Online journal of public health informatics最新文献

Objective: 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.

Methods: 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.

Results: 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%).

Discussion: Privacy concerns, portal knowledge, and conversations between primary care providers and patients predict portal access.

Conclusion: Methods described here may be employed to personalize methods of patient engagement during new patient registration.

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.

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.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Discussion: 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.

Conclusion: When considering COVID-19 immunization efforts, the VPI tool can serve as an added component in the decision-making process.

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.

Background: 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.

Methods: Strategies to integrate FI medical and SS data were compared using a cohort versus case control (CC) study design.

Results: 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).

Conclusions: 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.

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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Stakeholders are coming together to develop a vision for increasing access to family planning (FP) by 2030. Of the 923 million women in the developing world who wish to avoid a pregnancy, 218 million women are not using a modern contraceptive (Guttmacher Institute, 2020). In 2016, over 3.4 billion people were using the internet (https://ourworldindata.org/internet 2016). Moreover, internet users in the developing world use social media more frequently than Internet users in the U.S. and Europe. Of the many proposed actions to accelerate progress in family planning, the use of Twitter should be a key component. In this commentary, we describe the use of Twitter in a select group of low-and-middle-income countries that have made commitments to the family planning 2020 initiative (FP2020 countries and have the potential to leverage Twitter with current and potential family planning users. We examine Twitter feeds in eight key FP2020 countries, and we look at the content of Tweets issued by the ministries of health in most of these same countries. Our view is that it is feasible and easy to access Twitter feeds in low-and -middle income countries. We base our view on the types of reproductive health and family planning terms discussed in a public forum such as Twitter by current and potential users and their partners and ministries of health. We highlight two broad considerations that merit discussion among interested stakeholders, including policy makers, program designers, and health advocates. The first relates to the use of Twitter within family planning programs, and the second relates to themes that require more significant research. Data coupled with analytical capacity will help policy makers and program designers to effectively leverage Twitter for expanding the reach of family planning services and influencing social media policy. Our aim is to not only to contribute to the body of knowledge but also to spur greater engagement by program personnel, researchers, health advocates and contraceptive users.

The diagnosis of tuberculosis (TB) disease remains a global challenge, and the need for innovative diagnostic approaches is inevitable. Trained African giant pouched rats are the scent TB detection technology for operational research. The adoption of this technology is beneficial to countries with a high TB burden due to its cost-effectiveness and speed than microscopy. However, rats with some factors perform better. Thus, more insights on factors that may affect performance is important to increase rats' TB detection performance. This paper intends to provide understanding on the factors that influence rats TB detection performance using visual analytics approach. Visual analytics provide insight of data through the combination of computational predictive models and interactive visualizations. Three algorithms such as Decision tree, Random Forest and Naive Bayes were used to predict the factors that influence rats TB detection performance. Hence, our study found that age is the most significant factor, and rats of ages between 3.1 to 6 years portrayed potentiality. The algorithms were validated using the same test data to check their prediction accuracy. The accuracy check showed that the random forest outperforms with an accuracy of 78.82% than the two. However, their accuracies difference is small. The study findings may help rats TB trainers, researchers in rats TB and Information systems, and decision makers to improve detection performance. This study recommends further research that incorporates gender factors and a large sample size.