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16 The Uniform Clinical Data Set Henry Krakauer To properly assess the Uniform Clinical Data Set, it must be clearly understood that it was designed for a very specific purpose, namely, to meet the operational needs of the Health Care Financing Administration (HCFA) in assuring, through the Peer Review Organizations (PROs), the quality of the care that Medicare beneficiaries receive. It will, therefore, satisfy a limited array of needs for clinical data, but the extent to which it does will have to be determined empirically, that is, through experience with its use. PROs exert considerable influence on the practice of medicine through the financial and disciplinary actions at their disposal. They are authorized to: · deny reimbursement for inappropriate admissions, · deny reimbursement for substandard care, · initiate sanctions by the Inspector General, and correct aberrant patterns of medical care. The difficulties inherent in these activities may be illustrated by the process of denial of reimbursement for care judged to have been substandard. Table 1 presents a theoretical but reasonable algorithm leading to such a denial. First, it is necessary to demonstrate that the patient suffered harm, that is, an adverse outcome. Beyond that, it is necessary to demonstrate that the adverse outcome was avoidable; that is, it should not have been predictable with a reasonably high level of probability from the condition of the patient at admission. Finally, it is necessary to establish that a breach of protocol occurred, in other words that there was negligence or incompetence, in order to establish culpability. 120
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COLLECTION OF PRIMARY DATA TABLE 1 Problems in Denying Reimbursement for Substandard Care 121 SPECIFIC Denials for substandard care Harm death, disability, physiological impairment, Increased intensity or duration of care SCREENING Avoidable physiological condition at admission Objective standards CULPABILITY Breach of protocol negligence, incompetence THE PRO CASE REVIEW PROCESS The current process of case review by the PROs begins with a screening of the medical record to identify instances in which the care is suspicious enough to merit further expert attention. A case identified in the screening is then referred to a physician advisor for review. If the system of peer review is not to be perceived as arbitrary and capricious, it is necessary to (a) develop and uniformly apply well-defined screening criteria that identify where there is a reasonable probability of a deficiency and (b) develop and uniformly apply objective standards that would permit the physician reviewer to ascertain with a high level of probability that a deficiency in care did occur. The best guide in these matters is actual experience. Experience permits one to judge that an act of omission or commission results in harm reason- ably often and that it was therefore reasonably likely to have done so in the case in question. Given the realities of medical practice, that experience should be passed through the filter of consensus to make it acceptable. Once this has been accomplished, the devising of consistent screening criteria and objective standards and their uniform application become straightforward. A strategy for the efficient accumulation and evaluation of experience in the Medicare environment is displayed in Table 2. It consists of a sequen- tial process that begins with assessment of the health of the Medicare ben- eficiaries and of the time trends and geographic variations therein two problem-finding tools and proceeds with the assessment of the effectiveness of interventions, be they medical or administrative, as the problem-solving step. The final step is feedback of the results of the evaluations, coupled with disciplinary activities and financial incentives to ensure their proper and timely use. This approach makes extensive use of observational techniques to evaluate the natural history of conditions as they are currently being
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22 EFFECTIVENESS AND OUTCOMES IN HEALTH CARE TABLE 2 Strategy for Improvement of the Effectiveness of Medical Interventions for Medicare Beneficiaries Health Care Financing Administration 1. Monitoring time trends a. population-based b. medical interventions (feasible with available billing and census data) 2. Analyzing geographic variations a. population-based b. medical interventions (feasible with available billing arid census data) 3. Assessing effectiveness of interventions (longitudinal, to develop objective star~dards) a. monitoring, as in step 1 above (retrospective, based on billing data) b. use of data from medical records (retrospective, natural history) Uniform Clinical Data Set (also for case finding for PRO review [medicolegal] ~ National Center for Health Services Research, HCFA c. clinical demonstrations (prospective, natural history, especially for emerging technologies) National Institutes of Health, NCHSR, HCFA d. randomized, controlled clinical trials Health Resources arid Services Administration, NIH, HCFA 4. Feedback (educational, disciplinary, financial) treated and to begin identifying which of the available and competing courses of treatment appear most beneficial. Because the approach is sequential and begins with an assessment of the universe of patients at risk for a condition, every successive step will, while decreasing the number of pa- tients and increasing the detail of the pertinent data (down to the randomized clinical trial), allow the researcher to generalize the findings to the patient universe. In addition, each earlier, broader step informs planning for the subsequent, more specific step in the analytic sequence. THE UNIFORM CLINICAL DATA SET AND PRO NEEDS The Uniform Clinical Data Set occupies a specific niche in this process. It is a tool for extracting data from medical records to permit effective risk adjustment in assessing the treatment of a given patient. The composition of the Uniform Clinical Data Set is dictated, as was indicated above, by two requirements. It must enable the PROs to screen cases efficiently and uniformly in order to identify those in which the effec- tiveness of the care delivered was problematic, and it must enable reviewing physicians to develop objective tools by which to judge the cases. Thus, it
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COLLECTION OF PRIMARY DATA 123 must satisfy an operational need, screening, and simultaneously must sup- port an epidemiological activity, the development of objective standards through evaluation of the effectiveness of medical interventions. The contents of the data set were defined by a task force of clinicians and research personnel who had these two objectives clearly in mind. USING THE UNIFORM CLINICAL DATA SET The content of the Uniform Clinical Data Set is illustrated by Tables 3 through 7. These tables are excerpted computer screens, or menus, that prompt the entry of data from the medical record. Table 3 displays the main menu, which lists the major classes of information that are to be extracted from the hospitalization record. The order of the screens follows roughly the order in which the data are likely to be encountered in the medical record. The hardware and software are, however, so quick that the order of abstraction is dictated by the order of appearance of data in the medical record rather than the order presented on the list. The richness of the data collected is best illustrated with specific examples, TABLE 3 Excerpt from Uniform Clinical Data Set Computer Screen: Peer Review Screens (Main Menu) A. Sociodemographic data B. Admission status Admission medication history History permanent anatomic changes History and physical Laboratory: chemistry/blood gases Laboratory: hematology/urinalysis Laboratory: microbiology J. Laboratory: cytologylhistology K. Admission diagnostic tests Endoscopy M. Operative episodes N. Treatment interventions O. Recovery phase P. Discharge status Q. Discharge planning R. HDI master Type letter corresponding to a menu item: F3=Set HDI_ID F10=Leave
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124 EFFECTIVENESS AND OUTCOMES IN HEALTH CARE such as the screens which address the patient's history and physical exami- nation. These are accessed by striking the letter that labels the "history and physical" menu entry. The subscreens (Table 4, the first of three "history and physical" screens) provide a menu from which more specific subscreens (which refer to organ systems) may be selected and indicate whether data pertaining to the organ system identified have been entered. When data have been entered, the flag "F" (false) next to that item changes to "T" (true), as shown for several entries in Table 4. At the next (organ) level of data, using the cardiovascular examination as an example (Table S), the data recorded include specific abnormalities, findings within normal limits ("normal"), and "other findings". Because more data may be recorded than can fit on one screen, an additional screen may be entered by striking "+". The abstractor toggles the F to a T for any finding by striking the appropriate letter, resulting in the recording of that datum (for example, for jugular venous distention in Table 5~. The results of diagnostic tests are also recorded for specific periods dur- ing the hospitalization. For the laboratory tests (chemistry, hematology, enzymes, urinalysis, microbiology, and cytology), the worst result obtained within the first 24 hours of hospitalization is recorded as the "initial" value; in the case of some enzymes, such as the cardiac enzymes, a window of 48 hours is specified. If no admission data are on the record, preadmission results obtained within a week before admission are accepted. TABLE 4 Excerpt from the Uniform Clinical Data Set Computer Screen: Peer Reviews Screens History and Physical A Chronic necrologic disease History of necrologic surgery Current neuroloic exam findings Chronic cardiac disease Chronic vascular disease History of cardiovascular surgery Current cardiovascular exam findings I. Chronic pulmonary disease History of pulmonary surgery K. Current pulmonary exam findings Chronic psychiatric disease M. Current psychiatric exam findings N. History of cancer F F F T F T F F F F +/- GO TO OTHER HISTORY AND PHYSICAL INFORMATION (+ = SCREEN B AND - = SCREEN C) Blank=Criteria F10=Leave Type letter corresponding to item _
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COLLECTION OF PRIMARY DATA 125 TABLE 5 Excerpt from the Uniform Clinical Data Set Computer Screen: Cardiovascular Examination Findings Enter item letter on 1st line to change or enter T or F beside item ENTER ITEM LETTER: CHANGE ITEM ENTER = ITEM BY ITEM, F10 = LEAVE PRESS "+" FOR OTHER CV EXAM FINDINGS Item Description Value A B C D E F G H I K L M N o p Normal Shock Pulmonary edema Peripheral edema Jugular venous distension T Tachycardia Bradycardia Murmur Arrhythmia Cardiomegaly Gallop rhythm Peripheral pallor Bruit Thrill Friction rub Pulse Deficit-peripheral F F F F F F F F F F F Current CV exam Findings Enter item letter on 1st line to change or enter T or F beside item - ENTER ITEM LETTER: CHANGE ITEM ENTER = ITEM BY ITEM, F10 = LEAVE PRESS "+" FOR OTHER CV EXAM FINDINGS Item Description Value A Ischemic ulcers F B Stasis ulcers F C Venous/varicose ulcer F D Gangrene F E Dependent rubor F F Delayed capillary fill F G Chest pain (steady) F H Other findings F
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26 EFFECTIVENESS AND OUTCOMES IN HEALTH CARE TABLE 6 Excerpt from the Uniform Clinical Data Set Computer Screen: Peer Reviews Screens-Treatment Interventions Nonsurgical procedures A. Blood products F T T B. Inhalation therapy C. Professional services Medication therapy in hospital D. Prescribed medications E. Adverse reaction to medications F F. T Delivery systems for medications T Blank=Criteria F10=Leave Type letter for item desired Prescribed medication Use Arrows, Home, End, Pgup, Pgdn and Enter to Choose. FlOtoCarlcel Drug name Route Start End Heparin 2 05/09/88 05/12/88 Theophylline, anhydrous 1 05/09/88 05/15/88 Bactr~m 1 05/12/88 05/15/88 Pepcid 1 05/13/88 05/15/88 Carafate 1 05/14/88 05/16/88 Codeine 1 07/08/88 07/10/88 In addition to the laboratory findings that apply to the period immedi- ately surrounding the admission, the results of the last test prior to discharge are recorded (although this may have occurred considerably before discharge) and, for selected tests, the worst value between the initial and final value (the "interim" value) are recorded. The date a test was drawn is also recorded, as well as whether, given the calibration of the equipment on which the test was performed, the result fell outside the hospital's normal limits. Although the worst interim result is not usable for epidemiological analyses, it is used in screening cases by means of the HCFA Generic Quality Screens and so must be collected. Table 6 illustrates the kinds of information being collected about treat- ment, such as nonsurgical procedures and drugs administered during the hospitalization. The medications data sought include route of administra- tion (self-administrable, thus not requiring specific skills, or invasive, requiring specific skills for administration), the start date (the date when the drug was first given), and the end date (the last date of administration). The entry of drugs is guided by a dictionary that contains about 6,000 trade and generic names. This ensures that the drugs recorded are recognizable and associates
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COLLECTION OF PRIMARY DATA 127 the names with therapeutic categories used by the expert system that as- sesses the appropriateness of admission and the quality of care. APPLICATION OF FINDINGS This expert system represents one of the two applications of the ab- stracted data. Immediately following abstraction, a sequence of about 3,000 logical rules is applied to the data. These rules embody the criteria currently used by the PROs to (a) verify that the patient's illness was sufficiently severe to justify the admission and that services that require the patient to be hospitalized were in fact rendered (the "admission necessity" algorithms) and (b) ascertain whether breaches of protocol pertaining to inpatient man- agement and the discharge of the patient may have occurred (the "generic quality" and the "discharge" algorithms). The product, placed on the screen of the computer in about two and a half minutes, is a case summary that contains the results of the evaluation (Table 7) and an ordered listing of all the findings abstracted (not shown). TABLE 7 Excerpt from the Uniform Clinical Data Set: Flag Settings and Reasonsa Algorithm Flags AD00 Admission necessary PA CASE FAILS ADMISSION NECESSITY SCREENS. REFER TO PA. ES00 Elective admission PR SP Elective admission flag. ES04 Cardiac revascularization PR 2B INDICATIONS FOR INPATIENT ELECTIVE SURGERY NOT PRESENT. PA FLAG ES04 PA DP01 Ischemic heart disease / chest pain PR 8D APPROPRIATE HOSPITALIZATION AND SERVICES. OK FLAG DPO1 OK OP09 Central nervous system MO A CASE REQUIRES MONITORING FOR SEVERITY OF ILLNESS DS01 Discharge status/disposition MO N17 Surgical patient with final hemoglobin missing or result less than admission result with difference >= 3 and < 4 grams per deciliter, discharge pulse > 110 DS01 Discharge status/disposition N29 No creatinines MO aExample for actual hospitalization
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28 EFFECTIVENESS AND OUTCOMES IN HEALTH CARE The case shown represents an actual hospitalization in which a coronary angioplasty was performed, ostensibly for a malfunction of a prior coronary artery bypass graft. The "flag" report states that the admission was elec- tive, for cardiac revascularization, but that sufficient indications for the procedure were not present. In fact, the results of cardiac catheterization, specified in the case summary, included a left ventricular ejection fraction of 56 percent and a 50 percent stenosis of the right coronary. To justify revascularization, the algorithms require at least 70 percent stenosis. Con- sequently, the case is to be referred to a physician advisor (PA) for further review of the necessity for the admission. The result of the surgical procedure algorithm (those labeled ES) results, in turn, in the summary recommendation (AD00) on admission necessity because elective surgery was performed. Had the admission not been elec- tive, the results of the "disease-specific" (DP) and the more generic "organ- specific" algorithms would also have been considered, and an "OK" in any of the admission necessity algorithms would have resulted in no referral for further review. In this instance, there were enough findings and services to justify the hospitalization (but not the angioplasty) for ischemic heart dis ease. There were some signs of disease of the central nervous system (OP09), but not enough to either justify or deny the admission, resulting in a recom- mendation that the case be accumulated in a data base for monitoring (MO) for patterns, but only if the monitoring flag appeared in the absence of an "OK" flag. In this case, it is disregarded. No generic quality screens (DS) were failed, but two problems were identified by the discharge screen. Neither of these was severe enough to merit referral to a physician advisor, but they were serious enough to merit tracking (MO) to ascertain whether they are recurring problems at the hos- pital that cared for the patient. The results of the case-finding algorithms suggest the level of clinical judgment they incorporate: rather rudimentary, but sufficient to give rise to controversy. This is a problem that, in the current environment of medical uncertainty, will dog the application of any expert system to the evaluation of medical practices. The other application of the data acquisition system, the development of an epidemiological data base, has as its ultimate purpose the reduction of that uncertainty so that case-finding rules and judgments rendered by PRO physician advisors might be more more objective and substantial. In a more general sense, when patterns of care and patterns of outcomes are compared among providers of medical care, adjustment for risks contributed by patients and therefore not attributable to care, must be made. The first example of the epidemiological application of abstracted clinical data is, in fact, an adjustment of mortality rates of hospitalized patients, grouped by hospital (the hospital is the provider). Table 8 and Figure 1
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COLLECTION OF PRIMARY DATA 129 present results obtained with data abstracted from medical records using MedisGroups, a commercial system. Table 8 compares measures of goodness-of-fit of models that employ data available from the HCFA claims files (core model), the core model plus the MedisGroups admission severity grade (ASG) (a measure that makes use of the abstracted data but selects findings and weights them according to clinical judgment)' and a model that consists of the core variables plus specific clinical findings as abstracted. The outcome is the probability of death of individual patients. The fit improves progressively as the compre- hensiveness of the model increases. Variables that identify hospitals and whose regression coefficients estimate the contribution of the hospital to the probability of patient death (patient risk factors being equal) contribute little at any level, but progressively less as patient risk factors are included in greater detail. The two measures of goodness-of-fit the proportion of concordant pairs or area under the ROC (receiver operating characteristics curve, and the rank order correlation coefficient of observed and predicted probabilities of death-are directly related. The area under the ROC curve ranges from 0.5, if the model is totally ineffective, to 1.0, if it is perfectly predictive. The value of 0.9 achieved by inclusion of the specific clinical findings is sub- stantial. It indicates that in about 90 percent of pairs of patients, one of whom died and one of whom did not, the patient who died had the higher predicted probability of death. I am not expert in these matters, but I am TABLE X Evaluation of Goodness-of-Fit of Regression Models of the Probability of Death of Individual Patients Variables in Model Demographic only Demographic and hospital Core Core and hospital Core and MedisGroups ASGb Core, MedisGroups ASG, and hospital Core and clinical findings Core, clinical findings, and hospital Proportion of Concordant Pairsa Rank Correlation of Observed and Predicted Deaths 0.640 0.689 0.838 0.852 0.883 0.890 0.896 0.902 0.279 0.378 0.675 0.704 0.767 0.781 0.792 0.804 aConcordant pairs: in pairs consisting of one patient who did and one who did not die, those pairs in which the patient who died had the higher predicted probability of dying. bASG admission severity grade.
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130 EFFECTIVENESS AND OUTCOMES IN HEALTH CARE rather impressed by the power of the clinical findings to improve the good- ness-of-fit of the model. A more precise indication of what improvement is achieved in assessing the hospital's contribution to the probability of patient death when clinical data are added to the model is suggested by Figure 1. The figure plots estimates of that contribution obtained with claims data alone (core model) and with claims and clinical data (full model). Further discussion of this matter is best left for another occasion, but the potential uses of detailed clinical data in risk adjustment should be clear. A more compelling application of detailed clinical data is to the estima- tion of the influence of patient risk factors and of treatments on outcomes, illustrated in Table 9. It presents a very useful example because of its 2 ~1 =) - z O - o In -1 I -2 · id · · ^' _ _ ~e- . . / /; ~ . · )~~ ~ ~ ~ ·.,p, ~ . - , ·~ . ~ . -2 -1 0 1 2 HOSPITAL COEFFICIENTS, CORE MODEL FIGURE 1 Comparison of Hospital Regression Coefficients from Core and Full Models
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COLLECTION OF PRIMARY DATA TABLE 9 Risk Factors for Death Up to Two Years After Admission for Acute Myocardial Infarction 131 Risk Factor Relative Risk of Dyinga Age, 80 vs. 65 years Leukocytosis, 20,000 vs. 7,000 Hypokalemia, 3.2 vs. 4.3 Alkalosis, pH 7.49 vs. 7.41 Prior admission within 30 days, yes vs. no Myocardial ischemia (EKG), yes vs. no Myocardial infarction, age undetermined, yes vs. no Blood glucose, 300 vs. 90 Atrioventricular dissociation, yes vs. no Congestive heart failure (X-ray), yes vs. no Blood urea nitrogen, 60 vs. 15 Arterial oxygen pressure, 60 vs. 90 mm Hg Disoriented, x2 or x3, yes vs. no Coma/stupor, yes vs. no Heart murmur, yes vs. no Systolic blood pressure, 60 vs. 120 Tachypnea, 32 vs. 12 per minute History of diabetes, yes vs. no History of stroke or transient ischemic attack, yes vs. no History of congestive heart failure, yes vs. no History of myocardial infarction Comorbidities (by ICD-9-CM codes) cancer, yes vs. no Chronic renal disease, yes vs. no Streptokinase (IV or IC), yes vs. no Coronary angioplasty, yes vs. no Coronary bypass surgery, yes vs. no Parenteral drugs (first 48 hours) Beta blocker, yes vs. no Calcium channel blocker, yes vs. no Digitalis, yes vs. no Intravenous nitroglycerine, yes vs. no Loop diuretic, yes vs. no Pressor agent, yes vs. no Short-acting nitroglycerine, yes vs. no 1.25 1.15 0.87 1.14 1.34 0.83 0.84 1.19 2.64 1.54 1.24 1.22 1.59 2.46 1.48 1.71 1.27 1.29 1.37 1.22 1.16 1.88 1.49 Treatment Covariates 0.87(P > 0.5) 0.47(P < 0.001) 0.48(P < 0.001) 0.66(P > 0.1) 1.12(P > 0.5) 0.92(P > 0.4) 0.76(P < 0.003) 0.72(P ~ 0.1) 1.48(P < 0.001) 1.68(P ~ 0.2) aBased on the Cox proportional hazards model and stepwise regression. Follow- up is 12 to 24 months. All patient-specific risk factors are statistically significant at P < 0.05.
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132 EFFECTIVENESS AND OUTCOMES IN HEALTH CARE complexity and the difficulty of its interpretation. The results shown were obtained by the application of the Cox proportional hazards model. The upper portion is straightforward, consisting of estimates of changes in the risk (relative risks) of death due to acute myocardial infarction (AMI) associated with the specified risk factors, all other risk factors being held constant. Only highly statistically significant (P < 0.05) predictors of death are listed in this portion. The risk factors include demographic characteris- tics, results of the admission physical examination, laboratory tests and other diagnostic tests carried out in the first 48 hours of hospitalization (or prior to surgery), and historical data. The lower portion, which addresses treatments, is intriguing. The data suggest that coronary angioplasty and bypass are (or were in 1985, the year the treatments were administered) highly effective tools for the treatment of patients with AMI, controlling for the patient risks specified in the upper portion of the table. In fact, they reduced the probability of death by about half. This effect persists if patients who died on the day of admission are excluded, because they may not have lived long enough to become candidates TABLE 10 Risk Factors for Rehospitalization Following Acute Myocardial Infarction Risk Factor Relative Risk of Rehospitalizationa Leukocytosis, 20,000 vs. 7,000 Hypocalcemia, 7 vs. 9.5 Hypokalemia, 3.2 vs. 4.3 Prior admission within 30 days, yes vs. no Blood urea nitrogen, 60 vs. 15 Arterial oxygen pressure below 75 mm Hg Edema, >2+ Systolic blood pressure, 60 vs. 120 Tachypnea, 32 vs. 12 per minute Left ventricular ejection fraction, 35 vs. 62% History of diabetes, yes vs. no History of congestive heart failure, yes vs. no History of chronic obstructive pulmonary disease, yes vs. no History of immunosuppressive therapy, yes vs. no Currently on anticoagulants' yes vs. no Comorbidities (by ICD-9-CM code) cancer, yes vs. no 1.18 0.66 0.85 1.54 1.23 1.16 1.34 0.78 1.19 1.33 1.15 1.23 1.30 1.26 1.41 1.61 aBased on the Cox proportional hazards model and stepwise regression. Follow- up is 12 to 24 months. All patient-specific risk factors are statistically significant at P < 0.05.
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COLLECTION OF PRIMARY DATA 133 for revascularization, and if the "center effect" is controlled for, because the superior outcome associated with revascularization may reflect the fact that patients admitted to hospitals that perform coronary revascularization may have received better care overall. The adverse effect of the use of presser agents, controlling for hypotension, is also intriguing. The analyses presented in Table 9 are observational and must, therefore, be approached with some caution. Nevertheless, the power of detailed clinical data in describing the natural history of conditions as they are currently being treated is well illustrated. Of course, mortality is not the only outcome that may be addressed by the combination of detailed clinical and claims data. Table 10 illustrates, in a fashion analogous to Table 9, analyses addressing rehospitalization rates. In all, to characterize adequately the effectiveness of medical interventions, and therefore their impacts on the health of patients, at least from a public health perspective, it is necessary to measure mortality, morbidity, disabil- ity, and expenditures for health services, in order to try to track the effec- tiveness of interventions. CONCLUSIONS HCFA's objectives in assessing the effectiveness of interventions were as follows: · to assess the overall merits of competing procedures, · to provide information to assist clinicians in the management of patients, · to provide information to assist in peer review of care, · to guide in the formulation of policy on the allocation of resources. The initial intent was to provide PROs with more effective tools for the review and evaluation of patient care. Clearly, the information generated in this process has broader applications, the most important being to assist clinicians in the treatment of patients by providing assessments of the rela- tive merits of treatment strategies overall and for patients with specific risk factors. A further useful by-product is guidance for the allocation of resources, at whatever level such decisions are made, by providing measures of the impacts of those decisions on the health of patients.
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Development and Use of Outcome Measures: Introduction G. Richard Smith, Session Moderator The primary thrust of the Effectiveness Initiative is to determine what works in the practice of medicine. One can determine if something works only by knowing what happens to the patient. Therefore, a major emphasis of the committee's work has been on the outcomes of patient care. Previously, we could determine outcomes only in terms of whether a patient was alive or dead or by using some kind of medical test. We had very limited knowledge about what effect various medical interventions had on patients. Over the past 15 years, a new technology has been developed to enable us to understand some of the effects of medical care. This technology is called health status assessment, functional status assessment, or even at times quality of life. As a result of this new technology, we are now able to quantify a number of aspects of the state of a patient's health. For example, we can determine the effect on a patient's physical health of developing asthma or the effect on a patient's mental health of being told that she has breast cancer. When these tools are applied in a systematic and thoughtful fashion, we can tell much about the effect of our medical care system not only on our people as a whole, but upon our individual patients. Donald L. Patrick is currently professor of health services and director of the Social and Behavioral Sciences Program at the University of Washington School of Public Health. Dr. Patrick discusses selection of outcomes; use of generic and disease-specific measures; progress toward short, reliable, valid, and responsive measures; and interpreting observed changes in measures and what these changes mean. Paul D. Cleary is an associate professor in the Department of Health Care Policy at the Harvard Medical School. His chapter describes current 135
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136 EFFECTIVENESS AND OUTCOMES IN HEALTH CARE and recent research efforts in the development and use of outcome measures. The presentation highlights the use of patient self-reports. Audrey Burnam is a senior behavioral scientist at The RAND Corporation. Her chapter concentrates on the depression part of the Medical Outcomes Study and summarizes the group's approach to studying depression outcomes. Initial findings from the study are presented.
Representative terms from entire chapter: