Improving Healthcare by Reducing Patient Handoffs
A guest article written by student Kritin Shanmugam, edited by Tyler Perini.
A 1999 report in the Institution of Medicine [1] declared that over 100,000 deaths and nearly 15 million injuries occur each year due to medical errors. More recent reports across multiple journals account for almost triple the number of deaths and injuries. Medical errors act as one of the leading causes of death and illness in the United States, ahead of breast cancer, and they cost tens of billions of dollars to the healthcare system. An influential source of these errors are communication errors between healthcare providers. Furthermore, these errors are further related to fatigue, as a consequence of residents’ working hours. In an effort to reduce the fatigue in residents and fellows, the Accreditation Council for Graduate Medical Education, eleven years ago, executed regulations on their hourly length of shifts, amount of weekly working hours, off time between shifts, and the regularity of successive days and nights of duty. The regulations on the shifts dramatically increased the number of communication mistakes and patient handoffs between providers. A patient handoff constitutes the transfer of a patient and any necessary information from one provider to another. Even though improved patient handoffs can slightly decrease communication errors, a reduction in handoffs themselves will immensely prevent dangerous mistakes from being made. A research study by Kazemian, P., Dong, Y., Rohleder, T.R. et al. (2014) is unique in that it focuses on redesigning physician schedules using an integer linear program (ILP) to reduce the number of handoffs. Therefore, the objective function of the researchers’ ILP is to minimize the number of patient handoffs.
There are a three primary decision variables in this model.
First, X is a binary variable with a purpose of assigning shifts to the trainees. The variable is relevant to the idea of limiting the weekly working hours and the length of shifts for the residents and fellows to prevent fatigue. An upper bound of X requires its summation to be at most 80/(the length of each time block in hours). This upper bound limits the weekly duty hours to 80 hours, and it is important because a trainee is likely to become dangerously fatigued if they work more than that amount. The lower bound of the summation must be greater than or equal to the minimum number of trainees needed to be in the hospital at time block k of day j. So, this constraint is ensuring that necessary coverage in the hospital is always met.
Second, W is a binary variable that is relevant to scheduling trainees to the night shift for specific days. The upper bound prevents the trainees from being scheduled for more than 4 consecutive night shifts. The parameter in this constraint is a decision of the researchers; the official restriction from the ACGME is 6 nights, but the researchers believe that extreme fatigue and insomnia is better averted by a limit of 4 nights. A large stretch of night shifts also limits the healthcare providers from properly resting, which is essential to prevent communication and fatigue-related errors. The lower bound states that W must be greater than or equal to X, for all k in the set of night time blocks available. This constraint is used to ensure that W is equal to 1 when trainee i works at night on day j.
Third, Z is a binary variable that corresponds to the days off of each trainee. There is no upper bound on this variable because an increasing quantity of days off does not lead to fatigue or insomnia. However, the summation of Z over the length of an entire month must be greater than or equal to 4, requiring that each trainee must have at least one day off per week.
The researchers also include numerous constraints in the ILP to produce implementable solutions. For instance, the maximum shift length is 16 hours for residents and 24 hours for senior trainees, and these lengths are decided by the ACGME. The first simple constraint ensures that a trainee is not scheduled for more than the maximum hours of continuous duty. The second simple constraint prevents shift changes at late nights or early mornings in an effort to allow healthy sleeping schedules for the trainees. An advanced constraint ensures that when Z(i,j) is 1, or when trainee i is off-duty day j, the trainee i has no scheduled shifts on that day.
The graphic illustrates the reasoning behind limiting the number of consecutive night shifts to 4. As the number of consecutive night shifts increases, the level of fatigue dangerously rises. In order to maintain an error-free medical environment, the physician schedule should aim to keep the number of consecutive night shifts under 4.
Integer linear programs can be implemented to improve the state of healthcare. Tens of thousands of lives and millions of injuries will hopefully be prevented through optimization strategies. As healthcare continuously grows in scale and complexity, it is essential to use operations research to ensure its efficiency and safety.
[1] Kohn LT, Corrigan J, Donaldson MS, Institute of Medicine (U.S.). Committee on Quality of Health Care in America (1999) “To err is human building a safer health system.”
[2] Kazemian, P., Dong, Y., Rohleder, T.R. et al. An IP-based healthcare provider shift design approach to minimize patient handoffs. Health Care Manag Sci 17, 1–14 (2014). https://doi.org/10.1007/s10729-013-9237-z
