Modelling patient flows as an aid to decision making for critical care capacities and organisation

Abstract

SummaryUsing real data from a number of hospitals, we predicted the patient flows following a capacity or organisational change. Clinically recognisable patient groups obtained through classification and regression tree analysis were used to tune a simulation model for the flow of patients in critical care units. A tuned model which accurately reflected the base case of the flow of patients was used to predict alterations in service provision in a number of scenarios which included increases in bed numbers, alterations in patients' lengths of stay, fewer delayed discharges, caring for long stay patients outside the formal intensive care unit and amalgamating small units. Where available the predictions' accuracy was checked by comparison with real hospital data collected after an actual capacity change. The model takes variability and uncertainty properly into account and it provides the necessary information for making better decisions about critical care capacity and organisation. Critical care involves uncertainty, variability, nonlinearity, complexity, numerous constraints and expensive resources. It is very difficult, if at all possible, to use randomised controlled trials to obtain the necessary evidence for making good decisions about critical care capacity and organisation.Critical care is expensive by comparison with general ward care and so over-provision is not desirable. Underprovision, on the other hand, can have serious consequences for patients with life-threatening illnesses or awaiting major surgery. Historically, additional critical care capacity in a hospital has usually been provided in response to repeated crises. This reactive approach is often based on simple calculations involving average number of patients expected in a year, average length of stay, and a target bed occupancy level. Such simple calculations do not take the complexity, variability, and the nonlinearity involved in the flow of critical care patients into account. They provide incorrect information about required capacity and may give false assurances about the levels of the service that will be provided. It is a well-known mathematical fact that required capacities in the face of nonlinearity and considerable variability depend not only on the average values but also the amount of variability [1].In a review of critical care [2], the Department of Health recommended the use of good capacity planning to avoid crises that result from inadequate capacity. In this paper a combined intensive care unit (ICU) and high dependency unit (HDU) is called a critical care unit (CCU). Good decisions about the capacity and the organisation of a CCU require an understanding of the existing demands and the likely future demands for critical care beds together with predictions about likely effects of changing the capacity and the organisation of a CCU. These requirements can be met through an appropriate statistical analysis of data on individual critical care patients and detailed mathematical models based on individual patients. Models for...

Modeling the critical care pathway for cardiothoracic surgery

“…Many authors have used simulation to solve complex hospital operations problems with compelling results [19][20][21][22][23][24]. Recent advances in capturing large data is carving the way for such advanced analytical tools.…”

Integrated decision making in healthcare : an operations research and management science perspective

“…Each care unit has its designated staff, equipment and beds (in one or more wards). The objective is to guarantee care from appropriately skilled nurses and required equipment to patients with specific diagnoses, while making efficient use of scarce resources [26,156,157,206,243,251,439,499]. First, the desirability of opening shared higher-level care units like Intensive Care Units (ICU) or Medium Care Units (MCU) should be considered [480].…”

“…For geriatric departments, it has to be decided whether to separate or consolidate assessment, rehabilitation and long-stay care [359,360]. Also, multi-specialty wards can be created for patients of similar length of stay, such as day-care, short-, week-and long-stay units [439,499], or for acute patients [259,482]. Concentrating emergency activities in one area (a Medical Assessment Unit) can improve efficiency and minimize disruption to other hospital services [376].…”

“…Methods: computer simulation [156,157,206,243,259,439], heuristics [26,309,482], mathematical programming [376], queueing theory [217,251,355,359,360,480,522].…”

“…Models that consider only a single unit neglect the possibility of admitting patients in a less appropriate care unit and thus the interaction between patient flows and the interrelationship between care units. Next to estimating utilization and the probability of admission rejections or delays, models that do incorporate multiple care units, also focus on the percentage of time that patients are placed in a care unit of a lower level or less appropriate care unit, or in a higher level care unit [11,108,202,217,327,439]. The first situation negatively impacts quality of care as it can lead to increased morbidity and mortality [478] and the second negatively impacts both quality of care, as it may block admission of another patient, and efficient resource use [217,439].…”