This research project creates an innovative methodology based upon enterprise-wide patient flow modeling to guide operational admission and scheduling practices to smooth hospital census/occupancy. Our research will provide theoretical foundations as well as a practical decision support methodology incorporating (1) the stochasticity of unscheduled/emergent patient arrivals, (2) scheduled elective surgical and medical inpatients, (3) the stochastic system dynamics of bed block and occupancy by bed unit/ward and (4) the patient's treatment trajectory through the bed units and (5) other critical hospital resources (i.e. physicians, nurses, equipment, specialized beds, operating time etc.). Analytical models will be developed to approximate the hospital beds as a dynamically controlled queueing network. A theoretical patient flow modeling methodology will be used to capture the stochastic evolution of the patient's bed resource needs and thereby census. New optimization/control models will provide methods for (1) elective admission planning and (2) census recourse/control.
Hospitals frequently lack rigorous, accurate enterprise level planning and daily census management tools developed from a systems perspective. This research will provide the theoretical foundations, practical methodologies, and proof of concept for a novel online decision support approach to achieve increased access by reducing turnaways and delays to access, better matching of the care workload to the scheduled staff for improved quality of patient care, and reduced hospital operating costs. This research engages partner hospitals that are recognized as leaders in innovation. Outcomes will be disseminated to engineering and public health communities through publications targeting highly visible journals in engineering, and medicine/healthcare. Graduate and undergraduate students in engineering, business, and public health will benefit through classroom instruction (including teaching tools) and involvement in the research. The highly relevant application will equip students to be change agents in improving hospital operations.
This research targets the modeling and optimization of flow in hospitals, healthcare settings, and service sector operations more generally. The primary goal of the research was to generate ne methodologies for mathematically quantifying how patients move through healthcare institutions (e.g., how they spend time in various hospital units during their entire stay). The core idea of this patient flow modeling is to create the statistical patterns for categories/types of patients that are useful for understanding how the healthcare resources/capacities are used and how appointment scheduling (referred to as admissions scheduling for hospital inpatient stays) can be managed in a better way. That is, mathematical models that can effectively predict the service of healthcare patients to provide a means to optimize the upfront decisions of how many non-emergent patients of each category to allow into the hospital so that it functions better. Both elective and emergency patient arrivals were incorporated into unified models of the census in each unit/ward of the hospital on each day of the week so that the outcomes under various admission or management policies can be predicted. The following are examples of the types of waste or dysfunction that these new methods try to improve. We seek to limit the amount of waiting time in the Emergency Room/Department, which is called boarding when the patient must wait hours for an inpatient bed to become available in the hospital. There is usually a most desirable bed unit/ward to which an inpatient should go at any point of transfer. When the desired ward is full, patients are placed "off-unit," and we seek to reduce this because it reduces the quality of care. Proper nurse staffing levels and appropriate nurse daily assignments are harder to achieve and/or more costly when the census of the hospital is more variable. Further, poor patient flow management leads to ambulance diversions (when the emergency department becomes saturated) and operational chaos. Part of the reason for the potential to improve these issues is that hospitals often lack effective enterprise level strategic planning of bed and care resources. Many hospitals are significantly more utilized during the middle of each week, but Mondays, Tuesdays, and Fridays tend to have usable bed capacity that is wasted. Furthermore, there are many other flow-related issues that can all be modeled, analyzed, and improved. These include reducing bed block/access block, reducing census variability from day to day and week to week, reducing the number of elective surgeries canceled for lack of a bed, and the number of patients placed off unit. By creating new methods to analyze and improve the functioning of the hospital in terms of the logistical flow of patients, there is great potential to reduce the cost of services, increase the number of services that can be provided, increase patient satisfaction (e.g., through reduced waits and fewer off-unit placements), and improve the quality of care provided. Our approach addresses hospitals' internal costs and resource utilization, thereby addressing cost containment while attending to multiple other important dimensions as well. Key performance objectives included: reduced waits for patient access, reduced variation in patient flows, and fewer elective services/procedures canceled for lack of beds/capacity. This research develops new analytical models of controlled hospital census that can, for the first time, be incorporated into a Mixed Integer Programming model to optimize the planning problem. This work provides the theoretical foundations for an efficient scheduled admissions planning system. Through various sub-projects, the effort forged new ground in developing data-driven approaches to patient flow modeling, resource allocation, operational flexibility, priority systems for improved flow, and related issues. The research included research that was an important part of the doctoral dissertations of three doctoral students who have now received their degrees. Multiple masters and undergraduate students were involved in this research effort. The results of the research project have been incorporated into university courses, and the results have been shared broadly through publications and the internet to increase their broader impact. It benefited from the collaborative efforts of engineering faculty, physicians, and medical staff as a cross-cutting collaborative effort.
