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27 Sep 2019 00:00
The COPC model
Because healthcare is complex and costly it is imperative to efficiently and equitably define resource needs. In the 21st Century it is possible to bring technology together with big data in a way that simultaneously supports macro, meso and micro level planning and implementation support.
Five years of developing and implementing ICT enabled community oriented primary care in Mamelodi and other part of Tshwane, the University of Pretoria’s Department of Family Medicine set about creating the COPC planning toolkit.
According to Rod Bennett, the lead developer of the toolkit: “We know that a normative approach to healthcare resource allocation can’t account for socio-economic and geographic risk factors that affect healthcare demand and service access.”
The toolkit sets out to achieve the highest level of granularity. Geoff Abbott, a member of the planning toolkit team, says: “We started with Stats SA small area layer census data. Because communities are dynamic, this is supplemented by satellite imagery from a local tech company. It provides us with up-to-date housing and population data.”
The mapping application of the toolkit links the population data of each small area to the nearest healthcare facility and calculates their relative distance from it.
Gender, age, income and burden of disease data from provincial and national statistics are used in the calculations in order can take into account “multidimensional poverty, risk adjusted burden of disease and available work time adjusted for conditions of employment and geography”.
Through computer modelling the COPC toolkit then calculates demand, workload, staffing need, and the cost of implementation. These calculations for demand can be made any level of care, be it community-based, general PHC, 24 hour or specialised services.
In 2018 the team used the COPC toolkit to model community based service workforce numbers and service costs delivered to the poorest 60% of the Gauteng population. Comparing three scenarios, their modelling showed that CHWs were significantly more efficient as a regularised workforce, with the security and stability of full-time employment, and when supported by ICT and continuous workplace learning. The study also showed that by drilling down into a specific community’s needs, it was possible to streamline resource allocation and CHW deployment. This means the CPC toolkit can be used to plan for progressive reduction in wastage of infrastructure and human resources whilst saving money.
In subsequent work, the UP team has used the COPC toolkit to calculate cost effectiveness and benefit cost ratios. They showed that the planned application of integrated, ICT enabled COPC will benefit patients and yield social returns on investment that reduce poverty, increase GDP, reduce hospital admissions and contribute to improved health outcomes.
Generally, they argue that “the COPC planning toolkit can be used to assist the planning and mobilisation of resources to go to scale in an equitable and sustainable way as a managed process over time”.
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