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3. ASSESSING THE FEASIBILITY OF MEASURING QUALITY WITH CLAIMS DATA
Chapter 2 highlighted that claims data are widely available,
relatively inexpensive, and amenable to analysis because they are
readily available in an electronic format.
However, standardized
quality measurement efforts that rely primarily on claims data are based
on just a handful of indicators.
Chapters 3 and 4 characterize the dimensions of clinical quality
that could be evaluated with claims data.
With this knowledge, we can
identify the situations where the additional costs of using medical
records might be justified by sufficiently improved quality information
and also identify some incremental changes that could be made to
administrative databases that would allow for significant gains in
quality measurement without medical record review.
This chapter addresses two questions:
(1)
What dimensions of technical quality can be measured with
claims data?
(2)
If incremental steps were taken to supplement the information
in claims data, how would the capacity of electronic data for
quality measurement increase?
I address these questions by assessing the feasibility of constructing
more than 550 quality of care indicators with claims data.
In this
context, feasibility is defined by whether all of the information
required to construct an indicator is typically available, or could be
reasonably well approximated, from claims data.
Then, data elements
that are not currently contained in claims data, but are found in other
electronic clinical information systems are used to study how much the
utility of claims data for quality measurement might increase if the
additional data were available.
The accuracy of quality measurement
with claims data is addressed in Chapter 4.
To provide background, the following are described: (1) the
quality of care indicators that are used in this analysis, (2) how
quality of care indicators are constructed with claims data, and (3) a
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framework for characterizing the dimensions of quality that are more or
less well suited for assessment with claims data.
SELECTED INDICATOR SET: QA TOOLS
Indicators from the RAND Quality Assessment Tools (QA Tools) system
were used to evaluate the feasibility of quality measurement with claims
data.
QA Tools is a comprehensive and clinically detailed approach to
quality measurement (McGlynn, Damberg et al. 2000).9
The QA Tools
system includes 553 indicators that relate to the adult population and
span 37 clinical areas (Table 3.1).10
The QA Tools system measures
processes of care and has been used to assess quality in communities
across the nation, as well as within individual HMOs and provider groups
(Asch, Kerr et al. 2001).
___________
9 Other indicator sets that measure processes include the
Healthplan Employer Data and Information Set (HEDIS), the CMS’s Health
Care Quality Improvement Program (HCQIP), and the Joint Commission on
Accreditation of Healthcare Organization’s (JCAHO) ORYX/Performance
Measurement System. However, I selected the QA Tools system over these
alternatives because it is the most extensive and included indicators
corresponding to all of those found in the alternate measurement
systems.
10 QA Tools also includes quality indicators specific to children.
However, to limit the scope of this analysis, these indicators were not
considered.
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Table 3.1
Adult Clinical Areas in the QA Tools System
Adult screening and prevention
Hormone replacement therapy
Alcohol dependence
Human immunodeficiency virus
Asthma
Hyperlipidemia
Atrial fibrillation
Hypertension
Benign prostatic hyperplasia
Immunizations
Breast cancer
Low back pain
Cataracts
Orthopedic conditions
Cerebrovascular disease
Osteoarthritis
Cervical cancer
Pain management for cancer
Cesarean delivery
Peptic ulcer disease and dyspepsia
COPD
Pneumonia
Colorectal cancer
Prenatal care and delivery
Congestive heart failure
Prostate cancer
Coronary artery disease
Tuberculosis
Depression
Upper respiratory tract infection
Diabetes mellitus
Urinary tract infections
Family planning
Uterine bleeding and hysterectomy
Headache
Vaginitis & STDs
Hip fracture
SOURCE: (Asch, Kerr et al. 2000; Kerr, Asch et al. 2000; Kerr, Asch
et al. 2000)
Indicators included in the QA Tools systems depended on (a) the
presence of evidence indicating a specific intervention improves care,
(b) the proximity of the intervention to improved health, and (c) the
data that were believed to be available from medical record data.
The types of health care processes assessed by the indicators was
determined, in part, by the variation in the level of evidence to
support causal relationships between health care processes and outcomes.
The quantity and rigor of scientific evidence linking patient education
to improved health outcomes, for example, is not as substantial as the
literature that demonstrates links between specific medications and
improved health outcomes.11
Without an established process-outcome
link, a valid indicator cannot be developed.
___________
11 The varying degrees and quality of evidence for the different
modes of care is reflected by the contents of The Cochrane Library, an
electronic publication that is published quarterly
(www.updateusa.com/cochrane). The Library includes nearly 2500
abstracts of systematic reviews from around the world on the effects of
health care interventions. The Library contains, for example, 772
reviews on “drug-therapy,” but only 52 reviews on “patient-education.”
-32-
It is the provision of specific services that are most closely
linked to good outcomes.
Therefore, relatively few QA Tools indicators
assess the care modalities of admission and visit.
What is actually
done during an admission or visit is more important clinically than the
encounter itself.
The QA Tools indicators were written to be constructed with data
abstracted from medical records (McGlynn, Damberg et al. 2000).
If a
proposed indicator required information that could not be obtained from
medical records, then it was not accepted into the QA Tools system.
For example, medical records are generally not considered to be a good
data source to assess education because providers often fail to document
the education and counseling delivered during the course of a health
care encounter (Stange, Zyzanski et al. 1998).12
Thus, relatively few
indicators assess whether education was provided. However, education
indicators, such as advising smokers to quit or providing diabetics with
dietary counseling, were included in the QA Tools system if there was
professional consensus that failure to document the provision of the
indicated care is consistent with poor quality.
The fact that the availability of data from medical records framed
the development of the indicators represents the principal value of
using the QA Tools indicators for this analysis.
That is, the QA Tools
indicators allow me to consider a large set of indicators that represent
what we would like to know about the delivery of care without the bias
due to what information is typically considered to be unavailable from
claims data.
CONSTRUCTING QUALITY OF CARE INDICATORS
Data elements are the building blocks for constructing quality of
care indicators.
As illustrated by the following example, one or more
data elements are required to construct an indicator:
___________
12 The dearth of good information on the provision of education
from any type of data limits the ability of researchers to develop valid
quality measures to assess education.
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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.
To construct the indicator, the criteria must be identified to define
whether an individual is eligible for and has received the indicated
care.
The following types of data elements are needed to determine
whether a patient satisfies the eligibility criteria: gender, age,
disease history, and medication use.
Among eligible patients, those who
had their total cholesterol, HDL, and LDL tested in the past five years
would pass the indicator.
To assess whether it is feasible to use a specific data source to
construct a quality of care indicator, we need to know whether each of
the data elements involved in the eligibility and scoring criteria are
either available or can be reasonably well approximated from the data
source of interest.
Data elements may be available from more than one
type of data source (e.g., a patient’s gender might be determined
through claims data, medical records or a patient survey), and some
quality measurement efforts may be able to employ more than one data
source.
However, the goal of the current analysis is to determine the
dimensions of clinical quality that could be assessed with claims data
alone.
CONCEPTUAL MODEL FOR CHARACTERIZING QUALITY MEASURES
This analysis focuses on quality of care indicators that measure
processes.
Processes can be described by the following:
• Setting: inpatient, ambulatory, home
• Appropriateness of care: underuse, overuse, misuse
• Specific conditions: diabetes, asthma, urinary tract infection,
atrial fibrillation, etc.
• Function of care: screening, diagnostic, treatment, follow-up
• Type of care: preventive, acute, chronic
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• Modality of care: admission, education, history, immunizations,
laboratory services, medication, physical examination, surgery,
visits to a health professional
Although processes can be classified in many ways, I use the
modality of care to organize and guide the analyses reported here.
The
function and type of care are used as the secondary means of
classification to analyze and describe the type of indicators that can
be constructed with claims data.
A discussion of these classifications
and how the QA Tools indicators are distributed across them is presented
below.
Primary Means of Indicator Classification: Modality
The modality of care characterizes the intervention or service
provided to a patient.
I selected modality to characterize the
dimensions of quality that can be assessed with claims data because the
availability of information from claims data depends directly on the
modality of care.
Specifically, entries in claims records are triggered
by claims for payment (to providers) or reimbursement (to consumers) for
billable events.
Therefore claims data include information about
billable modes of care such as office visits, inpatient stays,
laboratory tests, or filled prescriptions.
In contrast, claims data do
not have information about care modalities such as taking a patient
history, providing health education and counseling, or performing the
basic elements of a physical examination because these interventions
generally do not qualify for separate payments and thus do not result in
a claim for payment.
Table 3.2 defines the common modes of care, specifies the number
of QA Tools indicators that assess each of them, and indicates whether
the mode is typically billable.
The developers of the QA Tools systems
classified the modes of care assessed by each indicator.
Table 3.2
reflects the primary classification; of the 553 indicators, 36 had a
secondary classification for mode.
classifications were not referenced.
In this analysis, secondary
Modes of care were classified as
typically billable if there are corresponding standardized codes (from
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ICD-9-PCS, CPT, HCPCS, UB-92, NDC) that can be entered on claims forms.
For example, claims for hospital admissions can be submitted for payment
using procedure and revenue codes, but there are no procedure codes
specific to education activities.
There are ICD-9 V-codes that can be
coded for education, if people are seeking consultation without
complaint or sickness, however these codes are not used to determine the
rate of payment.
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Table 3.2
Modalities of Care: Definitions, Distribution in QA Tools, and
Relationship to Billing
Modality
Definition
Number of QA
Tools
Indicators
9
Billable?
Yes
Admission
Patient admitted to a hospital
for care in an inpatient
setting.
Education
Counseling or information
provided by a health care
provider that informs patients
of health behaviors (e.g.,
smoking cessation, blood sugar
monitoring, dietary
modifications) that contribute
to improved health outcomes.
32
No
History
Provider documentation of
chief complaint, associated
symptoms, review of systems,
medication use, allergies, and
prior medical problems and
treatments.
75
No
Immunization
Administration of a vaccine.
18
Yes
Laboratory
Services
Performance of laboratory and
radiology tests.
149
Yes
Medication
Medications prescribed by the
physician.
120
Yes
Physical
examination
An examination of specified
system functions,
documentation of vital signs
and appearance of the patient.
88
No
Surgery
Performance of a surgical
procedure.
36
Yes
Visit
A patient encounter with a
health care provider in an
ambulatory setting (i.e.,
doctor’s office, outpatient
clinic, emergency room).
10
Yes
Other
intervention
Delivery of health care
services not defined by the
modes of care listed above
16
Yes
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(e.g., physical therapy,
hearing evaluation).
Since claims data exist primarily to pay for health care, I
anticipate that modality of care will provide a useful framework for
understanding the types of quality of care indicators that can be
constructed with claims data.
To better understand the capacity of
claims data for quality assessment, I will also characterize the
indicators that can be constructed with claims data by the function and
type of care.
Secondary Means of Indicator Classification
Function.
The function of care (screening, diagnosis, treatment,
and follow-up) relates to why a service was performed.
Figure 3.1 shows
that among the QA Tools indicators, various modalities of care can be
employed to screen, diagnose, treat or follow-up on a condition.
Some
modes of care are used primarily to implement a single function of care,
while other modes are employed across the continuum of care.
For
example, medication is generally used to treat a condition, but is not a
common intervention for screening, diagnosis, or following-up on a
condition.
In contrast, laboratory tests are performed to screen,
diagnose, inform treatment and provide follow-up for a disease.
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Type.
Many quality of care reporting efforts evaluate preventive,
acute and chronic care separately.13
Given this tendency, it is
important to consider which types of care are more or less amenable to
measurement with claims data.
Figure 3.3 illustrates that multiple care
modalities are used to assess each type of care.
Again, if the modes of
care are categorized as being billable or not, it suggests that claims
data exist for the measurement of some indicators of preventive, acute
and chronic care (Figure 3.4).
___________
13 The Foundation for Accountability (FACCT) has developed a
framework to communicate health care quality information to consumers
(Lansky 1998). Many organizations, including NCQA, have adopted the
FACCT framework for widespread quality reporting efforts. The FACCT
model organizes comparative information about quality performance into
five categories: The Basics, Staying Healthy, Getting Better, Living
with Illness, and Changing Needs. The Staying Health, Getting Better,
and Living with Illness categories involve information on the quality of
care in preventive, acute and chronic care, respectively.
-43-
Summary
The availability of information from claims data is directly
related to the primary function of claims data, namely, payment for
health care services.
Although all modes of care can contribute to
overall quality, services within only some modes typically affect
payment. Given the relationship between the modality of care and the
likelihood of information being available from claims data, I will use
the modality of care to predict the dimensions of quality that can be
assessed with claims data.
I will also describe the dimensions of
quality that can be measured with claims data in terms of why care
should be delivered (i.e., the function of care) and for what kind of
condition (i.e., type of care).
METHODS
Four steps were taken to characterize the dimensions of quality
that can be assessed with claims data.
First, I identified the data
elements required to construct each of the 553 QA Tools indicators.
Next, I classified each of the data elements into 22 general categories.
I then assessed whether each data element is available from claims data.
Finally, I determined whether it is feasible to construct the indicators
with claims data based on the availability of data elements.
These four
steps are now detailed.
Step 1: Identifying Data Elements
To identify the data elements required to construct each of the QA
Tools indicators, I used the analysis plans developed at RAND to
construct the indicators with medical records data.
The RAND analysis
plans have a single eligibility and scoring statement for each
indicator; these statements refer to specific variables that were
defined through the medical records data abstraction process.
I
decomposed these eligibility and scoring statements into individual data
elements.
Consider, as an example, the following indicator:
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A theophylline level should be obtained for patients on
theophylline who present with an exacerbation of COPD.
For this indicator, the data elements required to determine eligibility
are:
•
•
Date of COPD exacerbation
Dates of prescriptions for theophylline
Similarly, the data element for passing is:
•
Dates theophllyine level checked
A total of 982 unique data elements were identified as being
required to construct the 553 indicators from the QA Tools system.
If a
data element was required to construct more than one indicator, it was
counted only once.
For example, gender was required to construct 27 of
the indicators, but represents just one of the total 982 data
elements.14
For the current analysis, general descriptions of the data
elements were listed.
Detailed specifications such as the relevant ICD-
9-CM, CPT, or NDC codes were not identified for analysis in this
chapter.15
The analysis described in Chapter 4 generates detailed
specifications for a sub-set of the QA Tools indicators.
Step 2: Classifying the Data Elements
After the data elements required to construct each of the QA Tools
indicators were identified, they were classified into 22 primary
categories.
These categories provide a mechanism for describing the
types of information that can and cannot be obtained from claims data.
Sub-categories were also assigned to some of the data elements to
further detail the types of information needed to assess the quality of
care.
Table 3.3 lists the primary and secondary data element categories
that were assigned.
___________
14 If data elements were counted each time they were required to
construct an indicator, the count of data elements would have been
2,463.
15 Specific code values would be required to actually construct the
indicators with claims data.
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Table 3.3
Categories Used to Classify Data Elements
Primary Category
Age
Sub-Categories
Counseling/discussion
Diagnosis
Current diagnosis, duration, history, new, ruleout, severity
Documentation
Contraindication, other
Encounter
Follow-up, new to provider, provider type,
routine care, specific service, time
Gender
History
Diagnosis, drug/alcohol use, family, functional
status, immunization, lab result, medication,
medication allergy, physical activity,
procedure, sexual, smoking status, social,
treatment, other
Immunization
Laboratory service
performed
Laboratory-results
Laboratory-time
Medical equipment
Patient preference
Physical exam
Findings, vitals, other
Prescription
Daily dose, duration, filled, hospital
administration, initial, ordered, route
Procedure-findings
Procedure-performed
Procedure-time
Race
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Referral
Setting
Ambulatory, critical care bed, ER, ER/hospital,
hospital, office, phone
Signs & symptoms
Table 3.4 summarizes the distribution of the types of data elements
required to construct the 553 QA Tools indicators and highlights that
the data elements required to define eligibility typically differ from
those required to score a quality of care indicator.
For example, data
elements that characterize a patient, such as age, diagnosis and gender,
are generally needed for eligibility, while data elements for health
care interventions such as counseling or discussion, performing
laboratory tests and procedures, and prescribing medications are more
frequently required for scoring.
-47-
Table 3.4
Distribution of Data Element Types across Components of Indicators*
Age
Counseling/discussion
Diagnosis
Documentation
Encounter
Gender
History
Immunization
Laboratory service
performed
Laboratory-results
Laboratory-time
Medical equipment
Patient preference
Physical exam
Prescription
Procedure-findings
Procedure-performed
Procedure-time
Race
Referral
Setting
Signs & symptoms
Eligibility
Scoring
Indicator
64
5
406
2
27
27
90
0
3
0
41
8
12
35
0
60
19
68
64
44
409
14
62
27
140
19
70
64
6
0
2
23
78
26
57
2
1
0
99
95
9
1
3
1
76
127
4
141
6
0
1
16
30
64
7
3
3
98
168
30
166
8
1
1
108
120
* The columns in this table report the number of eligibility
statements, scoring statements, and complete indicators that
require the different types of data elements.
Step 3: Assessing the Availability of Data Elements in Claims Data
After the data elements required to construct the QA Tools
indicators were identified and classified, I assessed whether they were
available in claims data.
To do so, I assumed claims data to include
basic demographic information (e.g., gender, age), and claims for
hospitalizations, ambulatory care, and outpatient pharmacy.16
Although
___________
16 The availability of pharmacy claims data depends on the benefit
package of the health plan. If a health plan does not offer a pharmacy
benefit or if outpatient prescriptions are carved-out and paid by
another entity, then pharmacy claims are not likely to be a component of
the health plan’s claims data. However, for the purpose of this
feasibility analysis, I have assumed outpatient pharmacy claims to be a
component of a health plan’s claims data.
-48-
there are operational systems that electronically capture other types of
information, including laboratory results, radiology findings, and
medications administered during hospitalizations, they were not
considered to be routinely available in claims data because these data
are generally used and controlled by providers, not the health plan
where the claims data reside.
A data element was considered to be available if it could be found
directly in claims data by referencing a specific type of code (e.g.,
ICD-9-CM, CPT, or NDC) or data field (e.g., gender), or if the data
element could be approximated with claims data through reasonable
assumptions.
Information that can be directly obtained from claims data
includes (a) hospital admissions or office visits for a specific
condition, (b) the types of medications dispensed at outpatient
pharmacies, and (c) various laboratory tests or other procedures.
For
many other types of information, claims data do not directly represent
the information of interest.
For example, there are no data fields in
claims files for aspects of a patient’s diagnostic history.
However,
claims data can be used to gauge imperfectly whether a patient has a
history of a certain condition by looking in the past for the diagnosis
codes of interest.
If the claims data did not include the diagnosis
code of interest for the patient, it suggests the patient does not have
a history of the condition.
Step 4: Feasibility of Constructing Indicators with Claims Data
Based on the availability of data elements, I determined whether
each QA Tools indicator could be constructed with claims data.
I
concluded it is feasible to construct the indicator with claims data if
all data elements required to determine both eligibility and scoring
were available or could be approximated.
The process of listing, classifying and coding the availability of
data elements is illustrated below for two indicators and depicted in
Figures 3.5 and 3.6.
Example 1: An indicator that can be constructed with claims data
Figure 3.5 expands upon the data element analysis for the following
indicator discussed earlier in this chapter:
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A theophylline level should be obtained for patients on
theophylline who present with an exacerbation of COPD.
The three data elements required to construct the indicator are date of
COPD exacerbation, dates of prescriptions for theophylline and the dates
on which the patient’s theophllyine level was checked.
Row 1 of Figure 3.5 indicates that a visit for a COPD exacerbation
is required to construct eligibility; the data element was classified as
a diagnosis with a secondary classification of severity.
Since there is
a specific ICD-9-CM code (491.21) that indicates a COPD exacerbation,
there is a “Y” in the column titled “Available” to indicate that the
data element can be obtained from claims data.
Only patients who were taking theophylline at the time of the COPD
exacerbation are eligible for this indicator.
Although the provider is
likely to document in the medical record the medications that the
patient is taking at the time of the exacerbation, this would not be
entered in claims data.
However, pharmacy claims can be used to
approximate whether the patient was on theophylline at the time of the
exacerbation (Row 2, Figure 3.5).
To do this, we must assume that if a
patient was on theophylline, that he filled the prescription within the
health plan so that a claim was generated, and took the medication
regularly.
Then we can use the days supplied variable from the pharmacy
claims to see whether a sufficient amount of medication was dispensed
for the patient to be taking the theophylline at the time of the
exacerbation.
These assumptions are likely to exclude some patients who
were taking theophylline and include others who were not taking the
medication.
Nevertheless, employing some assumptions, it is feasible to
construct the eligible population with claims data (Row 3, Figure 3.5).
The only data element required to score the indicator in this
example is a list of the dates on which the patient had his theophylline
level checked (row 4).
Laboratory tests such as measurement of
theophylline, can be identified with CPT or ICD-9 procedure codes;
therefore, the data element is coded as being available from claims
data.
Since both the eligibility and scoring populations for this
-50-
indicator can be determined with claims data, it is feasible to
construct this indicator with claims data.
Figure 3.5
Illustrating the 4 Steps of the Feasibility Analysis: Example 1
Quality of Care Indicator: A theophylline
level should be obtained for patients on
theophylline who present with an exacerbation
of COPD.
Primary
Row Data element
classification
ELIGIBILITY
1 Date of COPD
exacerbation
Diagnosis
2 Dates of prescriptions
for theophylline
Prescription
3 Feasible to determine eligibility?
SCORING
4 Theophllyine level
checked
6 Feasible to construct indicator?
Severity
Y
Filled
Y
NA
Y
Yes
Lab-performed
5 Feasible to determine scoring?
Mode:
Laboratory
Function: Diagnosis
Type:
Acute
Secondary
Class
Available
Yes
Yes
Example 2: An indicator that cannot be constructed with claims data
Consider the following indicator:
Type 2 diabetics who have failed dietary therapy should
receive oral hypoglycemic therapy.
This indicator cannot be constructed with claims data.
Eligibility is
defined by people who have a diagnosis of Type 2 diabetes and continue
to have uncontrolled blood sugar values after trying dietary therapy.
The diagnosis of Type 2 diabetes (Figure 3.6, row 1) can be obtained
from claims data.
However, determining whether a patient has tried
dietary therapy generally involves significant inaccuracy.
If the
-51-
patient had a specific visit with a nutritionist or diabetic educator
(row 2), there could be a procedure or revenue code for the visit that
would indicate that the patient tried dietary therapy.
On the other
hand, if the patient’s physician provided dietary counseling during a
regular office visit (row 3), the claims data are unlikely to capture
that activity because education and counseling during office visits are
rarely distinct billable services.
An alternate way to discern whether
a patient has tried dietary therapy is to assume that in the absence of
pharmacotherapy (rows 4 and 5), dietary therapy is being used to treat
the diabetes.
With this asssumption, claims data can reasonably
approximate whether the patient tried dietary therapy.
If hemoglobin A1c (HbA1c) values are high, this suggests that the
current therapy is not sufficiently controlling the patient’s blood
sugar.
Therefore, HbA1c values are needed to determine whether a
patient fails dietary therapy.
Although claims data can be used to
determine whether HbA1c has been measured, the result of a laboratory
test is not captured in claims data (row 6).
Since claims data cannot
be used to identify the diabetics on dietary therapy or those patients
that are failing their current therapy, eligibility (Figure 3.6, row 7)
cannot be determined with claims data.
To score this indicator, we need to know whether an oral
hypoglycemic agent was prescribed.
is available in claims data.
It is expected that this information
When prescriptions are filled within the
given health care system a claim is generated.
This claim indicates the
medication prescribed by the provider (row 8).
However, no claim will
exist if a patient fails to fill the prescription or obtains the
medication outside the payment system.
Nevertheless, the data elements
required to score this indicator are expected to be in claims data
because I assumed most filled prescriptions will result in a claim.
-52-
Figure 3.6
Illustrating the 4 Steps of the Feasibility Analysis: Example 2
Quality of Care Measure: Type 2 diabetics
who have failed dietary therapy should
receive oral hypoglycemic therapy.
Primary
Row Data element
classification
ELIGIBILITY
1 Diagnosis of Type 2
diabetes
Diagnosis
2 Dietary counseling visit Encounter
Mode:
Medication
Function: Treatment
Type:
Chronic
Secondary
Class
Available
NA
Y
Specific
service
Y
NA
N
3 Dietary/nutritional
issues discussed
Counseling/
discussion
4 Prescription for oral
hypoglycemic agent
Prescription
ordered
Y
5 Prescription for insulin Prescription
ordered
Y
NA
N
ordered
Y
6 HbA1C values
Laboratory results
7 Feasible to determine eligibility?
SCORING
8 Prescription for oral
hypoglycemic agent
No
Prescription
9 Feasible to determine scoring?
10 Feasible to construct indicator?
Yes
No
Summary of approach to feasibility analysis
The data elements required to construct each of the 553 QA Tools
indicators were identified,
assigned to one of 22 categories, and
assessed according to whether they could be determined or reasonably
well approximated from claims data.
The feasibility of constructing
each indicator was then determined.
Figures 3.5 and 3.6 illustrate this
process.
Some indicators can be constructed completely with claims data
(Figure 3.5).
For other indicators it is feasible to identify the
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eligibility or scoring populations for the indicator, but not both
(Figure 3.6).
There are also QA Tools indicators for which claims data
cannot be used to determine whether the eligibility or scoring criteria
were satisfied.
The distribution of the types of data elements
available in claims data and the kinds of indicators that could be
constructed with claims data are described below.
FINDINGS: DATA ELEMENT ANALYSIS
Almost 1000 unique data elements were identified as being required
to construct the 553 QA Tools measures; 59% of these elements were
categorized as being available from claims data.
the data element analysis.
Table 3.5 summarizes
For each of the 22 primary data element
classifications, the table includes (a) the number of data elements that
were assigned to that category and (b) the proportion of the data
elements in the category that were classified as being available in
claims data.
The categories are presented in descending order of
proportion available from claims data.
-54-
Table 3.5
Number of Data Elements by Classification and Proportion Available in
Claims Data
Data element
classification
Age
Gender
Immunization
Medical equipment
Setting
Laboratory service
performed
Procedure-performed
Encounter
Diagnosis
Prescription
Procedure-time
Signs & symptoms
History
Physical exam
Laboratory-results
Counseling/discussion
Documentation
Laboratory-time
Patient preference
Procedure-findings
Race
Referral
Total
Count of
Data
Elements
Proportion
Available in
Claims Data
1
1
7
3
27
61
1.00
1.00
1.00
1.00
1.00
0.95
84
23
219
174
4
71
117
68
42
30
16
2
3
26
1
1
982
0.92
0.91
0.90
0.88
0.25
0.23
0.15
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.59
As shown in Table 3.5, basic demographic information, namely age
and gender, is readily available from claims data.
Other information
usually available in claims data includes diagnoses, the types of
laboratory tests or procedures that were performed, the types of
prescriptions dispensed, and the setting of the delivered care.
The
availability of these data elements is consistent with the types of
information required to administer (e.g., identify health plan
enrollees) and pay for health care services.
At the other end of the
spectrum, none of the data elements could be obtained from claims data
in the counseling/discussion, documentation, laboratory-time, patient
preference, procedure-findings, race, or referral classifications.
-55-
As described in Chapter 2, ICD-9-CM, CPT, HCPCS, UB-92 Revenue
Codes and NDC are standard coding systems used in claims data.
Although
these coding systems convey information on diagnoses, delivered
services, and dispensed medications, the availability of information
within each of these domains is variable.
The potential availability of
this information from claims data is described below.
Availability of information on laboratory tests
Three primary classifications of data elements were assigned to
laboratory services: performed, results and time.
As shown in Table
3.5, information on whether laboratory tests were performed can almost
always be determined with claims data (95% of the laboratory-performed
data elements could be obtained from claims data).17
However, data
elements related to the results or timing of laboratory services do not
affect payment and are not a required component on submitted claims.
Availability of information on procedures
The data elements related to procedures were assigned to one of the
following categories: performed, timing, and findings.
The proportions
of data elements for each of these classifications that could be
obtained from claims data are 0.92, 0.25, and 0.00 respectively (see
Table 3.5).
Neither the findings of procedures (e.g., ejection
fractions, mammogram suggestive of malignancy) nor the time18 at which
procedures are performed are coded in claims data.
The data elements in
the procedure-performed category that could not be determined from
___________
17 The three data elements from the laboratory-performed category
that could not be obtained from claims data are whether the fluid from
arthrocentesis of the knee was analyzed for (1) cell count, (2) gram
stain, and (3) crystals. CPT code 20610 represents arthrocentesis of
the knee, but there are no other codes to specify the types of analyses
that were performed on the fluid.
18 The date on which a procedure is performed is available in
claims data, but detailed information such as the hour and minute at
which a procedure is performed is not available. Therefore, claims data
are not sensitive to time-frames that are less than 24 hours. The one
data element for time that was coded as being available from claims data
was that an EKG be performed within 24 hours of admission. The other
indicators required that procedures be performed in less than 24 hours,
in these cases the “procedure-performed” data elements were coded as
unavailable.
-56-
claims data were patient care services that generally are not paid
separately (e.g., repositioning the patient) and therefore do not have a
specific procedure code.
Availability of information on medications
Eighty-eight percent of the data elements in the prescription
category could potentially be obtained from claims data (see Table 3.5).
The sub-classifications assigned to data elements in the prescription
category included daily dose, duration, filled, initial, hospital
administration, ordered, and route (see Table 3.6).
With the exception
of the sub-category of hospital administration, all data elements in the
prescription category were coded as being available from claims data.
Although a hospital may have an information system that logs all
medications administered to a patient during a hospitalization, the
information tends not to become a part of health plans’ claims data
because the administered medications typically do not affect payment to
the hospital.
Another issue related to inpatient medications is the
ability to assess the timeliness with which they were administered.
Even if health plans had access to inpatient pharmacy information, the
time of administration relative to time of admission would remain
unknown.
-57-
Table 3.6
Assessment of Data Elements in Claims Data: Prescription SubClassifications
Prescription subclassification
Count
Daily dose
Duration
Filled
Hospital administration
Initial
Ordered
Route
Total
11
6
47
12
8
72
10
174
Proportion
Available from
Claims Data
1.00
1.00
1.00
0.00
1.00
1.00
1.00
0.88
Availability of information on diagnoses
The availability of the data elements in the diagnosis category
also varies by sub-classification.
As shown in Table 3.7, none of the
data elements relevant to the duration of a diagnosis and one-quarter of
the data elements that describe the severity of a diagnosis are
potentially available in claims data.
Disease severity can be
determined with ICD-9-CM codes for only a few conditions.
However, for
some conditions a combination of pharmacy and encounter data can be used
to characterize disease severity.
For example, if a patient receives
multiple asthma medications within a year, or has an emergency room
visit or hospitalization with a primary diagnosis of asthma, the pattern
of care warrants an inference of moderate to severe asthma (rather than
mild or exercise induced asthma).
-58-
Table 3.7
Assessment of Data Elements in Claims Data: Diagnosis SubClassifications
Diagnosis subclassification
Count
Current diagnosis
Duration
History
New
Rule-out
Severity
Total
141
2
28
31
1
16
219
Proportion
Available from
Claims Data
0.97
0.00
0.98
0.97
1.00
0.46
0.90
Almost all of the data elements relevant to whether a patient has a
history of a diagnosis or a new diagnosis were classified as being
available from claims data (see Table 3.7), even though ICD-9-CM codes
typically do not specify new or preexisting conditions.
Historical and
new diagnoses were considered to be potentially available from claims
data, assuming access to longitudinal claims data that could provide
information that would with reasonable accuracy establish some of these
clinical details.
For example, if there is a diagnosis code for a
specific condition, and no prior ICD-9-CM code for the condition of
interest, then one could assume the diagnosis is new.
Obviously,
looking further back in claims or encounter data (e.g., 3 years rather
than 6 months) increases the level of confidence associated with an
inference concerning whether a diagnosis is new or preexisting.
Summary of data element availability
Almost 1000 data elements were identified as being required to
construct the 553 QA Tools indicators.
While 59% of these data elements
were judged to be potentially available from claims data, availability
varied widely by data element type (see Table 3.5).
Although
substantial information on diagnoses, laboratory tests, procedures, and
medications is captured in claims data for payment, not all data
elements related to these domains can be obtained from claims data.
Clinically detailed information, such as the severity of a condition or
the results and timing of laboratory services and procedures, is
sometimes relevant to an indicator, but such information is not usually
-59-
available from claims data.
Data elements related to history, physical
exam, and signs and symptoms, which are frequently required for quality
measurement, are also frequently unavailable from claims data.
The
implications of data availability for the types of indicators that are
feasible to construct with claims data are described below.
FINDINGS: INDICATOR ANALYSIS
Table 3.8 reports the number of QA Tools indicators, by modality,
that could be constructed with claims data.
Of the 553 QA Tools
indicators that were reviewed, 186 (34%) were characterized as being
feasible to construct with claims data.
This finding has strong
practical implications because HEDIS, the most common set of measures
used to measure performance, includes only 26 indicators of technical
quality.
This suggests that current measurement efforts fail to fully
recognize the capacity of claims data for quality measurement.
-60-
Table 3.8
Number and Proportion of Indicators that can be Constructed with Claims
Data – by Modality
Modality
Total # of
Indicators
Non-billable modes of care
Education
32
History
75
Physical exam
88
Non-billable sub-total
Billable modes of care
Admission
Immunization
195
9
18
Laboratory services
149
Medication
120
Other intervention
16
Surgery
36
Visit
10
Billable sub-total
TOTAL
358
553
# of Indicators Feasible to Construct
with Claims Data
(proportion)
Eligibility
Score
Indicator
(Eligibility +
Scoring)
11
(0.34)
63
(0.84)
67
(0.76)
141
(0.72)
3
(0.09)
3
(0.04)
20
(0.23)
26
(0.13)
1
(0.03)
1
(0.01)
11
(0.13)
13
(0.07)
3
(0.33)
12
(0.67)
99
(0.66)
56
(0.47)
9
(0.56)
10
(0.28)
10
(1.00)
199
(0.56)
340
(0.61)
9
(1.00)
18
(1.00)
124
(0.83)
91
(0.76)
10
(0.63)
31
(0.86)
10
(1.00)
293
(0.82)
319
(0.58)
3
(0.33)
12
(0.67)
87
(0.58)
48
(0.40)
5
(0.31)
8
(0.22)
10
(1.00)
173
(0.48)
186
(0.34)
As depicted in Figure 3.7, only 7% of the indicators assessing nonbillable modes of care (education, history, and physical exam) could
potentially be constructed with claims data.19
Among the indicators
___________
19 I coded indicators that pertained to either physical
reassessment or additional counseling as being feasible to construct
with claims data based on the assumption that a follow-up encounter
within a reasonably short time-interval could reveal whether the
indicated care was delivered. Therefore, 14 of the indicators assessing
-61-
assessing modes that are typically billable, 48% could be constructed
with claims data.
non-billable modes of care were considered to be feasible to construct
with claims data.
-63-
Inability to determine eligibility frequently limits the utility of
claims data to construct indicators for billable modes of care.
Although the scoring components of 82% (N=293) of the indicators for
billable modes could potentially be constructed with claims data, there
is sufficient information to determine the eligible population for only
56% (N=199) of these indicators.
The proportion of indicators that can be constructed with claims
data ranged from 0.27 to 0.55 by the function of indicated care (Table
3.9) and from 0.28 to 0.36 by the type of care being assessed (Table
3.10).
This suggests that it is feasible to use claims data to measure
the quality of clinical care for selected aspects of all types of
conditions across the continuum of the care process.
In contrast, the
current set of HEDIS Effectiveness of Care measures, includes no
measures for the quality of diagnostic care processes or for acute care.
Table 3.9
Number and Proportion of Indicators that can be Constructed with Claims
Data – by Function of Care
Function of
Care
Total # of
Indicators
Screening
41
Diagnosis
208
Treatment
238
Follow-up
66
Total
553
# of Indicators Feasible to Construct with
Claims Data
(proportion)
Eligibility
Score
Indicator
(Eligibility +
Scoring)
27
18
11
(0.66)
(0.44)
(0.27)
150
99
64
(0.72)
(0.48)
(0.31)
105
160
75
(0.44)
(0.67)
(0.32)
58
42
36
(0.88)
(0.64)
(0.55)
340
319
186
(0.61)
(0.58)
(0.34)
-64-
Table 3.10
Number and Proportion of Indicators that can be Constructed with Claims
Data -- by Type of Care
Type of
Care
Preventive
Total # of
Indicators
65
Acute
174
Chronic
314
Total
553
# of Indicators Feasible to Construct with
Claims Data
(proportion)
Eligibility
Score
Complete
Measure
31
40
18
(0.48)
(0.62)
(0.28)
119
96
63
(0.68)
(0.55)
(0.36)
190
183
105
(0.61)
(0.58)
(0.33)
340
319
186
(0.61)
(0.58)
(0.34)
Summary of Feasibility Assessment
Relative to current applications of claims data, broader
measurements of quality with claims data appear to be feasible.
The
analysis identified 186 (34%) adult QA Tools indicators that could be
constructed with claims data.
Feasibility of constructing indicators is
much more common when the relevant health care services are typically
billable.
However, lack of data elements required to determine
eligibility frequently preclude construction of these indicators.
Less
than 60% of the eligibility statements associated with measures
involving billable modes of care can be constructed with claims data.
This is because clinically detailed information such as laboratory
results, the findings of procedures, and signs and symptoms are
frequently required to determine eligibility, but are not available from
claims data.
In sum, it is usually feasible to construct quality of
care indicators with claims data if they (a) rely on diagnoses and
demographic information to determine eligibility and (b) assess billable
modes of care such as encounters, immunizations, laboratory tests, and
outpatient prescriptions.
The accuracy of using claims data to construct the feasible
indicators is analyzed in the next chapter.
The remainder of this
-65-
chapter investigates how the capacity for quality measurement could
increase if additional information were available in claims data.
INCREASING THE CAPACITY OF CLAIMS DATA FOR QUALITY MEASUREMENT
The primary advantage of using claims data for quality measurement
is that they are widely available in an electronic format for large
numbers of patients.
However, the assessment of data availability
points to several types of information that generally cannot be obtained
from claims data (see Table 3.5).
Leading examples are examination
findings, test results, timing of events, patients’ signs and symptoms,
and the content of education and counseling activities.
When these
types of information are required to construct quality of care
indicators, an alternate data source, such as medical records, is
needed.
Looking forward, standardized electronic medical records (EMRs)
may one day be available to provide all information required for quality
measurement in a standardized electronic format.
In the interim
however, I consider whether there are incremental steps that could be
taken to supplement the information currently available in claims data.
If so, how would the capacity for quality measurement with claims data
increase?
The analysis described presently addresses these questions.
How Could Additional Information be Obtained?
To analyze how supplemental information would affect quality
measurement with claims data, I considered what health plans could do
without computerizing medical records to incorporate additional
electronic information with their claims data.
Although other
stakeholders—including providers, purchasers, and regulators—could take
actions to improve quality measurement, the perspective of health plans
is taken because they possess claims data and have the most capability
to shape how they are used.
For example, to increase their capacity for
quality measurement with electronic data, health plans could: (a) link
electronic data that already exist in operational systems20 to claims
___________
20 Operational systems in laboratories, hospital pharmacies, and
electrocardiography carts, for example, include most data electronically
on laboratory results, medications administered during an inpatient
-66-
data files, (b) add data fields to existing claims forms, or (c) change
the way the data currently submitted in claims are coded. If a health
plan were to pursue one or more of these options, it would need to
balance the potential gains for quality measurement and other claims
functions against the costs of implementation.
To frame this analysis, I identified four types of information—
laboratory results, procedure findings, vital signs, and signs and
symptoms—that are not in claims data, but are potentially amenable to
quality measurement because they can be communicated through
standardized codes.
While claims data are typically limited to
diagnosis (ICD-9-CM), procedure (CPT, ICD-PCS, HCPCS) and medication
(NDC) codes, there are other standardized terminology systems, such as
the Logical Observation Identifier Names and Codes (LOINC®) system21 and
the Systemized Nomenclature of Medicine Reference Terminology
(SNOMED®)22 that include codes for many more clinical concepts.
How
information about laboratory results, procedure findings, vital signs,
stay, and electrocardiographic measurements (McDonald, Overhage et al.
1997).
21 LOINC was released publicly in 1996. The Regenstrief Institute
at the University of Indiana developed and maintains the LOINC database.
The database includes more than 25,000 observation concepts, including
laboratory tests, vital signs, electrocardiographic measurements, intake
and output measures, critical care, overall clinical impressions, and
discharge summary. The American Clinical Laboratory Association (ACLA),
an association of large referral laboratories whose members are
responsible for more than 60% of U.S. outpatient laboratory test volume,
has recommended LOINC for adoption by its members. The three largest
commercial laboratories in the U.S. (representing approximately 30% of
the market) have adopted LOINC as their code system for reportable test
results, as have several health care systems (McDonald, Overhage, et al.
1997; Regenstrief Institute 2002).
22 SNOMED Clinical Terms (SNOMED CT®) was released in 2002 and is a
concept-based reference terminology that includes more than 333,000
concepts related to pathology, diagnoses, findings, and symptoms.
SNOMED CT is the combination of two established terminologies: SNOMED
and Clinical Terms Version 3. SNOMED is a copyrighted work of the
College of American Pathologists and was introduced in the 1970s.
Clinical Terms Version 3 (formerly known as the Read Codes) is a
copyrighted work of the National Health Service in the United Kingdom
and was introduced in the 1980s. The National Health Service has
mandated that electronic health records in the United Kingdom implement
SNOMED CT in 2003; there is no similar mandate in the US. For more
information about SNOMED, see www.snomed.org (accessed 2/18/2002).
-67-
and signs and symptoms might be obtained to supplement claims data is
described presently.
Laboratory results.
Most, if not all, producer laboratories (i.e.,
where laboratory test results are determined) have electronic systems
that identify the patient for whom the test was performed, the exact
name of the test, and the result.
Health plans, however, do not
typically have access to this information.
In order to obtain
laboratory results, health plans could either (a) have a contractual
agreement that source laboratories provide the data files that include
laboratory test results, or (b) require that test results be submitted
with claims for payment.
Regardless of the mechanism for obtaining the
laboratory results, a health plan needs to be able to understand the
information it receives from the laboratories.
Coding systems used to
identify laboratory tests vary by laboratory, and health plans would
need to receive information from multiple sources (e.g., commercial,
hospital, and nursing home laboratories).
If the source laboratories
were all to use a single code system, such as LOINC, then the health
plan would need to understand only the one system.
However, if
laboratories use alternate coding systems, including their own internal
code values, the ability to link laboratory data with claims data
becomes significantly more difficult.
Although the laboratory results
are likely to exist in an electronic form, the ability to use the
information in conjunction with claims data will vary among health plans
depending on the number of laboratories with which the health plan needs
to coordinate and the degree of centralization among the producer
laboratories about a common coding system.
Procedure findings.
In the absence of an electronic medical record
(EMR), it is unlikely that providers have a centralized database that
contains the findings from multiple types of procedures such as
biopsies, ultrasounds, and electrocardiograms.
To incorporate procedure
findings with claims data, health plans would need to link across
multiple operational systems, if they exist, or require providers to
include results when they submit claims for payment.
The ability of
health plans to obtain data on procedure findings is likely to vary by
-68-
the providers with which they contract.
Health plans with a relatively
centralized system of providers, for example, will be in a better
position to make these associations because the number of potential
operational systems from which results would be obtained would be fewer.
Among the providers with which health plans contract, those who have
invested in information systems and have been cognizant of the ability
to share information across multiple parties will be better able to
share their information with health plans relative to providers who do
not keep procedure findings in an electronic format, or keep them in an
electronic format that does not use standardized terminologies and
codes.
Vital signs.
Unless providers are using EMRs, it is unlikely that
blood pressure values and heart rates are electronically reported in any
database.
Therefore, linking existing data or changing the way claims
data are coded are not viable routes to obtain information about vital
signs.
For health plans to gain access to this information, they would
need to modify their claims forms and require that health care providers
submit this information on their claims for payment.
The burden would
be relatively low because only three numeric values (systolic blood
pressure, diastolic blood pressure, and heart rate) would need to be
recorded.
Signs and symptoms.
If all providers would maintain patient
problem lists electronically and in a standard code system that could be
understood by others, then health plans could link these problem lists
to the existing claims data and have significantly richer information
about patients’ conditions.
lists is rare.23
However, electronic maintenance of problems
Therefore, to obtain detailed information about
patients’ presenting signs and symptoms the coding system currently used
to communicate information about diagnoses would need to be altered.
Currently, ICD-9-CM codes are used to describe the type of condition
that is being addressed during the encounter, but they are often unable
___________
23 Survey by medical records institute and SNOMED found that less
than 1.5% of surveyed providers are using components of an EMR to
support electronic problem list.
http://www.medrecinst.com/resources/survey2002/overview.shtml (accessed
11/3/02).
-69-
to accurately represent the clinical problems being addressed.
A more
detailed coding system, such as SNOMED, has the potential to communicate
a more clinically accurate description of patients’ presenting signs and
symptoms and diagnoses in a standardized format.
SNOMED has been shown
to have more complete case identification for conditions relative to
ICD-9-CM codes (Elkin, Ruggieri et al. 2001).
Switching to an alternate system of diagnostic codes would be a
significant change.
To move from ICD-9 coding to SNOMED or another more
clinically detailed system, health plans would need to provide
significant support to providers.
For example, a structured paper form
or an electronic interface that allows providers to easily select the
diagnostic observations and symptoms for a patient could be introduced
or coding specialists could be hired to translate visit notes to SNOMED
codes.
Regardless of the approach, it would take significant resources
to successfully change to a coding system that would provide meaningful
information about signs and symptoms.
Methods
To analyze the potential gains of additional information to quality
measurement, I examined the QA Tools indicators that cannot be
constructed with claims data alone, but that could be constructed if the
following types of data were available:
•
Laboratory results
•
Procedure findings
•
Vital signs
•
Signs and symptoms
Of the 982 data elements required to construct the QA Tools
indicators, 42 were for laboratory results, 26 for procedure findings,
11 for vital signs, and 71 for signs and symptoms.
One or more of these
data elements were required to construct 204 indicators.24
To analyze
___________
24 Analysis earlier in this chapter found that of these 204
indicators, 184 (90%) cannot be constructed with claims data.
Laboratory results were required for 64 indicators, procedure findings
for 30 indicators, signs and symptoms for 120 indicators and vital signs
were required for 31 indicators. Nineteen of the indicators that
required signs and symptoms could be constructed with the current
contents of claims data.
-70-
how quality measurement with electronic data would be enhanced if these
types of data were available, I calculated the increases in the numbers
and types of quality indicators that could be constructed by having each
one of the additional data types available.
The increase in capacity
for quality measurement was also calculated assuming that all four types
of data were available.
The current analysis provides optimistic estimates of how much the
capacity for quality measurement could be improved because I assumed
that the additional information would meet all of the QA Tools
requirements, and this is unlikely.
For example, an indicator may
require information about a symptom that is not coded in LOINC or
SNOMED.
Therefore, the results indicate the “upper bounds” for
increases in the capacity for quality measurement with the additional
data.
Even if information about laboratory results, procedure findings,
vital signs, and signs and symptoms required to construct the QA Tools
indicators were available, there would still be indicators that could
not be constructed because other types of data would remain unavailable
(e.g., the content of patient education and counseling activities,
components of patient history and provider documentation habits).
Findings and Discussion
Without additional information, the claims data for most health
plans25 could support the construction of 186 of the 553 QA Tools
indicators (Table 3.10).
If in addition, a health plan was able to
incorporate laboratory results, procedure findings, vital signs, and
signs and symptoms with claims data, 260, or nearly half, of the QA
Tools indicators could be constructed without use of medical records
(Table 3.11).
Information about signs and symptoms had the largest
effect on the number of indicators that could be constructed and
findings from procedures had the smallest effect.
However, no single
type of information increased the proportion of indicators that could be
constructed by more than 6%.
___________
25 In this context, the contents of claims data include claims for
ambulatory encounters, outpatient ancillary services (e.g., laboratory
and radiology tests), outpatient pharmacy, and inpatient encounters.
-71-
The increase in the number of indicators that could be constructed
with the additional information is reported in Table 3.11 and depicted
in Figure 3.9.
Figures 3.9-3.11 show the results by mode, function and
type of care; a table with the detailed numerical results is in Appendix
A.
Following Figures 3.8-3.11, there is a discussion of how each type
of information could increase the capacity for quality measurement.
Table 3.11
Potential Increase in Capacity for Quality Measurement – All Indicators
Number of Indicators Feasible to Construct
(%)
Total # of
Indicators
553
Original
Lab
Procedure
Vitals +
Signs &
All
Analysis
Results +
Findings +
Original
Symptoms
additional
Original
Original
+
elements +
196
(0.35)
Original
221
(0.40)
Original
260
(0.47)
186
(0.34)
209
(0.38)
202
(0.37)
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Description of effects of additional information on quality measurement.
Laboratory results. If laboratory results were available and linked
to claims data, then 23 additional QA Tools indicators could be
constructed.
The increased capacity for quality measurement comes from
the ability to identify patients who satisfy eligibility criteria based
on specific laboratory results (e.g., people with LDL > 130 mg/dl or
HbA1c >= 9.5%).
Since the QA Tools indicators measure processes, and
not intermediate outcomes, laboratory results do not change the number
of scoring statements that can be constructed.
Procedure Findings.
The availability of procedure findings, such
as biopsy results, ejection fraction measurements, and ultrasound
findings, increases the number of QA Tools indicators that can be
constructed from 186 to 196.
Of the four additional types of data
elements that were examined, the availability of procedure findings had
the smallest effect on increasing the capacity for quality measurement.
Similar to the pattern with laboratory results, the availability of
procedure findings increases the ability to determine who is eligible
for an indicator, but does not help determine whether the indicated
process was delivered.
Blood Pressure Values and Heart Rates.
If health plans had
information on their enrollees’ blood pressures and heart rates, an
additional 16 QA Tools indicators could be constructed.
The
availability of blood pressure values and heart rates would improve the
ability to measure the quality of care delivered for cardiovascular
conditions, especially hypertension.
However, the ability to measure
the quality of care for other types of conditions would remain
unchanged.
Signs and Symptoms.
Of the four types of information that were
analyzed, signs and symptoms would lead to the largest increase in the
number of QA Tools indicators that could be constructed.
If health
plans were better able to identify their enrollees by the types of signs
and symptoms for which they present for care, 35 additional QA Tools
indicators could be constructed.
Information about signs and symptoms
would not increase the capacity to measure preventive care, but
measurement of care for acute and chronic conditions would increase,
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especially when assessing the diagnostic function of care (see Figure
3.11).
Summary
The purpose of this analysis was to determine whether incremental
changes (i.e., changes short of implementing EMRs) could lead to
significant gains in quality measurement.
The results of this analysis
suggest that the addition of any one type of information would not have
a very large contribution to the number or type of indicators that could
be constructed.
However, if information about laboratory results,
procedure findings, vital signs, and signs and symptoms were available
to supplement claims data, the proportion of QA Tools indicators that
could be constructed without medical record data would increase from
about one-third to about one-half.
Thirty-five additional indicators
could be assessed if information on signs and symptoms were available,
however results from laboratory tests and vital signs, which may be
easier to obtain, would allow an additional 23 and 16 QA Tools
indicators respectively to be constructed with claims data.
As health plans consider what information to obtain for quality
measurement, the gains to quality measurement must be compared to the
costs associated with obtaining the data.
Perceived benefits from
quality measurement activities and the ability to obtain the different
types of data elements are likely to vary considerably between health
plans.
For example, a health plan with a very young enrollment
population would be much less interested in being able to measure
quality about hypertension than a plan dominated by elderly enrollees,
and therefore not be willing to invest the resources required to change
their claims forms to include data fields for blood pressure values and
to impose the reporting requirement on providers.
Similarly, health
plans with a more concentrated network of providers may be able to
obtain additional data at a lower cost than a health plan with a very
open network of providers.
For example, a health plan that pays claims
to two commercial laboratories rather than 15 is in a much better
position to try to link laboratory results to claims data, other things
equal.
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Standard coding systems have been developed to communicate detailed
clinical data electronically.
However, minimal adoption of these
standards limits a health plan’s ability to readily incorporate
additional information with their claims data for quality measurement.
Quality measurement will be significantly enhanced as additional pieces
of clinical information are collected and stored in a standardized
electronic fashion, but no one type of information would provide a
dramatic improvement by itself.