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Do Physicians Influence Each Other’s Performance? Evidence from the Emergency Department
01 May 2019-Research Papers in Economics (Harvard University, John F. Kennedy School of Government)-
TL;DR: The results show that newly-hired and/or high-performing physicians are typically more influenced than others by their peers, and can be utilized by hospital administrators to improve the overall performance of physicians via better scheduling patterns and/ or training programs that require physicians to work during same shifts.
Abstract: Understanding potential ways through which physicians impact each other's performance can yield new insights into better management of hospitals' operations. We use evidence from Emergency Medicine to study whether and how physicians who work alongside each other during same shifts affect each other's performance. We find strong empirical evidence that physicians affect each other's speed and quality, and scheduling diverse peers during the same shift could have a positive net impact on the operations of a hospital Emergency Department (ED). Specifically, our results show that a faster (slower) peer decreases (increases) the average speed of a focal physician compared to a same-speed peer. Similarly, a higher- (lower-) quality peer decreases (increases) a focal physician's average quality. Furthermore, the presence of a less-experienced peer improves a focal physician's average speed. However, in contrast to the conventional wisdom, we do not find any evidence that more-experienced physicians can affect the performance of their less-experienced peers. We investigate various mechanisms that might be the driving force behind our findings, including psychological channels such as learning, social influence, and homophily as well as resource spillover. We identify resource spillover as the main driver of the effects we observe and show that, under high ED volumes (i.e., when the shared resources are most constrained), the magnitude of the observed effects increases. While some of these observed effects tend to be long-lived, we find that their magnitudes are fairly heterogeneous among physicians. In particular, our results show that newly-hired and/or high-performing physicians are typically more influenced than others by their peers. Finally, we draw conclusions from our results and discuss how they can be utilized by hospital administrators to improve the overall performance of physicians via better scheduling patterns and/or training programs that require physicians to work during same shifts.
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TL;DR: It is found that in the healthcare setting HRRP led to improvements of 3% to 6% in 30-day readmissions (the target metric) for those targeted by the policy as well as for those with non-targeted insurance types or clinical conditions.
Abstract: Financial incentives are commonly used to encourage improvements in quality. However, the presence of spillovers can make managing these incentives difficult. Using a large, national dataset, we study the spillover effects of a national healthcare quality improvement policy, the Hospital Readmissions Reduction Program (HRRP), on patients and metrics not targeted by the policy. Whereas prior work has shown limited spillover of quality improvement initiatives in manufacturing settings, we find that in the healthcare setting HRRP led to improvements of 3% to 6% in 30-day readmissions (the target metric) for those targeted by the policy as well as for those with non-targeted insurance types or clinical conditions. We also find significant improvements in non-targeted measures such as 31-60-day readmissions and hospitalization cost. These findings provide insight into how hospitals operationalized quality improvements, as the legislation had beneficial spillover effects beyond the narrow focus of the policy.
21 citations
TL;DR: This paper focuses on the response of human servers to the design and congestion level of the queueing system in which they operate, and incorporates two behavioral factors into multi-server analytical queueing models: server speedup due to increase of workload and server slowdown due to social loafing when multiple workers share the workload.
Abstract: Recent studies have shown that the processing speed of employees in service-based queueing systems is impacted by various behavioral factors. Limited analytical work, however, has been done to investigate how these behavioral factors affect the overall performance of different queueing system designs. In this paper, we focus on the response of human servers to the design and congestion level of the queueing system in which they operate. Specifically, we incorporate two behavioral factors into multi-server analytical queueing models: (1) server speedup due to increase of workload, and (2) server slowdown due to social loafing when multiple workers share the workload. We evaluate how these factors affect the performance of both the multi-server single-queue (SQ) and multi-server parallel-queue (PQ) system and the relative superiority of each system with respect to the number of customers in queue and the expected wait time in queue. We show that the impact of workload-dependent speedup on the queue size can be decomposed into a direct impact that reduces the queue size due to an increase in the expected service rate, and an indirect impact that further reduces the queue size due to smoothing. We quantify the performance impacts associated with both behavioral factors and clearly illustrate the conditions where each effect dominates and derive threshold values for these behavioral effects beyond which PQ systems outperform SQ systems. Finally, we consider strategic routing and its impact on the performance of the PQ system. Our analytical contributions and numerical analyses offer generalized managerial guidance regarding the choice of the queueing system design and provide a theoretical foundation for future research in behavioral queueing.
5 citations
TL;DR: In this article, the potential spillover effect of state guidances for non-essential surgeries on patients' access to essential health services, using deceased-donor kidney transplantation as the clinical setting, was estimated.
Abstract: The COVID-19 pandemic has posed an epic challenge to healthcare operations across the U.S. Between March and May 2020, multiple states issued guidances to suspend non-essential surgery. The suspensions prompted the healthcare industry to shed millions of jobs and limit the availability of resources necessary for essential procedures. In this paper, we estimate the potential spillover effect of state guidances for non-essential surgeries on patients’ access to essential health services, using deceased-donor kidney transplantation as the clinical setting. Through analyzing a unique nationwide dataset, we observe a steep reduction in the number of kidney transplants across all states amid the pandemic. However, the states with guidances for non-essential surgery experienced far steeper reductions than those without. Using a difference-in-differences approach, we estimate the issuance of a state guidance for non-essential surgery led to a 20.7% drop in the number of deceased-donor kidney transplants. The subsequent resumption of non-essential surgery, after each guidance expired, led to a 15.7% rebound in the transplant volume. By analyzing this unique “on” and “off” setting, our study reveals the spillover effect of state guidances on patients’ access to essential services such as deceased-donor kidney transplantation. Our results suggest that attempts to curb non-essential procedures may end up hurting the availability of essential ones, because hospitals may respond to such attempts by curbing support needed across all procedures. In a future pandemic, instead of suspending all non-essential surgery, policymakers should explore more granular approaches to safeguard the healthcare workforce and resources critical for supporting essential care.
2 citations
TL;DR: It is found that highly effective physicians order less tests compared to their peers and maintain their effectiveness when working under high workloads, and peer influence on a focal physician's effectiveness and efficiency is found, suggesting an opportunity to improve system performance by taking physician characteristics into account when determining the set of physicians that should be scheduled during the same shifts.
Abstract: Improving the performance of the healthcare sector requires an understanding of the effectiveness and efficiency of care delivered by providers. Although this topic is of great interest to policymakers, researchers, and hospital managers, rigorous methods of measuring effectiveness and efficiency of care delivery have proven elusive. Through Data Envelopment Analysis (DEA), we make use of evidence from care delivered by emergency physicians, and develop scores that gauge physicians' performance in terms of effectiveness and efficiency. In order to validate our DEA scores, we independently use various Machine Learning (ML) algorithms, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Random Forest (RF), a Generalized Linear Model (GLM), and Least Absolute Shrinkage and Selection Operator (LASSO). After validating our DEA scores via comparison with predictions made by these algorithms, we make use of them to identify the distinguishing behaviors of highly effective and efficient physicians. We find that highly effective physicians order less tests compared to their peers and maintain their effectiveness when working under high workloads. We also observe that highly efficient physicians order less tests on average and become even more efficient during high-volume shifts. Importantly, our results indicate a statistically significant positive relationship between a physician's effectiveness and efficiency scores suggesting that, contrary to conventional wisdom, effectiveness and efficiency in care delivery should be viewed as compliments not substitutes. In addition, we find that effectiveness is lower among physicians who have higher job tenure or average test order count. Efficiency, however, is lower among physicians with less experience (measured in number of years after graduation from medical school) or high average test order count. Furthermore, our results indicate an increase in a physician's average efficiency and a decrease in his/her average effectiveness when faced with high workloads. Finally, we find evidence of peer influence on a focal physician's effectiveness and efficiency, which suggests an opportunity to improve system performance by taking physician characteristics into account when determining the set of physicians that should be scheduled during the same shifts.
2 citations
References
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TL;DR: In this paper, the authors focus on communication processes and understand how messages have an effect on some outcome of focus in a focus-based focus-oriented focus-set problem, which is the goal of most communication researchers.
Abstract: Understanding communication processes is the goal of most communication researchers. Rarely are we satisfied merely ascertaining whether messages have an effect on some outcome of focus in a specif...
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TL;DR: This article used multivariate matching methods in an observational study of the effects of prenatal exposure to barbiturates on subsequent psychological development, using the propensity score as a distinct matching variable.
Abstract: Matched sampling is a method for selecting units from a large reservoir of potential controls to produce a control group of modest size that is similar to a treated group with respect to the distribution of observed covariates. We illustrate the use of multivariate matching methods in an observational study of the effects of prenatal exposure to barbiturates on subsequent psychological development. A key idea is the use of the propensity score as a distinct matching variable.
5,633 citations
"Do Physicians Influence Each Otherâ..." refers background in this paper
...1 times the pooled standard deviation of the logit of the propensity score (Rosenbaum and Rubin 1985)....
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01 Jan 1998
TL;DR: SMART as discussed by the authors is a web-based tool that allows rapid identification and annotation of signaling domain sequences, which can be used to determine the modular architectures of single sequences or genomes.
Abstract: Accurate multiple alignments of 86 domains that occur in signaling proteins have been constructed and used to provide a Web-based tool (SMART: simple modular architecture research tool) that allows rapid identification and annotation of signaling domain sequences. The majority of signaling proteins are multidomain in character with a considerable variety of domain combinations known. Com- parison with established databases showed that 25% of our domain set could not be deduced from SwissProt and 41% could not be annotated by Pfam. SMART is able to determine the modular architectures of single sequences or genomes; application to the entire yeast genome revealed that at least 6.7% of its genes contain one or more signaling domains, approximately 350 greater than previously annotated. The process of constructing SMART predicted (i) novel domain homologues in unexpected locations such as band 4.1- homologous domains in focal adhesion kinases; (ii) previously unknown domain families, including a citron-homology do- main; (iii) putative functions of domain families after identi- fication of additional family members, for example, a ubiq- uitin-binding role for ubiquitin-associated domains (UBA); (iv) cellular roles for proteins, such predicted DEATH do- mains in netrin receptors further implicating these molecules in axonal guidance; (v) signaling domains in known disease genes such as SPRY domains in both marenostrinypyrin and Midline 1; (vi) domains in unexpected phylogenetic contexts such as diacylglycerol kinase homologues in yeast and bacte- ria; and (vii) likely protein misclassifications exemplified by a predicted pleckstrin homology domain in a Candida albicans protein, previously described as an integrin.
3,157 citations
TL;DR: Analysis of qualitative data suggests that implementation involved four process steps: enrollment, preparation, trials, and reflection, which illuminating the collective learning process among those directly responsible for technology implementation contributes to organizational research on routines and technology adoption.
Abstract: This paper reports on a qualitative field study of 16 hospitals implementing an innovative technology for cardiac surgery. We examine how new routines are developed in organizations in which existing routines are reinforced by the technological and organizational context All hospitals studied had top-tier cardiac surgery departments with excellent reputations and patient outcomes yet exhibited striking differences in the extent to which they were able to implement a new technology that required substantial changes in the operating-room-team work routine. Successful implementers underwent a qualitatively different team learning process than those who were unsuccessful. Analysis of qualitative data suggests that implementation involved four process steps: enrollment, preparation, trials, and reflection. Successful implementers used enrollment to motivate the team, designed preparatory practice sessions and early trials to create psychological safety and encourage new behaviors, and promoted shared meaning a...
1,560 citations
"Do Physicians Influence Each Otherâ..." refers background in this paper
...Prior research has shown that an individual’s long-term performance improves over time as a result of learning from peers (Chan et al. 2014, Edmondson et al. 2001)....
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Posted Content•
TL;DR: This article used a confidential version of the National Longitudinal Survey of Youth (NLSY) to estimate a model of non-random selection of workers among cities and then investigated the hypothesis that the correlation between college share and wages is due to unobservable individual characteristics that may raise wages and be correlated with college share.
Abstract: Economists have speculated for at least a century that the social return to education may exceed the private return. In this paper, I estimate spillovers from college education by comparing wages for otherwise similar individuals who work in cities with different shares of college graduates in the labor force. OLS estimates show a large positive relationship between the share of college graduates in a city and individual wages, over and above the private return to education. A key issue in this comparison is the presence of unobservable individual characteristics, such as ability, that may raise wages and be correlated with college share. I use a confidential version of the National Longitudinal Survey of Youth (NLSY) to estimate a model of non-random selection of workers among cities. By observing the same individual over time, I can control for differences in unobserved ability across individuals and differences in the return to skills across cities. I then investigate the hypothesis that the correlation between college share and wages is due to unobservable city-specific shocks that may raise wages and attract more highly educated workers to different cities. To control for this source of potential bias, I turn to Census data and use two instrumental variables: the lagged city demographic structure and the presence of a land--grant college. The results from Census data are remarkably consistent with those based on the NLSY sample. A percentage point increase in the supply of college graduates raises high school drop-outs' wages by 1.9%, high school graduates' wages by 1.6%, and college graduates wages by 0.4%. The effect is larger for less educated groups, as predicted by a conventional demand and supply model. But even for college graduates, an increase in the supply of college graduates increases wages, as predicted by a model that includes conventional demand and supply factors as well as spillovers.
1,083 citations
"Do Physicians Influence Each Otherâ..." refers background in this paper
...Hence, it is unlikely that they would be responsive to some social cost they might impose on each other (Mas and Moretti 2009)....
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...Mas and Moretti (2009) study peer effects among cashiers in a supermarket chain and attribute the positive effect of productive peers on a worker’s productivity to increased social pressure....
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...For example, the literature on peer effects (see, e.g., Mas and Moretti 2009, Chan et al. 2014, Steinbach et al. 2016) has generally focused on settings where peers work in teams towards a common goal....
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...…a worker’s workload due to a slowdown behavior by the peer) are significant, actions and performance outcomes are observable, and the work environment resembles a teamwork setting (in which workers pursue a common goal and are compensated similarly and as a team; see, e.g., Mas and Moretti 2009)....
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