. 1998).
___________
4 Congress enacted the Health Insurance Portability and
Accountability Act (HIPAA) in 1996 and the Standards for Privacy of
Individually Identifiable Health Information (the Privacy Rule) was
finalized on August 14, 2002. The Privacy Rule protects any health
information that can be used to identify an individual. Protected
information includes an individual’s medical records and other personal
health information, and it applies to information in any form of
communication, electronic, oral, or written (Friedrich 2001; Gostin
2001). The rule states that health information may be disclosed for
research without the person’s permission, provided that the study
obtains a waiver from an IRB or privacy board (Code of Federal
Regulations, 2002). However, the financial and incarceration penalties
for compliance failures with HIPAA may lead institutions and IRBs to be
too cautious and to act defensively. Further, providers, such as
community physicians and hospitals, may decide not to give researchers
access to medical records (Kulynych and Korn 2002).
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Although claims data have less clinical information than medical
records, they are widely available and relatively inexpensive to analyze
(Lohr 1990; Dresser, Feingold et al. 1997; Steinwachs, Stuart et al.
1998). In contrast to medical records, claims can be easily deidentified which minimizes concerns about privacy and HIPAA compliance.
Since claims data are routinely collected and computerized, quality of
care indicators can be calculated repeatedly to identify trends and
progress in quality (Asch, Sloss et al. 2000).
The large numbers of
cases generally contained in claims files also permit multiple
comparisons, the testing of hypotheses about population subgroups, and
comparisons across statistical models (Lohr 1990).
Since claims data are easy to use and less costly than other data
sources, they have the potential to contribute to the knowledge base
about the quality of care.
However, any data source used in quality
measurement should be evaluated with regard to two criteria -availability and accuracy.
Evaluating the availability of a data source
means understanding who and what activities are included in the data
source and exactly what type of information the data contain.
Accuracy
addresses whether the data source can generate reliable answers to the
quality question at hand.
The remainder of this chapter reviews what is
known about the availability and accuracy of claims data and provides
examples of how claims data have been used to measure quality.
AVAILABILITY OF CLAIMS DATA
Whose information is included in claims data?
Claims data are a by-product of reimbursing for health care
services.
Therefore, claims data generally include people who have
health insurance, receive health care, and file a claim.5
Further,
___________
5 Other data sources, such as medical records and direct
observation, also depend on patients having an encounter with the health
care system. When people who do not use health care services are
omitted from quality measurement, the extent of problems of access and
under-use of recommended care are likely to be underestimated.
Population based surveys about individuals’ health are an alternate data
source that does not depend on patient encounters with the health care
system.
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individual payers, including private health plans, as well as the State
and Federal governments, possess claims data for only those people for
whom they are responsible for payment.
To compare quality across
payers, claims data need to be pooled.
Although there is no coordinated
strategy to capture all claims data in the US, some organizations have
combined data from multiple plans for comparative purposes.
For
example, private information management companies such as MEDSTAT,
Health Benchmarks, and Ingenix have pooled the claims data they receive
from their different clients.
Similarly, claims data are used to
construct many of the measures in NCQA’s Quality Compass® -- a database
with quality of care information from hundreds of HMOs.
What information is included in claims data?
Although the precise contents of claims files vary by health plan
or insurer, most claims forms capture patient characteristics, provider
identifiers, treatment and diagnostic information, and payment
information.
Health plans tend to use claims forms similar to those
used by the Centers for Medicare and Medicaid Services (CMS) to process
claims for government beneficiaries (Weiner, Parente et al. 1995;
McGlynn, Damberg et al. 1998).
In particular, the Uniform Bill (UB-92)
is the CMS form used to bill for inpatient hospital services and the
CMS-1500 is used to bill for outpatient services.
Tables 2.1 and 2.2
list the standard data elements for hospital and outpatient claims forms
respectively.
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Table 2.1
List of Standard Data Elements: Hospital Claims Forms
Patient Characteristics
Patient identifiers
Name (last, first, middle initial)
Address (street, city, state, zip code)
Date of birth
Gender
Marital status
Provider identifiers
Hospital/facility identifier
Physician identifier
Diagnostic and treatment information
Admission date
Discharge date
Type of admission
Admitting diagnosis code
Codes and description of service
Service date
Service units
Principal and other diagnoses (up to 9)
Principal and other procedures (up to 6)
Date of procedure(s)
Discharge status
Insurance/payment information
Payer identifier (e.g., Medicare or Medicaid)
Group name
Insured’s name
Insured’s identification number
Insured’s group number
Insured’s address and telephone number
Relationship to insured
Employer name
Employer location
Treatment authorization codes
Total charges
Amount paid
SOURCE: (CMS 1998)
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Table 2.2
List of Standard Data Elements: Outpatient Claims Forms
Patient characteristics
Patient identifier
Name (last, first, middle initial)
Address (street, city, state, zip code)
Telephone number
Date of birth
Marital status
Employment/student status
Other health insurance coverage
Provider identifiers
Physician identifier
Physician’s employer identification number
Diagnostic and treatment information
Date of current illness, injury or pregnancy
Condition related to employment or accident
Admission and discharge dates related to service
Principal and other diagnoses (up to 4)
Place of service
Date of service
Code for procedures, services or supplies
Name and identifier of referring/ordering physician
Date of disability
Date patient able to return to work
Insurance/payment information
Payer identifier (e.g., Medicare or Medicaid)
Group name
Insured’s name
Insured’s identification number
Insured’s group number
Insured’s address and telephone number
Relationship to insured
Employer name
Employer location
Treatment authorization codes
Accept assignment of Medicare benefits
Total charges
Amount paid
SOURCE: (CMS 1998)
Since the Medicare program does not include pharmacy benefits,
there is not a US government prototype for the contents of a
prescription claim form.
Therefore, I reviewed the prescription claims
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forms used by three private payers6 to gauge the typical contents of
prescription claims forms (see Table 2.3).
I did not find any
significant differences in the contents among the reviewed claims forms.
Table 2.3
List of Standard Data Elements: Prescription Claims Forms
Patient characteristics
Patient identifier
Name (last, first, middle initial)
Address (street, city, state, zip code)
Date of birth
Gender
Provider identifiers
Physician identifier
Pharmacy address
Pharmacy identifier
Treatment information
Prescription number
Code for dispensed medication
Date prescription filled
New versus refill prescription
Drug name and strength
Quantity
Days supply
Insurance/payment information
Group name
Insured’s name
Insured’s identification number
Insured’s group number
Insured’s address and telephone number
Relationship to insured
Secondary insurance
Total cost of prescription
___________
6 I reviewed the contents of the prescription claims forms for two
health plans (Health Net and PCS HealthSystems®) and one pharmacy
benefit management company (AdvanceRx). The forms were available for
download via the Internet at the following web sites:
http://www.healthnet.com/members/forms/pdf/8670.pdf (accessed October
21, 2001)
http://statenc.advparadigm.com/pdf/APCS_CLAIM_FORM.pdf (accessed October
21, 2001)
http://www.pcshs.com/benefits/forms/standard.pdf (accessed October 21,
2001)
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The clinical data contained in hospital, outpatient, and
prescription claims are limited to codes for diagnoses, services, and
medications.
These codes are frequently from, or can be linked to,
standardized coding systems.
The standardized coding systems for
diagnoses, services, and pharmaceuticals are described below.
Diagnostic codes.
Information in claims data about patients’
clinical conditions is most often in the form of diagnostic codes
specified by the International Classification of Diseases, Ninth
Revision, Clinical Modification (ICD-9-CM).
Use of ICD-9-CM codes is
widespread since providers are generally required to report their
diagnostic assessments via ICD-9-CM codes to be reimbursed for a patient
encounter, especially hospitalizations.
10,300 codes.
ICD-9-CM contains more than
The National Center for Health Statistics (NCHS) and the
CMS are the U.S. governmental agencies responsible for overseeing
changes and modifications to the ICD-9-CM (Iezzoni 1990; Centers for
Disease Control and Prevention 2001).
The diagnostic codes of ICD-9-CM are organized within broad
categories.
Some of these categories represent various types of
conditions (e.g., infectious and parasitic diseases, neoplasms), while
others reflect anatomic locations (e.g., circulatory, digestive,
respiratory systems) and one category is reserved for “symptoms, signs,
and ill-defined conditions.”
Three-, four-, and five-digit codes are
listed, representing increasing levels of specificity.
For example, the
three-digit code 250 indicates diabetes mellitus, while the fourth digit
specifies the manifestation (e.g., 250.5, diabetes with ophthalmic
manifestations) and the fifth digit reflects the type (e.g., 250.52,
diabetes with ophthalmic manifestations, adult-onset type).
For some
disease classifications, only four digits are specified.
Service codes.
Information about performed procedures or
delivered services may be in the form of ICD-9-CM procedure codes, codes
specified by the Current Procedure Terminology, Fourth Edition (CPT4), codes from the Healthcare Common Procedure Coding System (HCPCS),
or Uniform Billing (UB-92) Revenue Codes.
HIPPA final rules require the
use of different coding systems for procedures depending on where the
procedure was performed (Code of Federal Regulations 2000).
Inpatient
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hospital procedures must be reported using the ICD-9 Procedure Coding
System (ICD-9-PCS), while a combination of CPT-4 or HCPCS codes are
required for physician services and other health care services.
ICD-9-PCS.
Volume III of ICD-9-CM includes procedure codes that
are maintained by the CMS. There are almost 4,300 ICD-9 procedure codes;
the codes contain up to four digits.
CPT-4.
The CPT is a systematic listing and coding of health care
procedures and services.
five-digit code.
in 1966.
Each procedure or service is identified with a
The American Medical Association (AMA) developed CPT
The codes are organized into six sections: evaluation and
management, anesthesiology, surgery, radiology (including nuclear
medicine and diagnostic ultrasound), pathology and laboratory, and
medicine (except anesthesiology).
Within each section there are
subsections with anatomic, procedural, condition, or descriptor
subheadings.
The CPT system is revised annually to reflect significant
updates in medical technology and practice. The most recent version of
CPT, CPT 2001, contains 7,928 codes and descriptors.
HCPCS.
The HCPCS reports supplies, professional services, and
procedures for payment.
HCPCS is a three-level coding system, where
level I is equivalent to CPT.
Level II, or National, HCPCS codes are
five-digit alpha-numeric codes used to identify those coding categories
not included in CPT such as ambulance services and durable medical
equipment, prosthetics, othotics and supplies (DMEPOS).
Level II codes
are the result of the combined work of CMS, the Health Insurance
Association of America (HIAA), the American Dental Association (ADA),
and the Blue Cross/Blue Shield Association (BCBSA).
The level III, or
local, HCPCS codes are maintained and assigned by Medicaid State
agencies, Medicare contractors, and private insurers for use in their
specific programs or local areas of jurisdiction.
Level III codes are
established for items or services not having the frequency of use, wide
geographic use, or general applicability needed to establish a new level
I or level II code.
Revenue codes.
UB-92 revenue codes are frequently entered on
claims for payment for the cost centers within a hospital that have
separate charges.
The codes help identify some of the services that are
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delivered to patients, but CPT, ICD-9, and HCPCS codes are much more
specific.
The revenue codes have 3 digits and are listed in two
sections: accommodations and ancillary.
The accommodations revenue
codes specify the type of room the patient had (e.g., private or semiprivate room and the number of beds), the type of unit where the care
was received (e.g., medical/surgical, OB, or psychiatric), and whether
the provided care was more intensive than is typically rendered in the
general medical or surgical units.
The ancillary revenue codes include
charges for items such as nursing services, durable medical equipment,
laboratory or radiology services, and other therapeutic services.
The
National Uniform Billing Committee (NUBC) maintains the list of UB-92
Revenue Codes.
Pharmaceutical codes.
Claims files that track dispensed
medications generally use the National Drug Code (NDC) System.
Each
human drug is assigned a unique 10-digit, 3-segment NDC. The FDA assigns
the first segment, the labeler code. A labeler is any firm that
manufactures, repacks or distributes a drug product. The second segment,
the product code, identifies a drug’s strength, dose, and formulation
for a particular firm. The third segment, the package code, identifies
package sizes. The labeler assigns both the product and package codes.
Summary: Availability of Claims Data for Quality Measurement
Administrative data are generated when a person is covered by
private or public insurance and when claims are filed so that patients
or providers can be reimbursed for health services.
Widespread
availability of claims data and the use of standardized coding systems
facilitate quality measurement.
However, the clinical data found in
claims files are usually limited to codes for diagnoses, services,
procedures, and pharmaceuticals.
ACCURACY OF CLAIMS DATA
When selecting a data source for quality measurement, it is
important to assess accuracy.
Below, findings from previous studies
about agreement between medical records and claims data are reviewed and
sources of error in claims data are described.
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Agreement between medical record and claims data
The accuracy of claims data has been evaluated in several studies
(Romano and Mark 1994; Dresser, Feingold et al. 1997; Steinwachs, Stuart
et al. 1998; Lawthers, McCarthy et al. 2000).
Generally, agreement with
medical records is used to gauge the accuracy of claims data.
medical records review is also subject to measurement error.
However,
Errors in
data abstracted from medical records have several sources including
incorrect or incomplete documentation, illegibility of provider notes,
missing laboratory or other reports, and varying levels of abstractor
skills (Quam, Ellis et al. 1993; Luck, Peabody et al. 2000).
Nevertheless, medical records are rich in clinical data and are
frequently used as the standard against which to judge the accuracy of
other data sources (Fowles, Fowler et al. 1997; Bergmann, Byers et al.
1998).
Studies assessing the accuracy of coding in claims data generally
refer to the overall rate of agreement between claims data and medical
records, the sensitivity of the claims data, and the specificity of the
claims data.
Overall agreement is the rate at which the claims and
medical records data agree about whether a patient has a given medical
condition or received a specific service.
of identifying true positives.
Sensitivity is the likelihood
Therefore, the sensitivity of claims
data is the rate at which they are able to identify patients who,
according to medical records, have the condition or received the health
care intervention of interest.
Specificity is the likelihood of
identifying true negatives, the rate at which claims data correctly
indicate that the condition or procedure of interest did not exist or
occur, assuming the medical records are correct.
For most conditions
and procedures, claims data have better specificity than sensitivity
(Fisher, Whaley et al. 1992; Jollis, Ancukiewicz et al. 1993; Romano and
Mark 1994; Dresser, Feingold et al. 1997; Fowles, Fowler et al. 1998).
In regard to quality measurement, good specificity suggests that among
those patients identified by the medical records data as not meeting the
eligibility and scoring criteria, it is likely that the claims data
assessments will agree.
However, weak sensitivity implies that there
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will be measurement error when claims data are used to construct an
indicator because eligibility and scoring will be underestimated
relative to medical record assessments.
Most studies that have evaluated agreement between claims data and
medical records have used data from hospitalizations.
The National
Diagnosis Related Group (DRG) Validation Study used data from 1984-1985
and found that the overall agreement rate between diagnoses coded in the
claims data and documented in the medical record was 78.2%, but the
level of agreement ranged from 52.7 to 91.4% across conditions (Fisher,
Whaley et al. 1992).
A study of 1988 data in a hospital discharge
database in California found that the sensitivity of coding for eight
conditions ranged from 65 to 100%, while the specificity ranged from
98.8 to 100%.
Hypertension was the most under-reported condition;
sensitivity for the remaining conditions7 was 88% or more.
In the same
study the ranges for the sensitivity and specificity of coding for 16
procedures were 21 to 94% and 99.5 to 100% respectively (Romano and Mark
1994).
Non-invasive procedures tended to be under-reported, while the
sensitivity of coding exceeded 90% for bronchoscopy, hemodialysis,
endoscopy, ateriography, mechanical ventilation, and chemotherapy.8
Some evidence suggests that the quality of claims data has improved over
time, apparently because accurate discharge information is now a
requirement for reimbursement (Fisher, Whaley et al. 1992; Jollis,
Ancukiewicz et al. 1993).
A few studies have analyzed the ability of ambulatory claims data
to identify patients with specific conditions and whether particular
services were provided.
When using a combination of encounter and
pharmacy claims to identify health plan members with hypertension, there
was a 96% agreement rate with medical records about who had hypertension
___________
7 In addition to hypertension, the study by Romano and Mark (1994)
analyzed the coding of cancer, chronic liver disease, chronic renal
disease, chronic cardiovascular disease, chronic lung disease,
cerebrovascular degeneration, and diabetes.
8 The remaining procedures evaluated in the study were: lumbar
puncture, barium radiograph, computed tomography scan,
electroencephalogram, cardiac stress test, electrocardiographic (ECG)
monitoring, pulmonary capillary wedge pressure (PCWP) monitoring,
ultrasound, radionuclide scan, and packed red blood cells transfusion.
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(Quam, Ellis et al. 1993).
Other studies have found high rates of
agreement, ranging from 95 to 98%, about whether Pap smears, cholesterol
screening, and mammograms were performed.
The administration of
immunizations to children and early initiation of prenatal care had
agreement rates of 70 and 67% respectively (Dresser, Feingold et al.
1997; Fowles, Fowler et al. 1997).
The lower rates of agreement for
immunization and prenatal care were attributed to reimbursement policies
where these services did not need to be separately billed for
reimbursement (i.e., global billing) and thus were not captured in
claims data.
Sources of Inaccuracy in Claims Data
The accuracy of claims data is affected by inappropriate or
incomplete coding, whether the health plan is responsible for payment,
reimbursement policies, and the clinical content and coding guidelines
in the standardized coding systems.
These sources of inaccuracy are
described below.
Inappropriate and incomplete coding.
Codes may be either incorrect or absent from a claims file for a
variety of reasons.
To begin, medical record technicians may make
transcription errors (e.g., transpose numbers in the codes), apply the
incorrect code to what the physician has written in the chart (e.g.,
code a new myocardial infarction (MI) as an old MI), or fail to code all
of the diagnoses documented in the medical record (Jollis, Ancukiewicz
et al. 1993; Dans 1998).
Claims records may also be inaccurate because
of provider coding practices.
The provider, for example, may try to
protect patient confidentiality and insurability by recording an
alternate diagnosis for sensitive conditions such as mental illness or
HIV (Dans 1998), or may miscode conditions (by exaggerating condition
severity or changing billing diagnoses) to assure the health insurance
company pays for the care and that the patient can avoid an appeals
process for care that the provider perceives to be necessary (Wynia,
Cummins et al. 2000; Werner, Alexander et al. 2002).
Further, as a way
to reduce administrative burden, providers may not use all applicable
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codes; they may use “super bills” that list the most common diagnoses
and procedures so that the closest code is checked off instead of
writing in a more accurate code (Quam, Ellis et al. 1993; Garnick,
Hendricks et al. 1994).
No record of rendered service.
A health plan’s claims data are generally limited to claims for the
services for which they must pay.
Several factors affect whether a
health plan is responsible for payment.
To begin, if claims are not
submitted to the insurance company by either the patient or provider,
then no record of the service rendered will exist.
Another example of
when health plans do not need to pay claims is when a patient receives
services outside the health plan (e.g., at a public health clinic) or
obtains care not included in his or her benefits package.
Similarly, a
health plan does not need to pay pharmacy claims if a patient pays for
the prescription medication out-of-pocket or purchases an over-thecounter substitute.
Further, if the patient or provider submits claims
before a deductible is exceeded, it may not be maintained in the files
because many insurers save information only on paid claims.
Finally,
health insurance plans are less likely to have information about
services related to long-term care, workers’ compensation, and injuries
resulting from accidents because other organizations are responsible for
payment.
Reimbursement policies.
Health plan reimbursement policies such as capitation, bundling,
and carve-outs, can also limit one’s ability to identify delivered care
through claims data (Dresser, Feingold et al. 1997; Dans 1998).
For
example, prenatal care may be capitated and billed once toward the end
of the pregnancy.
This makes it difficult to identify when the initial
prenatal care visit actually occurred and what services were delivered.
Childhood immunizations are an example of bundled services because they
may be billed as a component of “well-child” visits and thus not
identifiable as separate services in claims data.
Some types of
services, such as mental health or pharmacy benefits, may be “carved-
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out” so that a separate organization is responsible for payment.
In
these cases, the primary insurer may not have access to claims data for
the carved-out service, and these data may be required for quality
measurement.
Coding content and guidelines.
For quality measurement, we often need to group people by diagnosis
to determine who is eligible for any given quality of care indicator.
Unfortunately, using ICD-9-CM codes to identify similar people is
frequently less precise than would be ideal.
To begin, there generally are no ICD-9-CM codes to specify the
severity of a condition (e.g., ICD-9-CM codes do not differentiate
between mild asthma and moderate to severe asthma).
However, additional
information such as pharmacy claims or the types of encounters (e.g.,
emergency room visits or hospitalizations rather than office visits),
can be used to infer disease severity.
ICD-9-CM codes also fail to
distinguish between suspected and confirmed diagnoses and whether a
diagnosis is new or pre-existing.
For example, a single diagnostic code
for diabetes could mean (a) the patient is being evaluated for diabetes,
but the diagnosis has not been confirmed (i.e., the diagnostic code is
used to describe a rule out diagnosis of diabetes), (b) the patient has
been newly diagnosed with diabetes, or (c) the patient has a history
(i.e., prevalent diagnosis) of diabetes.
Examining claims data over a period of time, rather than for a
single encounter, can better discern whether a patient has a specific
disease and distinguish between rule-out, new, and prevalent diagnoses.
For instance, if there is only one encounter coded for diabetes over a
two-year period that includes several encounters and it is early in the
period, then it is more likely that the diagnostic code was used as a
rule-out code and the patient does not have diabetes. A new diagnosis of
diabetes would be best indicated when the first coding of diabetes is
identified, there are subsequent codes for diabetes, and earlier
encounters are not coded for a diagnosis diabetes.
If the patient had
multiple prior visits with a diagnostic code for diabetes, then one
could more confidently infer that the patient had a prevalent diagnosis
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of diabetes.
Increasing the number of diabetic coded visits required to
infer the diagnosis highlights the inherent trade-off between
sensitivity and specificity.
Specifically, if multiple encounters are
required to establish that a patient has diabetes, it is more likely
that diabetics will be missed, but also less likely that people will be
erroneously identified as being diabetic.
If there is not a visit coded
for diabetes, it suggests that the patient is not diabetic.
However,
the absence of a code for diabetes could also occur because the patient
did not seek care for the condition, the provider failed to code the
diagnosis during the time for which the claims data are available, or
the visit occurred before the deductible was met.
As the amount of time
for which data are available increases, the likelihood of errors
associated with being unable to detect the presence of a condition and
whether the diagnosis is new or prevalent decreases.
Identifying patients with a particular diagnosis for quality
measurement is further complicated by vague ICD-9-CM coding guidelines
(Iezzoni 1990; McCarthy, Iezzoni et al. 2000).
The rules that govern
ICD-9-CM coding frequently lack specific clinical definitions and this
increases the likelihood that patients with identical presentations and
diagnoses will be given different codes.
A patient admitted to the
hospital for chest pain, for example, could be assigned a code
corresponding to either precordial pain or angina. Therefore, when using
claims data to identify people with similar diagnoses, it may be
appropriate to use multiple codes.
However, as additional codes are
introduced, the likelihood of including people who do not fit the
criteria of the quality indicator increases.
In sum, using diagnostic codes to group people by diagnosis is
subject to error.
Errors are more likely to occur as the level of
clinical detail increases, such as needing to distinguish disease
severity or identify new diagnoses, and when multiple ICD-9 codes could
be used to communicate identical conditions.
Summary: Accuracy of Claims Data
The accuracy of claims data is frequently evaluated by assessing
agreement with information from medical records.
Agreement varies by
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the diagnoses and procedures being compared and the associated billing
practices.
However, the specificity of claims data is typically better
than its sensitivity.
Errors in claims data are typically due to
inaccurate or incomplete coding by providers, claims not being submitted
for reimbursement, insufficiently detailed diagnosis codes, and vague
guidelines for using ICD-9-CM codes.
APPLICATIONS OF CLAIMS DATA TO MEASURE QUALITY
Claims data have been used to study several aspects of health care
performance.
Beginning in the 1970s, claims data were used to quantify
dramatic variations in medical practice across geographic areas
(Wennberg and Gittelsohn 1973; Wilt, Cowper et al. 1999).
Claims data
have also been used to assess patient access to and utilization of
health services (Lozano, Connell et al. 1995; Lo Sasso and Freund 2000;
Fortney, Borowsky et al. 2002).
Clinical outcomes are measured with
claims data through efforts such as the Complications Screening Program
(CSP) and the Healthcare Cost and Utilization Project Quality Indicators
(HCUP QIs) (Johantgen, Elixhauser et al. 1998).
There are several efforts that successfully measure processes with
claims data.
The Health Plan Employer Data and Information Set (HEDIS)
draws on claims data to construct 26 indicators of technical quality.
HEDIS is primarily used to compare the performance of HMOs, but other
types of health plans such as PPOs are also beginning to use and report
their performance on HEDIS measures.
Quality of care indicators have
also been developed by the CMS and its contractors, Quality Improvement
Organizations (QIOs), to monitor the quality of care provided to
Medicare beneficiaries for six conditions.
However, monitoring of only
two of these conditions (breast cancer and diabetes) rely solely on
claims data (Jencks, Cuerdon et al. 2000).
Although these measurement
activities provide information about quality, they represent just a
fraction of the processes that are known to improve outcomes.
The objective of this dissertation is to provide a foundation that
could lead to broader use of claims data for quality measurement.
Essential to this objective is the answer to the following: Are there
dimensions of technical quality that are not measured with claims data,
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but could be?
To address this question, my analysis begins with an
extensive list of health care processes that would be ideally measured
to assess the quality of technical care.
Then, I identify what is
feasible to measure with claims data from this comprehensive list of
indicators.
I examine hundreds of quality of care indicators to characterize
the dimensions of clinical quality that could be measured with claims
data.
The types of data elements that would increase the capacity for
quality measurement with claims data are also identified.
To gauge the
validity of quality of care measurement with claims data, I then analyze
the factors associated with better and worse agreement between claims
and medical records data.