Team Relationships and Performance: Evidence from Healthcare Referral Networks.

We examine the teams that emerge when a primary care physician (PCP) refers patients to specialists. When PCPs concentrate their specialist referrals-for instance, by sending their cardiology patients to fewer distinct cardiologists-repeat interactions between PCPs and specialists are encouraged. Repeated interactions provide more opportunities and incentives to develop productive team relationships. Using data from the Massachusetts All Payer Claims Database, we construct a new measure of PCP team referral concentration and document that it varies widely across PCPs, even among PCPs in the same organization. Chronically ill patients treated by PCPs with a one standard deviation higher team referral concentration have 4% lower health care utilization on average, with no discernible reduction in quality. We corroborate this finding using a national sample of Medicare claims and show that it holds under various identification strategies that account for observed and unobserved patient and physician characteristics. The results suggest that repeated PCP-specialist interactions improve team performance.

Information technology and medical missteps: evidence from a randomized trial.

We analyze the effect of a decision support tool designed to help physicians detect and correct medical "missteps". The data comes from a randomized trial of the technology on a population of commercial HMO patients. The key findings are that the new information technology lowers average charges by 6% relative to the control group. This reduction in resource utilization was the result of reduced in-patient charges (and associated professional charges) for the most costly patients. The rate at which identified issues were resolved was generally higher in the study group than in the control group, suggesting the possibility of improvements in care quality along measured dimensions and enhanced diffusion of new protocols based on new clinical evidence.

Care fragmentation, quality, and costs among chronically ill patients.

OBJECTIVES: To assess the relationship between care fragmentation and both quality and costs of care for commercially insured, chronically ill patients. STUDY DESIGN: We used claims data from 2004 to 2008 for 506,376 chronically ill, privately insured enrollees of a large commercial insurance company to construct measures of fragmentation. We included patients in the sample if they had chronic conditions in any of the following categories: cardiovascular disease, diabetes, asthma, arthritis, or migraine. METHODS: We assigned each patient a fragmentation index based on the patterns of care of their primary care provider (PCP), with care patterns spread across a higher number of providers considered to be more fragmented. We used regression analysis to examine the relationship between fragmentation and both quality and cost outcomes. RESULTS: Patients of PCPs in the highest quartile of fragmentation had a higher chance of having a departure from clinical best practice (32.8%, vs 25.9% among patients of PCPs in the lowest quartile of fragmentation; P < .001). Similarly, patients of PCPs with high fragmentation had higher rates of preventable hospitalizations (9.1% in highest quartile vs 7.1% in lowest quartile; P < .001). High fragmentation was associated with $4542 higher healthcare spending ($10,396 in the highest quartile vs $5854 in the lowest quartile; P < .001). We found similar or larger effects on quality and costs among patients when we examined the most frequently occurring disease groups individually. CONCLUSIONS: Chronically ill patients whose primary care providers offer highly fragmented care more often experience lapses in care quality and incur greater healthcare costs.

Influence, information overload, and information technology in health care.

We investigate whether information technology (IT) can help physicians more efficiently acquire new knowledge in a clinical environment characterized by information overload. We combine analysis of data from a randomized trial with a theoretical model of the influence that IT has on the acquisition of new medical knowledge. Although the theoretical framework we develop is conventionally microeconomic, the model highlights the non-market and non-pecuniary influence activities that have been emphasized in the sociological literature on technology diffusion. We report three findings. First, empirical evidence and theoretical reasoning suggests that computer-based decision support will speed the diffusion of new medical knowledge when physicians are coping with information overload. Second, spillover effects will likely lead to "underinvestment" in this decision support technology. Third, alternative financing strategies common to new IT, such as the use of marketing dollars to pay for the decision support systems, may lead to undesirable outcomes if physician information overload is sufficiently severe and if there is significant ambiguity in how best to respond to the clinical issues identified by the computer. This is the first paper to analyze empirically and theoretically how computer-based decision support influences the acquisition of new knowledge by physicians.