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BACKGROUND: Hereditary angioedema (HAE), a rare genetic disease, induces acute attacks of swelling in various regions of the body. Its prevalence is estimated to be 1 in 50,000 people, with no reported bias among different ethnic groups. However, considering the estimated prevalence, the number of patients in Japan diagnosed with HAE remains approximately 1 in 250,000, which means that only 20% of potential HAE cases are identified. OBJECTIVE: This study aimed to develop an artificial intelligence (AI) model that can detect patients with suspected HAE using medical history data (medical claims, prescriptions, and electronic medical records [EMRs]) in the United States. We also aimed to validate the detection performance of the model for HAE cases using the Japanese dataset. METHODS: The HAE patient and control groups were identified using the US claims and EMR datasets. We analyzed the characteristics of the diagnostic history of patients with HAE and developed an AI model to predict the probability of HAE based on a generalized linear model and bootstrap method. The model was then applied to the EMR data of the Kyoto University Hospital to verify its applicability to the Japanese dataset. RESULTS: Precision and sensitivity were measured to validate the model performance. Using the comprehensive US dataset, the precision score was 2% in the initial model development step. Our model can screen out suspected patients, where 1 in 50 of these patients have HAE. In addition, in the validation step with Japanese EMR data, the precision score was 23.6%, which exceeded our expectations. We achieved a sensitivity score of 61.5% for the US dataset and 37.6% for the validation exercise using data from a single Japanese hospital. Overall, our model could predict patients with typical HAE symptoms. CONCLUSIONS: This study indicates that our AI model can detect HAE in patients with typical symptoms and is effective in Japanese data. However, further prospective clinical studies are required to investigate whether this model can be used to diagnose HAE.

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This paper focuses on defining a framework to allow individual patients to track their own health related data. We propose a Patient Centered Information Framework (PCIF) allowing patient to manage their own data by using discharge letters. Discharge letters summarize information from a hospital stay, such as medical history, diagnoses, treatments and follow up, needed for continuity of care. It enables patients to share data with different organizations ensuring personal data protection, even when moving from different places and countries. A record of clinical management may thus be guaranteed when moving among different health structures as well as simplifying obtaining medications. We propose an approach to allow citizens to manage their health related data in a cross borders fashion. We compare the regulation of discharge letters among a sample of countries. We propose a management protocol for using a commonly adopted patient discharge letter framework within a PCIF.

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BACKGROUND: Population viral load (VL), the most comprehensive measure of the HIV transmission potential, cannot be directly measured due to lack of complete sampling of all people with HIV. OBJECTIVE: A given HIV clinic's electronic health record (EHR), a biased sample of this population, may be used to attempt to impute this measure. METHODS: We simulated a population of 10,000 individuals with VL calibrated to surveillance data with a geometric mean of 4449 copies/mL. We sampled 3 hypothetical EHRs from (A) the source population, (B) those diagnosed, and (C) those retained in care. Our analysis imputed population VL from each EHR using sampling weights followed by Bayesian adjustment. These methods were then tested using EHR data from an HIV clinic in Delaware. RESULTS: Following weighting, the estimates moved in the direction of the population value with correspondingly wider 95% intervals as follows: clinic A: 4364 (95% interval 1963-11,132) copies/mL; clinic B: 4420 (95% interval 1913-10,199) copies/mL; and clinic C: 242 (95% interval 113-563) copies/mL. Bayesian-adjusted weighting further improved the estimate. CONCLUSIONS: These findings suggest that methodological adjustments are ineffective for estimating population VL from a single clinic's EHR without the resource-intensive elucidation of an informative prior.

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Background: Multiple sclerosis (MS) is a persistent inflammatory condition impacting the brain and spinal cord, affecting globally approximately 2.8 million individuals. Effective self-management plays a crucial role in the treatment of chronic diseases, including MS, significantly influencing health outcomes. A personal health record (PHR) is a promising tool to support self-management, potentially empowering patients and enhancing their engagement in treatment and health. Despite these promising aspects, challenges in implementation persist and PHRs are still a relatively new concept undergoing rapid development. Objective: This study aimed to assess the feasibility and usability of the PHR. Secondary objectives included evaluating implementation determinants, and exploring preliminary effects on quality of care for both patients and healthcare professionals (HCPs), self-management, self-efficacy for patients, job satisfaction, efficiency, and demand for HCPs, and preliminary effects on costs and health-related quality of life. Methods: This study had a mixed-methods design. Quantitative data of patients (n = 80) and HCPs (n = 12) were collected via self-reported questionnaires at baseline (T0), after one year (T1), and after two years (T2). One focus group interview was conducted at T2 with patients (n = 7), and another one with HCPs (n = 4), to get a more in-depth understanding of the feasibility and usability of the PHR via the Unified Theory of Acceptance and Use of Technology framework, and to further explore the secondary objectives in-depth. Results: Most patients never logged in during the first year and logged in a couple of times per year during the second year, averaging around 15 min per log-in session. The HCPs mainly logged in a couple of times per year over the two years with an average use of six minutes per session. Patient usability and satisfaction scores were below average and moderate, respectively: with SUS-scores of 59.9 (SD = 14.2, n = 33) at T1 and 59.0 (SD = 16.3, n = 37) at T2, and CSQ-8 scores of 21.4 (SD = 5.0, n = 34) at T1, and 22.1 (SD = 5.0, n = 39) at T2. HCPs had similar usability and satisfaction scores. Multiple facilitators and barriers were identified by both patients and HCPs, such as (in)sufficient knowledge of how to use the PHR, lack of staff capacity and ICT obstacles. No significant differences were found in the preliminary effects. Qualitative data showed, among others, that both patients and HCPs saw the benefit of the PHR in terms of performance expectancy, by gaining more insight into health and health data, but challenges remained regarding effort expectancy, such as log-in issues and experiencing difficulties with information retrieval. Conclusion: The feasibility and usability were considered moderate by patients and HCPs; however, potential regarding the performance of the PHR was observed. Implementation challenges, such as the complexity of usage, lowered the adoption of the PHR. The evolving nature of PHRs requires ongoing evaluation and adaptation to optimize their potential benefits. Utilizing a participatory design approach and a dedicated implementation team could help in achieving this optimization, ultimately enhancing their adoption.

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The use of mobile-based personal health record (m-PHR) applications at the hospital level has been minimally studied. This study aimed to investigate the relationship between m-PHR use and quality of care. A cross-sectional study design was employed, analyzing data from 99 hospitals. Two data sources were utilized: a previous m-PHR investigation conducted from 26 May to 30 June 2022 and a hospital evaluation dataset on quality of care. The use of m-PHR applications was measured by the number of m-PHR application downloads. Three independent variables were assessed: quality of care in the use of antibiotic drugs, injection drugs, and polypharmacy with ≥6 drugs. A generalized linear model was used for the analysis. The hospitals providing high-quality care, as evaluated based on the rate of antibiotic prescription (relative risk [RR], 3.328; 95% confidence interval [CI], 1.840 to 6.020; p < 0.001) and polypharmacy (RR, 2.092; 95% CI, 1.027 to 4.261; p = 0.042), showed an increased number of m-PHR downloads. Among the hospital covariates, public foundation status and being part of multi-hospital systems were associated with the number of m-PHR downloads (p < 0.05). This exploratory study found a positive relationship between quality of care and m-PHR use. Hospitals providing high-quality care may also excel in various activities, including m-PHR application use.

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Background: In many countries, health care professionals are legally obliged to share information from electronic health records with patients. However, concerns have been raised regarding the sharing of notes with adolescents in mental health care, and health care professionals have called for recommendations to guide this practice. Objective: The aim was to reach a consensus among authors of scientific papers on recommendations for health care professionals' digital sharing of notes with adolescents in mental health care and to investigate whether staff at child and adolescent specialist mental health care clinics agreed with the recommendations. Methods: A Delphi study was conducted with authors of scientific papers to reach a consensus on recommendations. The process of making the recommendations involved three steps. First, scientific papers meeting the eligibility criteria were identified through a PubMed search where the references were screened. Second, the results from the included papers were coded and transformed into recommendations in an iterative process. Third, the authors of the included papers were asked to provide feedback and consider their agreement with each of the suggested recommendations in two rounds. After the Delphi process, a cross-sectional study was conducted among staff at specialist child and adolescent mental health care clinics to assess whether they agreed with the recommendations that reached a consensus. Results: Of the 84 invited authors, 27 responded. A consensus was reached on 17 recommendations on areas related to digital sharing of notes with adolescents in mental health care. The recommendations considered how to introduce digital access to notes, write notes, and support health care professionals, and when to withhold notes. Of the 41 staff members at child and adolescent specialist mental health care clinics, 60% or more agreed with the 17 recommendations. No consensus was reached regarding the age at which adolescents should receive digital access to their notes and the timing of digitally sharing notes with parents. Conclusions: A total of 17 recommendations related to key aspects of health care professionals' digital sharing of notes with adolescents in mental health care achieved consensus. Health care professionals can use these recommendations to guide their practice of sharing notes with adolescents in mental health care. However, the effects and experiences of following these recommendations should be tested in clinical practice.

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BACKGROUND: Supervised machine learning models have been widely used to predict and get insight into diseases by classifying patients based on personal health records. However, a class imbalance is an obstacle that disrupts the training of the models. In this study, we aimed to address class imbalance with a conditional normalizing flow model, one of the deep-learning-based semi-supervised models for anomaly detection. It is the first introduction of the normalizing flow algorithm for tabular biomedical data. METHODS: We collected personal health records from South Korean citizens (n = 706), featuring genetic data obtained from direct-to-customer service (microarray chip), medical health check-ups, and lifestyle log data. Based on the health check-up data, six chronic diseases were labeled (obesity, diabetes, hypertriglyceridemia, dyslipidemia, liver dysfunction, and hypertension). After preprocessing, supervised classification models and semi-supervised anomaly detection models, including conditional normalizing flow, were evaluated for the classification of diabetes, which had extreme target imbalance (about 2%), based on AUROC and AUPRC. In addition, we evaluated their performance under the assumption of insufficient collection for patients with other chronic diseases by undersampling disease-affected samples. RESULTS: While LightGBM (the best-performing model among supervised classification models) showed AUPRC 0.16 and AUROC 0.82, conditional normalizing flow achieved AUPRC 0.34 and AUROC 0.83 during fifty evaluations of the classification of diabetes, whose base rate was very low, at 0.02. Moreover, conditional normalizing flow performed better than the supervised model under a few disease-affected data numbers for the other five chronic diseases - obesity, hypertriglyceridemia, dyslipidemia, liver dysfunction, and hypertension. For example, while LightGBM performed AUPRC 0.20 and AUROC 0.75, conditional normalizing flow showed AUPRC 0.30 and AUROC 0.74 when predicting obesity, while undersampling disease-affected samples (positive undersampling) lowered the base rate to 0.02. CONCLUSIONS: Our research suggests the utility of conditional normalizing flow, particularly when the available cases are limited, for predicting chronic diseases using personal health records. This approach offers an effective solution to deal with sparse data and extreme class imbalances commonly encountered in the biomedical context.

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BACKGROUND: Patient-accessible electronic health records (PAEHRs) are assumed to enhance the quality of care, expressed in terms of safety, effectiveness, timeliness, person centeredness, efficiency, and equity. However, research on the impact of PAEHRs on the perceived quality of care among parents, children, and adolescents is largely lacking. In the Netherlands, a PAEHR (Iuvenelis) was developed for preventive child health care and youth care. Parents and adolescents had access to its full content, could manage appointments, ask questions, and comment on written reports. OBJECTIVE: This study aims to assess whether and how using this PAEHR contributes to perceived quality of care from a client's perspective. METHODS: We chose a qualitative design with a phenomenological approach to explore how parents and adolescents perceived the impact of using a PAEHR on quality of care. In-depth interviews that simultaneously included 1 to 3 people were conducted in 2021. In total, 20 participants were included in the study, representing parents and adolescents, both sexes, different educational levels, different native countries, and all participating municipalities. Within this group, 7 of 13 (54%) parents had not previously been informed about the existence of a client portal. Their expectations of using the client portal, in relation to quality of care, were discussed after a demonstration of the portal. RESULTS: Parents and adolescents perceived that using Iuvenelis contributed to the quality of care because they felt better informed and more involved in the care process than before the introduction of Iuvenelis. Moreover, they experienced more control over their health data, faster and simpler access to their health information, and found it easier to manage appointments or ask questions at their convenience. Parents from a migratory background, among whom 6 of 7 (86%) had not previously been informed about the portal, expected that portal access would enhance their understanding of and control over their care processes. The parents expressed concerns about equity because parents from a migratory background might have less access to the service. Nevertheless, portal usability was regarded as high. Furthermore, both parents and adolescents saw room for improvement in the broader interdisciplinary use of Iuvenelis and the quality of reporting. CONCLUSIONS: Using Iuvenelis can contribute to the client-experienced quality of care, more specifically to perceived person centeredness, timeliness, safety, efficiency, and integration of care. However, some quality aspects, such as equity, still need addressing. In general, client information about the portal needs to be improved, specifically focusing on people in vulnerable circumstances, such as those from migratory backgrounds. In addition, to maximize the potential benefit of using Iuvenelis, stimulating a person-centered attitude among professionals is important. Considering the small number of adolescent participants (n=7), adding quantitative data from a structured survey could strengthen the available evidence.

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Objective: To investigate the application of personal health record (PHR) and chronic disease management platform established on the basis of patient portal system (PPS) in managing older adults with coronary heart disease and to examine the effect on patients' self-care ability, coping mode, and quality of life. Methods: A total of 532 elderly patients with coronary heart disease were included in the study. All the participants enrolled were admitted to a tertiary-care hospital between January 2019 and June 2021. They were randomly assigned to the study group (269 cases) and the control group (263 cases). Patients in the control group were discharged with the routine discharge procedures and received the routine follow-up care. On the other hand, patients in the study group were discharged and followed up through the PHR and chronic disease management platform established on the basis of PPS. After 6 months, 12 months, and 18 months of patient management, the Exercise of Self-Care Agency (ESCA) Scale, Medical Coping Modes Questionnaire (MCMQ) and Seattle Angina Questionnaire (SAQ) were used to evaluate the patients' self-care ability, coping mode, and quality of life, respectively. The patient management effects of the two groups were analyzed. Results: Before the management programs started, there was no statistically significant difference in the scores for the scales between the two groups of patients. After 6 months, 12 months, and 18 months of patient management, the ESCA scores of both groups were higher than those before patient management started (P<0.05). Facing scores in the MCMQ of both groups were higher than those before patient management started (P<0.05), while the scores for avoidance and yielding were lower than those before patient management started (P<0.05). The SAQ scores of both groups were higher than those before patient management started (P<0.05). After 6 months, 12 months and 18 months of patient management, the ESCA scores of the study group were always higher compared with those of the control group (P<0.05). The facing score of the study group was higher, while the scores for avoidance and yielding were lower compared with those of the control group (P<0.05). The SAQ scores of the study group were higher compared with those of the control group (P<0.05). The medication compliance rate in the study group (83.27%) was higher than that in the control group (69.96%) (P<0.05). The incidence of adverse cardiovascular events in the study group (4.09%) was lower than that in the control group (10.27%) (P<0.05). The average times of emergency treatment and readmission in the study group were lower compared with those of the control group (P<0.05). The patient satisfaction score of the study group was higher compared with that of the control group (P<0.05). Conclusion: The PHR and chronic disease management platform established on the basis of PPS can increase the convenient access to medical care among elderly patients with coronary heart disease, which is conducive to improving their self-care ability, coping mode, and quality of life. In addition, the patient management effect is good.

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BACKGROUND: Understanding patient preference regarding taking tablet or capsule formulations plays a pivotal role in treatment efficacy and adherence. Therefore, these preferences should be taken into account when designing formulations and prescriptions. OBJECTIVE: This study investigates the factors affecting patient preference in patients who have difficulties swallowing large tablets or capsules and aims to identify appropriate sizes for tablets and capsules. METHODS: A robust data set was developed based on a questionnaire survey conducted from December 1, 2022, to December 7, 2022, using the harmo smartphone app operated by harmo Co, Ltd. The data set included patient input regarding their tablet and capsule preferences, personal health records (including dispensing history), and drug formulation information (available from package inserts). Based on the medication formulation information, 6 indices were set for each of the tablets or capsules that were considered difficult to swallow owing to their large size and concomitant tablets or capsules (used as controls). Receiver operating characteristic (ROC) analysis was used to evaluate the performance of each index. The index demonstrating the highest area under the curve of the ROC was selected as the best index to determine the tablet or capsule size that leads to swallowing difficulties. From the generated ROCs, the point with the highest discriminative performance that maximized the Youden index was identified, and the optimal threshold for each index was calculated. Multivariate logistic regression analysis was performed to identify the risk factors contributing to difficulty in swallowing oversized tablets or capsules. Additionally, decision tree analysis was performed to estimate the combined risk from several factors, using risk factors that were significant in the multivariate logistic regression analysis. RESULTS: This study analyzed 147 large tablets or capsules and 624 control tablets or capsules. The "long diameter + short diameter + thickness" index (with a 21.5 mm threshold) was identified as the best indicator for causing swallowing difficulties in patients. The multivariate logistic regression analysis (including 132 patients with swallowing difficulties and 1283 patients without) results identified the following contributory risk factors: aged <50 years (odds ratio [OR] 1.59, 95% CI 1.03-2.44), female (OR 2.54, 95% CI 1.70-3.78), dysphagia (OR 3.54, 95% CI 2.22-5.65), and taking large tablets or capsules (OR 9.74, 95% CI 5.19-18.29). The decision tree analysis results suggested an elevated risk of swallowing difficulties for patients with taking large tablets or capsules. CONCLUSIONS: This study identified the most appropriate index and threshold for indicating that a given tablet or capsule size will cause swallowing difficulties, as well as the contributory risk factors. Although some sampling biases (eg, only including smartphone users) may exist, our results can guide the design of patient-friendly formulations and prescriptions, promoting better medication adherence.

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BACKGROUND: Despite strong and growing interest in ending the ongoing opioid health crisis, there has been limited success in reducing the prevalence of opioid addiction and the number of deaths associated with opioid overdoses. Further, 1 explanation for this is that existing interventions target those who are opiate-dependent but do not prevent opioid-naïve patients from becoming addicted. OBJECTIVE: Leveraging behavioral economics at the patient level could help patients successfully use, discontinue, and dispose of their opioid medications in an acute pain setting. The primary goal of this project is to evaluate the effect of the 3 versions of the Opioid Management for You (OPY) tool on measures of opioid use relative to the standard of care by leveraging a pragmatic randomized controlled trial (RCT). METHODS: A team of researchers from the Center for Learning Health System Sciences (CLHSS) at the University of Minnesota partnered with M Health Fairview to design, build, and test the 3 versions of the OPY tool: social influence, precommitment, and testimonial version. The tool is being built using the Epic Care Companion (Epic Inc) platform and interacts with the patient through their existing MyChart (Epic Systems Corporation) personal health record account, and Epic patient portal, accessed through a phone app or the MyChart website. We have demonstrated feasibility with pilot data of the social influence version of the OPY app by targeting our pilot to a specific cohort of patients undergoing upper-extremity procedures. This study will use a group sequential RCT design to test the impact of this important health system initiative. Patients who meet OPY inclusion criteria will be stratified into low, intermediate, and high risk of opiate use based on their type of surgery. RESULTS: This study is being funded and supported by the CLHSS Rapid Prospective Evaluation and Digital Technology Innovation Programs, and M Health Fairview. Support and coordination provided by CLHSS include the structure of engagement, survey development, data collection, statistical analysis, and dissemination. The project was initially started in August 2022. The pilot was launched in February 2023 and is still running, with the data last counted in August 2023. The actual RCT is planned to start by early 2024. CONCLUSIONS: Through this RCT, we will test our hypothesis that patient opioid use and diverted prescription opioid availability can both be improved by information delivery applied through a behavioral economics lens via sending nudges directly to the opioid users through their personal health record. TRIAL REGISTRATION: ClinicalTrials.gov NCT06124079; https://clinicaltrials.gov/study/NCT06124079. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52882.

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The exact definition of a personal health record (PHR) is not clear, but it is generally defined as an electronic record of an individual's medical examination results and medication history that can be accurately tracked by the individual and family members. There are many opportunities to obtain personal PHR data in the field of occupational health, and the amount of information in a worker's PHR is enormous. It is necessary in this field to comprehensively integrate and accumulate not only information from medical examinations, but also records of the work environment and work records as personal health information.

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Fookkun® is a pill dispenser in which single doses of several medications intended to be taken simultaneously are sealed in single film bags rolled onto a medication rotating drum. The system makes musical alert sounds when it is time for the patient to take the medications. If the patient misses a dose, a designated contact, such as the patient's child, is alerted. We conducted an experiment monitoring the use of a pill dispenser (Fookkun®) by an older patient. The participant was a 71-year-old woman with diabetes living in a dwelling home. The experiment lasted approximately 6 months. Fookkun® and the prototype data transmitter were installed at the patient's home. Fookkun®'s medication history data are displayed on the electronic medication record book (E-MRB) and the patient's pharmacist checks the patient's medication history on the E-MRB. The Fookkun® was effective in facilitating medication adherence. The pharmacist and the patient's daughter did not need to check the E-MRB because Fookkun® alerted them when the patient missed her medication. We believe that if the medication history data linked between a pill dispenser and an E-MRB can be shared among medical staff, this will contribute to a medical digital transformation in Japan in the future.

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The desire to access personal and high-quality health information electronically is increasing, not only in Canada, but globally. With the advent of the COVID - 19 pandemic the desire and demand for telemedicine and timely access to personal health data such as online laboratory (lab) results has increased substantially. This study examines citizens' perspectives of being provided with high-quality information about a specific lab test (i.e., potassium) in the same display as a trend graph. Therefore, the objective of this study is to test how participants managed this additional information about the context of the test, understood, and applied it. The researchers analyzed the responses of semi-structured interviews with Canadian participants (N=24) using conventional content analysis. This paper examined four themes related to providing complementary information concurrently with lab results in the same display: 1) Benefits of Collocated Information, 2) Information Overload, 3) Misinterpretation, 4) Confusion. This study provided examples of some of the difficulties that the participants faced accessing their lab values online, while navigating and discerning complimentary high-quality health information available in their patient portal.

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BACKGROUND: Patient-provider communication can be assessed by the patient-centered communication (PCC) score. With rapid development of electronic health (eHealth) information usage, we are uncertain of their role in PCC. Our study aims to determine the association between PCC and eHealth usage with the analysis of national representative survey data. METHODS: This is a cross sectional analysis using the Health Information National Trends Survey 5 (HINTS 5) cycle 1 to cycle 4 data (2017-2020). Seven specific questions were used for PCC assessment, and eHealth usage was divided into two types (private-eHealth and public-eHealth usage). A multivariate logistic regression was performed to determine the association between PCC and eHealth usage after the adjustment of other social, demographic, and clinical variables. RESULTS: Our study analyzed a total of 13,055 unweighted participants representing a weighted population of 791,877,728. Approximately 43% of individuals used private eHealth and 19% used public eHealth. The adjusted odds ratio (AOR) of private-eHealth usage associated with positive PCC was 1.17 (95% CI 1.02-1.35, p = 0.027). The AOR of public-eHealth usage associated with positive PCC was 0.84 (95% CI 0.71-0.99, p = 0.043). CONCLUSION: Our study found that eHealth usage association with PCC varies. Private-eHealth usage was positively associated with PCC, whereas public-eHealth usage was negatively associated with PCC.

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Background: Although digital maternity records (DMRs) have been evaluated in the past, no previous work investigated usability or acceptance through an observational usability study. Objective: The primary objective was to assess the usability and perception of a DMR smartphone app for pregnant women. The secondary objective was to assess personal preferences and habits related to online information searching, wearable data presentation and interpretation, at-home examination, and sharing data for research purposes during pregnancy. Methods: A DMR smartphone app was developed. Key features such as wearable device integration, study functionalities (eg, questionnaires), and common pregnancy app functionalities (eg, mood tracker) were included. Women who had previously given birth were invited to participate. Participants completed 10 tasks while asked to think aloud. Sessions were conducted via Zoom. Video, audio, and the shared screen were recorded for analysis. Task completion times, task success, errors, and self-reported (free text) feedback were evaluated. Usability was measured through the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). Semistructured interviews were conducted to explore the secondary objective. Results: A total of 11 participants (mean age 34.6, SD 2.2 years) were included in the study. A mean SUS score of 79.09 (SD 18.38) was achieved. The app was rated "above average" in 4 of 6 UEQ categories. Sixteen unique features were requested. We found that 5 of 11 participants would only use wearables during pregnancy if requested to by their physician, while 10 of 11 stated they would share their data for research purposes. Conclusions: Pregnant women rely on their medical caregivers for advice, including on the use of mobile and ubiquitous health technology. Clear benefits must be communicated if issuing wearable devices to pregnant women. Participants that experienced pregnancy complications in the past were overall more open toward the use of wearable devices in pregnancy. Pregnant women have different opinions regarding access to, interpretation of, and reactions to alerts based on wearable data. Future work should investigate personalized concepts covering these aspects.

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Recent decades have brought enormous progress in both genetics and genomics, as well as in information technology (IT). The sequence of the human genome is now known, and although our knowledge is far from complete, great progress has been made in understanding how the genome works. With the developments in storage capacity, artificial intelligence, and learning algorithms, we are now able to learn and interpret complex systems such as the human genome in a very short time. Perhaps the most important goal of learning about the human genome is to understand diseases better: how they develop; how their processes can be prevented or slowed down; and after diseases have developed, how they can be cured or their symptoms alleviated. The vast majority of diseases have a genetic background, i.e., genes, sequence variations, and gene-gene interactions play a role in most diseases to a greater or lesser extent. Accordingly, the first step is to discover which genes, or genomic variants, cause or contribute to the development of a particular disease in a given patient. Given that an individual's genome remains virtually unchanged throughout their life (with one or two exceptions, such as in the case of cancer, which is caused by somatic mutations), it might be considered advantageous to sequence the genome of every person at birth. In this paper, we set out to show the possible benefits of sequencing the entire genome of every human being at birth, while also discussing the main arguments against it.

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Blockchain technology includes numerous elements such as distributed ledgers, decentralization, authenticity, privacy, and immutability. It has progressed past the hype to find actual use cases in industries like healthcare. Blockchain is an emerging area that relies on a consensus algorithm and the idea of a digitally distributed ledger to eliminate any intermediary risks. By enabling them to trace data provenance and any changes made, blockchain technology can enable different healthcare stakeholders to share access to their networks without violating data security and integrity. The healthcare industry faces challenges like fragmented data, security and privacy concerns, and interoperability issues. Blockchain technology offers potential solutions by ensuring secure, tamper-proof storage across multiple network nodes, improving interoperability and patient privacy. Encrypting patient data further enhances security and reduces unauthorized access concerns. Blockchain technology, deployed over the Internet, can potentially use the current healthcare data by using a patient-centric approach and removing the intermediaries. This paper discusses the effective utilization of blockchain technology in the healthcare industry. In contrast to other applications, the exoteric evaluation in this paper shows that the innovative technology called blockchain technology has a major role to play in the existing and future applications of the healthcare industry and has significant benefits.

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Discharge letters could be sent to the patients. This poster explores pros and cons of such a procedure.

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BACKGROUND: Electronic health record (EHR)-integrated digital personal health records (PHRs) via Fast Healthcare Interoperability Resources (FHIR) are promising digital health tools to support care coordination (CC) for children and youth with special health care needs but remain widely unadopted; as their adoption grows, mixed methods and implementation research could guide real-world implementation and evaluation. OBJECTIVE: This study (1) evaluates the feasibility of an FHIR-enabled digital PHR app for CC for children and youth with special health care needs, (2) characterizes determinants of implementation, and (3) explores associations between adoption and patient- or family-reported outcomes. METHODS: This nonrandomized, single-arm, prospective feasibility trial will test an FHIR-enabled digital PHR app's use among families of children and youth with special health care needs in primary care settings. Key app features are FHIR-enabled access to structured data from the child's medical record, families' abilities to longitudinally track patient- or family-centered care goals, and sharing progress toward care goals with the child's primary care provider via a clinician dashboard. We shall enroll 40 parents or caregivers of children and youth with special health care needs to use the app for 6 months. Inclusion criteria for children and youth with special health care needs are age 0-16 years; primary care at a participating site; complex needs benefiting from CC; high hospitalization risk in the next 6 months; English speaking; having requisite technology at home (internet access, Apple iOS mobile device); and an active web-based EHR patient portal account to which a parent or caregiver has full proxy access. Digital prescriptions will be used to disseminate study recruitment materials directly to eligible participants via their existing EHR patient portal accounts. We will apply an intervention mixed methods design to link quantitative and qualitative (semistructured interviews and family engagement panels with parents of children and youth with special health care needs) data and characterize implementation determinants. Two CC frameworks (Pediatric Care Coordination Framework; Patient-Centered Medical Home) and 2 evaluation frameworks (Consolidated Framework for Implementation Research; Technology Acceptance Model) provide theoretical foundations for this study. RESULTS: Participant recruitment began in fall 2022, before which we identified >300 potentially eligible patients in EHR data. A family engagement panel in fall 2021 generated formative feedback from family partners. Integrated analysis of pretrial quantitative and qualitative data informed family-centered enhancements to study procedures. CONCLUSIONS: Our findings will inform how to integrate an FHIR-enabled digital PHR app for children and youth with special health care needs into clinical care. Mixed methods and implementation research will help strengthen implementation in diverse clinical settings. The study is positioned to advance knowledge of how to use digital health innovations for improving care and outcomes for children and youth with special health care needs and their families. TRIAL REGISTRATION: ClinicalTrials.gov NCT05513235; https://clinicaltrials.gov/study/NCT05513235. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46847.