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Objective: Incomplete coding of secondary diagnoses may bias assessments of patient risks of poor outcomes using administrative health care databases, most of which allow only five diagnoses. The Medicare program is expanding the number of possible diagnoses from five to nine, aiming to improve coding completeness. We examined the impact of having more diagnosis codes available on assessments of risk of death.Design: We used 1988 computerized hospital discharge abstract data from California, which allow up to 25 diagnoses per discharge, to select a sample of hospitalized patients and assessed the relationship between the presence of 29 specific secondary diagnoses and the risk of in-hospital death.Setting: Nonfederal acute-care hospitals in California.Study Population: All patients at least 65 years of age who were hospitalized for stroke, pneumonia, acute myocardial infarction, or congestive heart failure in California in 1988 (N = 162,790).Main Outcome Measures: Relative risk of death for each specific secondary diagnosis.Results: Many conditions that on a clinical basis would be expected to increase the risk of death, such as adult-onset diabetes mellitus, previous myocardial infarction, angina, and ventricular premature beats, were associated with a lower risk of in-hospital death.Conclusions: Bias against coding of chronic or comorbid conditions on the computerized discharge abstracts of patients who die best explains these results. Efforts to improve diagnosis coding completeness solely by increasing the number of available coding spaces may not succeed. [ABSTRACT FROM AUTHOR]

This research examined the hypothesis that greater severity of illness explains the higher costs of hospitalizations at teaching compared with nonteaching hospitals. Medical records of 4439 cases within eight common conditions were reviewed at five tertiary teaching, five other teaching, and five nonteaching hospitals in metropolitan Boston, Mass. We assessed acute physiologic status, severity of the principal diagnosis, comorbidities, and functional status. The principal diagnosis was more severe for teaching hospital patients in four conditions, but few significant differences were found for the other severity dimensions by condition. Across all conditions combined, except for functional status, severity was significantly higher at teaching hospitals, but the absolute differences were small. After adjusting for diagnosis related groups, costs were higher at tertiary teaching compared with other teaching and nonteaching hospitals. Further adjusting for severity and other patient characteristics explained 18% (90% confidence interval, 4 to 33) of the higher costs at tertiary compared with nonteaching hospitals. [ABSTRACT FROM AUTHOR]

Timely use of necessary follow-up services is an im portant dimension of ambulatory care quality. Using a hospital-based walk-in center, this study identified pa tients who were referred for follow-up care and exam ined factors related to compliance with these referrals. The participants were 696 adults seen in a hospital- based walk-in unit between June 1, 1992, and December 1, 1992. Patients completed a self-administered ques tionnaire including questions about sociodemographic characteristics, prior use of health services, and the Medical Outcomes Study (MOS) 36-Item Health Survey. Medical findings, follow-up recommendations, insur ance status, and compliance with follow-up referrals were ascertained using chart review, the hospital's com puting system, and clinic records. Fifty percent of the patients were referred for follow-up medical care; 55% of these complied with follow-up referrals. Factors asso ciated with referral for follow-up care included older age, inability to afford a physician, longer duration of chief complaint, the patient's belief that follow-up care would be needed, and worse MOS pain score. The most important factor associated with compliance with fol low-up referral was scheduling appointments while pa tients were still in the walk-in unit. Patients with such scheduled appointments were almost 10 times more likely than others to receive follow-up (adjusted odds ratio = 9.6, 95% confidence interval = 4.4-21.2). The most important step a provider can take to improve compliance with follow-up referral is to schedule ap pointments before patients are sent home. This should presumably improve quality of ambulatory care. [ABSTRACT FROM PUBLISHER]

This research investigates the degree that estimates of the magnitude of small area variations in hospitalization rates depend on both the estimation method and the number of years of data used. Hospital discharge abstracts for patients 65 and older from acute care hospitals in Massachusetts from 1982 to 1987 were analyzed. The SCV statistic, the approach used in many current small area variation studies, and empirical Bayes (EB), an approach that adjusts more fully for the effect of random variation, were compared. EB estimates based on 3 years of data were best able to predict future area-specific hospitalization rates. Compared to EB estimates using 3 years of data, the SCV statistic with 1 year of data overestimated the median amount of systematic variation by over 70% for the 68 conditions studied; with 3 years of data, the SCV overestimated the median by 55%. Regardless of method, the same conditions were identified as relatively more variable and the same geographic areas were found to have higher than expected hospitalization rates. The magnitude of differences in hospitalization rates depends on how the data are analyzed and how many years of data are used. Hospitalization rates across small geographic areas may vary substantially less than reported previously.

Concerns about the insensitivity of Medicare's diagnosis-related groups (DRGs) to illness severity heightened interest in the potential of alternative patient classification systems to improve the fairness of hospital reimbursement. This article examines the ability of admission MedisGroups score to explain the costs of hospital stays. The database contained 54,112 patients 65 years or older discharged in 28 high-frequency DRGs from 1984 to the middle of 1986 from 24 hospitals across the country. Admission MedisGroups score alone explained 3% of costs using trimmed data. Addition of admission MedisGroups score to DRGs modestly improved ability to predict differences in cost: for trimmed data, DRGs alone explained 52% of the variation in costs, compared with 55% when admission MedisGroups score was added. Within individual DRGs, the explanatory power of admission MedisGroups score ranged from 0% to 21%. The level of explanatory power was not related to the spread of cases across admission MedisGroups scores within DRG. No consistent clinical pattern explained these differences across DRGs.