Development of policy research-evidence organizer and public health-policy evaluation tool (prophet): a computing paradigm for promoting evidence-informed policymaking in Nigeria
<p><strong><em>Background</em></strong><em>:</em> in vast majority of low-and middle-income countries, performance of health systems continues to be abysmally poor with unacceptably low health outcomes. This is not unconnected with implementation of evidence...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Academy Publishing Center
2024-12-01
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| Series: | Advances in Computing and Engineering |
| Subjects: | |
| Online Access: | http://apc.aast.edu/ojs/index.php/ACE/article/view/1076 |
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| Summary: | <p><strong><em>Background</em></strong><em>:</em> in vast majority of low-and middle-income countries, performance of health systems continues to be abysmally poor with unacceptably low health outcomes. This is not unconnected with implementation of evidence-deficient health policies. Critical research evidence contributes in strengthening health policies to ensure clear cut targets and context specifics that adequately addresses identified health challenges and inequities. This study modeled a computing paradigm for brokering knowledge translation process and assisting health policymakers in promoting evidenced-informed policymaking. It strategically evaluates and assess level of evidence content, and predict implementation prospects of health policy documents.</p><p><strong><em>Methods: </em></strong>its development process adopted object-oriented methodology for structural analysis and design specifications. Visual Basic.net and standard query language server were deployed at the front-end and back-end implementation processes respectively. The study designed an algorithm based on discrete choice experiment technique in an iterative four-scaled user-defined parametric options for rating policy features and assessment of overall policy prospect. Salient policy features/attributes were assembled as assessable variable entities. It adapted machine learning linear model to classify attributes into 6-domains to reflect the WHO promoted 6-policy cycle of a health system. Aggregated scores of policy features across all domains are utilized to compute policy overall grade-point in percentage weight.</p><p><strong><em>Results: </em></strong>PROPHET, was used to assess thirty-three (33) national health policies extracted from online repository warehousing health policy documents in Nigeria known as <em>policy information platform</em>. The result shows that only 11 out of the 33 (33.3%) policies passed with at least 50% grade-point fixed in this study as minimum benchmark for implementation considerations.</p><p><strong><em>Conclusion: </em></strong>This system rates policy features, assesses overall implementation prospect of policies with seamless real-time data validation and referencing across modules. PROPHET is expected to aid health policymakers in amplifying evidence-informed policymaking for improved health outcomes.</p><p> </p><p><strong>Received: 30 October 2024 </strong></p><p><strong>Accepted: 29 November 2024 </strong></p><p><strong>Published: 08 December 2024</strong></p> |
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| ISSN: | 2735-5977 2735-5985 |