Interventions and contextual factors to improve retention in care for patients with hypertension in primary care: Hermeneutic systematic review
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Cited by 6 publications
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Community-Based Participatory Research and System Dynamics Modeling for Improving Retention in Hypertension Care
ImportanceThe high prevalence of hypertension calls for broad, multisector responses that foster prevention and care services, with the goal of leveraging high-quality treatment as a means of reducing hypertension incidence. Health care system improvements require stakeholder input from across the care continuum to identify gaps and inform interventions that improve hypertension care service, delivery, and retention; system dynamics modeling offers a participatory research approach through which stakeholders learn about system complexity and ways to model sustainable system-level improvements.ObjectiveTo assess the association of simulated interventions with hypertension care retention rates in the Nigerian primary health care system using system dynamics modeling.Design, Setting, and ParticipantsThis decision analytical model used a participatory research approach involving stakeholder workshops conducted in July and October 2022 to gather insights and inform the development of a system dynamics model designed to simulate the association of various interventions with retention in hypertension care. The study focused on the primary health care system in Nigeria, engaging stakeholders from various sectors involved in hypertension care, including patients, community health extension workers, nurses, pharmacists, researchers, administrators, policymakers, and physicians.ExposureSimulated intervention packages.Main Outcomes and MeasuresRetention rate in hypertension care at 12, 24, and 36 months, modeled to estimate the effectiveness of the interventions.ResultsA total of 16 stakeholders participated in the workshops (mean [SD] age, 46.5 [8.6] years; 9 [56.3%] male). Training of health care workers was estimated to be the most effective single implementation strategy for improving retention in hypertension care in Nigeria, with estimated retention rates of 29.7% (95% CI, 27.8%-31.2%) at 12 months and 27.1% (95% CI, 26.0%-28.3%) at 24 months. Integrated intervention packages were associated with the greatest improvements in hypertension care retention overall, with modeled retention rates of 72.4% (95% CI, 68.4%-76.4%), 68.1% (95% CI, 64.5%-71.7%), and 67.1% (95% CI, 64.5%-71.1%) at 12, 24, and 36 months, respectively.Conclusions and RelevanceThis decision analytical model study showed that community-based participatory research could be used to estimate the potential effectiveness of interventions for improving retention in hypertension care. Integrated intervention packages may be the most promising strategies.
Community-Based Participatory Research and System Dynamics Modeling for Improving Retention in Hypertension Care
ImportanceThe high prevalence of hypertension calls for broad, multisector responses that foster prevention and care services, with the goal of leveraging high-quality treatment as a means of reducing hypertension incidence. Health care system improvements require stakeholder input from across the care continuum to identify gaps and inform interventions that improve hypertension care service, delivery, and retention; system dynamics modeling offers a participatory research approach through which stakeholders learn about system complexity and ways to model sustainable system-level improvements.ObjectiveTo assess the association of simulated interventions with hypertension care retention rates in the Nigerian primary health care system using system dynamics modeling.Design, Setting, and ParticipantsThis decision analytical model used a participatory research approach involving stakeholder workshops conducted in July and October 2022 to gather insights and inform the development of a system dynamics model designed to simulate the association of various interventions with retention in hypertension care. The study focused on the primary health care system in Nigeria, engaging stakeholders from various sectors involved in hypertension care, including patients, community health extension workers, nurses, pharmacists, researchers, administrators, policymakers, and physicians.ExposureSimulated intervention packages.Main Outcomes and MeasuresRetention rate in hypertension care at 12, 24, and 36 months, modeled to estimate the effectiveness of the interventions.ResultsA total of 16 stakeholders participated in the workshops (mean [SD] age, 46.5 [8.6] years; 9 [56.3%] male). Training of health care workers was estimated to be the most effective single implementation strategy for improving retention in hypertension care in Nigeria, with estimated retention rates of 29.7% (95% CI, 27.8%-31.2%) at 12 months and 27.1% (95% CI, 26.0%-28.3%) at 24 months. Integrated intervention packages were associated with the greatest improvements in hypertension care retention overall, with modeled retention rates of 72.4% (95% CI, 68.4%-76.4%), 68.1% (95% CI, 64.5%-71.7%), and 67.1% (95% CI, 64.5%-71.1%) at 12, 24, and 36 months, respectively.Conclusions and RelevanceThis decision analytical model study showed that community-based participatory research could be used to estimate the potential effectiveness of interventions for improving retention in hypertension care. Integrated intervention packages may be the most promising strategies.
Transforming and facilitating health care delivery through social networking platforms: evidences and implications from WeChat
Objectives Telehealth or remote care has been widely leveraged to provide health care support and has achieved tremendous developments and positive results, including in low- and middle-income countries (LMICs). Social networking platform, as an easy-to-use tool, has provided users with simplified means to collect data outside of the traditional clinical environment. WeChat, one of the most popular social networking platforms in many countries, has been leveraged to conduct telehealth and hosted a vast amount of patient-generated health data (PGHD), including text, voices, images, and videos. Its characteristics of convenience, promptness, and cross-platform support enrich and simplify health care delivery and communication, addressing some weaknesses of traditional clinical care during the pandemic. This study aims to systematically summarize how WeChat platform has been leveraged to facilitate health care delivery and how it improves the access to health care. Materials and Methods Utilizing Levesque’s health care accessibility model, the study explores WeChat’s impact across 5 domains: Approachability, Acceptability, Availability and accommodation, Affordability, and Appropriateness. Results The findings highlight WeChat’s diverse functionalities, ranging from telehealth consultations and remote patient monitoring to seamless PGHD exchange. WeChat’s integration with health tracking apps, support for telehealth consultations, and survey capabilities contribute significantly to disease management during the pandemic. Discussion and Conclusion The practices and implications from WeChat may provide experiences to utilize social networking platforms to facilitate health care delivery. The utilization of WeChat PGHD opens avenues for shared decision-making, prompting the need for further research to establish reporting guidelines and policies addressing privacy and ethical concerns associated with social networking platforms in health research.
Development and Application of Natural Language Processing on Unstructured Data in Hypertension: A Scoping Review
Background: Hypertension is a global health concern with a vast body of unstructured data, such as clinical notes, diagnosis reports, and discharge summaries, that can provide valuable insights. Natural Language Processing (NLP) has emerged as a powerful tool for extracting knowledge from unstructured data. This scoping review aims to explore the development and application of NLP on unstructured clinical data in hypertension, synthesizing existing research to identify trends, gaps, and underexplored areas for future investigation. Methods: We conducted a systematic search of electronic databases, including PubMed/MEDLINE, Embase, Cochrane Library, Scopus, Web of Science, ACM Digital Library, and IEEE Xplore Digital Library, to identify relevant studies published until the end of 2022. The search strategy included keywords related to hypertension, NLP, and unstructured data. Data extraction included study characteristics, NLP methods, types of unstructured data sources, and key findings and limitations. Results: The initial search yielded 951 articles, of which 45 met the inclusion criteria. The selected studies spanned various aspects of hypertension, including diagnosis, treatment, epidemiology, and clinical decision support. NLP was primarily used for extracting clinical information from unstructured electronic health records (EHRs) documents and text classification. Clinical notes were the most common sources of unstructured data. Key findings included improved diagnostic accuracy and the ability to comprehensively identify hypertensive patients with a combination of structured and unstructured data. However, the review revealed a lack of more advanced NLP techniques used in hypertension, generalization of NLP outside of benchmark datasets, and a limited focus on the integration of NLP tools into clinical practice. Discussion: This scoping review highlights the diverse applications of NLP in hypertension research, emphasizing its potential to transform the field by harnessing valuable insights from unstructured data sources. There is a need to adopt and customize more advanced NLP for hypertension research. Future research should prioritize the development of NLP tools that can be seamlessly integrated into clinical settings to enhance hypertension management. Conclusion: NLP demonstrates considerable promise in gleaning meaningful insights from the vast expanse of unstructured data within the field of hypertension, shedding light on diagnosis, treatment, and the identification of patient cohorts. As the field advances, there is a critical need to promote the use and development of advanced NLP methodologies that are tailored to hypertension and validated on real-world unstructured data.
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