Predictive Resident Schedules in the Time of COVID-19


Zana Hekmat, Analyst
Kyle Knepper, Manager
Authors: Zana Hekmat, Analyst &
Kyle Knepper, Manager
 


 



 


As the COVID-19 pandemic continues to develop, so do the Accreditation Council of Graduate Medical Education’s (ACGME) efforts to assist in managing key educational and operational concerns resulting from these disruptions. To date, the accrediting organization’s largest response to this devastating event is the implementation of two emergency states that help each Sponsoring Institution highlight the individualized level of effects being felt by this disaster. Each stage recognizes the severity of COVID-19 within a particular health system and/or community but also allows for temporary leniencies around education or administration-oriented requirements.

As of July 1, 2020, this binary approach includes the following emergency categorization:
  1. Non-Emergency Category: Default status wherein Sponsoring Institutions and their
    programs must operate as usual.
  2. Emergency Category: Sponsoring Institutions must continue to follow all ACGME Institutional Requirements and prioritize work hour requirements, adequate resources, and adequate supervision. However, fellows may function in a core (primary) specialty to meet patient care needs.
This flexibility granted by the ACGME helped many healthcare organizations maximize the use of their workforce when the pandemic began. For some, this continues. Though the results of this decision are overwhelmingly positive. Sponsoring Institutions and GME programs alike still wish to maintain a high-quality training experience for its learners while ensuring that key structured services, quality of care, and patient safety are not negatively affected.

Using the pandemic as a recent example, disasters add a significant amount of complexity and strain to GME programs. One critical way to improve a GME program’s disaster response preparedness is to limit the number of educational and operational variables that emerge during a moment of crisis. The easiest way to do this is through the development and implementation of a predictive training model. High quality and effective predictive training models satisfy all of the following:

 
  • Under normal conditions:
    • Ensures that all residents and faculty are scheduled for the entire academic year
    • Supports a safe learning environment that balances service and education
    • Satisfies all program-specific educational and service-oriented requirements
    • Ensures residents are consistently staffed across core and/or critical services
    • Ensures staffing coordination of available faculty to supervised residents is done so with a high degree of leverage
    • Achieves a balance of PGY levels across each rotation to best satisfy ACGME requirements, service needs, and enhanced perceptions of team development
    • Remains adaptable to account for situations like a leave of absence, vacation, continued medical education, and other approved time away that can be planned for
 
  • Under abnormal or emergency-related circumstances:
    • Easily identifies which rotations are non-critical and allows for a known number of
      resident resources to be reallocated to critical services until the crisis has been
      resolved
    • Ensures that in the event of resident reallocation, appropriate levels of faculty are
      available within critical services to safely supervise newly available resident resources
Overall, with a predictive model, there is a general understanding of how resident/fellow resources are currently being deployed at any given time throughout the academic year and what options for reallocation of those resources may be available in a state of emergency or disaster. Built around the specific parameters, preferences, and resources of a hospital, the predictive training model can be developed to minimize the operational impact, maximize the overall value of the program, and ensure consistency throughout the hospital’s key services. With a good representation of clinical commitments throughout the entire academic year, the predictive model allows for focused practice during specialty rotations by isolating clinical commitments, with increased flexibility to reallocate resources during times of crisis. As a simple example, the graphic below depicts how a 30-total resident (10/Year) internal medicine program would regularly deploy its resources throughout the health system using a 4+1 Training Model. It shows that at any given time of the year, there will be always 9 residents on inpatient coverage teams, 6 residents on specialty rotations, and 2 residents on electives.



During an emergency, such as COVID-19,
which requires extra hospital support and
patient care services, residents on the
elective rotation can be redistributed to
help in the department requiring coverage.




As a result, the predictive allows for a stronger emphasis on education, as well as more time in the clinic by minimizing the conflicts between inpatient and outpatient training functions. Contrary to this scheduling system, traditional models generally:
  • Conflict simultaneous inpatient and outpatient responsibilities
  • Prevent functional team structure
  • Neglect patient continuity
  • Disjoint residents/fellows from preceptors, therefore adversely impacting resident/fellow
    to faculty supervision ratios. With added variability and complexity from COVID-19, the
    4:1 resident/fellow to faculty supervision ratios may be exceeded, therefore putting the
    program in non-compliance with ACGME requirements.
If done correctly, the predictive model will demonstrate opportunities to account for future
disasters in the circumstance that residents/fellows need to be redistributed. Through built-
in capabilities from a predictive scheduling system, programs can ensure that the proper
levels of supervision will be available for those residents/fellows while structuring the
program to cover key service areas. Ultimately, predictive scheduling systems can align
ACGME requirements with fluctuations in patience care services to maintain educational
integrity and operational demands even with maximum pressures from disasters such as
COVID-19.

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