A Multi-dimensional Framework of Valued Output for Primary Care in England

Margherita Neri, Patricia Cubi-Molla and Graham Cookson
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Patricia Cubi-Molla: Office of Health Economics
Graham Cookson: Office of Health Economics

Applied Health Economics and Health Policy, 2024, vol. 22, issue 5, No 2, 609-617

Abstract: Abstract Improving efficiency and productivity are key aspects to ensure that general practices in England can meet the needs of a growing population with increasingly demanding and costly healthcare needs. However, current evidence on the efficiency and productivity of general practices is weak, partly due to suboptimal approaches to measure their ‘valued’ output. To overcome this limitation, this paper presents a multi-dimensional framework and indicators of valued output from the healthcare decision-maker’s perspective. We identified existing primary care performance frameworks through a targeted literature review. We reviewed the frameworks and selected the dimensions relating to the impact on patients’ health outcomes, corresponding with the definition of ‘valued’ output from the healthcare decision-maker perspective. For each dimension, we reviewed the National Institute for Health and Care Excellence (NICE) evidence base and guidance on best practice to develop indicators of valued output. Clinical experts and representatives of the main primary care stakeholders reviewed and validated the framework’s comprehensiveness and development process. Based on a review of three existing frameworks, we synthesised a multi-dimensional output framework comprising 13 dimensions for significant primary care-related conditions and services and 51 indicators of valued output. Each indicator of valued output measures a healthcare episode and the resulting impact on patient’s health. The multi-dimensional framework and indicators provide a theoretical tool to improve the measurement of primary care output in economic efficiency and productivity studies. Future research should explore the measurability of the indicators through available datasets and the implementation of the framework through analytical approaches for efficiency measurement.

Date: 2024
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DOI: 10.1007/s40258-024-00895-z

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