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Many people with HIV (PWH) in jail have not been well engaged in HIV treatment in the community. This study aimed to assess the impact of a data exchange paired with care coordination for PWH on HIV outcomes after jail release. We developed an automated process to match jail booking records with HIV surveillance data. Health department HIV relinkage specialists and jail release planners used the data in weekly case conferences to facilitate care coordination for PWH who were out of care (no CD4 or viral load [VL] reported in the past 12 months) or viremic (VL ≥ 200 copies/mL) before booking. We compared VL testing within 3 months after release and viral suppression (VL < 200 copies/mL) within 6 months after release in preintervention (October 1, 2016–October 1, 2017) versus postintervention (April 1, 2018–February 1, 2019) periods using Cox proportional hazard models. Comparing 153 bookings among PWH in the preintervention period to 80 in the postintervention period, VL testing after release did not differ (47% vs. 55%; adjusted hazard ratio [aHR]: 1.05, 95% confidence interval [CI]: 0.68–1.64) nor did viral suppression (35% vs. 44%; aHR: 1.28 [95% CI: 0.80–2.05]). Data exchange and care coordination are insufficient to improve postrelease outcomes among PWH without additional interventions in the jail and community. [ABSTRACT FROM AUTHOR]

Background and Objective: The data modernization initiative (DMI) is a multi‐year, multi‐billion‐dollar endeavor toward a robust public health information infrastructure. The various DMI projects (interoperability, analytics, workforce, governance) present an opportunity for a learning health system (LHS) framework in public health. The objective is to share an academic‐practice partnership model between the University of Minnesota (UMN) and the Minnesota Department of Health (MDH) in advancing public health informatics (PHI) and its relationship to an LHS model. Methods: The UMN‐MDH partnership was conceptualized in 2018 as a 1‐year pilot with annual renewals through a time/cost‐sharing faculty position with PHI expertise. The partnership focus was decided based on MDH's needs and mutual interests, with the core collaborating faculty (SR) being an embedded researcher at MDH. Responsibilities included supporting electronic case reporting (eCR), interoperability projects, and assisting MDH staff with PHI presentations/publications. The partnership has expanded to PHI workforce development through a national grant and now includes an interest in applying the LHS framework to MDH‐DMI work. Results: The MDH‐DMI team has embarked upon 13 projects for assessment through an LHS approach: systems interoperability projects between MDH and healthcare/local public health (n = 6); systems modernization for MDH programs (n = 5); informatics workforce development (n = 1); and program governance (n = 1). Each project has been evaluated and/or has current/future assessment plans to synthesize learnings and create a feedback loop for iterative improvement. The partnership has been mutually beneficial as it met agreed upon metrics across both institutions. The program's productivity is showcased with shared authorship in 10 peer‐reviewed proceedings/publications, 22 presentations and 16 posters across local/national conferences. Conclusion: The current case report of the UMN‐MDH partnership is a relatively recent exemplar to support tangible LHS demonstration in public health. Building LHS momentum at MDH and other public health entities will require LHS champion(s) and continued academic collaboration. [ABSTRACT FROM AUTHOR]

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The COVID-19 pandemic has accelerated the adoption of telemedicine in healthcare. This study explores the feasibility of telemedicine for foot and ankle care in primary settings, using a mixed-methods approach with online questionnaires, focus groups, and interviews. Stakeholders, including patients, podiatrists, and senior healthcare managers, agreed on the need for a telemedicine service. Recommendations include creating evidence-based guidelines, providing professional training, and enhancing community education. The research highlights the necessity for structured telemedicine services, identifying gaps in existing pandemic responses and the need for further guidelines and training. [ABSTRACT FROM AUTHOR]

Abstract This study investigates the utility of Google Trends and Google Search data in retrospectively analyzing the Flint Water Crisis, a significant public health event. By examining keywords relevant to the crisis, such as “lead,” “bottled water,” “water filters,” “pneumonia,” and “Legionnaires’ Disease,” we aimed to uncover patterns in public awareness and response during a public crisis and explore how such awareness and responses could potentially aid in reducing the risk of similar crises in the future. Our analysis reveals a clear correlation between search frequencies and the crisis timeline, with spikes in search terms corresponding to key events. This suggests that such data can serve as a valuable tool for understanding public sentiment and behavior in the face of environmental disasters. The research underscores the potential of “Big Data,” led by search engines and social media platforms, in shaping public policy and informed decision-making. However, it also addresses the limitations and challenges in using these data sources, including issues of data consistency, replicability, and the influence of sociocultural contexts on search behaviors. The study advocates for the combined use of Google Trends and Google Search data, complemented by other datasets, for a more comprehensive understanding of public engagement in environmental crises. This work contributes to the growing field of infodemiology, emphasizing the importance of big data analysis in environmental science and public health research.

This study investigates the utility of Google Trends and Google Search data in retrospectively analyzing the Flint Water Crisis, a significant public health event. By examining keywords relevant to the crisis, such as "lead," "bottled water," "water filters," "pneumonia," and "Legionnaires' Disease," we aimed to uncover patterns in public awareness and response during a public crisis and explore how such awareness and responses could potentially aid in reducing the risk of similar crises in the future. Our analysis reveals a clear correlation between search frequencies and the crisis timeline, with spikes in search terms corresponding to key events. This suggests that such data can serve as a valuable tool for understanding public sentiment and behavior in the face of environmental disasters. The research underscores the potential of "Big Data," led by search engines and social media platforms, in shaping public policy and informed decision-making. However, it also addresses the limitations and challenges in using these data sources, including issues of data consistency, replicability, and the influence of sociocultural contexts on search behaviors. The study advocates for the combined use of Google Trends and Google Search data, complemented by other datasets, for a more comprehensive understanding of public engagement in environmental crises. This work contributes to the growing field of infodemiology, emphasizing the importance of big data analysis in environmental science and public health research. [ABSTRACT FROM AUTHOR]

Background: Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. Objective: This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. Method: We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011–2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. Results: We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. Discussion: We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Conclusion: Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems. [ABSTRACT FROM AUTHOR]

The recent surge in the usage of e-cigarettes amongst youth has highlighted a long-standing societal crisis. To assist public health agencies in policymaking, past research often employed traditional survey-based methods to understand youth behavior, which suffer from response biases and scalability, are time-consuming, and their findings often lag the fast-changing public behavior. Our study fills this gap by using social media as a complementary data source to understand user intentions for vape usage at a large scale, thus, providing an alternative to traditional survey-based methods. In this paper, we propose a novel user intent mining framework under the guidance of social cognitive theory for health behavioral interventions that helps study user intentions across different social media platforms. We then employ this framework to investigate the feasibility of automated intent mining on social media by formulating a multi-class classification task, employing machine learning algorithms to classify a social media message across relevant intent classes: Accusational, Anecdotal, Informational, Justificational and Promotional. The analyses indicate that Accusational tweets and Anecdotal messages were most prevalent on X/Twitter and Reddit respectively. We further provide novel insights on the conversational context using topic modeling analysis and psychometric analysis consequently, informing intervention designs and assisting health analysts. [ABSTRACT FROM AUTHOR]

During the coronavirus disease 2019 (COVID-19) pandemic, the number and types of dashboards produced increased to convey complex information using digestible visualizations. The pandemic saw a notable increase in genomic surveillance data, which genomic epidemiology dashboards presented in an easily interpretable manner. These dashboards have the potential to increase the transparency between the scientists producing pathogen genomic data and policymakers, public health stakeholders, and the public. This scoping review discusses the data presented, functional and visual features, and the computational architecture of six publicly available SARS-CoV-2 genomic epidemiology dashboards. We found three main types of genomic epidemiology dashboards: phylogenetic, genomic surveillance, and mutational. We found that data were sourced from different databases, such as GISAID, GenBank, and specific country databases, and these dashboards were produced for specific geographic locations. The key performance indicators and visualization used were specific to the type of genomic epidemiology dashboard. The computational architecture of the dashboards was created according to the needs of the end user. The genomic surveillance of pathogens is set to become a more common tool used to track ongoing and future outbreaks, and genomic epidemiology dashboards are powerful and adaptable resources that can be used in the public health response. [ABSTRACT FROM AUTHOR]

Machine Translation (MT) has emerged as a crucial tool in bridging language barriers. In health settings, MT is increasingly relevant due to the diversity of patient populations, the dominance of English in medical research, and the limited availability of human translation services. Improvements in MT accuracy have prompted a re-evaluation of its suitability in contexts where it was once deemed impractical. This scoping review with meta-analysis delved into the appropriateness and limitations of MT in health, including in medical education, literature translation, and patient-provider communication. A keyword search in PubMed, PubMed Central, and IEEE Xplore produced peer-reviewed literature that focused on MT in a health context, published from 2018 to 2023. Analysis and mapping of full-text articles revealed 33 studies among 2,589 returned abstracts, indicating that MT is still unsuitable for direct use in patient interactions, due to clinical risks linked to insufficient accuracy. However, MT was showing promise further away from patients, for translation of medical articles, terminology, and educational content. Further research in improving MT performance in these contexts, coverage of understudied languages, and study of the existing usages of MT are recommended. [ABSTRACT FROM AUTHOR]

Abstract Background and Objective The data modernization initiative (DMI) is a multi‐year, multi‐billion‐dollar endeavor toward a robust public health information infrastructure. The various DMI projects (interoperability, analytics, workforce, governance) present an opportunity for a learning health system (LHS) framework in public health. The objective is to share an academic‐practice partnership model between the University of Minnesota (UMN) and the Minnesota Department of Health (MDH) in advancing public health informatics (PHI) and its relationship to an LHS model. Methods The UMN‐MDH partnership was conceptualized in 2018 as a 1‐year pilot with annual renewals through a time/cost‐sharing faculty position with PHI expertise. The partnership focus was decided based on MDH's needs and mutual interests, with the core collaborating faculty (SR) being an embedded researcher at MDH. Responsibilities included supporting electronic case reporting (eCR), interoperability projects, and assisting MDH staff with PHI presentations/publications. The partnership has expanded to PHI workforce development through a national grant and now includes an interest in applying the LHS framework to MDH‐DMI work. Results The MDH‐DMI team has embarked upon 13 projects for assessment through an LHS approach: systems interoperability projects between MDH and healthcare/local public health (n = 6); systems modernization for MDH programs (n = 5); informatics workforce development (n = 1); and program governance (n = 1). Each project has been evaluated and/or has current/future assessment plans to synthesize learnings and create a feedback loop for iterative improvement. The partnership has been mutually beneficial as it met agreed upon metrics across both institutions. The program's productivity is showcased with shared authorship in 10 peer‐reviewed proceedings/publications, 22 presentations and 16 posters across local/national conferences. Conclusion The current case report of the UMN‐MDH partnership is a relatively recent exemplar to support tangible LHS demonstration in public health. Building LHS momentum at MDH and other public health entities will require LHS champion(s) and continued academic collaboration.

Background and aims: The Sina Electronic Health Record (SinaEHR) system was developed to support primary care delivery across Iran by enabling chronic disease monitoring, infectious disease surveillance, health policy decision-making, and quality improvement initiatives. Methods: The SinaEHR system was implemented by Mashhad University of Medical Sciences which contains health records of over 5 million people across various regions of Iran. It utilizes the International Classification of Diseases (ICD) version 10, integrating laboratory results through a laboratory information system to facilitate real-time clinical decision-making. Results: Widespread implementation of the SinaEHR system has established an invaluable healthcare infrastructure resource for the improvement of primary care services across Iran. Strict security and confidentiality measures are enforced to protect sensitive patient information. Conclusion: The SinaEHR system significantly advances Iran’s healthcare infrastructure by providing a comprehensive electronic health record platform to improve healthcare delivery and promote evidence-based decision-making.

The traditional manual recording of vaccination records in Germany faced challenges during the COVID-19 pandemic, prompting the introduction of a COVID smartphone app with QR codes. However, this solution brought new challenges, emphasizing the need for a centrally managed European digital vaccination record for efficiency and validity. This study assesses the feasibility of using the HL7 FHIR standard in the healthcare industry for implementing a digital vaccination pass management and monitoring system. The system aims to offer convenience and improved efficiency for both patients and healthcare providers while promoting interoperability with other healthcare systems. To this end, we developed a prototype using modern technologies, such as React, Quarkus, and Keycloak. Results indicate potential benefits for patients and healthcare providers, offering access to immunization records, personalized recommendations, and streamlined management. However, integrating nuanced vaccination processes into the standardized FHIR system requires custom extensions, which might hinder interoperability. Manual data entry and the integration of an identity provider present further obstacles in industry scenarios. Despite these challenges, this study suggests that implementing HL7 FHIR can enhance efficiency, data accessibility, and accuracy in the vaccination process, supporting broader digitization efforts in the German healthcare system and beyond.

The traditional manual recording of vaccination records in Germany faced challenges during the COVID-19 pandemic, prompting the introduction of a COVID smartphone app with QR codes. However, this solution brought new challenges, emphasizing the need for a centrally managed European digital vaccination record for efficiency and validity. This study assesses the feasibility of using the HL7 FHIR standard in the healthcare industry for implementing a digital vaccination pass management and monitoring system. The system aims to offer convenience and improved efficiency for both patients and healthcare providers while promoting interoperability with other healthcare systems. To this end, we developed a prototype using modern technologies, such as React, Quarkus, and Keycloak. Results indicate potential benefits for patients and healthcare providers, offering access to immunization records, personalized recommendations, and streamlined management. However, integrating nuanced vaccination processes into the standardized FHIR system requires custom extensions, which might hinder interoperability. Manual data entry and the integration of an identity provider present further obstacles in industry scenarios. Despite these challenges, this study suggests that implementing HL7 FHIR can enhance efficiency, data accessibility, and accuracy in the vaccination process, supporting broader digitization efforts in the German healthcare system and beyond. [ABSTRACT FROM AUTHOR]

Context: Access to real-time data that provide accurate and timely information about the status and extent of disease spread could assist management of the COVID-19 pandemic and inform decision-making. Aim: To demonstrate our experience with regard to implementation of technical and architectural infrastructure for a near real-time electronic health record-based surveillance system for COVID-19 in Iran. Method: This COVID-19 surveillance system was developed from hospital information and electronic health record (EHR) systems available in the study hospitals in conjunction with a set of open-source solutions; and designed to integrate data from multiple resources to provide near real-time access to COVID-19 patients' data, as well as a pool of health data for analytical and decision-making purposes. Outcomes: Using this surveillance system, we were able to monitor confirmed and suspected cases of COVID-19 in our population and to automatically notify stakeholders. Based on aggregated data collected, this surveillance system was able to facilitate many activities, such as resource allocation for hospitals, including managing bed allocations, providing and distributing equipment and funding, and setting up isolation centres. Conclusion: Electronic health record systems and an integrated data analytics infrastructure are effective tools to enable policymakers to make better decisions, and for epidemiologists to conduct improved analyses regarding COVID-19. Implications: Improved quality of clinical coding for better case finding, improved quality of health information in data sources, data-sharing agreements, and increased EHR coverage in the population can empower EHR-based COVID-19 surveillance systems. [ABSTRACT FROM AUTHOR]

Objective To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule. Materials and Methods We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR. Results Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min. Discussion The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. Conclusion To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes. [ABSTRACT FROM AUTHOR]

Sleep quality is a critical factor in human health and well-being, with implications for various physiological and psychological processes. Traditional methods of sleep data collection are often limited by the quality and reliability of the data due to issues such as recall bias and subjective interpretation. This research aims to propose a novel framework that objectively measures and evaluates sleep quality using smart thermostats equipped with motion sensors, providing noninvasive and effortless sleep monitoring. The study conducts a comprehensive analysis of sleep patterns, exploring the relationship between activity sensors and sleep quality. By analyzing behavioral characteristics, the study identifies periods or clusters of days that require attention in terms of health and stress levels. The approach ensures privacy, ease of access, and integrates environmental factors, enabling a comprehensive understanding of an individual’s sleep health. The findings suggest that this zero-effort technology can significantly enhance sleep monitoring at both individual and population levels, with implications for health monitoring, stress management, and personalized healthcare interventions. Future work will focus on expanding the data set, incorporating more variables, and integrating contextual data to further improve sleep quality analysis and support real-time health interventions. [ABSTRACT FROM AUTHOR]

In Canada, extreme heat occurrences present significant risks to public health, particularly for vulnerable groups like older individuals and those with preexisting health conditions. Accurately predicting indoor temperatures during these events is crucial for informing public health strategies and mitigating the adverse impacts of extreme heat. While current systems rely on outdoor temperature data, incorporating real-time indoor temperature estimations can significantly enhance decision-making and strengthen overall health system responses. Sensor-based technologies, such as ecobee smart thermostats installed in homes, enable effortless collection of indoor temperature and humidity data. This study evaluates the efficacy of deep learning models in predicting indoor temperatures during heat waves using smart thermostat data, to enhance public health responses. Utilizing ecobee smart thermostats, we analyzed indoor temperature trends and developed forecasting models. Our findings indicate the potential of integrating IoT and deep learning into health warning systems, enabling proactive interventions, and improving sustainable health care practices in extreme heat scenarios. This approach highlights the role of digital health innovations in creating the resilient and sustainable healthcare systems against climate-related health adversities. [ABSTRACT FROM AUTHOR]

The growing challenges of healthcare systems pose a unique opportunity to leverage evidence-based digital health interventions. The WHO's SMART (Standards-based, Machine-readable, Adaptive, Requirements-based, and Testable) guidelines represent a significant advancement in this domain. This paper aims to summarize SMART guidelines authoring and implementation process, drawing on a comprehensive literature analysis. Our findings highlight critical success factors for national implementation, including stakeholder engagement, customization to local contexts, and leveraging international standards and digital technologies. We conclude with recommendations for countries aiming to implement WHO SMART guidelines, underscoring the need for a multi-disciplinary approach and the potential challenges to be navigated. [ABSTRACT FROM AUTHOR]

Environmental health (EH) services in the United States lag behind other areas of public health and health care with respect to information system interoperability and data sharing. This is partly due to an absence of well-defined use cases, the lack of direct economic drivers and resources to improve, the multiple jurisdictional elements that govern EH services across the United States, and no central organization to drive modernization of EH data. We summarize the status of EH information systems; argue for greater interoperability, including use cases for a messaging standard for environmental inspections; and present recommendations to better align EH services and data modernization efforts currently underway in other areas of public health. [ABSTRACT FROM AUTHOR]

Abstract Background Public Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards. Methodology This scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted. Results Findings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure. Conclusion Effective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.

Objective The increased availability of public data and accessible visualization technologies enhanced the popularity of public health data dashboards and broadened their audience from professionals to the general public. However, many dashboards have not achieved their full potential due to design complexities that are not optimized to users' needs. Material and Methods We used a 4-step human-centered design approach to develop a data dashboard of sexually transmitted infections for the New York State Department of Health: (1) stakeholder requirements gathering, (2) an expert review of existing data dashboards, (3) a user evaluation of existing data dashboards, and (4) an usability evaluation of the prototype dashboard with an embedded experiment about visualizing missing race and ethnicity data. Results Step 1 uncovered data limitations and software requirements that informed the platform choice and measures included. Step 2 yielded a checklist of general principles for dashboard design. Step 3 revealed user preferences that influenced the chart types and interactive features. Step 4 uncovered usability problems resulting in features such as prompts, data notes, and displaying imputed values for missing race and ethnicity data. Discussion Our final design was accepted by program stakeholders. Our modifications to traditional human-centered design methodologies to minimize stakeholders' time burden and collect data virtually enabled project success despite barriers to meeting participants in-person and limited public health agency staff capacity during the COVID-19 pandemic. Conclusion Our human-centered design approach and the final data dashboard architecture could serve as a template for designing public health data dashboards elsewhere. [ABSTRACT FROM AUTHOR]

ObjectiveThere is a need for a systematic method to implement the World Health Organization's Clinical Progression Scale (WHO-CPS), an ordinal clinical severity score for coronavirus disease 2019 patients, to electronic health record (EHR) data. We discuss our process of developing guiding principles mapping EHR data to WHO-CPS scores across multiple institutions.Materials and methodsUsing WHO-CPS as a guideline, we developed the technical blueprint to map EHR data to ordinal clinical severity scores. We applied our approach to data from 2 medical centers.ResultsOur method was able to classify clinical severity for 100% of patient days for 2756 patient encounters across 2 institutions.DiscussionImplementing new clinical scales can be challenging; strong understanding of health system data architecture was integral to meet the clinical intentions of the WHO-CPS.ConclusionWe describe a detailed blueprint for how to apply the WHO-CPS scale to patient data from the EHR.

BackgroundAccurate self-reported symptomatic toxicity documentation via the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) is essential throughout cancer treatment to ensure safety and understand therapeutic efficacy. However, the capture of accurate toxicities from patients undergoing radiation therapy is challenging because this is generally provided only at the time of scheduled visits.ObjectiveThis study seeks to establish the usability and feasibility of a mobile PRO-CTCAE Administration System (mPROS) to capture toxicities related to radiation therapy.MethodsEnglish-speaking adult patients who were undergoing radiation therapy for cancer were enrolled and given a brief demonstration of the Say All Your Symptoms (SAYS) and Symptom Tracking Entry Program (STEP) interfaces of the mPROS app, followed by a patient-use phase where patient actions were observed as they navigated mPROS to enter toxicities. Patient feedback was captured via a semistructured interview and brief questionnaire.ResultsWe enrolled 25 patients (age: mean 60.7 years; females: n=13, 52%; White patients: n=13; 52%; non-Hispanic patients: n=19, 76%; college graduates: n=17, 68%). Patients almost equally preferred the SAYS (n=14, 56%) or STEP (n=11, 44%) interfaces, with 21 patients (84%) agreeing that they would use mPROS to report their symptoms to their health care team and 19 patients (76%) agreeing that they would recommend mPROS to others.ConclusionsThe mPROS app is usable and feasible for facilitating the patient reporting of radiation therapy-related symptomatic toxicities. A revised version of mPROS that incorporates patient input and includes electronic health record integration is being developed and validated as part of a multicenter trial.

BackgroundGeriatric diseases (e.g., chronic diseases and geriatric syndromes) may result in impaired physical performance and a decline in the quality of life. The results of previous studies reported the positive effects of comprehensive community-based rehabilitation (CBR) services on physical and social functioning and psychosocial wellbeing. However, to provide adequate and personalised rehabilitation services, it is essential to understand the needs of the older adults population. There have been no studies on the need for CBR in older adults populations that consider their heterogeneity. Therefore, high-quality studies are required to recognise the heterogeneity and latent classes of CBR needs in older adults population groups. This study aims to identify the heterogeneity of the rehabilitation needs of older adults in the community and explore whether older adults with similar characteristics have similar needs through a cross-sectional survey and latent class analysis (LCA) to provide support for personalised rehabilitation services.MethodsThe study is structured into four phases. The first phase will focus on constructing a comprehensive questionnaire to assess rehabilitation needs. In the second phase, a pilot study will be conducted to evaluate the reliability and validity of the completed questionnaire. This step ensures the robustness of the instrument for data collection. The third phase will involve cross-sectional surveys using the finalised questionnaires to collect the necessary data from the targeted population. The fourth phase will focus on conducting LCA to determine the CBR needs of the older adult population.DiscussionThe results of this study will provide novel and critical information for a better understanding of the rehabilitation needs, potential categories, and influencing factors of older adults in the community. The study will be conducted in Guizhou Province in western China, where economic and social development is relatively low, and the results will inform and benefit other regions and developing countries facing similar challenges. However, because of the complete social security and rehabilitation service systems in developed areas, our research results may not fully reflect the situation in these areas. Future studies may need to be conducted in places with different levels of social development.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=191398, ChiCTR2300071478.

Purpose: To evaluate whether X, formerly known as Twitter, is being used effectively to advance the goals of International Volunteers in Urology (IVUmed). How is X activity associated with end-user engagement? Methods: Monthly analytics of the X account @IVUmed were reviewed between September 2014 and November 2022 using https://analytics.twitter.com/. Outcomes included tweets, mentions, impressions, engagements, interactions, followers, and profile visits. Statistical analysis using Mann–Whitney U test and Spearman's rank-order correlation was performed. Top tweet content between December 2020 and November 2022 was also analyzed and assigned one of seven different categories: research, workshops, mission statement, educational materials, fundraising, individual spotlight, and other. Results: Of @IVUmed's 1668 followers, 1334 (80.0%) were individuals. One thousand one hundred twenty-six (84.4%) individuals listed their locations with the majority (79.8%) residing in high-income countries. Tweet impressions have increased over time; they were significantly higher (p < 0.01) on average after the onset of COVID-19 in March 2020. From December 2020 to November 2022, new followers were positively correlated with tweet impressions (p < 0.01), total mentions (p < 0.01), and profile visits (p < 0.01). Profile visits were positively correlated with total tweets (p < 0.01). The content categories for monthly top tweets that proportionally garnered the most engagements were workshops (50%) and individual spotlight (29%), despite not being the most tweeted about content categories. Conclusion: Non-profit organizations wishing to increase their web-based outreach can benefit from increased primary X activity. While not evaluated in this study, it may also improve fundraising capabilities. Nevertheless, periodic review of account activity is important to ensure engagement of the targeted audience. [ABSTRACT FROM AUTHOR]

This study aimed to understand, from the users of Health Information Systems' perspectives, the essential components these technologies must present to be considered appropriate for incorporation and use in the Brazilian Unified Health System. As a secondary objective, we organized the results in the dimensions of the RE-AIM framework to facilitate future translation into practice. Qualitative exploratory research was carried out through a questionnaire with open-ended questions. We built the questions based on the RE-AIM and RE-AIM QuEST frameworks. For data organization and analysis, we used the NVivo 12® software, opting for the thematic categorical analysis strategy in three stages. Twenty-nine primary care and hospital services professionals participated, including nurses, physicians, pharmacists, dentists, community health workers, and management positions. Among the five dimensions of the framework used, sixteen analytical categories emerged. Constructs related to the interoperability between different healthcare services, implementation costs, infrastructure, reduction of bureaucracy, and guarantee of information security were deemed essential when considering implementing a new information system. The sustainability of technology in health services was directly associated with its performance quality to ensure that health professionals do not abandon the use of its functions. Plain Language Summary: Idealizing Information Systems: Perspectives of Health Professionals This study aimed to understand, from the users of Health Information Systems' perspectives, the essential components these technologies must present to be considered appropriate for incorporation and use in the Brazilian Unified Health System. As a secondary objective, we organized the results in the dimensions of the RE-AIM framework to facilitate future translation into practice. We conducted qualitative exploratory research using a questionnaire with open-ended questions. Twenty-nine primary care and hospital services professionals participated, including nurses, physicians, pharmacists, dentists, community health workers, and management positions. The results show that the interoperability between different healthcare services, implementation costs, infrastructure, bureaucracy reduction, and information security guarantee is essential when implementing a new information system. The sustainability of technology in health services was directly associated with its performance quality to ensure that health professionals do not abandon its functions. [ABSTRACT FROM AUTHOR]

ObjectiveThe study sought to describe the contributions of clinical informatics (CI) fellows to their institutions' coronavirus disease 2019 (COVID-19) response.Materials and methodsWe designed a survey to capture key domains of health informatics and perceptions regarding fellows' application of their CI skills. We also conducted detailed interviews with select fellows and described their specific projects in a brief case series.ResultsForty-one of the 99 CI fellows responded to our survey. Seventy-five percent agreed that they were "able to apply clinical informatics training and interest to the COVID-19 response." The most common project types were telemedicine (63%), reporting and analytics (49%), and electronic health record builds and governance (32%). Telehealth projects included training providers on existing telehealth tools, building entirely new virtual clinics for video triage of COVID-19 patients, and pioneering workflows and implementation of brand-new emergency department and inpatient video visit types. Analytics projects included reports and dashboards for institutional leadership, as well as developing digital contact tracing tools. For electronic health record builds, fellows directly contributed to note templates with embedded screening and testing guidance, adding COVID-19 tests to order sets, and validating clinical triage workflows.DiscussionFellows were engaged in projects that span the breadth of the CI specialty and were able to make system-wide contributions in line with their educational milestones.ConclusionsCI fellows contributed meaningfully and rapidly to their institutions' response to the COVID-19 pandemic.

Introduction: the Growing Expertise in E-health Knowledge and Skills (GEEKS) program is an applied apprenticeship program that aims to improve informatics capacity at various levels of the national health system and create a sustainable informatics workforce. Nigeria adapted the GEEKS model in 2019 as a mechanism to strengthen data quality and use of routine immunization (RI) and vaccine-preventable disease (VPD) surveillance data among Expanded Programme on Immunization (EPI) staff. Since the start of the GEEKS-EPI program, there has not been a formal assessment conducted to measure the extent to which GEEKS-EPI has been able to build local informatics workforce capacity and strengthen RI and VPD surveillance (VPDS) data quality and use in Nigeria. Methods: we conducted a qualitative assessment to inform the extent to which GEEKS-EPI has been able to build informatics skillsets to enhance local workforce capacity, foster collaboration across government agencies, and create a sustainable informatics workforce in Nigeria. In-Depth Interviews (IDIs) and Focus Group Discussions (FGDs) were held with GEEKS-EPI supervisors, mentors, and mentees from previous GEEKS-EPI cohorts. Results: while there were challenges reported during early implementation of the GEEKS-EPI program in Nigeria, particularly early on in the COVID-19 pandemic, participants and supervisors reported that the fellowship provided a framework for building a sustainable RI and VPDS informatics workforce through regular mentorship, peer-to-peer exchanges and Subject Matter Expert (SME)-led trainings. Conclusion: lessons learned from early implementation of GEEKS-EPI in Nigeria will help to inform its implementation in other countries, where strengthened national RI and VPDS informatics capacity is the primary objective. [ABSTRACT FROM AUTHOR]

The US public health infrastructure has been historically underfunded, a condition that was exacerbated by the COVID-19 pandemic. This was especially noted in the area of public health informatics. It was also acknowledged that the lack of a diverse public health workforce made it more difficult to address biases and disparities effectively. In 2021 the Office of the National Coordinator awarded $73 million to 10 awardees to develop public health informatics and technology (PHIT) workforce training. The Gaining Equity in Training for Public Health Informatics and Technology (GET PHIT) award utilizes various methods to train and engage minority and underserved populations in the field of public health informatics. Evaluations of the bootcamps and internships to date have shown generally positive results, both in terms of skills acquired and overall experiences. These results indicate that integrating the fields of public health and data science in non-degree, short-term experiences can have positive outcomes. [ABSTRACT FROM AUTHOR]

Objectives: Public health laboratories (PHLs) are essential components of US Public Health Service operations. The health information technology that supports PHLs is central to effective and efficient laboratory operations and overall public health response to infectious disease management. This analysis presents key information on how the Nebraska Public Health Laboratory (NPHL) information technology system evolved to meet the demands of the COVID-19 pandemic. Materials and Methods: COVID-19 presented numerous, unforeseen information technology system challenges. The most notable challenges requiring changes to NPHL software systems and capability were improving efficiency of the laboratory operation due to high-volume testing, responding daily to demands for timely data for analysis by partner systems, interfacing with multiple testing (equipment) platforms, and supporting community-based specimen collection programs. Results: Improvements to the NPHL information technology system enabled NPHL to perform >121 000 SARS-CoV-2 polymerase chain reaction tests from March 2020 through January 2022 at a sustainable rate of 2000 SARS-CoV-2 tests per day, with no increase in laboratory staffing. Electronic reporting of 62 000 rapid antigen tests eliminated paper reporting and extended testing services throughout the state. Collection of COVID-19 symptom data before specimen collection enabled NPHL to make data-driven decisions to perform pool testing and conserve testing kits when supplies were low. Practice Implications: NPHL information technology applications proved essential for managing health care provider workload, prioritizing the use of scarce testing supplies, and managing Nebraska's overall pandemic response. The NPHL experience provides useful examples of a highly capable information technology system and suggests areas for additional attention in the PHL environment, including a focus on end users, collaboration with various partners, and investment in information technology. [ABSTRACT FROM AUTHOR]

We have analysed the COVID-19 epidemic data of more than 174 countries (excluding China) in the period between 22 January and 28 March 2020. We found that some countries (such as the USA, the UK and Canada) follow an exponential epidemic growth, while others (like Italy and several other European countries) show a power law like growth. Regardless of the best fitting law, many countries can be shown to follow a common trajectory that is similar to Italy (the epicentre at the time of analysis), but with varying degrees of delay. We found that countries with 'younger' epidemics, i.e. countries where the epidemic started more recently, tend to exhibit more exponential like behaviour, while countries that were closer behind Italy tend to follow a power law growth. We hypothesize that there is a universal growth pattern of this infection that starts off as exponential and subsequently becomes more power law like. Although it cannot be excluded that this growth pattern is a consequence of social distancing measures, an alternative explanation is that it is an intrinsic epidemic growth law, dictated by a spatially distributed community structure, where the growth in individual highly mixed communities is exponential but the longer term, local geographical spread (in the absence of global mixing) results in a power law. This is supported by computer simulations of a metapopulation model that gives rise to predictions about the growth dynamics that are consistent with correlations found in the epidemiological data. Therefore, seeing a deviation from straight exponential growth may be a natural progression of the epidemic in each country. On the practical side, this indicates that (i) even in the absence of strict social distancing interventions, exponential growth is not an accurate predictor of longer term infection spread, and (ii) a deviation from exponential spread and a reduction of estimated doubling times do not necessarily indicate successful interventions, which are instead indicated by a transition to a reduced power or by a deviation from power law behaviour.

The Pan American Health Organization/ World Health Organization (PAHO/WHO) Anti-Infodemic Virtual Center for the Americas (AIVCA) is a project led by the Department of Evidence and Intelligence for Action in Health, PAHO and the Center for Health Informatics, PAHO/WHO Collaborating Center on Information Systems for Health, at the University of Illinois, with the participation of PAHO staff and consultants across the region. Its goal is to develop a set of tools—pairing AI with human judgment—to help ministries of health and related health institutions respond to infodemics. Public health officials will learn about emerging threats detected by the center and get recommendations on how to respond. The virtual center is structured with three parallel teams: detection, evidence, and response. The detection team will employ a mixture of advanced search queries, machine learning, and other AI techniques to sift through more than 800 million new public social media posts per day to identify emerging infodemic threats in both English and Spanish. The evidence team will use the EasySearch federated search engine backed by AI, PAHO’s knowledge management team, and the Librarian Reserve Corps to identify the most relevant authoritative sources. The response team will use a design approach to communicate recommended response strategies based on behavioural science, storytelling, and information design approaches.

Introduction: The use of technological resources to support processes in health systems has generated robust, interoperable and dynamic platforms. In the case of institutions working with neglected tropical diseases (NTD), there is a need for NTD-specific customizations. Objectives: To establish a medical records platform, specialized for NTD, which would facilitate the analysis of treatment evolution in patients, as well as generate more accurate data about various clinical aspects. Materials and methods: Here we developed a customized electronic medical record system based on OpenMRS for multiple NTDs. A set of forms and functionalities was developed under the OpenMRS guidelines, using shared community modules. Results: All the customized information was packaged in a distribution called NTD Health. The platform is web-based and can be upgraded and improved by users without technological barriers. Conclusions: The EMR system can become a useful tool for other institutions to improve their health practices as well as the quality of life for NTD patients, simplifying the customization of healthcare systems able to interoperate with other platforms.

Abstract Background Global public health action to address noncommunicable diseases (NCDs) requires new approaches. NCDs are primarily prevented and managed in the community where there is little investment in digital health systems and analytics; this has created a data chasm and relatively silent burden of disease. The nascent but rapidly emerging area of precision public health offers exciting new opportunities to transform our approach to NCD prevention. Precision public health uses routinely collected real-world data on determinants of health (social, environmental, behavioural, biomedical and commercial) to inform precision decision-making, interventions and policy based on social position, equity and disease risk, and continuously monitors outcomes – the right intervention for the right population at the right time. This scoping review aims to identify global exemplars of precision public health and the data sources and methods of their aggregation/application to NCD prevention. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Six databases were systematically searched for articles published until February 2021. Articles were included if they described digital aggregation of real-world data and ‘traditional’ data for applied community, population or public health management of NCDs. Real-world data was defined as routinely collected (1) Clinical, Medication and Family History (2) Claims/Billing (3) Mobile Health (4) Environmental (5) Social media (6) Molecular profiling (7) Patient-centred (e.g., personal health record). Results were analysed descriptively and mapped according to the three horizons framework for digital health transformation. Results Six studies were included. Studies developed population health surveillance methods and tools using diverse real-world data (e.g., electronic health records and health insurance providers) and traditional data (e.g., Census and administrative databases) for precision surveillance of 28 NCDs. Population health analytics were applied consistently with descriptive, geospatial and temporal functions. Evidence of using surveillance tools to create precision public health models of care or improve policy and practice decisions was unclear. Conclusions Applications of real-world data and designed data to address NCDs are emerging with greater precision. Digital transformation of the public health sector must be accelerated to create an efficient and sustainable predict-prevent healthcare system.

Objective The annual American College of Medical Informatics (ACMI) symposium focused discussion on the national public health information systems (PHIS) infrastructure to support public health goals. The objective of this article is to present the strengths, weaknesses, threats, and opportunities (SWOT) identified by public health and informatics leaders in attendance. Materials and Methods The Symposium provided a venue for experts in biomedical informatics and public health to brainstorm, identify, and discuss top PHIS challenges. Two conceptual frameworks, SWOT and the Informatics Stack, guided discussion and were used to organize factors and themes identified through a qualitative approach. Results A total of 57 unique factors related to the current PHIS were identified, including 9 strengths, 22 weaknesses, 14 opportunities, and 14 threats, which were consolidated into 22 themes according to the Stack. Most themes (68%) clustered at the top of the Stack. Three overarching opportunities were especially prominent: (1) addressing the needs for sustainable funding, (2) leveraging existing infrastructure and processes for information exchange and system development that meets public health goals, and (3) preparing the public health workforce to benefit from available resources. Discussion The PHIS is unarguably overdue for a strategically designed, technology-enabled, information infrastructure for delivering day-to-day essential public health services and to respond effectively to public health emergencies. Conclusion Most of the themes identified concerned context, people, and processes rather than technical elements. We recommend that public health leadership consider the possible actions and leverage informatics expertise as we collectively prepare for the future. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations. [ABSTRACT FROM AUTHOR]

Background: Information management capacity is crucial for controlling risks from health emergencies. But little is known about how sub-national public health bodies overcome public health intelligence challenges when responding to disease outbreaks. This paper describes a protocol for a mixed-methods systematic review to fill this knowledge gap. In addition to describing the evidence base and characterising public health intelligence responses, it will explore reported facilitators and barriers to response. Methods: Research on sub-national public health intelligence disease outbreak responses will be synthesised. The review will be limited to articles published in English, during or after 2019. Key electronic databases will be searched for peer-reviewed, primary research studies. Two reviewers will independently screen articles for relevance. Articles that refer to a public health intelligence response to a propagated disease outbreak by a sub-national Public Health Authority will be included. Quality assessment of included articles will be undertaken using published tools. Data integration will be by the Pillar Integration Process (PIP). Discussion: This review will describe and synthesise the recent literature on sub-national Public Health Authorities’ responses to propagated disease outbreaks. The systematic design will limit bias and the inclusion of data from quantitative, qualitative and mixed-methods studies will ensure relevant evidence is considered regardless of the methodology used to produce it. The review is part of a larger research project which aims to explore the role of sub-national Public Health Intelligence during the COVID-19 pandemic and investigate how public health intelligence preparedness could be improved in the future. This could provide information to support the development of training, preparedness indicators and/or ways of implementing directives. PROSPERO registration: CRD42022308042 (08/02/2022)

COVID-19 remains an important focus of study in the field of public health informatics. COVID-19 designated hospitals have played an important role in the management of patients affected by the disease. In this paper we describe our modelling of the needs and sources of information for infectious disease practitioners and hospital administrators used to manage a COVID-19 outbreak. Infectious disease practitioner and hospital administrator stakeholders were interviewed to learn about their information needs and where they obtained their information. Stakeholder interview data were transcribed and coded to extract use case information. The findings indicate that participants used many and varied sources of information in the management of COVID-19. The use of multiple, differing sources of data led to considerable effort. In modelling participants' activities, we identified potential subsystems that could be used as a basis for developing an information system specific to the public health needs of hospitals providing care to COVID-19 patients. [ABSTRACT FROM AUTHOR]

Objective: To analyze the use of the e-SUS Primary Care strategy by the Street Clinic teams, after a continuing education process. Methods: Descriptive-exploratory study with a qualitative research-intervention approach carried out with 23 professionals from three clinics in Rua da Centro-Oeste do Brasil in 2016. The intervention consisted of a theoretical-practical seminar on the e-SUS Primary Care strategy with assessment mediated by focus groups. Data were submitted to Content Analysis, Thematic modality, with the help of the ATLAS.ti software. Results: There were reflections on the need to transform professional practice and value electronic health records of the activities carried out by the teams to raise the quality of care and give more visibility to the work undertaken by professionals. They began to gradually incorporate the e-SUS Primary Care strategy into the work processes of the services. Conclusion: The continuing education process provided professionals and managers with a space for reflection and resignification of professional practice in relation to electronic health records, making them aware of the importance of computerization in work processes. Throughout the intervention process, it was evident that the participants were mobilized regarding their understanding and attitudes towards the e-SUS Primary Care strategy in their daily lives. [ABSTRACT FROM AUTHOR]

The Pan American Health Organization/ World Health Organization (PAHO/WHO) Anti-Infodemic Virtual Center for the Americas (AIVCA) is a project led by the Department of Evidence and Intelligence for Action in Health, PAHO and the Center for Health Informatics, PAHO/WHO Collaborating Center on Information Systems for Health, at the University of Illinois, with the participation of PAHO staff and consultants across the region. Its goal is to develop a set of tools--pairing AI with human judgment--to help ministries of health and related health institutions respond to infodemics. Public health officials will learn about emerging threats detected by the center and get recommendations on how to respond. The virtual center is structured with three parallel teams: detection, evidence, and response. The detection team will employ a mixture of advanced search queries, machine learning, and other AI techniques to sift through more than 800 million new public social media posts per day to identify emerging infodemic threats in both English and Spanish. The evidence team will use the EasySearch federated search engine backed by AI, PAHO's knowledge management team, and the Librarian Reserve Corps to identify the most relevant authoritative sources. The response team will use a design approach to communicate recommended response strategies based on behavioural science, storytelling, and information design approaches. [ABSTRACT FROM AUTHOR]