For many years it has been claimed that observational studies find stronger treatment effects than randomized, controlled trials. … We searched the Abridged Index Medicus and Cochrane data bases to identify observational studies reported between 1985 and 1998 that compared two or more treatments or interventions for the same condition. We then searched the Medline and Cochrane data bases to identify all the randomized, controlled trials and observational studies comparing the same treatments for these conditions. For each treatment, the magnitudes of the effects in the various observational studies were combined by the Mantel–Haenszel or weighted analysis-of-variance procedure and then compared with the combined magnitude of the effects in the randomized, controlled trials that evaluated the same treatment. … In most cases, the estimates of the treatment effects from observational studies and randomized, controlled trials were similar. In only 2 of the 19 analyses of treatment effects did the combined magnitude of the effect in observational studies lie outside the 95 percent confidence interval for the combined magnitude in the randomized,controlled trials.
Medical care is characterized by enormous inefficiency. Costs are higher and outcomes worse than almost all analyses of the industry suggest should occur. In other industries characterized by inefficiency, efficient firms expand to take over the market, or new firms enter to eliminate inefficiencies. This has not happened in medical care, however. This paper explores the reasons for this failure of innovation. I identify two factors as being particularly important in organizational stagnation: public insurance programs that are oriented to volume of care and not value, and inadequate information about quality of care. Recent reforms have aspects that bear on these problems.
In this paper we examine the causal impact of competition on management quality. We analyze the hospital sector where geographic proximity is a key determinant of competition, and English public hospitals where political competition can be used to construct instrumental variables for market structure. Since almost all major English hospitals are government run, closing hospitals in areas where the governing party has a small majority is rare due to fear of electoral punishment. We find that management quality – measured using a new survey tool – is strongly correlated with financial and clinical outcomes such as survival rates from emergency heart attack admissions (AMI). More importantly, we find that higher competition (as indicated by a greater number of neighboring hospitals) is positively correlated with increased management quality, and this relationship strengthens when we instrument the number of local hospitals with local political competition. Adding another rival hospital increases the index of management quality by one third of a standard deviation and leads to a 10.7% reduction in heart-attack mortality rates.
General medical care in the United States has historically been provided by physicians who care for their patients in both ambulatory and hospital settings. Care is now increasingly divided between physicians specializing in hospital care (hospitalists) and ambulatory-based care primary care physicians. We develop and find strong empirical support for a theoretical model of the division of labor in general medicine that views the use of hospitalists as balancing the costs of coordinating care across physicians in the hospitalist model against physicians costs switching between ambulatory and hospital settings in the traditional model. Our findings suggest opportunities to improve care.
[Book] Causality: Models, Reasoning and Inference, by Judea Pearl. The first edition, as summarized by the publisher:
Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.