|
Items in cart [0] |
Questions? Call 888-624-8373 |
| Copyright © 2009. National Academy of Sciences. All rights reserved. Terms of Use and Privacy Statement |
Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 65
11
Claims Data and Effectiveness:
Acute Myocardial Infarction and
Other Examples
Barbara ]. McNeil
The question of effectiveness of medical treatment is an extremely im-
portant one and one that will benefit from close collaboration between physicians
and social scientists. In this chapter, however, I confine my discussion to a
limited aspect of that collaboration- that is, to the analysis of claims data,
particularly analysis of Medicare claims data. My discussion is based on
the claims data as they exist today. It is important to note, however, that
since these data have begun to be used for prospective payment, their accuracy
has improved considerably. I think we can expect improvements of similar
magnitude once these data are used to a greater extent for research on
effectiveness and outcomes, particularly as they relate to medical technology.
The original definition of medical technology from the Office of Tech-
nology Assessment (OTA) considers two types of technologies. The first is
any medical device, drug, or surgical procedure used in the care of patients.
The second is any organizational or support system within which medical
care is delivered. It is unlikely that claims data in their current form will be
usable in the latter, so I will restrict my comments to the first type of
technology.
STRENGTHS OF CLAIMS DATA
Large claims data bases have a number of strengths. To illustrate these I
draw upon the experience of many other researchers and on my own experience
as a researcher and as a commissioner with the Prospective Payment Assessment
Commission (ProPAC). The following list illustrates the most notable strengths.
It applies principally to Medicare Part A data (primarily hospitalization
data) because that is where most of our experience has been thus far. Part B
(ambulatory) data, particularly when linked to Part A data, expand these
65
OCR for page 66
66
EFFECTIVENESS AND OUTCOMES IN HEALTH CARE
strengths still further. Such linkage is costly, however, and initial efforts
are just being completed. Strengths of claims data include the following:
1. They can be used to provide usage rates.
2. They can be used to indicate variations in use of technology by geog-
raphy, hospital type (e.g., teaching, nonteaching; urban, rural), age, sex, and
so on. This is the area in which John Wennberg has worked so successfully
over the years.
3. They can be linked to mortality data in order to define mortality rates
as a function of the above items and as a function of key diagnostic and
procedure codes. This is the basis of the Health Care Financing Administration's
(HCFA) initiative in providing mortality rate data to hospitals.
4. When linked with the Medicare Cost Report, claims data can be used
to estimate the costs of hospitalization. Comparative data can also be obtained
across types of institutions. If patients' records were linked over time and
Part B data were linked with Part A data, they could be used to provide
information on the costs of an episode of care. (This assumes that it is
possible to define an episode accurately.)
5. They can provide information on home health services.
Although this list of strengths is long, for our initial activities in the
Effectiveness Initiative, we will largely be talking about items 1, 2, and 3.
GENERAL LIMITATIONS
There are four serious limitations to these Medicare claims data. First,
there is very limited information on comorbidity and disease severity. Thus,
it is difficult, if not impossible, to define an "inception cohort" that is, a
homogeneous group of patients whose identity is clearly and reproducibly
defined at a particular time and who are then followed into the future.
Second, there is limited information on socioeconomic status, and much
recent literature has shown that socioeconomic status correlates well with
usage of certain health services and medical technologies.
Third, data on outcome are sparse. Currently, they allow us to measure
mortality rates and readmission rates; however, it is not always possible to
determine whether a readmission is related to the prior admission, is a
consequence of suboptimal care, or is an unrelated event. Because much of
medical care is designed to reduce morbidity rather than mortality, omission
of data on postdischarge functioning of the patient and on alleviation of the
symptoms that generated the hospitalization limits the usefulness of current
outcomes data to research. Moreover, as we think about incorporating
outcomes data, we should think about obtaining data at times after discharge
that reflect the expected results of the hospitalization. For example, outcomes
OCR for page 67
IOM CLINICAL CONDITION WORKSHOPS
67
after a cholecystectomy should probably be obtained at 3 months, but out-
comes after hip replacement surgery should probably wait for 6 to 12 months.
Fourth, many codes used to describe diagnoses and procedures are nonspe-
cific, as discussed below.
Recent work by Lisa Iezzoni and her colleagues on coding of acute
myocardial infarction illustrate some of these limitations (1~. This study
reports that more than one-quarter of the patients assigned an acute myocardial
infarction code from the International Classification of Diseases (ICD-9-
CM) at the time of discharge did not have the condition or receive active
treatment for the condition during hospitalization. Miscoding resulted most
often when patients were admitted with a "rule-out infarction" diagnosis.
Misspecification (that is, the physician failed to note explicitly the absence
of acute myocardial infarction) or failure of the medical abstracter to note
subsequent explicitly documented exclusion of the infarction resulted in the
largest number of coding errors. Admission of patients for cardiac catheterization
with coronary angiography within 8 weeks of acute myocardial infarction
(thus technically permitting the acute myocardial infarction code) was cited
as another major reason for misclassification.
The difficulties raised by the coding guidelines for the ICD-9-CM and
the diagnosis-related group (DRG) codes are further compounded when a
secondary diagnosis of acute myocardial infarction is used to assign the
infarction DRG to cases where another cardiac condition is the principal
diagnoses. The study supports the conclusion that previous hospital discharge
data on acute myocardial infarction lack sufficient validity in themselves to
define an inception cohort for effectiveness and outcomes research. As
coding rules change over the next year, however, to minimize some of the
above-mentioned problems, identification of an inception cohort from the
discharge codes will become more accurate.
Inaccuracy of diagnostic codes is not unique to acute myocardial infarction.
In the next section, I amplify on the four general limitations of claims data
in the context of assessment of three technologies: diagnostic devices,
drugs, and clinical trials.
LIMITATIONS FOR DIAGNOSTIC DEVICES
This is probably the area in which claims data are likely to be least
useful, in the absence of significant changes. The first limitation derives
from the fact that, for inpatients, Medicare claims files code for only three
procedures. Ill patients usually have significantly more than three diagnostic
procedures, and hence the list of coded diagnostic procedures is frequently
incomplete and biased. It is biased because sicker patients will not have
room on the claim for the diagnostic code, whereas healthier patients will.
OCR for page 68
68
EFFECTIVENESS AND OUTCOMES IN HEALTH CARE
An example of this phenomenon occurred when ProPAC tried to track the
use of magnetic resonance imaging (MRI) among Medicare beneficiaries.
There were far fewer MRIs reported than we estimated had been done. In
addition, there were far fewer done on sicker patients in the DRGs most
likely to make use of MRI. This is analogous to the phenomenon described
by Stephen Jencks regarding concurrent diagnoses among ill patients (21.
The second problem in the evaluation of the effectiveness of diagnostic
devices relates to time-lags between the use of new technologies and the
development of codes for them. Development of codes can take years, thus
preventing us from identifying the use of new devices. Although this is a
limitation primarily of inpatient records, it can occur in outpatient records
as well. Examples of coding omissions that hinder evaluation include MRI,
electrophysiological studies, and positron-emission tomography (PET). Third,
claims data do not provide any information on the type of equipment used.
For imaging technologies this is critical: major differences in effectiveness
can result from use of older generations of equipment. Fourth, it is seldom
possible to differentiate between tests done for diagnosis and those done for
screening. This is obviously important in the case of mammography. Finally,
there is no correlation of diagnostic test results with information from an
independent source (for example, pathology).
It is important to emphasize that, to the extent that we have information
from inpatient care (ICD-9-CM codes) and ambulatory sources (ICD-9-CM
or Current Procedural Terminology [CPI] codes) some of the above problems
can be alleviated. In any case, the limitations described above regarding
inpatient data have prompted the National Cancer Institute to conduct a
major prospective study of the effectiveness of diagnostic imaging procedures
in patients with one of five types of cancer. Nine institutions are currently
collaborating in this study, and six more are expected to be added next year.
LIMITATIONS FOR DRUGS
The problems of claims data for drugs are similar to those for diagnostic
devices. Codes for new drugs may lag their availability by many years.
The classic example of this relates to thrombolytic therapy. Most physicians,
policymakers, and researchers identified this as an extremely important area
for study two years ago; however, there were no codes for thrombolytic
therapy. There are still no codes for the therapy per se it can be identified
(and then not always) only when done in connection with an angioplasty.
Drugs are very complicated to evaluate because of multiple doses and
multiple forms, and it is going to be tricky to get information on outpatient
drug use. The repeal of the Medicare Catastrophic Coverage Act, with its
drug coverage, will make information on Medicare beneficiaries more difficult
to obtain. However, a number of researchers have been extraordinarily
OCR for page 69
IOM CLINICAL CONDITION WORKSHOPS
69
successful in using claims data from selected states (for example, New
Jersey) (3~.
LIMITATIONS FOR THERAPIES
There has been a tremendous amount of discussion about the use of
claims data for therapies, and much of it has been very negative. I think we
should recognize, however, that a number of useful things can be accomplished
with claims data for therapy. For one, we may not need to resort to randomized
trials for all interventions.
Some limitations remain, however. The first one is that the coding for
therapy is not always current. For example, two years ago ProPAC was
interested in studying cochlear implants as a new therapy for patients with
deafness. At the time there was no way of identifying these patients from
hospitalization claims data alone. The second problem with the coding for
therapies is that the code may not be specific enough. This is particularly
troublesome for ICD-9-CM codes used on inpatient records. CPT codes are
considerably more specific in reporting procedures although they have little
or no diagnostic information. Thus, if bills for physician services or outpatient
services are linked with hospitalization records, specificity is improved.
Failing that linkage, there are problems in four areas:
1. The ICD-9-CM codes do not reflect refinements in a procedure (for
example, a cementless instead of a cement hip prosthesis).
2. The codes frequently do not indicate whether a procedure was a
repeat one (for example, a first or a second coronary artery bypass graft).
Linking patient records over many years (for example, 10 years) would
solve this problem if the payer were the same during the entire period.
3. The codes are sometimes incomplete. A one-year study of total
parenteral nutrition (TPN) conducted by ProPAC illustrates this. At that
time it was believed that DRGs 296 and 182 (nutritional disorders and
miscellaneous digestive disorders) would contain many patients having TPN.
A review indicated that approximately 1,200 patients that year (less that 1
percent of all patients in those DRGs) were identified from the claims records.
Independent estimates suggested a number more like 100,000 to 200,000
patients. In this case, as with MRI, sicker patients had enough other procedures
done to them that TEN never reached the claims records.
4. Claims data seldom allow identification of an inception cohort. This
was mentioned under general limitations, but I repeat it here because of its
particular importance for evaluation of therapies. Elliot Fisher and John
Wennberg emphasize this in their discussion of the claims analyses of transurethral
prostatectomies (41. In general, it will be easier to define an inception
cohort for an acute event, such as acute myocardial infarction, than for a
chronic one.
OCR for page 70
70
EFFECTIVENESS AND OUTCOMES IN HEALTH CARE
CONCLUSION
Finally, where are we? I think we are at a point in the careers of a
number of health services researchers that is really quite rosy. We have a
data base that is constantly being improved and will continue to be improved
as a result of our research interests. Over the short term, I believe that these
data will be used primarily for generating hypothesis. Our resultant analy-
ses and studies will have an obvious impact on our ability to measure the
effectiveness of medical practice. Over a longer term, it is likely that some
of our results will be used to identify access problems. Who is not getting
what? For what reason? To accomplish both short- and long-term objectives,
we must work closely with policymakers on activities related to improving
the data base and the training of individuals capable of using it.
REFERENCES
1. Iezzoni, L.I., Burnside' S., Sickles, L., et al. Coding of Acute Myocardial
Infarction: Clinical and Policy Implications. Annals of Internal Medicine 109:745-751,
1988.
2. Jencks, S.J. Issues in the Use of Large Data Bases for Effectiveness Re-
search. Pp. 94-104 in Effectiveness and Outcomes in Health Care. Heithoff, K.A. and
Lohr, K.N., eds. Washington, DC: National Academy Press, 1990.
3. Avorn, J., Dreyer, P., Connelly, K., et al. Use of Psychoactive Drugs and
Quality of Care in Rest Homes. New England Journal of Medicine 320:227-232, 1989.
4. Fisher, E.S. and Wennberg, J.E. Administrative Data in Effectiveness Studies:
The Prostatectomy Assessment. Pp. 80-93 in Electiveness and Outcomes in Health
Care. Heithoff, K.A. and Lohr, K.N., eds. Washington, D.C.: National Academy
Press, 1990.
Representative terms from entire chapter:
acute myocardial
