# Simulating the blood transfusion system in Kenya: Modelling methods and exploratory analyses

## TL;DR

This paper uses simulation to study Kenya's blood transfusion system and explore ways to improve blood availability for patients.

## Contribution

The paper introduces the first discrete event simulation model of Kenya's blood system to explore system-level impacts on patient blood demand.

## Key findings

- A process map of Kenya's blood system was developed using interviews with hospital and blood bank personnel.
- The simulation model can explore impacts of changes in blood collection, demand, and distribution on patient-level blood availability.
- The model can identify process impediments and support decision-making for improving blood system efficiency.

## Abstract

The process of collecting blood from donors and making it available for transfusion requires a complex series of operations involving multiple actors and different resources at each step. Ensuring hospitals receive adequate and safe blood for transfusion is a common challenge across low- and middle-income countries, but is rarely addressed from a system level. This paper presents the first use of discrete event simulation to study the blood system in Kenya and to explore the effect of variations and perturbations at different steps of the system on meeting blood demand at patient level. A process map of the Kenyan blood system was developed to capture critical steps from blood donation to transfusion using interviews with blood bank, hospital and laboratory personnel at four public hospitals across three counties in Kenya. The blood system was simulated starting with blood collection, a blood bank where blood is tested and stored before it is issued, a major hospital attached to the blood bank, and several smaller hospitals served by the same blood bank. Values for supply-side parameters were based mainly on expert opinion; demand-side parameters were based on data from blood requisitions made in hospital wards, and dispatch of blood from the hospital laboratory. Illustrative examples demonstrate how the model can be used to explore impacts of changes in blood collection (e.g., prioritising different donor types), blood demand (e.g., differing clinical case mix), and blood distribution (e.g., restocking strategies) on meeting demand at patient level. The model can reveal potential process impediments in the blood system and aid in choosing between alternate strategies or policies for improving blood collection, testing, distribution or use. Such a systems approach allows for interventions at different steps in the blood continuum to be tested on blood availability for different patients presenting at diverse hospitals across the country.