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Mogamulizumab is a humanized antibody targeting CC chemokine receptor 4 (CCR4). This post‐marketing surveillance was conducted in Japan as a regulatory requirement from 2014 to 2020 to ensure the safety and effectiveness of mogamulizumab in patients with relapsed or refractory (r/r) CCR4‐positive peripheral T‐cell lymphoma (PTCL) or r/r cutaneous T‐cell lymphoma (CTCL). Safety and effectiveness data were collected for up to 31 weeks after treatment initiation. A total of 142 patients were registered; safety was evaluated in 136 patients. The median number of doses was 8.0 (range, 1–18). The main reasons for treatment termination were insufficient response (22.1%) and adverse events (13.2%). The frequency of any grade adverse drug reaction was 57.4%, including skin disorders (26.5%), infections and immune system disorders (16.2%), and infusion‐related reactions (13.2%). Graft‐versus‐host disease, grade 2, developed in one of two patients who underwent allogeneic‐hematopoietic stem cell transplantation after receiving mogamulizumab. Effectiveness was evaluated in 131 patients (103 with PTCL; 28 with CTCL). The best overall response rate was 45.8% (PTCL, 47.6%; CTCL, 39.3%). At week 31, the survival rate was 69.0% (95% confidence interval, 59.8%–76.5%) [PTCL, 64.4% (54.0%–73.0%); CTCL, 90.5% (67.0%–97.5%)]. Safety and effectiveness were comparable between patients <70 and ≥ 70 years old and between those with relapsed and refractory disease. The safety and effectiveness of mogamulizumab for PTCL and CTCL in the real world were comparable with the data reported in previous clinical trials. Clinical Trial Registration [ABSTRACT FROM AUTHOR]

Diabetes care has experienced great changes due to technological advancements. Recent notable innovations in diabetes technology include real-time continuous glucose monitors, automated insulin delivery systems, insulin patches, and health management applications based on smartwatches and smartphones. Along with the development of these digital devices, there have been efforts to integrate patient-generated health data, including blood glucose/blood pressure, diet, exercise, and sleep, with hospital electronic health records for direct use in diabetes care. In addition, advancements and cost reductions in omics technologies, including genomics, are expected to enable personalized diagnosis, treatment, and prevention of diabetes through modeling based on vast amounts of related data, from multi-omics to patient-generated health data, using artificial intelligence. This is ultimately expected to advance precision medicine for diabetes in the future. [ABSTRACT FROM AUTHOR]

Objective: To present the retrieval and analysis of clinical data from anamneses in the. Electronic Health Record (EHR), referred to in this research as Interface Terminology. Methods:The clinical data collection process in this research was carried out on electronic patient records from a private hospital. The data sample consisted of 18,256 anamneses from the field of gynecology in 2018. The clinical data was retrieved through Natural Language Processing using the Python language. The most frequent terms related to clinical data were analysed, such as abbreviations and acronyms, stop words, procedures, and n-grams. Results: Clinical data has the potential to be reused for scientific production, epidemiological profiling and in the creation of dictionaries and enrichment of controlled vocabularies for PEP and other health information systems. They are also important in defining algorithms for information retrieval. As a result, a repository was created in the OSF (https://osf.io/de43a/) containing spreadsheets and tables with clinical data for reuse in the delimitation of algorithms, as well as the creation of a word cloud to identify the most frequent terms in electronic patient records in the field of Gynecology. The algorithms used to retrieve the information were made available on the GitHub digital repository. Conclusions: Clinical data is information about the patient, used for care purposes, hospital administrative issues, allowing research related to the patient's health and illness. The Interface Terminology, exemplified in the research hospital's EHR, presented a diversity of clinical data in the anamneses. [ABSTRACT FROM AUTHOR]

BackgroundTailored Antiplatelet Initiation to Lessen Outcomes Due to Decreased Clopidogrel Response after Percutaneous Coronary Intervention (TAILOR-PCI) is the largest cardiovascular genotype-based randomized pragmatic trial (NCT#01742117) to evaluate the role of genotype-guided selection of oral P2Y12 inhibitor therapy in improving ischemic outcomes after PCI. The trial has been extended from the original 12- to 24-month follow-up, using study coordinator-initiated telephone visits. TAILOR-PCI Digital Study tests the feasibility of extending the trial follow-up in a subset of patients for up to 24 months using state-of-the-art digital solutions. The rationale, design, and approach of extended digital study of patients recruited into a large, international, multi-center clinical trial has not been previously described.MethodsA total of 930 patients from U.S. and Canadian sites previously enrolled in the 5,302 patient TAILOR-PCI trial within 23 months of randomization are invited by mail to the Digital Study website (http://tailorpci.eurekaplatform.org) and by up to 2 recruiting telephone calls. Eureka, a direct-to-participant digital research platform, is used to consent and collect prospective data on patients for the digital study. Patients are asked to answer health-related surveys at fixed intervals using the Eureka mobile app and or desktop platform. The likelihood of patients enrolled in a randomized clinical trial transitioning to a registry using digital technology, the reasons for nonparticipation and engagement rates are evaluated. To capture hospitalizations, patients may optionally enable geofencing, a process that allows background location tracking and triggering of surveys if a hospital visit greater than 4 hours is detected. In addition, patients answer digital hospitalization surveys every month. Hospitalization data received from the Digital Study will be compared to data collected from study coordinator telephone visits during the same time frame.ConclusionsThe TAILOR-PCI Digital Study evaluates the feasibility of transitioning a large multicenter randomized clinical trial to a digital registry. The study could provide evidence for the ability of digital technology to follow clinical trial patients and to ascertain trial-related events thus also building the foundation for conducting digital clinical trials. Such a digital approach may be especially pertinent in the era of COVID-19.

Background: Brief counseling and self-monitoring with a pedometer are common practice within primary care for physical activity promotion. It is unknown how high-tech electronic activity monitors compare to pedometers within this setting. This study aimed to investigate the outcomes, through effect size estimation, of an electronic activity monitor-based intervention to increase physical activity and decrease cardiovascular disease risk. Method: The pilot randomized controlled trial was pre-registered online at clinicaltrials.gov (NCT02554435). Forty overweight, sedentary participants 55-74 years of age were randomized to wear a pedometer or an electronic activity monitor for 12 weeks. Physical activity was measured objectively for 7 days at baseline and follow-up by a SenseWear monitor and cardiovascular disease risk was estimated by the Framingham risk calculator. Results: Effect sizes for behavioral and health outcomes ranged from small to medium. While these effect sizes were favorable to the intervention group for physical activity (PA) (d = 0.78) and general health (d = 0.39), they were not favorable for measures. Conclusion: The results of this pilot trial show promise for this low-intensity intervention strategy, but large-scale trials are needed to test its efficacy.

Background: The expert panel that conducted the Assessing Surgical Site Infection Surveillance Technologies (ASSIST) project elaborates on the key findings of the health technologies assessment (HTA) report in a series of articles addressing topics from workflow challenges to implementation strategies to new big data analytics tailored to incorporate serial patient-generated health data (PGHD). Conclusion: By reporting on the methodology, with an emphasis on stakeholder engagement, the ASSIST investigators provide the basis for a future deep dive into the next phase of PGHD integration into surgical site infection (SSI) surveillance.

BACKGROUND:The increase in availability of patient data through consumer health wearable devices and mobile phone sensors provides opportunities for mental health treatment beyond traditional self-report measurements. Previous studies have suggested that wearables can be effectively used to benefit the physical health of people with mental health issues, but little research has explored the integration of wearable devices into mental health care. As such, early research is still necessary to address factors that might impact integration including patients' motivations to use wearables and their subsequent data. OBJECTIVE:The aim of this study was to gain an understanding of patients' motivations to use or not to use wearables devices during an intensive treatment program for post-traumatic stress disorder (PTSD). During this treatment, they received a complementary Fitbit. We investigated the following research questions: How did the veterans in the intensive treatment program use their Fitbit? What are contributing motivators for the use and nonuse of the Fitbit? METHODS:We conducted semistructured interviews with 13 veterans who completed an intensive treatment program for PTSD. We transcribed and analyzed interviews using thematic analysis. RESULTS:We identified three major motivations for veterans to use the Fitbit during their time in the program: increase self-awareness, support social interactions, and give back to other veterans. We also identified three major reasons certain features of the Fitbit were not used: lack of clarity around the purpose of the Fitbit, lack of meaning in the Fitbit data, and challenges in the veteran-provider relationship. CONCLUSIONS:To integrate wearable data into mental health treatment programs, it is important to understand the patient's perspectives and motivations in using wearables. We also discuss how the military culture and PTSD may have contributed to our participants' behaviors and attitudes toward Fitbit usage. We conclude with possible approaches for integrating patient-generated data into mental health treatment settings that may address the challenges we identified.

People with diabetes-related foot ulcers (DFUs) need to perform self-care consistently over many months to promote healing and to mitigate risks of hospitalisation and amputation. However, during that time, improvement in their DFU can be hard to detect. Hence, there is a need for an accessible method to self-monitor DFUs at home. We developed a new mobile phone app, "MyFootCare", to self-monitor DFU healing progression from photos of the foot. The aim of this study is to evaluate the engagement and perceived value of MyFootCare for people with a plantar DFU over 3 months' duration. Data are collected through app log data and semi-structured interviews (weeks 0, 3, and 12) and analysed through descriptive statistics and thematic analysis. Ten out of 12 participants perceive MyFootCare as valuable to monitor progress and to reflect on events that affected self-care, and seven participants see it as potentially valuable to enhance consultations. Three app engagement patterns emerge: continuous, temporary, and failed engagement. These patterns highlight enablers for self-monitoring (such as having MyFootCare installed on the participant's phone) and barriers (such as usability issues and lack of healing progress). We conclude that while many people with DFUs perceive app-based self-monitoring as valuable, actual engagement can be achieved for some but not for all people because of various facilitators and barriers. Further research should target improving usability, accuracy and sharing with healthcare professionals and test clinical outcomes when using the app. [ABSTRACT FROM AUTHOR]

Background: The advancement of information and communication technologies has spurred a growing interest in and increased applications of patient-generated health data (PGHD). In particular, PGHD may be promising for older adults with cancer who have increased survival rates and experience a variety of symptoms., Objective: This scoping review aimed to identify the characteristics of research on PGHD as applied to older adults with cancer and to assess the current use of PGHD., Methods: Guided by Arksey and O'Malley as well as the JBI (Joanna Briggs Institute) methodology for scoping reviews, 6 electronic databases were searched: PubMed, Embase, CINAHL, Cochrane Library, Scopus, and Web of Science. In addition, the reference lists of the selected studies were screened to identify gray literature. The researchers independently screened the literature according to the predefined eligibility criteria. Data from the selected studies were extracted, capturing study, participant, and PGHD characteristics., Results: Of the 1090 identified studies, 88 were selected. The publication trend gradually increased, with a majority of studies published since 2017 (69/88, 78%). Almost half of the studies were conducted in North America (38/88, 43%), followed by Europe (30/88, 34%). The most common setting in which the studies were conducted was the participant's home (69/88, 78%). The treatment status varied; the median sample size was 50 (IQR 33.8-84.0). The devices that were used to measure the PGHD were classified as research-grade wearable devices (57/113, 50.4%), consumer-grade wearable devices (28/113, 24.8%), or smartphones or tablet PCs for mobile apps (23/113, 20.4%). More than half of the studies measured physical activity (69/123, 56.1%), followed by patient-reported outcomes (23/123, 18.7%), vital signs (13/123, 10.6%), and sleep (12/123, 9.8%). The PGHD were mainly collected passively (63/88, 72%), and active collection methods were used from 2015 onward (20/88, 23%). In this review, the stages of PGHD use were classified as follows: (1) identification, monitoring, review, and analysis (88/88, 100%); (2) feedback and reporting (32/88, 39%); (3) motivation (30/88, 34%); and (4) education and coaching (19/88, 22%)., Conclusions: This scoping review provides a comprehensive summary of the overall characteristics and use stages of PGHD in older adults with various types and stages of cancer. Future research should emphasize the use of PGHD, which interacts with patients to provide patient-centered care through patient engagement. By enhancing symptom monitoring, enabling timely interventions, and promoting patient involvement, PGHD have the potential to improve the well-being of older adults with cancer, contributing to better health management and quality of life. Therefore, our findings may provide valuable insights into PGHD that health care providers and researchers can use for geriatric cancer care., Trial Registration: Open Science Framework Registry OSF.IO/FZRD5; https://doi.org/10.17605/OSF.IO/FZRD5., (©Yesol Kim, Geonah Kim, Hyeonmi Cho, Yeonju Kim, Mona Choi. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.02.2025.)

Health data generated by apps and devices are increasingly popular and expected to affect various aspects of doctor-patient relationships. No longer confined to medically authorised and certified health technologies, a range of biomedical data-from heart rate to blood pressure or oxygen saturation-are captured and processed by consumer health devices. This article outlines different responses of physicians to patients collecting data with popular consumer devices and considers how the data may challenge or reify medical authority. Based on semi-structured interviews with doctors and chronically ill patients in Germany from 2021 to 2023, we compare cases from diabetes, sleep disorders, cardiovascular conditions, obesity and ME/CFS and explore when, how and for what reasons different medical specialists consider patient-generated data (PGD) from consumer devices in outpatient settings. Their response registers vary: whereas some physicians reject PGD that seem to compete with their diagnostic activities, others tolerate the data (collection), whereas still others more readily include them into their diagnostic practices. This suggests nuanced strategies for navigating the demarcation between accepting or rejecting 'uninvited' participation through PGD from consumer apps and devices., (© 2024 The Author(s). Sociology of Health & Illness published by John Wiley & Sons Ltd on behalf of Foundation for the Sociology of Health & Illness.)

Background: Mobile devices offer an emerging opportunity for research participants to contribute person-generated health data (PGHD). There is little guidance, however, on how to best report findings from studies leveraging those data. Thus, there is a need to characterize current reporting practices so as to better understand the potential implications for producing reproducible results., Objective: The primary objective of this scoping review was to characterize publications' reporting practices for research that collects PGHD using mobile devices., Methods: We comprehensively searched PubMed and screened the results. Qualifying publications were classified according to 6 dimensions-1 covering key bibliographic details (for all articles) and 5 covering reporting criteria considered necessary for reproducible and responsible research (ie, "participant," "data," "device," "study," and "ethics," for original research). For each of the 5 reporting dimensions, we also assessed reporting completeness., Results: Out of 3602 publications screened, 100 were included in this review. We observed a rapid increase in all publications from 2016 to 2021, with the largest contribution from US authors, with 1 exception, review articles. Few original research publications used crowdsourcing platforms (7%, 3/45). Among the original research publications that reported device ownership, most (75%, 21/28) reported using participant-owned devices for data collection (ie, a Bring-Your-Own-Device [BYOD] strategy). A significant deficiency in reporting completeness was observed for the "data" and "ethics" dimensions (5 reporting factors were missing in over half of the research publications). Reporting completeness for data ownership and participants' access to data after contribution worsened over time., Conclusions: Our work depicts the reporting practices in publications about research involving PGHD from mobile devices. We found that very few papers reported crowdsourcing platforms for data collection. BYOD strategies are increasingly popular; this creates an opportunity for improved mechanisms to transfer data from device owners to researchers on crowdsourcing platforms. Given substantial reporting deficiencies, we recommend reaching a consensus on best practices for research collecting PGHD from mobile devices. Drawing from the 5 reporting dimensions in this scoping review, we share our recommendations and justifications for 9 items. These items require improved reporting to enhance data representativeness and quality and empower participants., (©Shanshan Song, Micaela Ashton, Rebecca Hahn Yoo, Zoljargal Lkhagvajav, Robert Wright, Debra J H Mathews, Casey Overby Taylor. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.01.2025.)

Objectives: Patient-Generated Health Data (PGHD) is increasingly influential in therapy and diagnostic decisions. PGHD should be integrated into electronic health records (EHR) to maximize its utility. This study evaluates the openEHR Reference Model (RM) compatibility with the DH-Convener initiative's modules (Data Collection Module and Data Connector Module) as a potential concept for standardizing PGHD across wearable health devices, focusing on achieving interoperability., Materials and Methods: The study analyzes various types of PGHD, assessing the data formats and structures used by wearable tools. We evaluate openEHR RM specification with our initiative, DH-Convenor, focusing on PGHD semantic interoperability challenges. We evaluated current Archetypes and Templates that are now created and exist on openEHR Clinical Knowledge Management (CKM) and mapped them to our requirements. The DH-Convener modules are examined for their compatibility in standardizing PGHD integration into openEHR clinical workflows and compared with other existing standards for flexibility, scalability, and interoperability., Results: The findings indicate that the diversity in data formats across wearable tools and openEHR shows strong potential as unifying data models based on the DH-Convener's modules. It supports a wide range of PGHD types in existing archetypes and aligns well with our initiative's requirements for storing PGHD, enabling more seamless integration into EHR systems., Discussion: Integrating PGHD into EHR is crucial for personalized healthcare, but inconsistent device formats hinder interoperability. The DH-Convener leverages openEHR to provide a strong solution, though stakeholder collaboration remains essential. Our initiative demonstrates openEHR's ability to ensure consistency, particularly in Europe., Conclusion: We aligned the openEHR layers with the DH-Convener modules, demonstrating openEHR's compatibility for storing PGHD and supporting interoperability goals, such as standardized storage and seamless data transfer to Austria's national EHR. Future efforts should prioritize promoting these models and ensuring their adaptability to emerging wearable devices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Patients' health records have the potential to include patient generated health data (PGHD), which can aid in the provision of personalized care. Access to these data can allow healthcare professionals to receive additional information that will assist in decision-making and the provision of additional support. Given the diverse sources of PGHD, this review aims to provide evidence on PGHD integration with electronic health records (EHR), models and standards for PGHD exchange with EHR, and PGHD-EHR policy design and development. The review also addresses governance and socio-technical considerations in PGHD management. Databases used for the review include PubMed, Scopus, ScienceDirect, IEEE Xplore, SpringerLink and ACM Digital Library. The review reveals the significance, but current deficiency, of provenance, trust and contextual information as part of PGHD integration with EHR. Also, we find that there is limited work on data quality, and on new data sources and associated data elements, within the design of existing standards developed for PGHD integration. New data sources from emerging technologies like mixed reality, virtual reality, interactive voice response system, and social media are rarely considered. The review recommends the need for well-developed designs and policies for PGHD-EHR integration that promote data quality, patient autonomy, privacy, and enhanced trust.

Technological advancements have introduced wearable and passive monitoring tools that can capture aspects of daily living, health and well-being in homes and communities. Personal Health Informatics is the study of any information system, tool or platform designed for individuals and their families/ communities with the goal to facilitate decision making, access to information, education, managing health, prevent disease, and improve communication whereby the individual patient or consumer is the end user whose needs and preferences inform the design and implementation of the system. Examples of such tools include digital phenotyping, consumer genomics and smart homes. Such platforms capture patient generated health data and allow for proactive models of care, whereby patterns or trends are analyzed to identify opportunities for tailored interventions. Future research needs to address the impact of personal health informatics systems on health outcomes, shared decision making and patient empowerment. We discuss new roles and opportunities that arise with the growth of this field, including the role of the personal health data navigator who can guide and assist patients and families in navigating the complex digital landscape in order to most effectively utilize inclusive personal health informatics tools. [ABSTRACT FROM AUTHOR]

BACKGROUND:Chronic diseases, including diabetes mellitus, are the leading cause of mortality and disability in the United States. Current solutions focus primarily on diagnosis and pharmacological treatment, yet there is increasing evidence that patient-centered models of care are more successful in improving and addressing chronic disease outcomes. OBJECTIVE:The objective of this clinical trial is to evaluate the impact of a mobile health (mHealth) enabled nurse health coaching intervention on self-efficacy among adults with type-2 diabetes mellitus. METHODS:A randomized controlled trial was conducted at an academic health system in Northern California. A total of 300 participants with type-2 diabetes were scheduled to be enrolled through three primary care clinics. Participants were randomized to either usual care or intervention. All participants received training on use of the health system patient portal. Participants in the intervention arm received six scheduled health-coaching telephone calls with a registered nurse and were provided with an activity tracker and mobile application that integrated data into the electronic health record (EHR) to track their daily activity and health behavior decisions. All participants completed a baseline survey and follow-up surveys at 3 and 9 months. Primary and secondary outcomes include diabetes self-efficacy, hemoglobin A1c (HbA1c), and quality of life measures. RESULTS:Data collection for this trial, funded by the Patient-Centered Outcomes Research Institute, will be completed by December 2017. Results from the trial will be available mid-2018. CONCLUSIONS:This protocol details a patient-centered intervention using nurse health coaching, mHealth technologies, and integration of patient-generated data into the EHR. The aim of the intervention is to enhance self-efficacy and health outcomes by providing participants with a mechanism to track daily activity by offering coaching support to set reasonable and attainable health goals, and by creating a complete feedback loop by bringing patient-generated data into the EHR. TRIAL REGISTRATION:ClinicalTrials.gov NCT02672176; https://clinicaltrials.gov/ct2/show/NCT02672176 (Archived by WebCite at http://www.webcitation.org/6xEQXe1M5).

Objectives An increasing emphasis has been placed on the integration of clinical data and patient-generated health data (PGHD), which are generated outside of hospitals. This study explored the possibility of using standard terminologies to represent PGHD for data integration. Methods We chose the 2020 general health checkup questionnaire of the Korean Health Screening Program as a resource. We divided every component of the questionnaire into entities and values, which were mapped to standard terminologies—Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) version 2020-07-31 and Logical Observation Identifiers Names and Codes (LOINC) version 2.68. Results Eighty-nine items were derived from the 17 questions of the 2020 health examination questionnaire, of which 76 (85.4%) were mapped to standard terms. Fifty-two items were mapped to SNOMED CT and 24 items were mapped to LOINC. Among the items mapped to SNOMED CT, 35 were mapped to pre-coordinated expressions and 17 to post-coordinated expressions. Forty items had one-to-one relationships, and 17 items had one-to-many relationships. Conclusions We achieved a high mapping rate (85.4%) by using both SNOMED CT and LOINC. However, we noticed some issues while mapping the Korean general health checkup questionnaire (i.e., lack of explanations, vague questions, and overly narrow concepts). In particular, items combining two or more concepts into a single item were not appropriate for mapping using standard terminologies. Although it is not the case that all items need to be expressed in standard terminology, essential items should be presented in a way suitable for mapping to standard terminology by revising the questionnaire in the future.

Background: Routine health information systems (RHISs) are crucial to informing decision-making at all levels of the health system. However, the use of RHIS data in low- and middle-income countries (LMICs) is limited due to concerns regarding quality, accuracy, timeliness, completeness and representativeness. Objective: This study systematically reviewed technical, behavioural and organisational/environmental challenges that hinder the use of RHIS data in LMICs and strategies implemented to overcome these challenges. Method: Four electronic databases were searched for studies describing challenges associated with the use of RHIS data and/or strategies implemented to circumvent these challenges in LMICs. Identified articles were screened against inclusion and exclusion criteria by two independent reviewers. Results: Sixty studies met the inclusion criteria and were included in this review, 55 of which described challenges in using RHIS data and 20 of which focused on strategies to address these challenges. Identified challenges and strategies were organised by their technical, behavioural and organisational/environmental determinants and by the core steps of the data process. Organisational/environmental challenges were the most commonly reported barriers to data use, while technical challenges were the most commonly addressed with strategies. Conclusion: Despite the known benefits of RHIS data for health system strengthening, numerous challenges continue to impede their use in practice. Implications: Additional research is needed to identify effective strategies for addressing the determinants of RHIS use, particularly given the disconnect identified between the type of challenge most commonly described in the literature and the type of challenge most commonly targeted for interventions. [ABSTRACT FROM AUTHOR]

Unplanned surgical readmissions pose a challenging problem for the American healthcare system. We propose to combine consumer electronic voice recognition technology with the FHIR standard to create a post-surgical discharge monitoring app to identify and alert physicians to a patient's deteriorating status.

Background: Therapists and their patients increasingly discuss digital data from social media, smartphone sensors, and other online engagements within the context of psychotherapy., Objective: We examined patients' and mental health therapists' experiences and perceptions following a randomized controlled trial in which they both received regular summaries of patients' digital data (eg, dashboard) to review and discuss in session. The dashboard included data that patients consented to share from their social media posts, phone usage, and online searches., Methods: Following the randomized controlled trial, patient (n=56) and therapist (n=44) participants completed a debriefing survey after their study completion (from December 2021 to January 2022). Participants were asked about their experience receiving a digital data dashboard in psychotherapy via closed- and open-ended questions. We calculated descriptive statistics for closed-ended questions and conducted qualitative coding via NVivo (version 10; Lumivero) and natural language processing using the machine learning tool latent Dirichlet allocation to analyze open-ended questions., Results: Of 100 participants, nearly half (n=48, 49%) described their experience with the dashboard as "positive," while the other half noted a "neutral" experience. Responses to the open-ended questions resulted in three thematic areas (nine subcategories): (1) dashboard experience (positive, neutral or negative, and comfortable); (2) perception of the dashboard's impact on enhancing therapy (accountability, increased awareness over time, and objectivity); and (3) dashboard refinements (additional sources, tailored content, and privacy)., Conclusions: Patients reported that receiving their digital data helped them stay "accountable," while therapists indicated that the dashboard helped "tailor treatment plans." Patient and therapist surveys provided important feedback on their experience regularly discussing dashboards in psychotherapy., (© Lauren Southwick, Meghana Sharma, Sunny Rai, Rinad S Beidas, David S Mandell, David A Asch, Brenda Curtis, Sharath Chandra Guntuku, Raina M Merchant. Originally published in JMIR Mental Health (https://mental.jmir.org).)

Objectives: To understand healthcare providers' experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians' use of patientgenerated data from Type 1 diabetes devices., Materials and Methods: This qualitative study was conducted in three phases. In Phase 1, 11 clinicians reviewed data using commercial diabetes platforms in a think-aloud data walkthrough activity followed by semistructured interviews. In Phase 2, GlucoGuide was developed. In Phase 3, the same clinicians reviewed data using GlucoGuide in a think-aloud activity followed by semistructured interviews. Inductive thematic analysis was used to analyze transcripts of Phase 1 and Phase 3 think-aloud activity and interview., Results: 3 high level tasks, 8 sub-tasks, and 4 challenges were identified in Phase 1. In Phase 2, 3 requirements for GlucoGuide were identified. Phase 3 results suggested that clinicians found GlucoGuide easier to use and experienced a lower cognitive burden as compared to the commercial diabetes data reports that were used in Phase 1. Additionally, GlucoGuide addressed the challenges experienced in Phase 1., Discussion: The study suggests that the knowledge of analytical tasks and task-specific visualization strategies in implementing features of data interfaces can result in tools that lower the perceived burden of engaging with data. Additionally, supporting clinicians in contextualizing algorithmic insights by visual analysis of relevant data can positively influence clinicians' willingness to leverage algorithmic support., Conclusion: Task-aligned tools that combine multiple data-driven approaches, such as visualization strategies and algorithmic insights, can improve clinicians' experience in reviewing device data., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

In this case series, we present several examples from participants (2 patients and 1 healthy control) of a 12-week pilot feasibility study to create a digital phenotype of depression (unipolar or bipolar) through active and passive data collection from a smartphone and a wearable device combined with routine clinical care for mood disorders. The selected cases represent real clinical examples that highlight the intrinsic challenges that should be expected when conducting similar studies, including appropriate health data privacy protection, clinical standardization, and interindividual differences in levels of engagement and acceptability of active and passive data collection (ie, self-reported, behavioral, cognitive, and physiological data), particularly with patient-generated data in mobile apps, digital proficiency habituation, and consistent use of wearable devices. In the context of the rapidly growing use of digital technologies in psychiatry, anticipating challenges for the integration of personal mobile devices and smartphone mental health apps as aides to track specific aspects of depressive disorders is critical for a clinically meaningful digital transformation of mood disorders care., Competing Interests: S.B. is a consultant and equity stakeholder in Mental Health Solutions, Inc. M.A.F. reports grant support from Assurex Health, Mayo Foundation, and intellectual property licensed to Chymia LLC with rights to receive future royalties. The remaining authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Rationale: Patients with rheumatoid arthritis (RA) experience a range of symptoms including joint pain and inflammation, stiffness, fatigue, anxiety, and low mood. Similar to patients with other long-term conditions, they may have periods of time when their disease is under control, and times when their condition is less stable, requiring treatment adjustments. The REMORA2 feasibility study explored the implementation of an integrated symptom-tracking system using a smartphone application (app), enabling patients to track day-to-day symptoms. The data was available in the electronic health record to be viewed at subsequent consultations., Aims and Objectives: This paper explores patients' comments on living with RA, and how patient-reported symptom data supports informed interactions as patients and clinicians work together to coproduce meaning from the data., Method: Individual semi-structured interviews were conducted with 21 patients and 7 clinicians, supplemented by nonparticipant observations of 5 clinical appointments. Thematic analysis was used to analyse data from the interviews, with an ethnographic approach used to analyse the observational data., Results: Both clinicians and patients reported the benefits of reviewing the data in the clinic together. This helped inform decisions about pain management and identified patients who might otherwise have dismissed symptoms such as pain, because of their natural inclination to be stoical., Conclusion: Improved insights on the care of RA were generated as patients and clinicians discuss symptom tracking data. This can assist the patient-clinician dyad in the process of two-way learning and shared decision-making on the management of a long-term condition., (© 2024 The Author(s). Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

Background:  Evidence supports using patient-generated blood pressure data for better outcomes in hypertension management. However, obstacles like dealing with home-generated paper data sets and questions of validity slowed the meaningful incorporation of home blood pressure into clinical care. As clinicians value patient data more, reliance on digital health solutions for data collection and shared decision-making grows., Objectives:  The purpose of this study is to evaluate the design and early implementation of an electronic health record (EHR)-based data visualization tool and explore the barriers or facilitators to integrating) patients' home blood pressure data into the electronic workflow in the clinical setting. Findings can inform potential next steps for implementation and provide recommendations for leveraging patient-generated health data (PGHD) in hypertension management., Methods:  We qualitatively explored pre- and early-implementation factors for integrating PGHD into clinicians' EHR interfaces intended to support shared decision-making using the Consolidated Framework for Implementation Research (CFIR). We collected data in the form of notes and transcripts from clinician focus groups, administrative leadership feedback sessions, research team observations, and recurring team meetings. This study took place at a midwestern academic health center., Results:  We identify implementation facilitating factors, adoption considerations, and next steps across CFIR domains focusing on large-scale implementation. Key recommendations include aligning internal and external priorities, empowering champions to facilitate uptake, using intuitive design, and anticipating and planning for unintended consequences., Conclusion:  These findings can guide future efforts to include PGHD in workflows, thus enhancing shared decision-making and laying the groundwork for larger implementations. Understanding the implementation barriers and facilitators to connect PGHD to clinician apps in the EHR workspace can promote their adoption and maintenance., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: Many people are motivated to self-track their health and optimize their well-being through mobile health apps and wearable devices. The diversity and complexity of these systems have evolved over time, resulting in a large amount of data referred to as patient-generated health data (PGHD), which has recently emerged as a useful set of data elements in health care systems around the world. Despite the increased interest in PGHD, clinicians and older adults' perceptions of PGHD are poorly understood. In particular, although some clinician barriers to using PGHD have been identified, such as concerns about data quality, ease of use, reliability, privacy, and regulatory issues, little is known from the perspectives of older adults. Objective: This study aims to explore the similarities and differences in the perceptions of older adults and clinicians with regard to how various types of PGHD can be used to care for older adults. Methods: A mixed methods study was conducted to explore clinicians and older adults' perceptions of PGHD. Focus groups were conducted with older adults and health care providers from the Greater Toronto area and the Kitchener-Waterloo region. The participants were asked to discuss their perceptions of PGHD, including facilitators and barriers. A questionnaire aimed at exploring the perceived usefulness of a range of different PGHD was also embedded in the study design. Focus group interviews were transcribed for thematic analysis, whereas the questionnaire results were analyzed using descriptive statistics. Results: Of the 9 participants, 4 (44%) were clinicians (average age 38.3 years, SD 7 years), and 5 (56%) were older adults (average age 81.0 years, SD 9.1 years). Four main themes were identified from the focus group interviews: influence of PGHD on patient-provider trust, reliability of PGHD, meaningful use of PGHD and PGHD-based decision support systems, and perceived clinical benefits and intrusiveness of PGHD. The questionnaire results were significantly correlated with the frequency of PGHD mentioned in the focus group interviews (r=0.42; P=.03) and demonstrated that older adults and clinicians perceived blood glucose, step count, physical activity, sleep, blood pressure, and stress level as the most useful data for managing health and delivering high-quality care. Conclusions: This embedded mixed methods study generated several important findings about older adults and clinicians' perceptions and perceived usefulness of a range of PGHD. Owing to the exploratory nature of this study, further research is needed to understand the concerns about data privacy, potential negative impact on the trust between older adults and clinicians, data quality and quantity, and usability of PGHD-related technologies for older adults. [ABSTRACT FROM AUTHOR]

Objective: Despite a proliferation of applications (apps) to conveniently collect patient-reported outcomes (PROs) from patients, PRO data are yet to be seamlessly integrated with electronic health records (EHRs) in a way that improves interoperability and scalability. We applied the newly created PRO standards from the Office of the National Coordinator for Health Information Technology to facilitate the collection and integration of standardized PRO data. A novel multitiered architecture was created to enable seamless integration of PRO data via Substitutable Medical Apps and Reusable Technologies on Fast Healthcare Interoperability Resources apps and scaled to different EHR platforms in multiple ambulatory settings.Materials and Methods: We used a standards-based approach to deploy 2 apps that source and surface PRO data in real-time for provider use within the EHR and which rely on PRO assessments from an external center to streamline app and EHR integration.Results: The apps were developed to enable patients to answer validated assessments (eg, a Patient-Reported Outcomes Measurement Information System including using a Computer Adaptive Test format). Both apps were developed to populate the EHR in real time using the Health Level Seven FHIR standard allowing providers to view patients' data during the clinical encounter. The process of implementing this architecture with 2 different apps across 18 ambulatory care sites and 3 different EHR platforms is described.Conclusion: Our approach and solution proved feasible, secure, and time- and resource-efficient. We offer actionable guidance for this technology to be scaled and adapted to promote adoption in diverse ambulatory care settings and across different EHRs. [ABSTRACT FROM AUTHOR]

Person-generated health data (PGHD) are valuable for studying outcomes relevant to everyday living, for obtaining information not otherwise available, for long-term follow-up, and in situations where decisions cannot wait for traditional clinical research to be completed. While there is no dispute that these data are subject to bias, insights gained may be better than having an information void, provided the biases are understood and addressed. People will share information known uniquely to them about exposures that may affect drug tolerance, safety, and effectiveness (eg, nonprescription and complementary medications, alcohol, tobacco, illicit drugs, exercise, etc). Patients may be the best source of safety information when long-term follow-up is needed (eg, the 5- to 15-year follow-up required for some gene therapies). Validation studies must be performed to evaluate what people can accurately report and when supplementary confirmation information is needed. However, PGHD has already proven valuable in quantifying and contrasting COVID-19 vaccine benefits and risks and for evaluating disease transmission and the accuracy of COVID-19 testing. Going forward, PGHD will be used for patient-measured and patient-relevant outcomes, including for regulatory purposes, and will be linked to broader health data networks using tokenization, becoming a mainstay for signals about risks and benefits for diverse populations. This article is part of a Special Collection on Pharmacoepidemiology., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In recent years, the adoption of wearable gadgets such as Fitbit has revolutionized the way individuals track and monitor their personal activity data. These devices provide valuable in-sights into an individual's physical activity levels, sleep patterns, and overall health metrics. Integrating this data into healthcare informatics systems can offer significant benefits in terms of personalized healthcare delivery and improved patient outcomes. This paper explores the synergistic integration of Fitbit-generated personal activity data using the openEHR Reference Model in healthcare informatics as a practical case study in patient-generated health data (PGHD) integration based on health informatics standards as a framework for the representation and exchange of Electronic Health Records (EHRs). The synergistic integration of Fitbit-generated personal activity data through openEHR and FHIR standards models also covers the way for advanced analytics and population health management. By linking and analyzing data from various sources, including sensors and wearable devices, healthcare organizations can identify trends, patterns, and insights that can guide population health strategies, preventive care initiatives, and personalized treatment plans, in addition to aiding physicians in follow-up care.

Patient-generated health data (PGHD) is the person's health-related data collected outside the clinical environment. Integrating this data into the electronic health record (EHR) supports better patient-provider communication and shared decision-making, empowering patients to actively manage their health conditions. In this study, we investigated the essential features needed for patients and healthcare providers to effectively integrate PGHD functionality into the EHR system. Through our collaborative design approach involving healthcare professionals (HCPs) and patients, we developed a prototype and suggestion, using Estonia as a model, which is the ideal approach for collecting and integrating PGHD into the EHR.

One approach to enriching the Learning Health System (LHS) is leveraging vital signs and data from wearable technologies. Blood oxygen, heart rate, respiration rates, and other data collected by wearables (like sleep and exercise patterns) can be used to monitor and predict health conditions. This data is already being collected and could be used to improve healthcare in several ways. Our approach will be health data interoperability with HL7 FHIR (for data exchange between different systems), openEHR (to store researchable data separated from software but connected to ontologies, external terminologies and code sets) and maintain the semantics of data. OpenEHR is a standard that has an important role in modelling processes and clinical decisions. The six pillars of Lifestyle Medicine can be a first attempt to change how patients see their daily decisions, affecting the mid to long-term evolution of their health. Our objective is to develop the first stage of the LHS based on a co-produced personal health recording (CoPHR) built on top of a local LLM that interoperates health data through HL7 FHIR, openEHR, OHDSI and terminologies that can ingest external evidence and produces clinical and personal decision support and, when combined with many other patients, can produce or confirm evidence.

Objective: To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows., Methods: To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee., Results and Discussion: We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new or modified policies, processes, procedures, technology, and expertise to: (1) Ensure patient-generated health data (PGHD), including patient-reported outcomes (PROs), are documented, reviewed, and managed by appropriately trained clinicians, between visits and after regular working hours. (2) Educate patients to use connected medical devices and handle technical issues. (3) Facilitate collection and incorporation of PGHD, PROs, patient preferences, and social determinants of health into existing electronic health records. (4) Troubleshoot erroneous data received from devices. (5) Develop dashboards to display longitudinal patient-reported data. (6) Provide reimbursement to support new models of care. (7) Support patient engagement with remote devices., Conclusion: Several new policies, processes, technologies, and expertise are required to ensure safe and effective implementation and use of PC CDS. As we gain more experience implementing and working with PC CDS, we should be able to begin realizing the long-term positive impact on patient health that the patient-centered movement in healthcare promises., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

A As health technology advances, this study aims to develop an innovative nutritional intake management system that integrates artificial intelligence technology and social media software to achieve precise analysis of patient-generated data and comprehensive management in continuous care. Our system is built on the Line Bot platform, allowing users to easily and intuitively obtain detailed analyses of their individual nutritional intake by reporting dietary information. While users report their dietary habits through the Line Bot, our AI model conducts real-time analysis of nutrient intake, providing personalized nutritional recommendations. This instantaneous feedback not only enhances user engagement in nutritional management but also aids in establishing healthy habits. Additionally, through integration with social media software, our system facilitates information sharing and community support among users, promoting the exchange of nutritional knowledge and mutual assistance. This study further explores the specific needs of patients with chronic diseases, collecting individual data on chronic conditions and total nutritional intake. Based on the nutritional intake guidelines proposed by the Health Promotion Administration in Taiwan, more precise nutritional management recommendations are provided to meet the unique health needs of each patient. This study introduces a comprehensive, patient-generated data-based approach for precision nutrition management in continuous care. By integrating artificial intelligence, social media software, and data analysis, our system not only offers effective tools for monitoring and managing patients' nutritional intake but also fosters interaction and support among patients, driving the implementation of continuous care practices.

This scoping review aimed to identify and synthesize the literature related to patient-generated health data (PGHD) among older adults with cancer in home setting. Of the 1,090 articles extracted through six databases searches, 53 were selected. Studies were published from 2007 to 2022 and the types of devices to generate PGHD included research-grade and consumer-grade wearable devices. PGHD was assessed for physical activity, vital signs, and sleep. PGHD utilization was categorized: 1) identification, monitoring, review, and analysis (100%); 2) feedback and information report (32.1%); 3) motivation (26.4%); and 4) education and coaching (17.0%). Our study reveals that various PGHDs from older adults with cancer are mainly collected passively, with limited use for interaction with healthcare providers. These results may provide valuable insights for healthcare providers into potential PGHD applications in geriatric cancer care.

Purpose: Little is known concerning the impact of chronic spasticity on physical activities, social participation, and well-being, and whether patients' needs are addressed by current treatments. This study aims to investigate these lacunas in persons with a pure form of hereditary spastic paraplegia (HSP), in whom spasticity is a prominent symptom. Methods: Fourteen patients with a pure form of HSP were interviewed. These interviews were recorded, verbally transcribed, and thematically analyzed. Results: Four themes were identified which can be reflected by the phrases: (1) 'I stumble', (2) 'I struggle', (3) 'I feel ashamed', and (4) 'I need support'. Balance and gait problems led to limitations in domestic activities, employment, and recreation. 'Stumbling' also occurred due to pain, stiffness, and fatigue. Struggling was related to the continuous need for adaptation strategies, including the abandonment of some activities. Participants further reported feelings of shame, fear, and frustration. Lastly, they needed more support in daily activities than currently provided. Conclusion: Besides treating spasticity-related motor impairments, patients with HSP need practical support for optimizing their physical activities and social participation. They also seek attention for the non-motor consequences of their chronic spasticity to improve their well-being. Patient-reported outcomes might help to address these needs. According to patients with hereditary spastic paraplegia, interventions for spasticity should not only be aimed at reducing motor impairments, but also on reducing pain and fatigue, improving nighttime rest and general well-being, and optimizing the performance of relevant personal activities. Medical, role and emotional management in patients with hereditary spastic paraplegia can be improved only when individual needs are identified and monitored over the course of the disease. Besides assessment of bodily functions and physical capacities, systematic evaluation of patient-reported outcomes will help both patients and professionals to monitor the functional impact of disease progression and to evaluate the effects of interventions aimed at retarding this progression. [ABSTRACT FROM AUTHOR]

Epidemiology and pharmacoepidemiology frequently employ Real‐World Data (RWD) from healthcare teams to inform research. These data sources usually include signs, symptoms, tests, and treatments, but may lack important information such as the patient's diet or adherence or quality of life. By harnessing digital tools a new fount of evidence, Patient (or Citizen/Person) Generated Health Data (PGHD), is becoming more readily available. This review focusses on the advantages and considerations in using PGHD for pharmacoepidemiological research. New and corroborative types of data can be collected directly from patients using digital devices, both passively and actively. Practical issues such as patient engagement, data linking, validation, and analysis are among important considerations in the use of PGHD. In our ever increasingly patient‐centric world, PGHD incorporated into more traditional Real‐Word data sources offers innovative opportunities to expand our understanding of the complex factors involved in health and the safety and effectiveness of disease treatments. Pharmacoepidemiologists have a unique role in realizing the potential of PGHD by ensuring that robust methodology, governance, and analytical techniques underpin its use to generate meaningful research results. [ABSTRACT FROM AUTHOR]

Patient-generated health data (PGHD) are health-related data generated, recorded, and collected by patients or caregivers. Its main advantage is that patients can actively participate in their own health care, since the data-generating agents are patients and caregivers, not hospitals. Due to the development and popularization of information and communications technology and digital devices, the number of studies using PGHD for better health care is increasing. When PGHD was used in the outpatient setting, healthcare providers were better able to understand each patients’ condition using more accurate data, and to monitor patient health status between visits. In particular, to manage chronic diseases such as diabetes, it is essential to monitor daily blood sugar and change nutrient intake in the context of medication, overall diet, and exercise. However, problems associated with data quality, data extraction, and insufficient evidence and research to guide use of this kind of data in clinical setting are yet to be solved. Further, the gap between patient and healthcare providers’ perceptions of PGHD persists. We suggest that PGHD, electronic medical record data in hospitals, and claims and genome data could be combined to good effect. This combination can help patients and healthcare providers make better decisions with respect to patient health and to maintain patient engagement. In addition, the collection of PGHD through sophisticated sensors, and data analysis through advanced portals could combine medical big data with daily big data. Eventually, a personalized healthcare automation system through PGHD-based algorithms could provide healthcare artificial intelligence services. [ABSTRACT FROM AUTHOR]

Background: Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients' behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context., Objective: This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them., Methods: A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses., Results: The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients' devices. PGHD about patients' behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies., Conclusions: PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce., International Registered Report Identifier (irrid): RR2-10.2196/39389., (©Sharon Guardado, Maria Karampela, Minna Isomursu, Casandra Grundstrom. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.05.2024.)

Background: The increased pervasiveness of digital health technology is producing large amounts of person-generated health data (PGHD). These data can empower people to monitor their health to promote prevention and management of disease. Women make up one of the largest groups of consumers of digital self-tracking technology., Objective: In this scoping review, we aimed to (1) identify the different areas of women's health monitored using PGHD from connected health devices, (2) explore personal metrics collected through these technologies, and (3) synthesize facilitators of and barriers to women's adoption and use of connected health devices., Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews, we searched 5 databases for articles published between January 1, 2015, and February 29, 2020. Papers were included if they targeted women or female individuals and incorporated digital health tools that collected PGHD outside a clinical setting., Results: We included a total of 406 papers in this review. Articles on the use of PGHD for women steadily increased from 2015 to 2020. The health areas that the articles focused on spanned several topics, with pregnancy and the postpartum period being the most prevalent followed by cancer. Types of digital health used to collect PGHD included mobile apps, wearables, websites, the Internet of Things or smart devices, 2-way messaging, interactive voice response, and implantable devices. A thematic analysis of 41.4% (168/406) of the papers revealed 6 themes regarding facilitators of and barriers to women's use of digital health technology for collecting PGHD: (1) accessibility and connectivity, (2) design and functionality, (3) accuracy and credibility, (4) audience and adoption, (5) impact on community and health service, and (6) impact on health and behavior., Conclusions: Leading up to the COVID-19 pandemic, the adoption of digital health tools to address women's health concerns was on a steady rise. The prominence of tools related to pregnancy and the postpartum period reflects the strong focus on reproductive health in women's health research and highlights opportunities for digital technology development in other women's health topics. Digital health technology was most acceptable when it was relevant to the target audience, was seen as user-friendly, and considered women's personalization preferences while also ensuring accuracy of measurements and credibility of information. The integration of digital technologies into clinical care will continue to evolve, and factors such as liability and health care provider workload need to be considered. While acknowledging the diversity of individual needs, the use of PGHD can positively impact the self-care management of numerous women's health journeys. The COVID-19 pandemic has ushered in increased adoption and acceptance of digital health technology. This study could serve as a baseline comparison for how this field has evolved as a result., International Registered Report Identifier (irrid): RR2-10.2196/26110., (©Jalisa Lynn Karim, Rachel Wan, Rhea S Tabet, Derek S Chiu, Aline Talhouk. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.05.2024.)

Purpose: Central cancer registries collect data and provide population-level statistics that can be tracked over time; yet registries may not capture the full range of clinically relevant outcomes. Patient-generated health data (PGHD) include health/treatment history, biometrics, and patient-reported outcomes (PROs). Collection of PGHD would broaden registry outcomes to better inform research, policy, and care. However, this is dependent on the willingness of patients to share such data. This study examines cancer survivors' perspectives about sharing PGHD with central cancer registries.Methods: Three U.S. central registries sampled colorectal, non-Hodgkin lymphoma, and metastatic breast cancer survivors 1-4 years after diagnosis, recruiting them via mail to participate in one of seven focus groups (n = 52). Group discussions were recorded, transcribed, and thematically analyzed.Results: Most survivor-participants were unaware of the existence of registries. After having registries explained, all participants expressed their willingness to share PGHD with them if treated confidentially. Participants were willing to provide information on a variety of topics (e.g., medical history, medications, symptoms, financial difficulties, quality of life, biometrics, nutrition, exercise, and mental health), with a focus on long-term effects of cancer and its treatment. Participants' preferred mode for providing data varied. Participants were also interested in receiving information from registries.Conclusions: Our results suggest that registry-based collection of PGHD is acceptable to most cancer survivors and could facilitate registry-based efforts to collect PGHD/PROs. Central cancer registry-based collection of PGHD/PROs, especially on long-term effects, could enhance registry support of cancer control efforts including research and population health management. [ABSTRACT FROM AUTHOR]

Today, medical data such as diagnoses, procedures, imaging reports and laboratory tests, are not only collected in context of primary research and clinical studies. In addition, citizens are tracking their daily steps, food intake, sport exercises, and disease symptoms via mobile phones and wearable devices. In this context, the topic of "data donation" is drawing increased attention in science, politics, ethics and practice. This paper provides insights into the status quo of personal data donation in Germany and from a global perspective. As this topic requires a consideration of several perspectives, potential benefits and related, multifaceted challenges for citizens, patients and researchers are discussed. This includes aspects such as data quality & accessibility, privacy and ethical considerations. [ABSTRACT FROM AUTHOR]

Objective: Although patient generated health data (PGHD) has stimulated excitement about its potential to increase patient engagement and to offer clinicians new insights into patient health status, we know little about these efforts at scale and whether they align with patient preferences. This study sought to characterize provider-led PGHD approaches, assess whether they aligned with patient preferences, and identify challenges to scale and impact.Materials and Methods: We interviewed leaders from a geographically diverse set of health systems (n = 6), leaders from large electronic health record vendors (n = 3), and leaders from vendors providing PGHD solutions to health systems (n = 3). Next, we interviewed patients with 1 or more chronic conditions (n = 10), half of whom had PGHD experience. We conducted content analysis to characterize health system PGHD approaches, assess alignment with patient preferences, and identify challenges.Results: In this study, 3 primary approaches were identified, and each was designed to support collection of a different type of PGHD: 1) health history, 2) validated questionnaires and surveys, and 3) biometric and health activity. Whereas patient preferences aligned with health system approaches, patients raised concerns about data security and the value of reporting. Health systems cited challenges related to lack of reimbursement, data quality, and clinical usefulness of PGHD.Discussion: Despite a federal policy focus on PGHD, it is not yet being pursued at scale. Whereas many barriers contribute to this narrow pursuit, uncertainty around the value of PGHD, from both patients and providers, is a primary inhibitor.Conclusion: Our results reveal a fairly narrow set of approaches to PGHD currently pursued by health systems at scale. [ABSTRACT FROM AUTHOR]

Background: Internet‐based participatory surveillance systems, such as the German GrippeWeb, monitor the frequency of acute respiratory illnesses on population level. In order to interpret syndromic information better, we devised a microbiological feasibility study (GrippeWeb‐Plus) to test whether self‐collection of anterior nasal swabs is operationally possible, acceptable for participants and can yield valid data. Methods: We recruited 103 GrippeWeb participants (73 adults and 30 children) and provided them with a kit, instructions and a questionnaire for each sample. In the first half of 2016, participants took an anterior nasal swab and sent it to the Robert Koch Institute whenever an acute respiratory illness occurred. Reporting of illnesses through the GrippeWeb platform continued as usual. We analysed swabs for the presence of human c‐myc‐DNA and 22 viral and bacterial pathogens. After the study, we sent participants an evaluation questionnaire. We analysed timeliness, completeness, acceptability and validity. Results: One hundred and two participants submitted 225 analysable swabs. Ninety per cent of swabs were taken within 3 days of symptom onset. Eighty‐nine per cent of swabs had a corresponding reported illness in the GrippeWeb system. Ninety‐nine per cent of adults and 96% of children would be willing to participate in a self‐swabbing scheme for a longer period. All swabs contained c‐myc‐DNA. In 119 swabs, we identified any of 14 viruses but no bacteria. The positivity rate of influenza was similar to that in the German physician sentinel. Conclusion: Self‐collection of anterior nasal swabs proofed to be feasible, was well accepted by participants, gave valid results and was an informative adjunct to syndromic data. [ABSTRACT FROM AUTHOR]

Telemedicine can facilitate population health management by extending the reach of providers to efficiently care for high-risk, high-utilization populations. However, for telemedicine to be maximally useful, data collected using telemedicine technologies must be reliable and readily available to healthcare providers. To address current gaps in integration of patient-generated health data into the electronic health record (EHR), we examined 2 patient-facing platforms, Epic MyChart and Apple HealthKit, both of which facilitated the uploading of blood glucose data into the EHR as part of a diabetes telemedicine intervention. All patients were offered use of the MyChart platform; we subsequently invited a purposive sample of patients who used the MyChart platform effectively (n = 5) to also use the Apple HealthKit platform. Patients reported both platforms helped with diabetes self-management, and providers appreciated the convenience of the processes for obtaining patient data. Providers stated that the EHR data presentation format for Apple HealthKit was challenging to interpret; however, they also valued the greater perceived accuracy the Apple HealthKit data. Our findings indicate that patient-facing platforms can feasibly facilitate transmission of patient-generated health data into the EHR and support telemedicine-based care. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to describe the 4-step process (consent, selection, protection, and abstraction) of acquiring a large sample of chiropractic patient records from multiple practices and subsequent data abstraction. From April 2017 to December 2017, RAND acquired patient records from 99 chiropractic practices across the United States. The records included patients enrolled in a survey e-study (prospective sample) and a random sample of all clinic patients (retrospective sample) with chronic back or neck pain. Clinic staff were trained to collect the sample, scan, and transfer the records. We designed an online data collection tool for abstraction. Protocols were instituted to protect patient confidentiality. Doctors of chiropractic were selected and trained as abstractors, and a system was established to monitor data collection. In compliance with data protection protocols, 3603 patient records were scanned, including 1475 in the prospective sample and 2128 in the random sample. A total of 1716 patients (prospective sample) consented to having their records scanned, but only 1475 could be retrieved. Of records scanned, 19% were unusable owing to illegibility, no care during the period of interest, or poor scanning. The abstractor interrater reliability for appropriateness of care decisions was fair to moderate (κ.38-.48). The acquisition, handling, and abstraction of a large sample of chiropractic records was a complex task with challenges that necessitated adapting planned approaches. Of the records abstracted, many revealed incomplete provider documentation regarding the details of and rationale for care. Better documentation and more standardized record keeping would facilitate future research using patient records. [ABSTRACT FROM AUTHOR]

This paper presents the extension of a service-oriented architecture framework for precision oncology to the management of patient generated health data from wearables. The solution follows the indication provided by the Health Level 7 (HL7) and Object Management Group (OMG) initiative Healthcare Service Specification Project (HSSP) and is compliant to Retrieve Locate and Update Service (RLUS) Release 1 standard adopting Clinical Document Architecture Release 2 (CDA R2) as semantic signifier. The system which has been developed supports the management of visits, the setting up of a clinical diary and a comprehensive view of the patients from the wearables data for improve clinical care and for research. The system structure is highly modular and the parameters relating to wearables data are only present in one module. Extension of the systems to other aspects, such as genomics and immune therapy, are planned following the same modular design criteria. [ABSTRACT FROM AUTHOR]

Health care professionals are in the front line to identify signals of life issues. In Finnish school healthcare 25% of the children are raising concern from the professional’s perspective. In oral health care, ill oral health may be an indicator of issues and vice versa. However, the early inter-professional cross-talk is minimal. The aim of this project was to recognize the patients that would raise concern among oral health care professionals. Holistic interventions of these patients would secondarily improve oral health. Targeting the group with heaviest disease and social burden, the morbidity would cease in the total population. The staff of Kuopio public oral health care was trained for early patient recognition and referral to an oral health intervention clinic. The clinic focused on speaking out the patient’s life situation and individual oral health education. If needed, a multi-professional primary healthcare support team was gathered. With the most complex problems, the family services and child protection services were consulted. In 2017 and 2018 there were 264 and 344 recognized concerning individuals. Through tailored intervention strategies, the target group was able to increase brushing, lower the plague index and improve self-reported oral health (SOH). Total population indicators (decayed-missing-filled index, DMFT and invasive treatment) showed decreasing trends but have too many background variables for reliable effect assessment. Oral health can play an integral role in recognizing compromised individuals. The operational changes created during the project will act as platform for future digital tools.