Predicting the Future: Using Simulation Modeling to Forecast Patient Flow on General Medicine Units
BACKGROUND: Hospitals are complex adaptive systems within which multiple components such as patients, practitioners, facilities, and technology interact. A careful approach to optimization of this complex system is needed because any change can result in unexpected deleterious effects. One such approach is discrete event simulation, in which what-if scenarios allow researchers to predict the impact of a proposed change on the system. However, studies illustrating the application of simulation in optimization of general internal medicine (GIM) team inpatient operations are lacking.
METHODS: Administrative data about admissions and discharges, data from a time-motion study, and expert opinion on workflow were used to construct the simulation model. Then, the impact of four changes – aligning medical teams with nursing units, adding a hospitalist team, adding a nursing unit, and adding both a nursing unit and hospitalist team with higher admission volume – were modeled on key hospital operational metrics.
RESULTS: Aligning medical teams with nursing units improved team metrics for aligned teams but shifted patients to unaligned teams. Adding a hospitalist team had little benefit, but adding a nursing unit improved system metrics. Both adding a hospitalist team and a nursing unit would be required to maintain operational metrics with increased patient volume.
CONCLUSION: Using simulation modeling, we provided data on the implications of four possible strategic changes on GIM inpatient units, providers, and patient throughput. Such analyses may be a worthwhile investment to study strategic decisions and make better choices with fewer unintended consequences.
© 2019 Society of Hospital Medicine
Hospitals are complex adaptive systems within which practitioners, technology, physical resources, and other components adapt interdependently to attempt to best meet the needs of patients.1 Hospitals must provide a stable, dependable level of care while also surging to respond to times of high demand, such as patient emergencies or swells in patient volume. Given the critical and resource-intensive nature of this work, optimizing the system is essential; however, because of the complexity of the system, making changes can result in unexpected and possibly deleterious effects. We need to approach change in hospital processes carefully and thoughtfully.
The Institute of Medicine, the National Academy of Engineering, and the President’s Council of Advisors on Science and Technology have recommended the application of systems engineering approaches to improve health care delivery.2,3 Systems engineering seeks to coordinate, synchronize, and integrate complex systems of people, information, materials, technology, and financial resources.4,5 To determine how complex systems can be improved, engineers apply analytic methods to describe how such systems operate and what the impact of changes might be. These methodologies have improved patient care and reduced costs at several hospitals.6 For example, a decision support system that combined simulation, optimization, and machine learning methods in an emergency department (ED) resulted in a 33% reduction in length of stay (LOS) and a 28% decrease in ED readmissions.7 Other strategies to improve patient flow include shaping demand (decreasing variation in surgical scheduling, relocating low acuity care ED visit to primary care, etc.), redesigning systems (early discharges, improving efficiency, and coordination of hospital discharge process, decreasing care variation, etc.), or aligning capacity and demand. Another approach, real-time demand capacity (RTDC), is based on management principles and queuing and constraint theory and has been implemented successfully in a variety of health care organizations. RTDC represents a promising approach to improve hospitalwide patient flow and can be integrated into current bed management processes.8 Unfortunately, many of these approaches are not well known to clinicians and would benefit from greater awareness and input from healthcare practitioners.
One systems engineering tool that can be used to describe, analyze, and evaluate proposed changes in care is simulation.9 Simulation creates a model within which what-if scenarios (ie, adjusting various inputs into the simulation) allow researchers to define the likelihood of consequences from various courses of action and determine the optimal change to a system. Such analyses can predict the impact of a proposed change on patients and healthcare practitioners.10-13
A critical concern for hospitals that simulation may help address is managing the volume of inpatients. A high inpatient census is necessary for financial solvency, yet too high a census of inpatients or an unexpected surge in acuity can overwhelm hospital resources. Many hospitals, pressured by growing numbers of increasingly complex patients, have seen medical inpatients spread across multiple nonmedical nursing units (NUs) of their institution such that a particular medical team may have only a couple patients assigned to each nursing unit.14 This dispersion may hinder communication between physicians and nurses and limits the time physicians have to interact with patients.15 Additionally, coordination of care may become more challenging for discharge planning.16 Aligning medical teams with NUs may benefit the quality and efficiency of care or may create a barrier to patient flow, which worsens these problems.15,17 Alternatively, hospitals might meet the increasing demands for care by choosing to add capacity by opening new NUs or hiring additional healthcare providers.
This article describes the application of simulation to model the interconnected variables and subsequent future states created by several possible
