StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions

Abstract

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.

Rapid cancer diagnosis for patients with vague symptoms: a cost-effectiveness study

“…DES has proven valid and useful in various healthcare settings to model the impact of patient pathway changes, 8 redesign, 10 and capacity changes. 9 However, the complexity of the service, patient population, and case mix required simplifications and assumptions to be made. These were verified by RDC key team members.…”

Clinical pathway modelling: a literature review

“…It is important to note that only 12 papers [3,22,52,60,73,93,96,119,120,124,143,170] state that their research/product was implemented/informed policythis is only 6.9% of the papers surveyed. Previous reviews have found similar results in regards to implementation (e.g.…”

“…Scope Hospital [8, 21, 23, 27, 29-31, 36, 41, 51, 64, 67, 74-76, 79, 85, 108, 111, 114-117, 134, 142, 145-148, 152, 158, 160, 165, 166, 170, 177, 178, 184, 190, 202, 203, 209] Department [12,14,15,17,18,46,50,52,57,62,68,100,113,125,129,141,151,153,154,161,163,164,167,169,171,182,185,187,212] Clinical [1, 3, 5, 10, 11, 13, 16, 19, 20, 22, 32-35, 40, 42, 43, 45, 47, 53, 60, 70-73, 80-83, 87-99, 102, 107, 110, 112, 121, 124, 126-128, 133, 136, 139, 140, 149, 162, 179, 183, 186, 188, 191, 192, 195, 197-201, 204-206, 210, 211] Disease [4, 7, 9, 13, 24, 25, 37, 39, 43, 48, 49, 54-56, 58, 66, 86, 101-104, 118-120, 122, 128, 131, 138, 143, 144, 150, 157, 172, 196, 211] Table A5…”

“…Investigating Type Mapping (Ma) [1, 4, 5, 7, 22, 29, 33, 37, 42, 47, 48, 54, 56, 57, 70, 71, 81, 83, 87, 88, 90, 91, 94-99, 104, 112, 121, 126, 127, 131, 136, 138, 139, 144, 149, 154, 157, 161, 162, 164, 170, 172, 177-179, 183-185, 192, 195, 197-203, 206, 209-211] Modelling (Mo) [15, 19-21, 23, 24, 30, 31, 36, 39, 43, 50, 55, 58, 64, 67, 76, 79, 82, 86, 89, 102, 103, 115, 117-119, 122, 129, 145-147, 150, 151, 160, 169, 171, 187, 188, 190, 191, 196, 212] Improving (I) [13,46] Ma & Mo [3, 9, 11, 17, 18, 40, 60, 62, 66, 72-74, 80, 85, 90, 93, 101, 107, 108, 114, 120, 124, 125, 128, 133, 134, 140, 143, 158, 205] Mo & I [8,16,25,32,34,41,45,52,53,68,75,110,111,113,116,141,148,165,182] All Types [10,12,14,27,35,49,51,100,142,152,153,163,166,167,186,204] Table A12. Outcome focus of the pathway.…”

The Impact of One-Dose Package of Medicines on Patient Waiting Time in Dispensing Pharmacy: Application of a Discrete Event Simulation Model

“…It has been used in studies in the field of hospital management, health resource planning, and to improve patient flow and waiting time at hospitals. [15][16][17][18][19][20][21] However, relatively few studies have focused on pharmacy systems. [22][23][24] In the present study, we aimed to clarify the impact of ODP on patient waiting time by conducting a survey at a dispensing pharmacy, and propose an improvement strategy with use of DES model.…”