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Distance, Quality, or Relationship? Interhospital Transfer of Heart Attack Patients

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  • Lauren Xiaoyuan Lu
  • Susan Feng Lu

Abstract

We empirically investigate the pattern of where heart attack patients are transferred between hospitals. Using 2011 Florida State Emergency Department and Inpatient Databases, we demonstrate the relative importance of three key factors in determining transfer destinations: (1) the distance between sending and receiving hospitals, (2) publicly reported quality measures of receiving hospitals, and (3) the relationship between sending and receiving hospitals as indicated by whether they are affiliated with the same multihospital system. Our conditional logit analysis shows that hospital relationship plays a more dominant role in determining transfer destinations than distance and quality. This result is robust to three alternative specifications of choice sets using distance ranking, radius circles, and Hospital Referral Regions, and also robust to alternative measures of distance and quality. Using 30‐day readmission rate to evaluate the health outcome of transferred patients, we find that relationship‐based transfers led to worse outcome than distance‐based and quality‐based transfers. We also find that nonprofit hospitals are more likely to conduct quality‐based transfers and less likely to conduct relationship‐based transfers than their for‐profit counterparts. Our study calls for reevaluating the current practice of relationship‐driven routing of heart attack patients—selecting transfer destinations based on quality or distance can potentially decrease hospital readmission rate.

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  • Lauren Xiaoyuan Lu & Susan Feng Lu, 2018. "Distance, Quality, or Relationship? Interhospital Transfer of Heart Attack Patients," Production and Operations Management, Production and Operations Management Society, vol. 27(12), pages 2251-2269, December.
  • Handle: RePEc:bla:popmgt:v:27:y:2018:i:12:p:2251-2269
    DOI: 10.1111/poms.12711
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    Cited by:

    1. Shujing Sun & Susan F. Lu & Huaxia Rui, 2020. "Does Telemedicine Reduce Emergency Room Congestion? Evidence from New York State," Information Systems Research, INFORMS, vol. 31(3), pages 972-986, September.
    2. Shen, Zuo-Jun Max & Xie, Jingui & Zheng, Zhichao & Zhou, Han, 2023. "Dynamic scheduling with uncertain job types," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1047-1060.
    3. Ginger Zhe Jin & Ajin Lee & Susan Feng Lu, 2022. "Patient Routing to Skilled Nursing Facilities: The Consequences of the Medicare Reimbursement Rule," Management Science, INFORMS, vol. 68(12), pages 8722-8740, December.
    4. Raymond Lei Fan & Ming Zhao & David Xiaosong Peng, 2021. "Differentiating Interhospital Transfer Types: Varied Impacts and Diverging Coordination Strategies," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3657-3678, October.
    5. Diwas Singh KC & Stefan Scholtes & Christian Terwiesch, 2020. "Empirical Research in Healthcare Operations: Past Research, Present Understanding, and Future Opportunities," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 73-83, January.
    6. Lauren Xiaoyuan Lu & Susan Feng Lu, 2022. "Does Nonprofit Ownership Matter for Firm Performance? Financial Distress and Ownership Conversion of Nursing Homes," Management Science, INFORMS, vol. 68(7), pages 5127-5145, July.
    7. Yingchao Lan & Deepa Goradia & Aravind Chandrasekaran, 2022. "Ancillary Cost Implications of Physicians Multisiting and Inter‐Organizational Collaboration During Healthcare Delivery," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 561-582, February.
    8. Liangfei Qiu & Subodha Kumar & Arun Sen & Atish P. Sinha, 2022. "Impact of the Hospital Readmission Reduction Program on hospital readmission and mortality: An economic analysis," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2341-2360, May.

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