Geisinger Health System, Danville, PA, USA.
J Med Syst. 2015 Oct;39(10):130. doi: 10.1007/s10916-015-0325-0. Epub 2015 Aug 27.
The ability to accurately measure and assess current and potential health care system capacities is an issue of local and national significance. Recent joint statements by the Institute of Medicine and the Agency for Healthcare Research and Quality have emphasized the need to apply industrial and systems engineering principles to improving health care quality and patient safety outcomes. To address this need, a decision support tool was developed for planning and budgeting of current and future bed capacity, and evaluating potential process improvement efforts. The Strategic Bed Analysis Model (StratBAM) is a discrete-event simulation model created after a thorough analysis of patient flow and data from Geisinger Health System's (GHS) electronic health records. Key inputs include: timing, quantity and category of patient arrivals and discharges; unit-level length of care; patient paths; and projected patient volume and length of stay. Key outputs include: admission wait time by arrival source and receiving unit, and occupancy rates. Electronic health records were used to estimate parameters for probability distributions and to build empirical distributions for unit-level length of care and for patient paths. Validation of the simulation model against GHS operational data confirmed its ability to model real-world data consistently and accurately. StratBAM was successfully used to evaluate the system impact of forecasted patient volumes and length of stay in terms of patient wait times, occupancy rates, and cost. The model is generalizable and can be appropriately scaled for larger and smaller health care settings.
准确测量和评估当前和潜在的医疗保健系统能力是具有地方和国家意义的问题。最近,医学研究所和医疗保健研究与质量局的联合声明强调了应用工业和系统工程原则来提高医疗保健质量和患者安全结果的必要性。为了满足这一需求,开发了一种决策支持工具,用于规划和预算当前和未来的床位容量,并评估潜在的流程改进工作。战略床位分析模型(StratBAM)是在对患者流量和 Geisinger 健康系统(GHS)电子健康记录数据进行彻底分析后创建的离散事件模拟模型。关键输入包括:患者到达和出院的时间、数量和类别;单位级护理时间;患者路径;以及预计的患者量和住院时间。关键输出包括:按到达源和接收单位划分的入院等待时间以及入住率。电子健康记录用于估计概率分布的参数,并为单位级护理时间和患者路径构建经验分布。模拟模型对 GHS 运营数据的验证证实了其一致且准确地模拟现实数据的能力。StratBAM 成功用于评估预测患者量和住院时间对患者等待时间、入住率和成本的系统影响。该模型具有通用性,可以针对更大和更小的医疗保健环境进行适当调整。