Managing unnecessary variability in patient demand to reduce nursing stress and improve patient safety. Joint Comm J Qual Patient Saf

Boston University, Boston, USA.
Joint Commission journal on quality and patient safety / Joint Commission Resources 07/2005; 31(6):330-8.
Source: PubMed


BACKGROUND: Increases in adverse clinical outcomes have been documented when hospital nurse staffing is inadequate. Since most hospitals limit nurse staffing to levels for average rather than peak patient census, substantial census increases create serious potential stresses for both patients and nurses. By reducing unnecessary variability, hospitals can reduce many of these stresses and thereby improve patient safety and quality of care. THE SOURCE AND NATURE OF VARIABILITY IN DEMAND: The variability in the daily patient census is a combination of the natural (uncontrollable) variability contributed by the emergency department and the artificial (potentially controllable) peaks and valleys of patient flow into the hospital fromelective admissions. Once artificial variability in demand is significantly reduced, a substantial portion of the peaks and valleys in census disappears; the remaining censsus variability is largely patient and disease driven. When artificial variability has been minimized, a hospital must have sufficient resources for the remaining patient-driven peaks in demand, over which it has no control, if it is to deliver an optimal level of care. DISCUSSION: Study of operational issues in health care delivery, and acting on what is learned, is critical. Al forms of artificial variation in the demand and supply of health care services should be identified, and pilot programs to test operational changes should be conducted.

    • "Health care delivery systems are often stressed by uncertain (i.e., variable) demand for services over time [1]. A large source of this variation stems from the natural pattern in which individuals within a population need specific health care resources [2] [3]. "
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    ABSTRACT: Efficient health care delivery systems aim to match resources to demand for services over time. Resource allocation decisions must be made under stochastic uncertainty. This includes uncertainty in the number of individuals (i.e., counts) in need of services over discrete time intervals. Examples include counts of patients arriving to emergency departments and counts of prescription medications distributed by pharmacies. Accurately forecasting count data in health care systems allows decision-makers to anticipate the need for services and make informed decisions about how to manage resources and purchase supplies over time.
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    • "McManus et al. [16] noted that natural variability can be used to optimize the allocation of resources, but no empirical model was included in the study. Managing the variability of patient flow has an effect on nurse staffing, quality of care, and the number of inpatient beds for ED admission and solves the overcrowding problem [17] [18]. However, there is a lack of quantitative analysis to demonstrate which flow variability parameter causes the impact. "
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    ABSTRACT: Surgery scheduling must balance capacity utilization and demand so that the arrival rate does not exceed the effective production rate. However, authorized overtime increases because of random patient arrivals and cycle times. This paper proposes an algorithm that allows the estimation of the mean effective process time and the coefficient of variation. The algorithm quantifies patient flow variability. When the parameters are identified, takt time approach gives a solution that minimizes the variability in production rates and workload, as mentioned in the literature. However, this approach has limitations for the problem of a flow shop with an unbalanced, highly variable cycle time process. The main contribution of the paper is to develop a method called takt time, which is based on group technology. A simulation model is combined with the case study, and the capacity buffers are optimized against the remaining variability for each group. The proposed methodology results in a decrease in the waiting time for each operating room from 46 minutes to 5 minutes and a decrease in overtime from 139 minutes to 75 minutes, which represents an improvement of 89% and 46%, respectively.
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    • "The issue of patient flow variability has also been extensively analyzed by other authors [9,34–39]. Particularly, Litvak makes a distinction between natural variability and artificial variability [10] [11] [36]: natural variability is uncontrollable and is due to the intrinsic characteristics of health care delivery (for example, patient flow from the ED), whereas artificial variability is potentially controllable through managerial intervention and is due to process defects or incorrect behaviors. According to Litvak [11], the key to improving patient flow management – and, consequently, accommodate growing demand – is to increase the bed occupancy by eliminating patient flow variability. "
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    ABSTRACT: Through a comparative study of six Italian hospitals, the paper develops and tests a framework to analyze hospital-wide patient flow performance. The framework adopts a system-wide approach to patient flow management and is structured around three different levels: (1) the hospital, (2) the pipelines (possible patient journeys within the hospital) and (3) the production units (physical spaces, such as operating rooms, where service delivery takes places). The focus groups and the data analysis conducted within the study support that the model is a useful tool to investigate hospital-wide implications of patient flows. The paper provides also evidence about the causes of hospital patient flow problems. Particularly, while shortage of capacity does not seem to be a relevant driver, our data shows that patient flow variability caused by inadequate allocation of capacity does represent a key problem. Results also show that the lack of coordination between different pipelines and production units is critical. Finally, the problem of overlapping between elective and unscheduled cases can be solved by setting aside a certain level of capacity for unexpected peaks.
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