Development and Optimization of the Veterans Affairs' National Heart Failure Dashboard for Population Health Management.

BACKGROUND: In 2020, the Veterans Affairs (VA) health care system deployed a heart failure (HF) dashboard for use nationally. The initial version was notably imprecise and unreliable for the identification of HF subtypes. We describe the development and subsequent optimization of the VA national HF dashboard. MATERIALS AND METHODS: This study describes the stepwise process for improving the accuracy of the VA national HF dashboard, including defining the initial dashboard, improving case definitions, using natural language processing for patient identification, and incorporating an imaging-quality hierarchy model. Optimization further included evaluating whether to require concurrent ICD-codes for inclusion in the dashboard and assessing various imaging modalities for patient characterization. RESULTS: Through multiple rounds of optimization, the dashboard accuracy (defined as the proportion of true results to the total population) was improved from 54.1% to 89.2% for the identification of HF with reduced ejection fraction (HFrEF) and from 53.9% to 88.0% for the identification of HF with preserved ejection fraction (HFpEF). To align with current guidelines, HF with mildly reduced ejection fraction (HFmrEF) was added to the dashboard output with 88.0% accuracy. CONCLUSIONS: The inclusion of an imaging-quality hierarchy model and natural-language processing algorithm improved the accuracy of the VA national HF dashboard. The revised dashboard informatics algorithm has higher use rates and improved reliability for the health management of the population.

Feasibility, accuracy, and usability analysis of MapAML, a first-in-class app for integrated diagnosis in acute myeloid leukemia.

Performing a comprehensive diagnosis of acute myeloid leukemia (AML) is complex and involves the integration of clinical information, bone marrow morphology, immunophenotyping, cytogenetic, and molecular analysis, which can be challenging to the general hematologist. The aim of this study was to evaluate the usability and accuracy of MapAML, a smartphone app for integrated diagnosis in AML, created to aid the hematologist in its clinical practice. App performance was evaluated in dedicated sessions, in which 21 hematologists or fellows in hematology performed an integrated diagnosis of deidentified real-world clinical AML cases, first without and posteriorly with MapAML use. Diagnosis accuracy increased after MapAML utilization, with the average score going from 7.08 without app to 8.88 with app use (on a scale from 0 to 10), representing a significant accuracy improvement (p = .002). Usability evaluation was very favorable, with 81% of users considering the app very or extremely simple to use. There was also a significant increase in confidence to perform a complete and accurate diagnosis in AML after app use, with 61.9% of the participants willing to use the app in their clinical practice. In this study, MapAML increased accuracy with excellent usability for integrated diagnosis in AML.

Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022.

OBJECTIVES: To map the clinical use of CE-marked artificial intelligence (AI)-based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022. MATERIALS AND METHODS: Our AI network (one radiologist or AI representative per Dutch hospital organization) received a questionnaire each spring from 2020 to 2022 about AI product usage, financing, and obstacles to adoption. Products that were not listed on www.AIforRadiology.com by July 2022 were excluded from the analysis. RESULTS: The number of respondents was 43 in 2020, 36 in 2021, and 33 in 2022. The number of departments using AI has been growing steadily (2020: 14, 2021: 19, 2022: 23). The diversity (2020: 7, 2021: 18, 2022: 34) and the number of total implementations (2020: 19, 2021: 38, 2022: 68) has rapidly increased. Seven implementations were discontinued in 2022. Four hospital organizations said to use an AI platform or marketplace for the deployment of AI solutions. AI is mostly used to support chest CT (17), neuro CT (17), and musculoskeletal radiograph (12) analysis. The budget for AI was reserved in 13 of the responding centers in both 2021 and 2022. The most important obstacles to the adoption of AI remained costs and IT integration. Of the respondents, 28% stated that the implemented AI products realized health improvement and 32% assumed both health improvement and cost savings. CONCLUSION: The adoption of AI products in radiology departments in the Netherlands is showing common signs of a developing market. The major obstacles to reaching widespread adoption are a lack of financial resources and IT integration difficulties. CLINICAL RELEVANCE STATEMENT: The clinical impact of AI starts with its adoption in daily clinical practice. Increased transparency around AI products being adopted, implementation obstacles, and impact may inspire increased collaboration and improved decision-making around the implementation and financing of AI products. KEY POINTS: • The adoption of artificial intelligence products for radiology has steadily increased since 2020 to at least a third of the centers using AI in clinical practice in the Netherlands in 2022. • The main areas in which artificial intelligence products are used are lung nodule detection on CT, aided stroke diagnosis, and bone age prediction. • The majority of respondents experienced added value (decreased costs and/or improved outcomes) from using artificial intelligence-based software; however, major obstacles to adoption remain the costs and IT-related difficulties.

Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics-Part I: Why Is It Needed?

A multitude of federally and industry-funded efforts are underway to generate and collect human, animal, microbial, and other sources of data on an unprecedented scale; the results are commonly referred to as "big data." Often vaguely defined, big data refers to large and complex datasets consisting of myriad datatypes that can be integrated to address complex questions. Big data offers a wealth of information that can be accessed only by those who pose the right questions and have sufficient technical knowhow and analytical skills. The intersection comprised of the gut-brain axis, the intestinal microbiome and multi-ome, and several other interconnected organ systems poses particular challenges and opportunities for those engaged in gastrointestinal and liver research. Unfortunately, there is currently a shortage of clinicians, scientists, and physician-scientists with the training needed to use and analyze big data at the scale necessary for widespread implementation of precision medicine. Here, we review the importance of training in the use of big data, the perils of insufficient training, and potential solutions that exist or can be developed to address the dearth of individuals in GI and hepatology research with the necessary level of big data expertise.

Gastroenterology Fellowship and Postdoctoral Training in Omics and Statistics-Part II: How Can It Be Achieved?

Data are being generated, collected, and aggregated in massive quantities at exponentially increasing rates. This "big data," discussed in depth in the first section of this two-part series, is increasingly important to understand the nuances of the gastrointestinal tract and its complex interactions and networks involving a host of other organ systems and microbes. Creating and using these datasets correctly requires comprehensive training; however, current instruction in the integration, analysis, and interpretation of big data appears to lag far behind data acquisition. While opportunities exist for those interested in acquiring the requisite training, these appear to be underutilized, in part due to widespread ignorance of their existence. Here, to address these gaps in knowledge, we highlight existing big data learning opportunities and propose innovative approaches to attain such training. We offer suggestions at both the undergraduate and graduate medical education levels for prospective clinical and basic investigators. Lastly, we categorize training opportunities that can be selected to fit specific needs and timeframes.

miRSNP rs188493331: A key player in genetic control of microRNA-induced pathway activation in hypertrophic scars and keloids.

BACKGROUND: Our study aims to delineate the miRSNP-microRNA-gene-pathway interactions in the context of hypertrophic scars (HS) and keloids. MATERIALS AND METHODS: We performed a computational biology study involving differential expression analysis to identify genes and their mRNAs in HS and keloid tissues compared to normal skin, identifying key hub genes and enriching their functional roles, comprehensively analyzing microRNA-target genes and related signaling pathways through bioinformatics, identifying MiRSNPs, and constructing a pathway-based network to illustrate miRSNP-miRNA-gene-signaling pathway interactions. RESULTS: Our results revealed a total of 429 hub genes, with a strong enrichment in signaling pathways related to proteoglycans in cancer, focal adhesion, TGF-ß, PI3K/Akt, and EGFR tyrosine kinase inhibitor resistance. Particularly noteworthy was the substantial crosstalk between the focal adhesion and PI3K/Akt signaling pathways, making them more susceptible to regulation by microRNAs. We also identified specific miRNAs, including miRNA-1279, miRNA-429, and miRNA-302e, which harbored multiple SNP loci, with miRSNPs rs188493331 and rs78979933 exerting control over a significant number of miRNA target genes. Furthermore, we observed that miRSNP rs188493331 shared a location with microRNA302e, microRNA202a-3p, and microRNA20b-5p, and these three microRNAs collectively targeted the gene LAMA3, which is integral to the focal adhesion signaling pathway. CONCLUSIONS: The study successfully unveils the complex interactions between miRSNPs, miRNAs, genes, and signaling pathways, shedding light on the genetic factors contributing to HS and keloid formation.

Factors associated with loss to follow-up in patients with advanced age-related macular degeneration: A telehealth recall initiative.

INTRODUCTION: Patients with advanced age-related macular degeneration (AMD) frequently experience loss to follow-up (LTFU), heightening the risk of vision loss from treatment delays. This study aimed to identify factors contributing to LTFU in patients with advanced AMD and assess the effectiveness of telephone-based outreach in reconnecting them with eye care. METHODS: A custom reporting tool identified patients with advanced AMD who had not returned for eye care between 31 October 2021 and 1 November 2022. Potentially LTFU patients were enrolled in a telephone outreach programme conducted by a telehealth extender to encourage their return for care. Linear regression analysis identified factors associated with being LTFU and likelihood of accepting care post-outreach. RESULTS: Out of 1269 patients with advanced AMD, 105 (8.3%) did not return for recommended eye care. Patients LTFU were generally older (89.2 ± 8.9 years vs. 87.2 ± 8.5 years, p = 0.02) and lived farther from the clinic (25 ± 43 miles vs. 17 ± 30 miles, p = 0.009). They also had a higher rate of advanced dry AMD (26.7% vs. 18.5%, p = 0.04) and experienced worse vision in both their better-seeing (0.683 logMAR vs. 0.566 logMAR, p = 0.03) and worse-seeing (1.388 logMAR vs. 1.235 logMAR, p = 0.04) eyes. Outreach by a telehealth extender reached 62 patients (59%), 43 through family members or healthcare proxies. Half of the cases where a proxy was contacted revealed that the patient in question had died. Among those contacted directly, one third expressed willingness to resume eye care (20 patients), with 11 scheduling appointments (55%). Despite only two patients returning for in-person eye care through the intervention, the LTFU rate halved to 4.4% by accounting for those patients who no longer needed eye care at the practice. CONCLUSIONS: There is a substantial risk that older patients with advanced AMD will become LTFU. Targeted telephone outreach can provide a pathway for vulnerable patients to return to care.

Characteristics of Japanese physician influencers on Twitter during the COVID-19 pandemic and fact-checking their tweets on COVID-19-related drugs.

BACKGROUND: During the coronavirus disease-2019 (COVID-19) pandemic, segments of the public relied on social media platforms such as Twitter for medical information shared by medical personnel. Although physicians are likely to disseminate more accurate information on Twitter than non-medical individuals, it cannot be taken for granted. As such, tweets written by physicians in Japan should also be scrutinized for accuracy. PURPOSE: The purpose of this study was to create a profile of the most popular physician influencers on Twitter in Japan, and to do a fact-check of their tweets regarding COVID-19-related drugs. DESIGN: This is a retrospective observational study. METHODS: We purchased Twitter data for Japan for the initial 9 months of the COVID-19 pandemic (from January 2020 to September 2020), and extracted tweets with keywords related to COVID-19 at a sampling rate of 3%. The most popular physicians were identified and selected consecutively by searching for the top 1000 accounts using Twitter's search function. These top accounts were considered influencers and their tweets and retweets concerning COVID-19-related drugs were fact-checked against scientific literature. RESULTS: We identified 21 physician influencers with real names: most were male in their 40s and 50s working at private medical facilities. The contents of their tweets were mainly sourced from scientific publications that were current at that time. The fact-check revealed that only one of 50 tweets was not correct while the others had no identifiable inaccuracies. CONCLUSIONS: Except for one tweet, tweets written and retweeted by Japanese physician influencers concerning the COVID-19-related drugs contained predominantly accurate information.

Use of an electronic wellness instrument in the integrated health and social care of older adults: a group concept mapping study.

BACKGROUND: Health system fragmentation directly contributes to poor health and social outcomes for older adults with multiple chronic conditions and their care partners. Older adults often require support from primary care, multiple specialists, home care, community support services, and other health-care sectors and communication between these providers is unstructured and not standardized. Integrated and interprofessional team-based models of care are a recommended strategy to improve health service delivery to older adults with complex needs. Standardized assessment instruments deployed on digital platforms are considered a necessary component of integrated care. The aim of this study was to develop strategies to leverage an electronic wellness instrument, interRAI Check Up Self Report, to support integrated health and social care for older adults and their care partners in a community in Southern Ontario, Canada. METHODS: Group concept mapping, a participatory mixed-methods approach, was conducted. Participants included older adults, care partners, and representatives from: home care, community support services, specialized geriatric services, primary care, and health informatics. In a series of virtual meetings, participants generated ideas to implement the interRAI Check Up and rated the relative importance of these ideas. Hierarchical cluster analysis was used to map the ideas into clusters of similar statements. Participants reviewed the map to co-create an action plan. RESULTS: Forty-one participants contributed to a cluster map of ten action areas (e.g., engagement of older adults and care partners, instrument's ease of use, accessibility of the assessment process, person-centred process, training and education for providers, provider coordination, health information integration, health system decision support and quality improvement, and privacy and confidentiality). The health system decision support cluster was rated as the lowest relative importance and the health information integration was cluster rated as the highest relative importance. CONCLUSIONS: Many person-, provider-, and system-level factors need to be considered when implementing and using an electronic wellness instrument across health- and social-care providers. These factors are highly relevant to the integration of other standardized instruments into interprofessional team care to ensure a compassionate care approach as technology is introduced.

Noninvasive monitoring technologies to identify discomfort and distressing symptoms in persons with limited communication at the end of life: a scoping review.

BACKGROUND: Discomfort and distressing symptoms are common at the end of life, while people in this stage are often no longer able to express themselves. Technologies may aid clinicians in detecting and treating these symptoms to improve end-of-life care. This review provides an overview of noninvasive monitoring technologies that may be applied to persons with limited communication at the end of life to identify discomfort. METHODS: A systematic search was performed in nine databases, and experts were consulted. Manuscripts were included if they were written in English, Dutch, German, French, Japanese or Chinese, if the monitoring technology measured discomfort or distressing symptoms, was noninvasive, could be continuously administered for 4 hours and was potentially applicable for bed-ridden people. The screening was performed by two researchers independently. Information about the technology, its clinimetrics (validity, reliability, sensitivity, specificity, responsiveness), acceptability, and feasibility were extracted. RESULTS: Of the 3,414 identified manuscripts, 229 met the eligibility criteria. A variety of monitoring technologies were identified, including actigraphy, brain activity monitoring, electrocardiography, electrodermal activity monitoring, surface electromyography, incontinence sensors, multimodal systems, and noncontact monitoring systems. The main indicators of discomfort monitored by these technologies were sleep, level of consciousness, risk of pressure ulcers, urinary incontinence, agitation, and pain. For the end-of-life phase, brain activity monitors could be helpful and acceptable to monitor the level of consciousness during palliative sedation. However, no manuscripts have reported on the clinimetrics, feasibility, and acceptability of the other technologies for the end-of-life phase. CONCLUSIONS: Noninvasive monitoring technologies are available to measure common symptoms at the end of life. Future research should evaluate the quality of evidence provided by existing studies and investigate the feasibility, acceptability, and usefulness of these technologies in the end-of-life setting. Guidelines for studies on healthcare technologies should be better implemented and further developed.

Pharmacogenetics Clinical Decision Support Systems for Primary Care in England: Co-Design Study.

BACKGROUND: Pharmacogenetics can impact patient care and outcomes through personalizing the selection of medicines, resulting in improved efficacy and a reduction in harmful side effects. Despite the existence of compelling clinical evidence and international guidelines highlighting the benefits of pharmacogenetics in clinical practice, implementation within the National Health Service in the United Kingdom is limited. An important barrier to overcome is the development of IT solutions that support the integration of pharmacogenetic data into health care systems. This necessitates a better understanding of the role of electronic health records (EHRs) and the design of clinical decision support systems that are acceptable to clinicians, particularly those in primary care. OBJECTIVE: Explore the needs and requirements of a pharmacogenetic service from the perspective of primary care clinicians with a view to co-design a prototype solution. METHODS: We used ethnographic and think-aloud observations, user research workshops, and prototyping. The participants for this study included general practitioners and pharmacists. In total, we undertook 5 sessions of ethnographic observation to understand current practices and workflows. This was followed by 3 user research workshops, each with its own topic guide starting with personas and early ideation, through to exploring the potential of clinical decision support systems and prototype design. We subsequently analyzed workshop data using affinity diagramming and refined the key requirements for the solution collaboratively as a multidisciplinary project team. RESULTS: User research results identified that pharmacogenetic data must be incorporated within existing EHRs rather than through a stand-alone portal. The information presented through clinical decision support systems must be clear, accessible, and user-friendly as the service will be used by a range of end users. Critically, the information should be displayed within the prescribing workflow, rather than discrete results stored statically in the EHR. Finally, the prescribing recommendations should be authoritative to provide confidence in the validity of the results. Based on these findings we co-designed an interactive prototype, demonstrating pharmacogenetic clinical decision support integrated within the prescribing workflow of an EHR. CONCLUSIONS: This study marks a significant step forward in the design of systems that support pharmacogenetic-guided prescribing in primary care settings. Clinical decision support systems have the potential to enhance the personalization of medicines, provided they are effectively implemented within EHRs and present pharmacogenetic data in a user-friendly, actionable, and standardized format. Achieving this requires the development of a decoupled, standards-based architecture that allows for the separation of data from application, facilitating integration across various EHRs through the use of application programming interfaces (APIs). More globally, this study demonstrates the role of health informatics and user-centered design in realizing the potential of personalized medicine at scale and ensuring that the benefits of genomic innovation reach patients and populations effectively.

Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.

BACKGROUND: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows. OBJECTIVE: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS. METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics. RESULTS: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework. CONCLUSIONS: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.

The Costs of Anonymization: Case Study Using Clinical Data.

BACKGROUND: Sharing data from clinical studies can accelerate scientific progress, improve transparency, and increase the potential for innovation and collaboration. However, privacy concerns remain a barrier to data sharing. Certain concerns, such as reidentification risk, can be addressed through the application of anonymization algorithms, whereby data are altered so that it is no longer reasonably related to a person. Yet, such alterations have the potential to influence the data set's statistical properties, such that the privacy-utility trade-off must be considered. This has been studied in theory, but evidence based on real-world individual-level clinical data is rare, and anonymization has not broadly been adopted in clinical practice. OBJECTIVE: The goal of this study is to contribute to a better understanding of anonymization in the real world by comprehensively evaluating the privacy-utility trade-off of differently anonymized data using data and scientific results from the German Chronic Kidney Disease (GCKD) study. METHODS: The GCKD data set extracted for this study consists of 5217 records and 70 variables. A 2-step procedure was followed to determine which variables constituted reidentification risks. To capture a large portion of the risk-utility space, we decided on risk thresholds ranging from 0.02 to 1. The data were then transformed via generalization and suppression, and the anonymization process was varied using a generic and a use case-specific configuration. To assess the utility of the anonymized GCKD data, general-purpose metrics (ie, data granularity and entropy), as well as use case-specific metrics (ie, reproducibility), were applied. Reproducibility was assessed by measuring the overlap of the 95% CI lengths between anonymized and original results. RESULTS: Reproducibility measured by 95% CI overlap was higher than utility obtained from general-purpose metrics. For example, granularity varied between 68.2% and 87.6%, and entropy varied between 25.5% and 46.2%, whereas the average 95% CI overlap was above 90% for all risk thresholds applied. A nonoverlapping 95% CI was detected in 6 estimates across all analyses, but the overwhelming majority of estimates exhibited an overlap over 50%. The use case-specific configuration outperformed the generic one in terms of actual utility (ie, reproducibility) at the same level of privacy. CONCLUSIONS: Our results illustrate the challenges that anonymization faces when aiming to support multiple likely and possibly competing uses, while use case-specific anonymization can provide greater utility. This aspect should be taken into account when evaluating the associated costs of anonymized data and attempting to maintain sufficiently high levels of privacy for anonymized data. TRIAL REGISTRATION: German Clinical Trials Register DRKS00003971; https://drks.de/search/en/trial/DRKS00003971. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1093/ndt/gfr456.

Twenty-Five Years of Progress-Lessons Learned From JMIR Publications to Address Gender Parity in Digital Health Authorships: Bibliometric Analysis.

BACKGROUND: Digital health research plays a vital role in advancing equitable health care. The diversity of research teams is thereby instrumental in capturing societal challenges, increasing productivity, and reducing bias in algorithms. Despite its importance, the gender distribution within digital health authorship remains largely unexplored. OBJECTIVE: This study aimed to investigate the gender distribution among first and last authors in digital health research, thereby identifying predicting factors of female authorship. METHODS: This bibliometric analysis examined the gender distribution across 59,980 publications from 1999 to 2023, spanning 42 digital health journals indexed in the Web of Science. To identify strategies ensuring equality in research, a detailed comparison of gender representation in JMIR journals was conducted within the field, as well as against a matched sample. Two-tailed Welch 2-sample t tests, Wilcoxon rank sum tests, and chi-square tests were used to assess differences. In addition, odds ratios were calculated to identify predictors of female authorship. RESULTS: The analysis revealed that 37% of first authors and 30% of last authors in digital health were female. JMIR journals demonstrated a higher representation, with 49% of first authors and 38% of last authors being female, yielding odds ratios of 1.96 (95% CI 1.90-2.03; P<.001) and 1.78 (95% CI 1.71-1.84; P<.001), respectively. Since 2008, JMIR journals have consistently featured a greater proportion of female first authors than male counterparts. Other factors that predicted female authorship included having female authors in other relevant positions and gender discordance, given the higher rate of male last authors in the field. CONCLUSIONS: There was an evident shift toward gender parity across publications in digital health, particularly from the publisher JMIR Publications. The specialized focus of its sister journals, equitable editorial policies, and transparency in the review process might contribute to these achievements. Further research is imperative to establish causality, enabling the replication of these successful strategies across other scientific fields to bridge the gender gap in digital health effectively.

Identification of health-related problems in youth: a mixed methods feasibility study evaluating the Youth Health Report System.

BACKGROUND: Because poor health in youth risk affecting their entry in adulthood, improved methods for their early identification are needed. Health and welfare technology is widely accepted by youth populations, presenting a potential method for identifying their health problems. However, healthcare technology must be evidence-based. Specifically, feasibility studies contribute valuable information prior to more complex effects-based research. The current study assessed the process, resource, management, and scientific feasibility of the Youth Health Report System prototype, developed within a youth health clinic context in advance of an intervention study. METHODS: This mixed-methods feasibility study was conducted in a clinical setting. The process, resource, management, and scientific feasibility of the Youth Health Report System were investigated, as recommended in the literature. Participants were youth aged 16-23 years old, attending a youth health clinic, and healthcare professionals from three clinics. The youth participants used their smart phones to respond to Youth Health Report System health questions and healthcare professionals used their computer to access the results and for registration system entries. Qualitative data were collected from interviews with healthcare professionals, which were described with thematic analysis. Youth participants' quantitative Youth Health Report System data were analyzed for descriptive statistics. RESULTS: Feasibility analysis of qualitative data from interviews with 11 healthcare professionals resulted in three themes: We expected it could be hard; Information and routines helped but time was an issue; and The electronic case report form was valuable in the health assessment. Qualitative data were collected from the Youth Health Report System. A total of 54 youth participants completed the evaluation questionnaire, and healthcare professionals retrieved information from, and made post-appointment system entries. Quantitative results revealed few missing items and acceptable data variability. An assessment template of merged qualitative and quantitative data guided a consensus discussion among the researchers, resulting in acceptable feasibility. CONCLUSIONS: The process-, resource-, management-, and scientific feasibility aspects were acceptable, with some modifications, strengthening the potential for a successful Youth Health Report System intervention study.

Electronic Health Records for Predicting Outcomes to Work-Related Musculoskeletal Disorders: A Scoping Review.

PURPOSE: Through electronic health records (EHRs), musculoskeletal (MSK) therapists such as chiropractors and physical therapists, as well as occupational medicine physicians could collect data on many variables that can be traditionally challenging to collect in managing work-related musculoskeletal disorders (WMSDs). The review's objectives were to explore the extent of research using EHRs in predicting outcomes of WMSDs by MSK therapists. METHOD: A systematic search was conducted in Medline, PubMed, CINAHL, and Embase. Grey literature was searched. 2156 unique papers were retrieved, of which 38 were included. Three themes were explored, the use of EHRs to predict outcomes to WMSDs, data sources for predicting outcomes to WMSDs, and adoption of standardised information for managing WMSDs. RESULTS: Predicting outcomes of all MSK disorders using EHRs has been researched in 6 studies, with only 3 focusing on MSK therapists and 4 addressing WMSDs. Similar to all secondary data source research, the challenges include data quality, missing data and unstructured data. There is not yet a standardised or minimum set of data that has been defined for MSK therapists to collect when managing WMSD. Further work based on existing frameworks is required to reduce the documentation burden and increase usability. CONCLUSION: The review outlines the limited research on using EHRs to predict outcomes of WMSDs. It highlights the need for EHR design to address data quality issues and develop a standardised data set in occupational healthcare that includes known factors that potentially predict outcomes to help regulators, research efforts, and practitioners make better informed clinical decisions.

Deep Deblurring in Teledermatology: Deep Learning Models Restore the Accuracy of Blurry Images' Classification.

Background: Blurry images in teledermatology and consultation increased the diagnostic difficulty for both deep learning models and physicians. We aim to determine the extent of restoration in diagnostic accuracy after blurry images are deblurred by deep learning models. Methods: We used 19,191 skin images from a public skin image dataset that includes 23 skin disease categories, 54 skin images from a public dataset of blurry skin images, and 53 blurry dermatology consultation photos in a medical center to compare the diagnosis accuracy of trained diagnostic deep learning models and subjective sharpness between blurry and deblurred images. We evaluated five different deblurring models, including models for motion blur, Gaussian blur, Bokeh blur, mixed slight blur, and mixed strong blur. Main Outcomes and Measures: Diagnostic accuracy was measured as sensitivity and precision of correct model prediction of the skin disease category. Sharpness rating was performed by board-certified dermatologists on a 4-point scale, with 4 being the highest image clarity. Results: The sensitivity of diagnostic models dropped 0.15 and 0.22 on slightly and strongly blurred images, respectively, and deblurring models restored 0.14 and 0.17 for each group. The sharpness ratings perceived by dermatologists improved from 1.87 to 2.51 after deblurring. Activation maps showed the focus of diagnostic models was compromised by the blurriness but was restored after deblurring. Conclusions: Deep learning models can restore the diagnostic accuracy of diagnostic models for blurry images and increase image sharpness perceived by dermatologists. The model can be incorporated into teledermatology to help the diagnosis of blurry images.

Factors identified as barriers or facilitators to EMR/EHR based interprofessional primary care: a scoping review.

As interprofessional collaboration (IPC) in primary care receives increasing attention, the role of electronic medical and health record (EMR/EHR) systems in supporting IPC is important to consider. A scoping review was conducted to synthesize the current literature on the barriers and facilitators of EMR/EHRs to interprofessional primary care. Four online databases (OVID Medline, EBSCO CINAHL, OVID EMBASE, and OVID PsycINFO) were searched without date restrictions. Twelve studies were included in the review. Of six facilitator and barrier themes identified, the key facilitator was teamwork support and a significant barrier was data management. Other important barriers included usability related mainly to interoperability, and practice support primarily in terms of patient care. Additional themes were organization attributes and user features. Although EMR/EHR systems facilitated teamwork support, there is potential for team features to be strengthened further. Persistent barriers may be partly addressed by advances in software design, particularly if interprofessional perspectives are included. Organizations and teams might also consider strategies for working with existing EMR/EHR systems, for instance by developing guidelines for interprofessional use. Further research concerning the use of electronic records in interprofessional contexts is needed to support IPC in primary care.

Automatic dental age calculation from panoramic radiographs using deep learning: a two-stage approach with object detection and image classification.

BACKGROUND: Dental age is crucial for treatment planning in pediatric and orthodontic dentistry. Dental age calculation methods can be categorized into morphological, biochemical, and radiological methods. Radiological methods are commonly used because they are non-invasive and reproducible. When radiographs are available, dental age can be calculated by evaluating the developmental stage of permanent teeth and converting it into an estimated age using a table, or by measuring the length between some landmarks such as the tooth, root, or pulp, and substituting them into regression formulas. However, these methods heavily depend on manual time-consuming processes. In this study, we proposed a novel and completely automatic dental age calculation method using panoramic radiographs and deep learning techniques. METHODS: Overall, 8,023 panoramic radiographs were used as training data for Scaled-YOLOv4 to detect dental germs and mean average precision were evaluated. In total, 18,485 single-root and 16,313 multi-root dental germ images were used as training data for EfficientNetV2 M to classify the developmental stages of detected dental germs and Top-3 accuracy was evaluated since the adjacent stages of the dental germ looks similar and the many variations of the morphological structure can be observed between developmental stages. Scaled-YOLOv4 and EfficientNetV2 M were trained using cross-validation. We evaluated a single selection, a weighted average, and an expected value to convert the probability of developmental stage classification to dental age. One hundred and fifty-seven panoramic radiographs were used to compare automatic and manual human experts' dental age calculations. RESULTS: Dental germ detection was achieved with a mean average precision of 98.26% and dental germ classifiers for single and multi-root were achieved with a Top-3 accuracy of 98.46% and 98.36%, respectively. The mean absolute errors between the automatic and manual dental age calculations using single selection, weighted average, and expected value were 0.274, 0.261, and 0.396, respectively. The weighted average was better than the other methods and was accurate by less than one developmental stage error. CONCLUSION: Our study demonstrates the feasibility of automatic dental age calculation using panoramic radiographs and a two-stage deep learning approach with a clinically acceptable level of accuracy.

Best practices for multimodal clinical data management and integration: An atopic dermatitis research case.

BACKGROUND: In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science. METHODS: We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis. RESULTS: MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA. CONCLUSIONS: The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.