Implementation and applications of PACE-HRH, a stochastic workload model of human resources for health

Abstract

Background Effective healthcare systems need adequate numbers of well-trained human resources for health (HRH). To support evidence-based strategic planning, modeling is sometimes used to estimate the number of required health workers and to allocate them appropriately. However, despite the demonstrated utility of models, there are several limitations to existing tools, including the inability to reflect the stochastic nature of workload and parameter uncertainty, or to incorporate seasonal variations. Additionally, some tools are proprietary or no longer supported, which makes them difficult for decision makers to adopt. Methods To address these issues, we have created an open-source, freely available modeling tool called the Population-Aware Capacity Estimator for Human Resources for Health (PACE-HRH). The modeling platform has two components: an Excel-based workbook for data input and scenario management, and a stochastic Monte Carlo simulation package and analysis pipeline written in R. PACE-HRH has a demographics model that projects future populations, a task time model that estimates workload from both variable responsibilities and overhead, an optional seasonality model, and an optional cadre allocation model. Results To establish the utility of PACE-HRH, we run a demonstrative model based on a subset of eight clinical service categories, populated with Ethiopian data. The projections show an increase in weekly workload for a baseline population from 37.8 (36.0, 39.7) hours in 2021 to 44.0 (37.9, 49.8) hours in 2035. The ability to calculate a confidence interval is unique to PACE-HRH, as is the option to calculate the monthly variation in workload, which in this case amounts to seasonal amplitude of 6.8%. These results are demonstrative only and more curated input assumptions would be needed in order for the results to support decision making. Conclusions Modeling HRH requirements is valuable to planning processes. The PACE-HRH modeling package takes a novel approach to generating these estimates and is designed to be an easy-to-use platform that reduces barriers to use. There is a shortage of observational data on task times, which are key model assumptions, and time and motion studies are needed. However, even without improved data, PACE-HRH is an advancement in the field of HRH modeling and can be used to support evidence-based planning processes.

One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level

“…We use a previously published, bottom-up time accounting approach (10) to estimate the level of workload for each primary healthcare service included in a comprehensive service package. The list of healthcare tasks ("services") is based on the Ethiopian HEP optimization roadmap for 2021-2035 (9) (complete list in Additional le 1, table 1).…”

“…This is a general primary healthcare service package with a broad range of services including immunization, antenatal care, family planning, nutrition, testing and treatment for both infectious and non-communicable diseases (NCDs), and routine preventive care and counseling. The analysis is run in R, based on the publicly available PACE-HRH software package (11).…”

“…The detailed model structure is outlined in a different publication (10) and we summarize it here. The model calculates the total amount of direct clinical contact time (in minutes) required to meet all of the healthcare needs for a baseline population.…”