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1.  Context and community. 
Health Services Research  2001;36(4):665-670.
PMCID: PMC1089250  PMID: 11508633
2.  Identifying and assessing the null hypothesis. 
Health Services Research  2000;34(6):1265-1271.
PMCID: PMC1089078  PMID: 10654829
3.  Pushing good research to go farther. 
Health Services Research  1998;33(5 Pt 1):1185-1190.
PMCID: PMC1070312  PMID: 9865216
4.  Use of risk-adjusted outcome data for quality improvement by public hospitals. 
Western Journal of Medicine  1996;164(5):410-414.
In 1993 the California Office of Statewide Health Planning and Development (OSHPD) began public release of risk-adjusted monitoring of outcomes (RAMO) under the California Hospital Outcomes Project. We studied how 17 acute are public hospitals in California used these RAMO data for quality improvement purposes following their initial distribution, first by analyzing the outcome data for San Francisco General Hospital Medical Center as recommended by OSHPD and, second, by querying the departments at the other 16 public hospitals to determine how their own analyses compared. We found that the hospitals generally did minimal analyses of the OSHPD RAMO data and considered the data of little value to them. Only 3 hospitals initiated quality improvement activities based on their data review. The major reasons given by the hospitals for not using the RAMO data were that their outcomes were adequate, as verified by a comparison of their observed outcomes and those expected after risk-adjustment; that the hospitals had too few patients in the diagnostic categories; that they had too few resources; and that they were not concerned with the data's public release. Other possible explanations were that awareness of the California Hospital Outcomes Project was not widespread at the time of the study, that the RAMO data were not distributed in a way that encouraged their use, and that public hospitals were not inclined to use the outcome data because the project was imposed on them. Whatever the explanation, our study suggests that the California Hospital Outcomes Project has had little effect on quality improvement in public hospitals.
PMCID: PMC1303538  PMID: 8686297
6.  Measuring hospital mortality rates: are 30-day data enough? Ischemic Heart Disease Patient Outcomes Research Team. 
Health Services Research  1995;29(6):679-695.
OBJECTIVE. We compare 30-day and 180-day postadmission hospital mortality rates for all Medicare patients and those in three categories of cardiac care: coronary artery bypass graft surgery, acute myocardial infarction, and congestive heart failure. DATA SOURCES/COLLECTION. Health Care Financing Administration (HCFA) hospital mortality data for FY 1989. STUDY DESIGN. Using hospital level public use files of actual and predicted mortality at 30 and 180 days, we constructed residual mortality measures for each hospital. We ranked hospitals and used receiver operating characteristic (ROC) curves to compare 0-30, 31-180, and 0-180-day postadmission mortality. PRINCIPAL FINDINGS. For the admissions we studied, we found a broad range of hospital performance when we ranked hospitals using the 30-day data; some hospitals had much lower than predicted 30-day mortality rates, while others had much higher than predicted mortality rates. Data from the time period 31-180 days postadmission yield results that corroborate the 0-30 day postadmission data. Moreover, we found evidence that hospital performance on one condition is related to performance on the other conditions, but that the correlation is much weaker in the 31-180-day interval than in the 0-30-day period. Using ROC curves, we found that the 30-day data discriminated the top and bottom fifths of the 180-day data extremely well, especially for AMI outcomes. CONCLUSIONS. Using data on cumulative hospital mortality from 180 days postadmission does not yield a different perspective from using data from 30 days postadmission for the conditions we studied.
PMCID: PMC1070038  PMID: 7860319
7.  Anatomy of health care reform proposals. 
Western Journal of Medicine  1993;159(4):494-500.
The current proliferation of proposals for health care reform makes it difficult to sort out the differences among plans and the likely outcome of different approaches to reform. The current health care system has two basic features. The first, enrollment and eligibility functions, includes how people get into the system and gain coverage for health care services. We describe 4 models, ranging from an individual, voluntary approach to a universal, tax-based model. The second, the provision of health care, includes how physician services are organized, how they are paid for, what mechanisms are in place for quality assurance, and the degree of organization and oversight of the health care system. We describe 7 models of the organization component, including the current fee-for-service system with no national health budget, managed care, salaried providers under a budget, and managed competition with and without a national health budget. These 2 components provide the building blocks for health care plans, presented as a matrix. We also evaluate several reform proposals by how they combine these 2 elements.
PMCID: PMC1022294  PMID: 8273344
8.  Calculating the probability of rare events: why settle for an approximation? 
Health Services Research  1993;28(4):419-439.
OBJECTIVE. Health services researchers often need to compute the probability of observing a certain number of events when only a few such events are expected. Our objective is to show that the standard approaches (Poisson, binomial, and normal approximations) are inappropriate in such instances, and to suggest an alternative. DATA SOURCES. Patients undergoing cholecystectomy (34,234) in 465 California hospitals in 1983 are used to demonstrate the biases arising from various methods of calculating the probability of observing a given number of deaths in each hospital. Similar data from other procedures and diagnoses with lower and higher mortality rates are also used for illustration. STUDY DESIGN. The computational methods to derive probabilities using the Poisson, normal, simulation, and exact probabilities are discussed. Using a previously developed risk factor model, the probability of observing the actual number of deaths (or more) is calculated given the expectation of death for each patient in each hospital. Results for the four methods are compared, showing the types of random and systematic errors in the Poisson, normal, and simulation approaches. DATA COLLECTION. Routinely collected hospital discharge abstract data were provided by the California Office of Statewide Planning and Development. PRINCIPAL FINDINGS. The Poisson and normal approximations are often biased substantially in calculating upper-tail p-values, especially when the expected number of adverse outcomes is less than five. Simulations allow unbiased calculations, and the degree of random error can be made arbitrarily small given enough trials. Exact calculations using a simple recursive algorithm can be done very efficiently on either a mainframe or personal computer. For example, the whole set of cholecystectomy patients can be assessed in less than 90 seconds on a Macintosh. CONCLUSIONS. Calculating the probability of observing a small number of events using standard approaches may result in substantial errors. The availability of a simple and inexpensive method of calculating these probabilities exactly can avoid these errors.
PMCID: PMC1069950  PMID: 8407336
9.  Choice of hospital for delivery: a comparison of high-risk and low-risk women. 
Health Services Research  1993;28(2):201-222.
OBJECTIVE. This article tests whether or not the factors that affect hospital choice differ for selected subgroups of the population. DATA SOURCES. 1985 California Office of Statewide Health Planning and Development (OSHPD) discharge abstracts and hospital financial data were used. STUDY DESIGN. Models for hospital choice were estimated using McFadden's conditional logit model. Separate models were estimated for high-risk and low-risk patients, and for high-risk and low-risk women covered either by private insurance or by California Medicaid. The model included independent variables to control for quality, price, ownership, and distance to the hospital. DATA EXTRACTION. Data covered all maternal deliveries in the San Francisco Bay Area in 1985 (N = 61,436). ICD-9 codes were used to classify patients as high-risk or low-risk. The expected payment code on the discharge abstract was used to identify insurance status. PRINCIPAL FINDINGS. The results strongly reject the hypothesis that high-risk and low-risk women have the same choice process. Hospital quality tended to be more important for high-risk than low-risk women. These results also reject the hypothesis that factors influencing choice of hospital are the same for women covered by private insurance as for those covered by Medicaid. Further, high-risk women covered by Medicaid were less likely than high-risk women covered by private insurance to deliver in hospitals with newborn intensive care units. CONCLUSIONS. The results show that the choice factors vary across several broadly defined subgroups of patients with a specific condition. Thus, estimates aggregating all patients may be misleading. Specifically, such estimates will understate actual patient response to quality of care indicators, since patient sensitivity to quality of care varies with the patients' risk status.
PMCID: PMC1069930  PMID: 8514500
10.  Costs and coverage. Pressures toward health care reform. 
Western Journal of Medicine  1992;157(5):576-583.
Signs of discontent with the health care system are growing. Calls for health care reform are largely motivated by the continued increase in health care costs and the large number of people without adequate health insurance. For the past 20 years, health care spending has risen at rates higher than the gross national product. As many as 35 million people are without health insurance. As proposals for health care reform are developed, it is useful to understand the roots of the cost problem. Causes of spiraling health care costs include "market failure" in the health care market, expansion in technology, excessive administrative costs, unnecessary care and defensive medicine, increased patient complexity, excess capacity within the health care system, and low productivity. Attempts to control costs, by the federal government for the Medicare program and then by the private sector, have to date been mostly unsuccessful. New proposals for health care reform are proliferating, and important changes in the health care system are likely.
PMCID: PMC1022049  PMID: 1441510
11.  The volume-outcome relationship: practice-makes-perfect or selective-referral patterns? 
Health Services Research  1987;22(2):157-182.
Various studies have demonstrated that hospitals with larger numbers of patients with a specific diagnosis or procedure have lower mortality rates. In some instances, these results have been interpreted to mean that physicians and hospital personnel with more of these patients develop greater skills and that this results in better outcomes--the "practice-makes-perfect" hypothesis. An alternative explanation is that physicians and hospitals with better outcomes attract more patients--the "selective-referral pattern" hypothesis. Using data for 17 categories of patients from a sample of over 900 hospitals, we examine the patterns of selected variables with respect to hospital volume. To explore the plausibility of each hypothesis, a simultaneous-equation model is also used to test the relative importance of the two explanations for each diagnosis or procedure. The results suggest that both explanations are valid, and that the relative importance of the practice or referral explanation varies by diagnosis or procedure, in ways consistent with clinical aspects of the various patient categories.
PMCID: PMC1065430  PMID: 3112042
12.  Appropriate measures of hospital market areas. 
Health Services Research  1987;22(1):69-89.
As public and private policymakers turn to market-oriented strategies to control hospital prices, it is necessary to understand the conceptual underpinnings of hospital market area measurement. This article provides a framework for evaluating which definitions of hospital market areas are suitable for various types of analyses. Hospital market areas can be defined from two perspectives: an individual hospital perspective and that of the overall market. From each perspective, empirical definitions can be based on geopolitical boundaries, distance between hospitals, and patient-origin data. In this article, market areas are compared based on various descriptions using data on California hospitals and patient discharge abstracts.
PMCID: PMC1065423  PMID: 3570813
13.  Health services research as a scientific process: the metamorphosis of an empirical research project from grant proposal to final report. 
Health Services Research  1986;21(4):563-584.
The process of health services research is rarely examined; attention is usually focused on results and policy implications. Large and small decisions made during the execution of a study, however, can have major impacts on its outcomes. This article describes a project that underwent major changes because of problems discovered in the basic data and threats to the valid interpretation of econometric results uncovered by qualitative case studies. Although the combination of difficulties encountered in this project may be unusual, it is likely that many similar problems and opportunities occur in other empirical studies.
PMCID: PMC1068973  PMID: 3771233
15.  Regionalization of services within a multihospital health maintenance organization. 
Health Services Research  1980;15(3):231-247.
Among the many factors that may explain lower costs for enrollees in Health Maintenance Organizations (HMOs) is the possibility that the HMO provides inpatient services more efficiently. While direct cost comparisons are in appropriate, it is reasonable to examine whether the Kaiser program in the San Francisco Bay Area regionalizes services among its ten hospitals. The presence of each of 43 facilities/services reported is examined in a regression model that includes type of hospital, size, a size-type interaction, and the distance to the nearest competing facility. When the generally smaller size of the Kaiser hospitals was controlled for, Kaiser hospitals had fewer technologically based services and concentrated these services in larger hospitals. Kaiser had more outpatient-oriented services. Among non-Kaiser hospitals, some specialized facilities were competitively distributed.
PMCID: PMC1072166  PMID: 7204063

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