Collaborative Research: Disruption and Resilience in Healthcare Routines Following Adverse Events

  • Funded by National Science Foundation (NSF)
  • Total publications:0 publications

Grant number: 2120014; 2120530

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2021
    2024
  • Known Financial Commitments (USD)

    $538,000
  • Funder

    National Science Foundation (NSF)
  • Principal Investigator

    Julie Wolf, Brian Pentland
  • Research Location

    United States of America
  • Lead Research Institution

    University of Rochester, Michigan State University
  • Research Priority Alignment

    N/A
  • Research Category

    Secondary impacts of disease, response & control measures

  • Research Subcategory

    Indirect health impacts

  • Special Interest Tags

    N/A

  • Study Type

    Unspecified

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

When routines are disrupted, people want to get back to normal. If the disruption is minor, like a flat tire, recovery is easy. If the disruption is major, recovery may be more difficult. For example, the shutdowns caused by the COVID pandemic forced doctors, nurses, and other clinical staff to find new ways to care for their patients. This research will use this example to study the effects of disruptions on healthcare routines. The expectation is that routines will "bounce back" from minor disruptions, but the effects of major disruptions are more difficult to predict. After major disruptions, some routines may return to normal, while others may not.

The goal of this project is to discover basic mechanisms that influence stability and change in routines. To understand what makes some routines stronger than others, the effects of the COVID pandemic will be studied in four medical fields at the University of Rochester Medical Center: dermatology, orthopedics, oncology, and cardiology. Data from electronic health records will be used to study the effects of shutdowns and other kinds of disruptions, such as changes in software and billing codes. Tools from network science will be used to model routines as patterns of action. These methods will allow comparisons to be made between patterns of action before and after a disruption with great precision. The extent to which the strength of a routine depends on the structure of the action pattern itself will be examined. Data will be produced on outpatient clinical routines during the COVID-19 pandemic along with a variety of materials to communicate the findings with a broader audience. Ultimately, this research will lead to a better understanding of how institutional routines can be made more reliable and effective in the face of disasters large and small.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.