Patients Managing Their Medical Data in Personal Electronic Health Records: Scoping Review.

BACKGROUND: Personal electronic health records (PEHRs) allow patients to view, generate, and manage their personal and medical data that are relevant across illness episodes, such as their medications, allergies, immunizations, and their medical, social, and family health history. Thus, patients can actively participate in the management of their health care by ensuring that their health care providers have an updated and accurate overview of the patients' medical records. However, the uptake of PEHRs remains low, especially in terms of patients entering and managing their personal and medical data in their PEHR. OBJECTIVE: This scoping review aimed to explore the barriers and facilitators that patients face when deciding to review, enter, update, or modify their personal and medical data in their PEHR. This review also explores the extent to which patient-generated and -managed data affect the quality and safety of care, patient engagement, patient satisfaction, and patients' health and health care services. METHODS: We searched the MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, Web of Science, and Google Scholar web-based databases, as well as reference lists of all primary and review articles using a predefined search query. RESULTS: Of the 182 eligible papers, 37 (20%) provided sufficient information about patients' data management activities. The results showed that patients tend to use their PEHRs passively rather than actively. Patients refrain from generating and managing their medical data in a PEHR, especially when these data are complex and sensitive. The reasons for patients' passive data management behavior were related to their concerns about the validity, applicability, and confidentiality of patient-generated data. Our synthesis also showed that patient-generated and -managed health data ensures that the medical record is complete and up to date and is positively associated with patient engagement and patient satisfaction. CONCLUSIONS: The findings of this study suggest recommendations for implementing design features within the PEHR and the construal of a dedicated policy to inform both clinical staff and patients about the added value of patient-generated data. Moreover, clinicians should be involved as important ambassadors in informing, reminding, and encouraging patients to manage the data in their PEHR.

Application of Patient-Generated Health Data in Managing Chronic Conditions in Hospital Kuala Lumpur: A Qualitative Study.

Background: Patient-generated health data (PGHD) is health-related data captured and recorded by patients which informs healthcare practitioners (HCP) about the patients' health status between clinic visits. PGHD could be attributed as part of digital health and technological advancement. Methods: This is an exploratory qualitative study to explore the current PGHD usage and the views and experience of HCP towards PGHD. Semi-structured in-depth online interviews based on the modified Unified Theory of Acceptance and Use of Technology (UTAUT) were conducted with seven Hospital Kuala Lumpur medical- and surgical-based HCP specialists between October 2019 and February 2020. Purposive sampling method was applied to ensure speciality diversity among study respondents. Thematic analysis was performed on the interview transcripts. Results: Four main themes were identified namely the PGHD usage among the study respondents, the benefits of PGHD, the challenges of PGHD usage and the effort needed to use the PGHD. The main finding of this study includes the exploration of the benefits of PGHD usage such as efficient data management in aiding clinical consultation. Nonetheless, the clinical decision making based on PGHD is limited due to poor adoption of PGHD and unavailability of electronic data. This could be due to the lack of awareness, ICT infrastructure, funding, poor health literacy and language and cultural problems. Conclusion: PGHD has huge potential to be adopted in the clinical setting and subsequently benefiting the patients. However, parallel supportive environment is essential in supporting the usage of PGHD in the clinical setting.

Patient generated health data use in clinical practice: A systematic review.

BACKGROUND: Precision health calls for collecting and analyzing large amounts of data to capture an individual's unique behavior, lifestyle, genetics, and environmental context. The diffusion of digital tools has led to a significant growth of patient generated health data (PGHD), defined as health-related data created, gathered or inferred by or from patients and for which the patient controls data collection and data sharing. PURPOSE: We assessed the current evidence of the impact of PGHD use in clinical practice and provide recommendations for the formal integration of PGHD in clinical care. METHODS: We searched PubMed, Ovid, Embase, CINAHL, Web of Science, and Scopus up to May 2018. Inclusion criteria were applied and four reviewers screened titles and abstracts and consequently full articles. FINDINGS: Our systematic literature review identified 21 studies that examined the use of PGHD in clinical settings. Integration of PGHD into electronic records was extremely limited, and decision support capabilities were for the most part basic. DISCUSSION: PGHD and other types of patient-reported data will be part of the health care system narrative and we must continue efforts to understand its impact on health outcomes, costs, and patient satisfaction. Nursing scientists need to lead the process of defining the role of PGHD in the era of precision health.

Assessing the usability by clinicians of VISION: A hierarchical display of patient-collected physiological information to clinicians.

BACKGROUND: The inability of patients to accurately and completely recount their clinical status between clinic visits reduces the clinician's ability to properly manage their patients. One way to improve this situation is to collect objective patient information while the patients are at home and display the collected multi-day clinical information in parallel on a single screen, highlighting threshold violations for each channel, and allowing the viewer to drill down to any analog signal on the same screen, while maintaining the overall physiological context of the patient. All this would be accomplished in a way that was easy for the clinician to view and use. METHODS: Patients used five mobile devices to collect six heart failure-related clinical variables: body weight, systolic and diastolic blood pressure, pulse rate, blood oxygen saturation, physical activity, and subjective input. Fourteen clinicians practicing in a heart failure clinic rated the display using the System Usability Scale that, for acceptability, had an expected mean of 68 (SD, 12.5). In addition, we calculated the Intraclass Correlation Coefficient of the clinician responses using a two-way, mixed effects model, ICC (3,1). RESULTS: We developed a single-screen temporal hierarchical display (VISION) that summarizes the patient's home monitoring activities between clinic visits. The overall System Usability Scale score was 92 (95% CI, 87-97), p < 0.0001; the ICC was 0.89 (CI, 0.79-0.97), p < 0.0001. CONCLUSION: Clinicians consistently found VISION to be highly usable. To our knowledge, this is the first single-screen, parallel variable, temporal hierarchical display of both continuous and discrete information acquired by patients at home between clinic visits that presents clinically significant information at the point of care in a manner that is usable by clinicians.

Mobile technology and the digitization of healthcare.

The convergence of science and technology in our dynamic digital era has resulted in the development of innovative digital health devices that allow easy and accurate characterization in health and disease. Technological advancements and the miniaturization of diagnostic instruments to modern smartphone-connected and mobile health (mHealth) devices such as the iECG, handheld ultrasound, and lab-on-a-chip technologies have led to increasing enthusiasm for patient care with promises to decrease healthcare costs and to improve outcomes. This 'hype' for mHealth has recently intersected with the 'real world' and is providing important insights into how patients and practitioners are utilizing digital health technologies. It is also raising important questions regarding the evidence supporting widespread device use. In this state-of-the-art review, we assess the current literature of mHealth and aim to provide a framework for the advances in mHealth by understanding the various device, patient, and clinical factors as they relate to digital health from device designs and patient engagement, to clinical workflow and device regulation. We also outline new strategies for generation and analysis of mHealth data at the individual and population-based levels.

Practical application of the patient data-based quality control method: the potassium example.

Introduction: Internal quality control (IQC) is a core pillar of laboratory quality control strategies. Internal quality control commercial materials lack the same characteristics as patient samples and IQC contributes to the costs of laboratory testing. Patient data-based quality control (PDB-QC) may be a valuable supplement to IQC; the smaller the biological variation, the stronger the ability to detect errors. Using the potassium concentration in serum as an example study compared error detection effectiveness between PDB-QC and IQC. Materials and methods: Serum potassium concentrations were measured by using an indirect ion-selective electrode method. For the training database, 23,772 patient-generated data and 366 IQC data from April 2022 to September 2022 were used; 15,351 patient-generated data and 246 IQC data from October 2022 to January 2023 were used as the testing database. For both PDB-QC and IQC, average values and standard deviations were calculated, and z-score charts were plotted for comparison purposes. Results: Five systematic and three random errors were detected using IQC. Nine systematic errors but no random errors were detected in PDB-QC. The PDB-QC showed systematic error warnings earlier than the IQC. Conclusions: The daily average value of patient-generated data was superior to IQC in terms of the efficiency and timeliness of detecting systematic errors but inferior to IQC in detecting random errors.

Augmenting clinicians' analytical workflow through task-based integration of data visualizations and algorithmic insights: a user-centered design study.

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.

Assessing Quality of Life Using FHIR - How to Combine Patient Reported Outcome with Patient Generated Data for Better Compliance.

Quality of life (QoL) is affected by environmental influences and varies between patients. A combined measurement through Patient Reported Outcomes (PROs) and Patient Generated Data (PGD) may enhance the detection of QoL impairments by a longitudinal survey. Leveraging different approaches of QoL measurement techniques, the challenge is to combine data in a standardized, interoperable way. We developed an app (Lion-App) to semantically annotate data from sensor systems as well as PROs to be merged in an overall analysis of QoL. A FHIR implementation guide was defined for a standardized assessment. To access sensor data the interfaces of Apple Health or Google Fit are used instead of integrating various provider directly into the system. Since QoL cannot be collected exclusively via sensor values, a combination of PROs and PGD is necessary. PGD enable a progression of QoL which offers more insight into personal limitations whereas PROs give insight about personal burden. The use of FHIR enables structured exchange of data while personalized analyses might improve therapy and outcome.

Co-creation of the Global Patient Experience Data Navigator: a multi-stakeholder initiative to ensure the patient voice is represented in health decision-making.

BACKGROUND: Putting patients' needs and priorities at the forefront of healthcare initiatives and medical product development is critical to achieve outcomes that matter most to patients. This relies on the integration of early, meaningful patient engagement (PE) to learn what is important to patients, and collection of representative patient experience data (PXD). The increased number of PE/PXD efforts across global regulatory, health technology assessment, and healthcare systems is an important step forward to deliver improved health outcomes for patients. However, these initiatives are fragmented and lack integration, which is necessary to maximize efforts and reduce burden on patients. To overcome these challenges, the Global Patient Experience Data Navigator has been co-created by Patient Focused Medicines Development to provide practical resources that can facilitate and optimize PXD generation, collection, analysis, and dissemination for patient benefit and aims to be applicable across all therapeutic areas for all stakeholders. METHODS: Co-creation of the Navigator took place through an iterative process of validation and formalization driven by a diverse, multi-stakeholder working group with individuals who have varying knowledge/experience in PE/PXD. RESULTS: A series of workshops took place to conduct a gap analysis, develop a taxonomy model, and integrate existing frameworks. The collective insights led to the development of the Navigator consisting of four specific tools in the form of downloadable templates, which can be used to: (1) prioritize outcomes that matter most to patients and their caregivers; (2) select appropriate measurement methods for these outcomes; (3) identify when and why PXD is used throughout the product development cycle for each stakeholder; (4) identify when and why PXD is used throughout the healthcare process for each stakeholder. A public consultation was carried out to collect user feedback before the Navigator was made publicly available in December 2022. CONCLUSION: To our knowledge, the Global Patient Experience Data Navigator is the only publicly available toolkit developed with a multi-stakeholder and disease-agnostic approach providing taxonomically grouped resources to optimize the collection and collation of PXD for patient benefit. Future work will aim to further engage patients by adding a PE dimension to the Navigator.

Engaging patients at the start of healthcare and medical product development projects can help better understand their experiences and what is most important to them. Ultimately this will achieve the best outcomes. However, if not carefully planned, projects that engage patients can lead to inefficiencies, such as patients being asked for the same information repeatedly. The collection of patient experience data­information related to patients' experiences, needs, and priorities­also needs to be carefully managed. To help solve this problem, Patient Focused Medicines Development developed a publicly available "toolkit" called the Global Patient Experience Data Navigator. The Navigator has downloadable templates that can be filled in and used for projects in any disease area and by anyone collecting patient experience data. To represent different perspectives, individuals with a range of experiences and understanding of patient engagement projects worked together to co-create the toolkit. Several meetings took place to understand what the toolkit needed to do and to help provide a structure for the templates. There are four templates in the toolkit. These can be used to: (1) prioritize outcomes that matter most to patients and their caregivers; (2) select the best ways of measuring these outcomes; (3) identify when, why, and by whom patient experience data is used throughout the product development cycle; and (4) identify when, why and by whom patient experience data is used throughout the healthcare process. Future work will utilize public feedback to make the toolkit more user-friendly and provide education on how the Navigator can be used.

Will Apple devices' passive atrial fibrillation detection prevent strokes? Estimating the proportion of high-risk actionable patients with real-world user data.

OBJECTIVE: Utilizing integrated electronic health record (EHR) and consumer-grade wearable device data, we sought to provide real-world estimates for the proportion of wearers that would likely benefit from anticoagulation if an atrial fibrillation (AFib) diagnosis was made based on wearable device data. MATERIALS AND METHODS: This study utilized EHR and Apple Watch data from an observational cohort of 1802 patients at Cedars-Sinai Medical Center who linked devices to the EHR between April 25, 2015 and November 16, 2018. Using these data, we estimated the number of high-risk patients who would be actionable for anticoagulation based on (1) medical history, (2) Apple Watch wear patterns, and (3) AFib risk, as determined by an existing validated model. RESULTS: Based on the characteristics of this cohort, a mean of 0.25% (n = 4.58, 95% CI, 2.0-8.0) of patients would be candidates for new anticoagulation based on AFib identified by their Apple Watch. Using EHR data alone, we find that only approximately 36% of the 1802 patients (n = 665.93, 95% CI, 626.0-706.0) would have anticoagulation recommended even after a new AFib diagnosis. DISCUSSION AND CONCLUSION: These data suggest that there is limited benefit to detect and treat AFib with anticoagulation among this cohort, but that accessing clinical and demographic data from the EHR could help target devices to the patients with the highest potential for benefit. Future research may analyze this relationship at other sites and among other wearable users, including among those who have not linked devices to their EHR.

Feasibility and Impact of Remote Glucose Monitoring Among Patients With Newly Diagnosed Type 1 Diabetes: Single-Center Pilot Study.

BACKGROUND: Caregivers of children with newly diagnosed type 1 diabetes (T1D) maintain close contact with providers for several weeks to facilitate rapid adjustments in insulin dosing regimens. Traditionally, patient glucose values are relayed by telephone for provider feedback, but digital health technology can now enable the remote sharing of glucose data via mobile apps. OBJECTIVE: The aim of this study was to test the feasibility of remote glucose monitoring in a population of children and adolescents with newly diagnosed T1D and to explore whether remote monitoring alters habits for self-review of glucose data or perceived ease of provider contact in this population as compared to a nonrandomized control group. METHODS: Data were collected from families who chose to participate in remote monitoring (intervention group) as well as from patients receiving usual care (control group). The intervention group received Bluetooth-capable glucose meters and Apple iPod Touch devices. Patient-generated glucose data were passively relayed from the meter to the iPod Touch and then to both the electronic health record (EHR) and a third-party diabetes data platform, Tidepool. The principal investigator reviewed glucose data daily in the EHR and Tidepool and contacted the participants as needed for insulin dose adjustments during the time between hospital discharge and first clinic appointment. Families in the control group received usual care, which involved keeping written records of glucose values and contacting the diabetes team daily by telephone to relay data and receive treatment recommendations. A total of 40 families (20 for the intervention group and 20 for the control group) participated in the study. All families were surveyed at 1 month and 6 months regarding self-review of glucose data and ease of contacting the diabetes team. RESULTS: Patient-generated glucose data were remotely accessible for 100% of the participants via Tidepool and for 85% via the EHR. Survey data indicated that families in the intervention group were more likely than those in the control group to review their glucose data using mobile health apps after 1 month (P<.001), but by 6 months, this difference had disappeared. Perceived ease of contacting the clinical team for assistance was lower for the intervention group after 6 months (when receiving usual care) in comparison to during the intervention period (P=.48) and compared with a control group who did not have exposure to remote monitoring (P=.03). CONCLUSIONS: Remote glucose monitoring is feasible among pediatric patients with newly diagnosed T1D and may be associated with the earlier adoption of mobile health apps for self-management. The use of broadscale remote monitoring for T1D in the future will depend on improved access to Bluetooth-enabled mobile devices for all patients, improved interoperability of mobile health apps to enable data transfer on Android as well as Apple devices, and new provider workflows to handle large-scale panel management based on patient-generated health data. TRIAL REGISTRATION: ClinicalTrials.gov NCT04106440; https://clinicaltrials.gov/ct2/show/NCT04106440.

Assessing Usability of Smartwatch Digital Health Devices for Home Blood Pressure Monitoring among Glaucoma Patients.

Glaucoma is a leading cause of blindness worldwide. Blood pressure (BP) dysregulation is a known risk factor, and home-based BP monitoring is increasingly used, but the usability of digital health devices to measure BP among glaucoma patients is not well studied. There may be particular usability challenges among this group, given that glaucoma disproportionately affects the elderly and can cause visual impairment. Therefore, the goal of this mixed-methods study was to assess the usability of a smart watch digital health device for home BP monitoring among glaucoma patients. Adult participants were recruited and given a smartwatch blood pressure monitor for at-home use. The eHEALS questionnaire was used to determine baseline digital health literacy. After a week of use, participants assessed the usability of the BP monitor and related mobile app using the Post-study System Usability Questionnaire (PSSUQ) and the System Usability Scale (SUS), standardized instruments to measure usability in health information technology interventions. Variations in scores were evaluated using ANOVA and open-ended responses about participants' experience were analyzed thematically. Overall, usability scores corresponded to the 80th-84th percentile, although older patients endorsed significantly worse usability based on quantitative scores and additionally provided qualitative feedback describing some difficulty using the device. Usability for older patients should be considered in the design of digital health devices for glaucoma given their disproportionate burden of disease and challenges in navigating digital health technologies, although the overall high usability scores for the device demonstrates promise for future clinical applications in glaucoma risk stratification.

Understanding the attitudes and experiences of people living with potentially stigmatised long-term health conditions with respect to collecting and sharing health and lifestyle data.

Background: The emerging landscape of patient-generated data (PGData) provides an opportunity to collect large quantities of information that can be used to develop our understanding of different health conditions and potentially improve the quality of life for those living with long-term health condition (LTHCs). If the potential benefits of PGData are to be realised, we need a better understanding of the psychological barriers and facilitators to the collection and beneficial sharing of health and lifestyle data. Due to the understudied role that stigma plays in sharing PGData, we explore the attitudes and experiences of those living with potentially stigmatised LTHCs with respect to collecting and sharing health and lifestyle data. Methods: This study used semi-structured interviews and a card sorting task to explore the attitudes and experiences of people living with potentially stigmatised LTHCs. Fourteen adult participants who reported having a range of conditions were recruited in England. Template analysis was used to analyse interview transcripts and descriptive statistics were used for the card sorting task. Results: The findings present four overarching themes: Preferences for collecting health and lifestyle data, Importance of anonymity, Expected use of data, and Sources of emotional support. Participants illustrated a general willingness to share health and lifestyle data; however, there were some notable differences in sharing experiences, varying both by information type and recipient group. Overall, participants did not identify health-related stigma as a barrier to collecting or sharing their personal health and lifestyle data. Conclusions: We outline a number of preferences that participants feel would encourage them to collect and share data more readily, which may be considered when developing data sharing tools for the future.

eHealth-Generated Patient Data in an Outpatient Setting after Hematopoietic Stem Cell Transplantation: A Scoping Review.

Hematopoietic stem cell transplantation (HCT) has the potential to cure malignant and nonmalignant diseases but remains associated with a wide range of complications, necessitating dedicated lifelong follow-up. While patients are monitored closely during the peri-HCT period, leaving the hospital setting after HCT introduces new challenges. This scoping review explores the current use of patient-generated eHealth data in the outpatient setting. A systematic search of the PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature, American Psychological Association PsycINFO, and International Health Technology Assessment databases in July 2021 identified the 22 studies (13 full text articles and 9 abstracts) included in this review. The large majority were small to medium-sized (n = 15; 68.2%) pilot or feasibility studies (n = 18; 81.8%) that were published between 2016 and 2021 (n = 16; 72.7%). Collection of patient-reported outcomes was the most frequently reported eHealth intervention (n = 14; 63.6%), followed by vital sign monitoring (n = 5; 22.7%) and home-based spirometry (n = 3; 13.6%), mostly in the early post-transplantation setting. eHealth interventions had favorable feasibility and acceptability profiles; however, we found little data on the efficacy, long-term monitoring, data security, and cost-effectiveness of eHealth interventions. Larger randomized studies are warranted to draw formal conclusions about the impact of eHealth on HCT outcomes and the best ways to incorporate eHealth in clinical practice.

A Versatile and Scalable Platform That Streamlines Data Collection for Patient-Centered Studies: Usability and Feasibility Study.

BACKGROUND: The Food and Drug Administration Center for Biologics Evaluation and Research (CBER) established the Biologics Effectiveness and Safety (BEST) Initiative with several objectives, including the expansion and enhancement of CBER's access to fit-for-purpose data sources, analytics, tools, and infrastructures to improve the understanding of patient experiences with conditions related to CBER-regulated products. Owing to existing challenges in data collection, especially for rare disease research, CBER recognized the need for a comprehensive platform where study coordinators can engage with study participants and design and deploy studies while patients or caregivers could enroll, consent, and securely participate as well. OBJECTIVE: This study aimed to increase awareness and describe the design, development, and novelty of the Survey of Health and Patient Experience (SHAPE) platform, its functionality and application, quality improvement efforts, open-source availability, and plans for enhancement. METHODS: SHAPE is hosted in a Google Cloud environment and comprises 3 parts: the administrator application, participant app, and application programming interface. The administrator can build a study comprising a set of questionnaires and self-report entries through the app. Once the study is deployed, the participant can access the app, consent to the study, and complete its components. To build SHAPE to be scalable and flexible, we leveraged the open-source software development kit, Ionic Framework. This enabled the building and deploying of apps across platforms, including iOS, Android, and progressive web applications, from a single codebase by using standardized web technologies. SHAPE has been integrated with a leading Health Level 7 (HL7®) Fast Healthcare Interoperability Resources (FHIR®) application programming interface platform, 1upHealth, which allows participants to consent to 1-time data pull of their electronic health records. We used an agile-based process that engaged multiple stakeholders in SHAPE's design and development. RESULTS: SHAPE allows study coordinators to plan, develop, and deploy questionnaires to obtain important end points directly from patients or caregivers. Electronic health record integration enables access to patient health records, which can validate and enhance the accuracy of data-capture methods. The administrator can then download the study data into HL7® FHIR®-formatted JSON files. In this paper, we illustrate how study coordinators can use SHAPE to design patient-centered studies. We demonstrate its broad applicability through a hypothetical type 1 diabetes cohort study and an ongoing pilot study on metachromatic leukodystrophy to implement best practices for designing a regulatory-grade natural history study for rare diseases. CONCLUSIONS: SHAPE is an intuitive and comprehensive data-collection tool for a variety of clinical studies. Further customization of this versatile and scalable platform allows for multiple use cases. SHAPE can capture patient perspectives and clinical data, thereby providing regulators, clinicians, researchers, and patient advocacy organizations with data to inform drug development and improve patient outcomes.

Validation Parameters of Patient-Generated Data for Digitally Recorded Allergic Rhinitis Symptom and Medication Scores in the @IT.2020 Project: Exploratory Study.

BACKGROUND: Mobile health technologies enable allergists to monitor disease trends by collecting daily patient-reported outcomes of allergic rhinitis. To this end, patients with allergies are usually required to enter their symptoms and medication repetitively over long time periods, which may present a risk to data completeness and quality in the case of insufficient effort reporting. Completeness of patient's recording is easily measured. In contrast, the intrinsic quality and accuracy of the data entered by the patients are more elusive. OBJECTIVE: The aim of this study was to explore the association of adherence to digital symptom recording with a predefined set of parameters of the patient-generated symptom and medication scores and to identify parameters that may serve as proxy measure of the quality and reliability of the information recorded by the patient. METHODS: The @IT.2020 project investigates the diagnostic synergy of mobile health and molecular allergology in patients with seasonal allergic rhinitis. In its pilot phase, 101 children with seasonal allergic rhinitis were recruited in Rome and instructed to record their symptoms, medication intake, and general conditions daily via a mobile app (AllergyMonitor) during the relevant pollen season. We measured adherence to daily recording as the percentage of days with data recording in the observation period. We examined the patient's trajectories of 3 disease indices (Rhinoconjunctivitis Total Symptom Score [RTSS], Combined Symptom and Medication Score [CSMS], and Visual Analogue Scale [VAS]) as putative proxies of data quality with the following 4 parameters: (1) intravariation index, (2) percentage of zero values, (3) coefficient of variation, and (4) percentage of changes in trend. Lastly, we examined the relationship between adherence to recording and each of the 4 proxy measures. RESULTS: Adherence to recording ranged from 20% (11/56) to 100% (56/56), with 64.4% (65/101) and 35.6% (36/101) of the patients' values above (highly adherent patients) or below (low adherent patients) the threshold of 80%, respectively. The percentage of zero values, the coefficient of variation, and the intravariation index did not significantly change with the adherence to recording. By contrast, the proportion of changes in trend was significantly higher among highly adherent patients, independently from the analyzed score (RTSS, CSMS, and VAS). CONCLUSIONS: The percentage of changes in the trend of RTSS, CSMS, and VAS is a valuable candidate to validate the quality and accuracy of the data recorded by patients with allergic rhinitis during the pollen season. The performance of this parameter must be further investigated in real-life conditions before it can be recommended for routine use in apps and electronic diaries devoted to the management of patients with allergic rhinitis.

Heterogeneous validity of daily data on symptoms of seasonal allergic rhinitis recorded by patients using the e-diary AllergyMonitor®.

BACKGROUND: Patient-generated symptom and medication scores are essential for diagnostic and therapeutic decisions in seasonal allergic rhinitis (SAR). Previous studies have shown solid consistencies between different scores at population level in real-life data and trials. For clinicians, the evaluation of individual data quality over time is essential to decide whether to rely on these data in clinical decision-making. OBJECTIVE: To analyze the consistency of different symptom (SS) and symptom medication scores (SMSs) at individual level in two study cohorts with different characteristics and explore individual patient trajectories over time. METHODS: Within the pilot phase of the @IT.2020 project on diagnostic synergy of mobile health and molecular IgE assessment in patients with SAR, we analyzed data of 101 children and 93 adults with SAR and instructed them to record their symptoms and medication intake daily via the mobile app AllergyMonitor®. We then assessed the correlation between different SMS and a visual analogue scale (VAS) on the impact of allergy symptoms on daily life at population and individual level. RESULTS: At population level, the Rhinoconjunctivitis total symptom score (RTSS) correlated better with VAS than the combined symptom and medication score (CSMS). At individual level, consistency among RTSS and VAS was highly heterogeneous and unrelated to disease severity or adherence to recording. Similar heterogeneity was observed for CSMS and VAS. CONCLUSIONS: The correlation of clinical information provided by different disease severity scores based on data collected via electronic diaries (e-diaries), is sufficient at population level, but broadly heterogeneous for individual patients. Consistency of the recorded data must be examined for each patient before remotely collected information is used for clinical decision making.

Sharing patient-generated data with healthcare providers: findings from a 2019 national survey.

OBJECTIVE: Our study estimates the prevalence and predictors of wearable device adoption and data sharing with healthcare providers in a nationally representative sample. MATERIALS AND METHODS: Data were obtained from the 2019 Health Information National Trend Survey. We conducted multivariable logistic regression to examine predictors of device adoption and data sharing. RESULTS: The sample contained 4159 individuals, 29.9% of whom had adopted a wearable device in 2019. Among adopters, 46.3% had shared data with their provider. Individuals with diabetes (odds ratio [OR], 2.39; 95% CI, 1.66-3.45; P < .0001), hypertension (OR, 2.80; 95% CI, 2.12-3.70; P < .0001), and multiple chronic conditions (OR, 1.55; 95% CI, 1.03-2.32; P < .0001) had significantly higher odds of wearable device adoption. Individuals with a usual source of care (OR, 2.44; 95% CI, 1.95-3.04; P < .0001), diabetes (OR, 1.66; 95% CI, 1.32-2.08; P < .0001), and hypertension (OR, 1.78; 95% CI, 1.44-2.20; P < .0001) had significantly higher odds of sharing data with providers. DISCUSSION: A third of individuals adopted a wearable medical device and nearly 50% of individuals who owned a device shared data with a provider in 2019. Patients with certain conditions, such as diabetes and hypertension, were more likely to adopt devices and share data with providers. Social determinants of health, such as income and usual source of care, negatively affected wearable device adoption and data sharing, similarly to other consumer health technologies. CONCLUSIONS: Wearable device adoption and data sharing with providers may be more common than prior studies have reported; however, digital disparities were noted. Studies are needed that test implementation strategies to expand wearable device use and data sharing into care delivery.

Quality of Life as an Indicator for Care Delivery in Clinical Oncology Using FHIR.

INTRODUCTION: Health-related quality of life (HR-QoL) as a parameter for patient well-being is becoming increasingly important.[1] Nevertheless, it is mainly used as an endpoint in studies rather than as an indicator for adjustments in therapy. In this paper we will present an approach to gradually integrate quality of life (QoL) as a control element into the care delivery of oncology. CONCEPT: Acceptance, usability, interoperability and data protection were identified and integrated as key indicators for the development. As an initial approach, a questionnaire tool was developed to provide patients a simplified answering of questionnaires and physicians a clearer presentation of the results. IMPLEMENTATION: As communication standard HL7 FHIR was used and known security concepts like OpenID Concept were integrated. In a usability study, first results were achieved by asking patients in the waiting room to answer a questionnaire, which will be discussed with the physician in the appointment. This study was conducted in 2019 at theSLK Clinics Heilbronn and achieved 86% participation of all respondents with an average age of 67 years. DISCUSSION: Although the evaluation study could prove positive results in usability and acceptance, it is necessary to aim for longitudinal surveys in order to include QoL as a control element in the therapy. However, a longitudinal survey through questionnaires leads to decreasing compliance and increasing response bias. [2] For this reason, the concept needs to be expanded. With sensors a continuous monitoring can be carried out and the data can be mapped to the individual, interpreted by machine learning. CONCLUSION: Questionnaires are a concept that has been successfully applied in studies for years. However, since care delivery poses different challenges, the integration of new concepts is inevitable. The authors are currently working on an extension of the use of questionnaires with patient generated data through sensors.

Patient-centered care and the electronic health record: exploring functionality and gaps.

OBJECTIVE: Healthcare systems have adopted electronic health records (EHRs) to support clinical care. Providing patient-centered care (PCC) is a goal of many healthcare systems. In this study, we sought to explore how existing EHR systems support PCC; defined as understanding the patient as a whole person, building relational connections between the clinician and patient, and supporting patients in health self-management. MATERIALS AND METHODS: We assessed availability of EHR functions consistent with providing PCC including patient goals and preferences, integrated care plans, and contextual and patient-generated data. We surveyed and then interviewed technical representatives and expert clinical users of 6 leading EHR systems. Questions focused on the availability of specific data and functions related to PCC (for technical representatives) and the clinical usefulness of PCC functions (for clinicians) in their EHR. RESULTS: Technical representatives (n = 6) reported that patient communication preferences, personalized indications for medications, and end of life preferences were functions implemented across 6 systems. Clinician users (n = 10) reported moderate usefulness of PCC functions (medians of 2-4 on a 5-pointy -35t scale), suggesting the potential for improvement across systems. Interviews revealed that clinicians do not have a shared conception of PCC. In many cases, data needed to deliver PCC was available in the EHR only in unstructured form. Data systems and functionality to support PCC are under development in these EHRs. DISCUSSION AND CONCLUSION: There are current gaps in PCC functionality in EHRs and opportunities to support the practice of PCC through EHR redesign.