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Home Research Research Library Primary Care Research Priorities in Low-and Middle-Income Countries Primary Care Research Priorities in Low-and Middle-Income Countries 2019 Topic(s) Role of Primary Care PURPOSE To identify and prioritize the needs for new research evidence for primary health care (PHC) in low-and middle-income countries (LMICs) about organization, models of care, and financing of PHC. METHODS Three-round expert panel consultation of LMIC PHC practitioners and academics sampled from global networks, via web-based surveys. Iterative literature review conducted in parallel. Round 1 (pre–Delphi survey) elicited possible research questions to address knowledge gaps about organization and models of care and about financing. Round 2 invited panelists to rate the importance of each question, and in round 3 panelists provided priority ranking. RESULTS One hundred forty-one practitioners and academics from 50 LMICs from all global regions participated and identified 744 knowledge gaps critical to improving PHC organization and 479 for financing. Four priority areas emerged: effective transition of primary and secondary services, horizontal integration within a multidisciplinary team and intersectoral referral, integration of private and public sectors, and ways to support successfully functioning PHC professionals. Financial evidence priorities were mechanisms to drive investment into PHC, redress inequities, increase service quality, and determine the minimum necessary budget for good PHC. CONCLUSIONS This novel approach toward PHC needs in LMICs, informed by local academics and professionals, created an expansive and prioritized list of critical knowledge gaps in PHC organization and financing. It resulted in research questions, offering valuable guidance to global supporters of primary care evaluation and implementation. Its source and context specificity, informed by LMIC practitioners and academics, should increase the likelihood of local relevance and eventual success in implementing research findings. Read More ABFM Research Read all 2021 Developing measures to capture the true value of primary care Go to Developing measures to capture the true value of primary care 2023 Interoperability among hospitals treating populations that have been marginalized Go to Interoperability among hospitals treating populations that have been marginalized 2013 Relying on NPs and PAs Does Not Avoid the Need for Policy Solutions for Primary Care Go to Relying on NPs and PAs Does Not Avoid the Need for Policy Solutions for Primary Care 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments
ABFM Research Read all 2021 Developing measures to capture the true value of primary care Go to Developing measures to capture the true value of primary care 2023 Interoperability among hospitals treating populations that have been marginalized Go to Interoperability among hospitals treating populations that have been marginalized 2013 Relying on NPs and PAs Does Not Avoid the Need for Policy Solutions for Primary Care Go to Relying on NPs and PAs Does Not Avoid the Need for Policy Solutions for Primary Care 2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments
2021 Developing measures to capture the true value of primary care Go to Developing measures to capture the true value of primary care
2023 Interoperability among hospitals treating populations that have been marginalized Go to Interoperability among hospitals treating populations that have been marginalized
2013 Relying on NPs and PAs Does Not Avoid the Need for Policy Solutions for Primary Care Go to Relying on NPs and PAs Does Not Avoid the Need for Policy Solutions for Primary Care
2020 Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments Go to Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments