The Impact of Health Information Exchanges on Emergency Department Length of Stay

Empirical research in operations management (OM) has made rapid strides in the last 30 years, and increasingly, OM researchers are leveraging methods used in the econometrics and statistics literature to assess the causal effects of interventions. We discuss the two key challenges in assessing causality with observational data (i.e., baseline bias, differential treatment effect bias) and how dominant identification approaches such as matching, instrumental variables, regression discontinuity, difference‐in‐differences, and fixed effects deal with such challenges. We surface the key underlying assumptions of different causal estimation methods and discuss how OM scholars have used these methods in the last few years. We hope that reflecting on the plausibility and substantive meaning of underlying assumptions regarding different identification strategies in a particular context will lead to a better conceptualization, execution, evaluation, dissemination, and consumption of OM research. We conclude with a few thoughts that authors and reviewers may find helpful in their research as they engage in discourse related to causality.

Section: Identification Strategies For Assessing Causality In Pom Emp...mentioning

confidence: 99%

On the causality and plausibility of treatment effects in operations management research

Mithas

Chen

Lin

et al. 2022

“…To the best of our knowledge, this is the first study to focus on the understudied domain of healthcare models with multiple forms of care coordination (Zepeda and Sinha 2016). A majority of healthcare delivery research in the operations, public policy, and general management literatures has examined care-coordination issues at either the organizational (David et al 2013 or technological level (Ayer et al 2019, Khuntia et al 2017) but has ignored individual levels. Although both structural and technological collaborations are important, it is rather the individual such as multisiting physicians who directly interact with patients and make treatment decisions.…”

Section: Implications For Theorymentioning

confidence: 99%

Ancillary Cost Implications of Physicians Multisiting and Inter‐Organizational Collaboration During Healthcare Delivery

Lan

Goradia

Chandrasekaran

2022

T his study investigates diagnostic testing and pharmacy expenditures, commonly referred to as ancillary costs in the United States acute-care hospitals. These ancillary costs are charges associated with services provided to support patient treatment, including laboratory, radiology, and pharmacy charges. Despite of investment in inter-organizational and technological collaboration practices, these costs continue to rise. Our research examines the role of individual healthcare providers, namely multisiting physicians, who practice at more than one hospital, in reducing ancillary cost. We also look at how inter-organizational collaboration measured by the hospital's affiliation in an accountable care organization (ACO) model affect this relationship. To do this, we assembled a unique data set of 163,617 patients treated by 4411 physicians at 182 hospitals in Florida from 2014 to 2016. Using an econometric estimation approach accounting for endogeneity, we find that patients treated by multisiting physicians experienced, on an average, per-hospitalization reduction in laboratory and radiology charges of 58.57% and 30.11%, respectively. These savings increase when multisiting physicians have less practice experience. We also find that hospitals participating in ACO model complement the savings achieved through multisiting physicians in the form of even lower pharmacy costs. In post-hoc analyses, we find such cost reduction comes from reducing the total number of procedures or tests ordered. We also show that the lower treatment charges do not compromise the clinical quality of patients treated by multisiting physicians. Collectively, the findings offer important insights to the healthcare operations management literature on the interaction between individual-organizational collaboration efforts and patient-level outcomes.

“…In our setting, the DID econometric approach helps to establish the causal effects of each process change while also accounting for differences in the baseline holding room times across the two groups, as well as any unobservable changes impacting patient flow over time (Wooldridge 2002). The DID econometric specification has proven effective in analyzing the causal effects of policy and process adoption in recent operations and health policy analysis literature (Ayer et al 2019, Gray et al 2015, Khurana et al 2019, Rajaram et al 2014, Schmitt 2017, Song et al 2015a,b, Song et al 2018.…”

Section: Identification Strategymentioning

confidence: 99%

The Effect of Operational Process Changes on Preoperative Patient Flow: Evidence from Field Research

Kistler

Janakiraman

Kumar

et al. 2021

W e partnered with a leading US academic medical center to empirically examine the impact of operational process changes designed to improve preoperative flow of patients through the perioperative environment. We focus on the implementation of centralized decision-making and the introduction of an information technology (IT)-enabled intraoperative prompt, on efficiency, as measured by preoperative patient processing time. We analyze over 33,000 individual surgical cases in a unique field experimental setting to conduct an empirical investigation of the effects of each intervention on patients' time spent in preoperative processing. To identify the causal effect of each process change, we leverage our field experimental research design and cast our analyses in the difference-in-differences modeling framework. We compare the preoperative patient processing time of two distinct patient groups, a treatment group that is impacted by the implemented operational changes and a control group that was not impacted by the changes, before and after each process change. Our results suggest a 3.4% reduction in preoperative processing time with only centralized decision-making in place, yet suggest a 10.8% reduction in preoperative processing time when centralized decision-making is paired with the IT-enabled intraoperative prompt. We also find evidence of a complementarity effect between our process changes and surgeon prior process experience. Our study contributes to the healthcare operations literature by demonstrating the benefits of coordinated information flow within the patient supply chain. We offer insights for hospital managers and healthcare operations scholars alike on the role of information coordination in improving preoperative patient flow with minimal impact on existing resources and staff.