A modelling tool for capacity planning in acute and community stroke services

Abstract

BackgroundMathematical capacity planning methods that can take account of variations in patient complexity, admission rates and delayed discharges have long been available, but their implementation in complex pathways such as stroke care remains limited. Instead simple average based estimates are commonplace. These methods often substantially underestimate capacity requirements.We analyse the capacity requirements for acute and community stroke services in a pathway with over 630 admissions per year. We sought to identify current capacity bottlenecks affecting patient flow, future capacity requirements in the presence of increased admissions, the impact of co-location and pooling of the acute and rehabilitation units and the impact of patient subgroups on capacity requirements. We contrast these results to the often used method of planning by average occupancy, often with arbitrary uplifts to cater for variability.MethodsWe developed a discrete-event simulation model using aggregate parameter values derived from routine administrative data on over 2000 anonymised admission and discharge timestamps. The model mimicked the flow of stroke, high risk TIA and complex neurological patients from admission to an acute ward through to community rehab and early supported discharge, and predicted the probability of admission delays.ResultsAn increase from 10 to 14 acute beds reduces the number of patients experiencing a delay to the acute stroke unit from 1 in every 7 to 1 in 50. Co-location of the acute and rehabilitation units and pooling eight beds out of a total bed stock of 26 reduce the number of delayed acute admissions to 1 in every 29 and the number of delayed rehabilitation admissions to 1 in every 20. Planning by average occupancy would resulted in delays for one in every five patients in the acute stroke unit.ConclusionsPlanning by average occupancy fails to provide appropriate reserve capacity to manage the variations seen in stroke pathways to desired service levels. An appropriate uplift from the average cannot be based simply on occupancy figures. Our method draws on long available, intuitive, but underused mathematical techniques for capacity planning. Implementation via simulation at our study hospital provided valuable decision support for planners to assess future bed numbers and organisation of the acute and rehabilitation services.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-016-1789-4) contains supplementary material, which is available to authorized users.

A discrete event simulation approach for reserving capacity for emergency patients in the radiology department

“…Bhattacharjee and Ray [ 20 ] discussed the reasons for selecting DES in the context of health care decision-making processes. Monks et al [ 21 ] developed a DES model for capacity planning in acute and community stroke services. Lebcir et al [ 22 ] adopted a DES model to evaluate the use of community services in the treatment of patients with Parkinson’s disease in the United Kingdom.…”

Clinical pathway modelling: a literature review

“…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.…”

Infinite-server queueing models of demand in healthcare: A review of applications and ideas for further work

“…More recently, Monks et al (2016), writing for a medical audience, used a three-node time-homogeneous infinite-server model to investigate capacity requirements for a stroke service comprising of an acute stroke unit, a rehabilitation unit and early supported discharge. They combine simulated infinite-server results with the Erlang Loss formula to estimate delay probabilities for patients needing acute beds and for patients needing rehabilitation beds under different scenarios, including current service, pooling (or partial pooling) of the acute and rehabilitation beds, and changes in the throughput of patients.…”