Online journal of public health informatics最新文献

{"title":"Your Tweets Matter: How Social Media Sentiments Associate with COVID-19 Vaccination Rates in the US.","authors":"Ana Aleksandric, Mercy Jesuloluwa Obasanya, Sarah Melcher, Shirin Nilizadeh, Gabriela Mustata Wilson","doi":"10.5210/ojphi.v14i1.12419","DOIUrl":"10.5210/ojphi.v14i1.12419","url":null,"abstract":"<p><strong>Objective: </strong>The aims of the study were to examine the association between social media sentiments surrounding COVID-19 vaccination and the effects on vaccination rates in the United States (US), as well as other contributing factors to the COVID-19 vaccine hesitancy.</p><p><strong>Method: </strong>The dataset used in this study consists of vaccine-related English tweets collected in real-time from January 4 - May 11, 2021, posted within the US, as well as health literacy (HL), social vulnerability index (SVI), and vaccination rates at the state level.</p><p><strong>Results: </strong>The findings presented in this study demonstrate a significant correlation between the sentiments of the tweets and the vaccination rate in the US. The results also suggest a significant negative association between HL and SVI and that the state demographics correlate with both HL and SVI.</p><p><strong>Discussion: </strong>Social media activity provides insights into public opinion about vaccinations and helps determine the required public health interventions to increase the vaccination rate in the US.</p><p><strong>Conclusion: </strong>Health literacy, social vulnerability index and monitoring of social media sentiments need to be considered in public health interventions as part of vaccination campaigns.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":" ","pages":"e2"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9473329/pdf/ojphi-14-1-e2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40369366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using a Machine Learning Algorithm to Predict Online Patient Portal Utilization: A Patient Engagement Study.","authors":"Ahmed U Otokiti,&nbsp;Colleen M Farrelly,&nbsp;Leyla Warsame,&nbsp;Angie Li","doi":"10.5210/ojphi.v14i1.12851","DOIUrl":"https://doi.org/10.5210/ojphi.v14i1.12851","url":null,"abstract":"<p><strong>Objective: </strong>There is a low rate of online patient portal utilization in the U.S. This study aimed to utilize a machine learning approach to predict access to online medical records through a patient portal.</p><p><strong>Methods: </strong>This is a cross-sectional predictive machine learning algorithm-based study of Health Information National Trends datasets (Cycles 1 and 2; 2017-2018 samples). Survey respondents were U.S. adults (≥18 years old). The primary outcome was a binary variable indicating that the patient had or had not accessed online medical records in the previous 12 months. We analyzed a subset of independent variables using k-means clustering with replicate samples. A cross-validated random forest-based algorithm was utilized to select features for a Cycle 1 split training sample. A logistic regression and an evolved decision tree were trained on the rest of the Cycle 1 training sample. The Cycle 1 test sample and Cycle 2 data were used to benchmark algorithm performance.</p><p><strong>Results: </strong>Lack of access to online systems was less of a barrier to online medical records in 2018 (14%) compared to 2017 (26%). Patients accessed medical records to refill medicines and message primary care providers more frequently in 2018 (45%) than in 2017 (25%).</p><p><strong>Discussion: </strong>Privacy concerns, portal knowledge, and conversations between primary care providers and patients predict portal access.</p><p><strong>Conclusion: </strong>Methods described here may be employed to personalize methods of patient engagement during new patient registration.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"14 1","pages":"e8"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9831291/pdf/ojphi-14-1-e8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10582086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons and Implementation Challenges of Community Health Information System in LMICs: A Scoping Review of Literature.","authors":"Zeleke Abebaw Mekonnen,&nbsp;Moges Asressie Chanyalew,&nbsp;Binyam Tilahun,&nbsp;Monika Knudsen Gullslett,&nbsp;Shegaw Anagaw Mengiste","doi":"10.5210/ojphi.v14i1.12731","DOIUrl":"https://doi.org/10.5210/ojphi.v14i1.12731","url":null,"abstract":"<p><strong>Background: </strong>Accurate and timely information on health intervention coverage, quality, and equity is the foundation of public health practice. To achieve this, countries have made efforts to improve the quality and availability of community health data by implementing the community health information system that is used to collect data in the field generated by community health workers and other community-facing providers. Despite all the efforts, evidence on the current state is scant in Low Middle Income Countries (LMICs).</p><p><strong>Objective: </strong>To summarize the available evidence on the current implementation status, lessons learned and implementation challenges of community health information system (CHIS) in LMICs.</p><p><strong>Methods: </strong>We conducted a scoping review that included studies searched using electronic databases like Pubmed/Medline, World Health Organization (WHO) Library, Science Direct, Cochrane Library. We also searched Google and Google Scholar using different combinations of search strategies. Studies that applied any study design, data collection and analysis methods related to CHIS were included. The review included all studies published until February 30, 2022. Two authors extracted the data and resolved disagreements by discussion consulting a third author.</p><p><strong>Results: </strong>A total of 1,552 potentially relevant articles/reports were generated from the initial search, of which 21 were considered for the final review. The review found that CHIS is implemented in various structures using various tools across different LMICs. For the CHIS implementation majority used registers, family folder/card, mobile technologies and chalk/white board. Community level information was fragmented, incomplete and in most cases flowed only one way, with a bottom-up approach. The review also indicated that, technology particularly Electronic Community Health Information System (eCHIS) and mobile applications plays a role in strengthening CHIS implementation in most LMICs. Many challenges remain for effective implementation of CHIS with unintegrated systems including existence of parallel recording & reporting tools. Besides, lack of resources, low technical capacity, shortage of human resource and poor Information Communication Technology (ICT) infrastructure were reported as barriers for effective implementation of CHIS in LMICs.</p><p><strong>Conclusion: </strong>Generally, community health information system implementation in LMICs is in its early stage. There was not a universal or standard CHIS design and implementation modality across countries. There are also promising practices on digitalizing the community health information systems. Different organizational, technical, behavioural and economic barriers exist for effective implementation of CHIS. Hence, greater collaboration, coordination, and joint action are needed to address these challenges. Strong leadership, motivation, capa","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"14 1","pages":"e5"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699826/pdf/ojphi-14-1-e5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9138358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strengthening eHealth Systems to Support Universal Health Coverage in sub-Saharan Africa.","authors":"Adebowale Ojo,&nbsp;Herman Tolentino,&nbsp;Steven S Yoon","doi":"10.5210/ojphi.v13i3.11550","DOIUrl":"10.5210/ojphi.v13i3.11550","url":null,"abstract":"<p><p>The aim of universal health coverage (UHC) is to ensure that all individuals in a country have access to quality healthcare services and do not suffer financial hardship in using these services. However, progress toward attaining UHC has been slow, particularly in sub-Saharan Africa. The use of information and communication technologies for healthcare, known as eHealth, can facilitate access to quality healthcare at minimal cost. eHealth systems also provide the information needed to monitor progress toward UHC. However, in most countries, eHealth systems are sometimes non-functional and do not serve programmatic purposes. Therefore, it is crucial to implement strategies to strengthen eHealth systems to support UHC. This perspective piece proposes a conceptual framework for strengthening eHealth systems to attain UHC goals and to help guide UHC and eHealth strategy development.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 3","pages":"E17"},"PeriodicalIF":0.0,"publicationDate":"2021-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769196/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39859243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring Older Adult Blood Pressure Trends at Home as a Proxy for Brain Health.","authors":"Nicole Cassarino,&nbsp;Blake Bergstrom,&nbsp;Christine Johannes,&nbsp;Lisa Gualtieri","doi":"10.5210/ojphi.v13i3.11842","DOIUrl":"https://doi.org/10.5210/ojphi.v13i3.11842","url":null,"abstract":"<p><p>Even when older adults monitor hypertension at home, it is difficult to understand trends and share them with their providers. MyHealthNetwork is a dashboard designed for patients and providers to monitor blood pressure readings to detect hypertension and ultimately warning signs of changes in brain health. A multidisciplinary group in a Digital Health course at Tufts University School of Medicine used Design Thinking to formulate a digital solution to promote brain health among older adults in the United States (US). Older adults (aged 65 and over) are a growing population in the US, with many having one or more chronic health conditions including hypertension. Nearly half of all American adults ages 50-64 worry about memory loss as they age and almost all (90%) wish to maintain independence and age in their homes. Given the well-studied association between hypertension and dementia, we designed a solution that would ultimately promote brain health among older adults by allowing them to measure and record their blood pressure readings at home on a regular basis. Going through each step in the Design Thinking process, we devised MyHealthNetwork, an application which connects to a smart blood pressure cuff and stores users' blood pressure readings in a digital dashboard which will alert users if readings are outside of the normal range. The dashboard also has a physician view where users' data can be reviewed by the physician and allow for shared treatment decisions. The authors developed a novel algorithm to visually display the blood pressure categories in the dashboard in a way straightforward enough that users with low health literacy could track and understand their blood pressure over time. Additional features of the dashboard include educational content about brain health and hypertension, a digital navigator to support users with application use and technical questions. Phase 1 in the development of our application includes a pilot study involving recruitment of Primary Care Providers with patients who are at risk of dementia to collect and monitor BP data with our prototype. Subsequent phases of development involve partnerships to provide primary users with a rewards program to promote continued use, additional connections to secondary users such as family members and expansion to capture other health metrics.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 3","pages":"e16"},"PeriodicalIF":0.0,"publicationDate":"2021-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39859244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19: A Vaccine Priority Index Mapping Tool for Rapidly Assessing Priority Populations in North Carolina.","authors":"Gregory D Kearney,&nbsp;Katherine Jones,&nbsp;Yoo Min Park,&nbsp;Rob Howard,&nbsp;Ray Hylock,&nbsp;Bennett Wall,&nbsp;Maria Clay,&nbsp;Peter Schmidt,&nbsp;John Silvernail","doi":"10.5210/ojphi.v13i3.11617","DOIUrl":"https://doi.org/10.5210/ojphi.v13i3.11617","url":null,"abstract":"<p><strong>Background: </strong>The initial limited supply of COVID-19 vaccine in the U.S. presented significant allocation, distribution, and delivery challenges. Information that can assist health officials, hospital administrators and other decision makers with readily identifying who and where to target vaccine resources and efforts can improve public health response.</p><p><strong>Objective: </strong>The objective of this project was to develop a publicly available geographical information system (GIS) web mapping tool that would assist North Carolina health officials readily identify high-risk, high priority population groups and facilities in the immunization decision making process.</p><p><strong>Methods: </strong>Publicly available data were used to identify 14 key health and socio-demographic variables and 5 differing themes (social and economic status; minority status and language; housing situation; at risk population; and health status). Vaccine priority population index (VPI) scores were created by calculating a percentile rank for each variable over each N.C. Census tract. All Census tracts (N = 2,195) values were ranked from lowest to highest (0.0 to 1.0) with a non-zero population and mapped using ArcGIS.</p><p><strong>Results: </strong>The VPI tool was made publicly available (https://enchealth.org/) during the pandemic to readily assist with identifying high risk population priority areas in N.C. for the planning, distribution, and delivery of COVID-19 vaccine.</p><p><strong>Discussion: </strong>While health officials may have benefitted by using the VPI tool during the pandemic, a more formal evaluation process is needed to fully assess its usefulness, functionality, and limitations.</p><p><strong>Conclusion: </strong>When considering COVID-19 immunization efforts, the VPI tool can serve as an added component in the decision-making process.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 3","pages":"E13"},"PeriodicalIF":0.0,"publicationDate":"2021-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8765798/pdf/ojphi-13-3-e13.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39862552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating multi-purpose syndromic surveillance systems - a complex problem.","authors":"Roger Morbey,&nbsp;Gillian Smith,&nbsp;Isabel Oliver,&nbsp;Obaghe Edeghere,&nbsp;Iain Lake,&nbsp;Richard Pebody,&nbsp;Dan Todkill,&nbsp;Noel McCarthy,&nbsp;Alex J Elliot","doi":"10.5210/ojphi.v13i3.10818","DOIUrl":"https://doi.org/10.5210/ojphi.v13i3.10818","url":null,"abstract":"<p><p>Surveillance systems need to be evaluated to understand what the system can or cannot detect. The measures commonly used to quantify detection capabilities are sensitivity, positive predictive value and timeliness. However, the practical application of these measures to multi-purpose syndromic surveillance services is complex. Specifically, it is very difficult to link definitive lists of what the service is intended to detect and what was detected. First, we discuss issues arising from a multi-purpose system, which is designed to detect a wide range of health threats, and where individual indicators, e.g. 'fever', are also multi-purpose. Secondly, we discuss different methods of defining what can be detected, including historical events and simulations. Finally, we consider the additional complexity of evaluating a service which incorporates human decision-making alongside an automated detection algorithm. Understanding the complexities involved in evaluating multi-purpose systems helps design appropriate methods to describe their detection capabilities.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 3","pages":"E15"},"PeriodicalIF":0.0,"publicationDate":"2021-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8765799/pdf/ojphi-13-3-e15.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39862554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Syndromic Surveillance Data for Accidental Fall Injury.","authors":"Donald E Brannen,&nbsp;Melissa Howell,&nbsp;Ashley Steveley,&nbsp;Jeff Webb,&nbsp;Deidre Owsley","doi":"10.5210/ojphi.v13i3.10264","DOIUrl":"https://doi.org/10.5210/ojphi.v13i3.10264","url":null,"abstract":"<p><strong>Background: </strong>Fall injuries (FI) are a priority for public health planning. Syndromic surveillance (SS) is used to detect outbreaks, environmental exposures, and bioterrorism in real time. Since information is gathered on patients, the utility of using this system for FI should be evaluated.</p><p><strong>Methods: </strong>Strategies to integrate FI medical and SS data were compared using a cohort versus case control (CC) study design.</p><p><strong>Results: </strong>The CC study was accurate 77.7% (57.7-91.3) of the time versus 100% for a cohort design. The CC study design found FI increased for older age groups, female gender, November, and December months. Dates with any freezing temperature had a higher case fatality rate. Repeat acute care visits increased the risk of FI diagnosis by over 6% and trended upward with each visit (R=.333, p<.001).</p><p><strong>Conclusions: </strong>The CC diagnostic quality of FI were better for age and gender than for area. The CC study found the indicators of increased risk of FI including freezing temperature, repeat acute care visits, older age groups, female gender, November, and December months. A gradient of increasing odds of FI with the number of acute care visits provides proof that community fall prevention programs should focus on those most likely to fall. A CC design of SS data can quickly identify indicators of FI with a lower accuracy but with less cost than a full cohort study, thus providing a method to focus local public health interventions.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 3","pages":"e18"},"PeriodicalIF":0.0,"publicationDate":"2021-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769255/pdf/ojphi-13-3-e18.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39859245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Virtual Data Repository Stimulates Data Sharing in a Consortium.","authors":"Suzanne Siminski,&nbsp;Soyeon Kim,&nbsp;Adel Ahmed,&nbsp;Jake Currie,&nbsp;Alex Benns,&nbsp;Amy Ragsdale,&nbsp;Marjan Javanbakht,&nbsp;Pamina M Gorbach","doi":"10.5210/ojphi.v13i3.10878","DOIUrl":"https://doi.org/10.5210/ojphi.v13i3.10878","url":null,"abstract":"<p><p>Research data may have substantial impact beyond the original study objectives. The Collaborating Consortium of Cohorts Producing NIDA Opportunities (C3PNO) facilitates the combination of data and access to specimens from nine NIDA-funded cohorts in a virtual data repository (VDR). Unique challenges were addressed to create the VDR. An initial set of common data elements was agreed upon, selected based on their importance for a wide range of research proposals. Data were mapped to a common set of values. Bioethics consultations resulted in the development of various controls and procedures to protect against inadvertent disclosure of personally identifiable information. Standard operating procedures govern the evaluation of proposed concepts, and specimen and data use agreements ensure proper data handling and storage. Data from eight cohorts have been loaded into a relational database with tables capturing substance use, available specimens, and other participant data. A total of 6,177 participants were seen at a study visit within the past six months and are considered under active follow-up for C3PNO cohort participation as of the third data transfer, which occurred in January 2020. A total of 70,391 biospecimens of various types are available for these participants to test approved scientific hypotheses. Sociodemographic and clinical data accompany these samples. The VDR is a web-based interactive, searchable database available in the public domain, accessed at www.c3pno.org. The VDR are available to inform both consortium and external investigators interested in submitting concept sheets to address novel scientific questions to address high priority research on HIV/AIDS in the context of substance use.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 3","pages":"e19"},"PeriodicalIF":0.0,"publicationDate":"2021-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769254/pdf/ojphi-13-3-e19.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39859246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Data and Digital Health Technologies to Assess and Impact Social Determinants of Health (SDoH): a State-of-the-Art Literature Review.","authors":"Kelly J Thomas Craig,&nbsp;Nicole Fusco,&nbsp;Thrudur Gunnarsdottir,&nbsp;Luc Chamberland,&nbsp;Jane L Snowdon,&nbsp;William J Kassler","doi":"10.5210/ojphi.v13i3.11081","DOIUrl":"https://doi.org/10.5210/ojphi.v13i3.11081","url":null,"abstract":"<p><strong>Objective: </strong>Identify how novel datasets and digital health technology, including both analytics-based and artificial intelligence (AI)-based tools, can be used to assess non-clinical, social determinants of health (SDoH) for population health improvement.</p><p><strong>Methods: </strong>A state-of-the-art literature review with systematic methods was performed on MEDLINE, Embase, and the Cochrane Library databases and the grey literature to identify recently published articles (2013-2018) for evidence-based qualitative synthesis. Following single review of titles and abstracts, two independent reviewers assessed eligibility of full-texts using predefined criteria and extracted data into predefined templates.</p><p><strong>Results: </strong>The search yielded 2,714 unique database records of which 65 met inclusion criteria. Most studies were conducted retrospectively in a United States community setting. Identity, behavioral, and economic factors were frequently identified social determinants, due to reliance on administrative data. Three main themes were identified: 1) improve access to data and technology with policy - advance the standardization and interoperability of data, and expand consumer access to digital health technologies; 2) leverage data aggregation - enrich SDoH insights using multiple data sources, and use analytics-based and AI-based methods to aggregate data; and 3) use analytics-based and AI-based methods to assess and address SDoH - retrieve SDoH in unstructured and structured data, and provide contextual care management sights and community-level interventions.</p><p><strong>Conclusions: </strong>If multiple datasets and advanced analytical technologies can be effectively integrated, and consumers have access to and literacy of technology, more SDoH insights can be identified and targeted to improve public health. This study identified examples of AI-based use cases in public health informatics, and this literature is very limited.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 3","pages":"E14"},"PeriodicalIF":0.0,"publicationDate":"2021-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8765800/pdf/ojphi-13-3-e14.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39862553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}