Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.

BACKGROUND: Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown. METHODS: Participants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10. RESULTS: We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events. CONCLUSIONS: The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).

Lessons learned in the Apple Heart Study and implications for the data management of future digital clinical trials.

The digital clinical trial is fast emerging as a pragmatic trial that can improve a trial's design including recruitment and retention, data collection and analytics. To that end, digital platforms such as electronic health records or wearable technologies that enable passive data collection can be leveraged, alleviating burden from the participant and study coordinator. However, there are challenges. For example, many of these data sources not originally intended for research may be noisier than traditionally obtained measures. Further, the secure flow of passively collected data and their integration for analysis is non-trivial. The Apple Heart Study was a prospective, single-arm, site-less digital trial designed to evaluate the ability of an app to detect atrial fibrillation. The study was designed with pragmatic features, such as an app for enrollment, a wearable device (the Apple Watch) for data collection, and electronic surveys for participant-reported outcomes that enabled a high volume of patient enrollment and accompanying data. These elements led to challenges including identifying the number of unique participants, maintaining participant-level linkage of multiple complex data streams, and participant adherence and engagement. Novel solutions were derived that inform future designs with an emphasis on data management. We build upon the excellent framework of the Clinical Trials Transformation Initiative to provide a comprehensive set of guidelines for data management of the digital clinical trial that include an increased role of collaborative data scientists in the design and conduct of the modern digital trial.

Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study.

BACKGROUND: Smartwatch and fitness band wearable consumer electronics can passively measure pulse rate from the wrist using photoplethysmography (PPG). Identification of pulse irregularity or variability from these data has the potential to identify atrial fibrillation or atrial flutter (AF, collectively). The rapidly expanding consumer base of these devices allows for detection of undiagnosed AF at scale. METHODS: The Apple Heart Study is a prospective, single arm pragmatic study that has enrolled 419,093 participants (NCT03335800). The primary objective is to measure the proportion of participants with an irregular pulse detected by the Apple Watch (Apple Inc, Cupertino, CA) with AF on subsequent ambulatory ECG patch monitoring. The secondary objectives are to: 1) characterize the concordance of pulse irregularity notification episodes from the Apple Watch with simultaneously recorded ambulatory ECGs; 2) estimate the rate of initial contact with a health care provider within 3 months after notification of pulse irregularity. The study is conducted virtually, with screening, consent and data collection performed electronically from within an accompanying smartphone app. Study visits are performed by telehealth study physicians via video chat through the app, and ambulatory ECG patches are mailed to the participants. CONCLUSIONS: The results of this trial will provide initial evidence for the ability of a smartwatch algorithm to identify pulse irregularity and variability which may reflect previously unknown AF. The Apple Heart Study will help provide a foundation for how wearable technology can inform the clinical approach to AF identification and screening.

Healthcare Service Utilization under a New Virtual Primary Care Delivery Model.

Background:Telemedicine holds great promise for changing healthcare delivery. While telemedicine has been used significantly in the direct-to-consumer setting, the use of telemedicine in a preventive primary care setting is not well studied.Introduction:ClickWell Care (CWC) is the first known implementation of a technology-enabled primary care model. We wanted to quantify healthcare utilization of primary care by patient characteristics and modality of care delivery.Materials and Methods:Our study population included those who completed a visit to a CWC clinic between January 1, 2015 and September 30, 2015. We compared patients based on utilization of CWCs in-person and virtual visits across the following domains: patient demographics, distance from clinic, responses to a Health Risk Assessment, and top 10 conditions treated.Results:Thousand two hundred seven patients completed a visit with a CWC physician in 2015. Nearly three-quarters of our patients were ≤40 years and sex was significantly different (p = 0.015) between visit cohorts. The greatest representation of men (47%) was seen in the virtual-only cohort. Patients' proximity to the clinic was also significantly different across visit cohorts (p = 0.018) with 44% of in-person-only and 34% of virtual-only patients living within 5 miles of Stanford Hospital.Discussion:We found men were more likely to engage in virtual-only care. Young patients are willing to accept virtual care although many prefer to complete an in-person visit first.Conclusions:Our findings suggest that a "bricks-and-clicks" care model where telemedicine is supported by a brick-and-mortar location may be an effective way to leverage telemedicine to deliver primary care.

New Delivery Model for Rising-Risk Patients: The Forgotten Lot?

BACKGROUND: Shared-risk models encourage providers to engage young patients early. Telemedicine may be well suited for younger, healthier patients although it is unclear how best to incorporate telemedicine into routine clinical care. INTRODUCTION: We test the assumptions surrounding the use of telemedicine, younger and rising-risk patients, and primary care in ClickWell Care (CWC), a care model developed at our institution for our own accountable care organization. MATERIALS AND METHODS: CWC's team of physicians and wellness coaches work together to provide comprehensive primary care through in-person, phone, and video visits. This study examines usage of the clinic over its initial year in operation. RESULTS: 1,464 unique patients conducted a total of 3,907 visits. 2,294 (58.7%) visits were completed virtually (1,382 [35.4%] by phone and 912 [23.3%] by video). Patients were more inclined to see the physician in-person for a new visit (1,065 visits [70.5%] vs. 362 [24%] phone and 83 [6%] video) and more likely to see the physician virtually for a return visit (606 [43.2%] phone and 249 [17.7%] video vs. 548 [39.1%] in-person), a statistically significant difference (X2 = 306.7, p < 0.00001). CONCLUSION: This new care model successfully engaged a younger population of patients. However, our data suggest young patients may not be inclined to establish care with a primary care physician virtually and, in fact, choose an initial in-person touch point, although many are willing to conduct return visits virtually. This new model of care could have a large impact on how care is delivered to low- and rising-risk patients.

The development of a mobile app-focused deduplication strategy for the Apple Heart Study that informs recommendations for future digital trials.

An app-based clinical trial enrolment process can contribute to duplicated records, carrying data management implications. Our objective was to identify duplicated records in real time in the Apple Heart Study (AHS). We leveraged personal identifiable information (PII) to develop a dissimilarity score (DS) using the Damerau-Levenshtein distance. For computational efficiency, we focused on four types of records at the highest risk of duplication. We used the receiver operating curve (ROC) and resampling methods to derive and validate a decision rule to classify duplicated records. We identified 16,398 (4%) duplicated participants, resulting in 419,297 unique participants out of a total of 438,435 possible. Our decision rule yielded a high positive predictive value (96%) with negligible impact on the trial's original findings. Our findings provide principled solutions for future digital trials. When establishing deduplication procedures for digital trials, we recommend collecting device identifiers in addition to participant identifiers; collecting and ensuring secure access to PII; conducting a pilot study to identify reasons for duplicated records; establishing an initial deduplication algorithm that can be refined; creating a data quality plan that informs refinement; and embedding the initial deduplication algorithm in the enrolment platform to ensure unique enrolment and linkage to previous records.

Arrhythmias Other Than Atrial Fibrillation in Those With an Irregular Pulse Detected With a Smartwatch: Findings From the Apple Heart Study.

[Figure: see text].

Utilizing a novel unified healthcare model to compare practice patterns between telemedicine and in-person visits.

OBJECTIVE: Telemedicine practice has been shown to vary from clinical guidelines. Variations in practice patterns may be caused by disruptions in the continuity of care between traditional and telemedicine providers. This study compares virtual and in-person visits in Stanford's ClickWell Care (CWC) - where patients see the same provider for both visit modalities. METHODS: Clinical data for two years of patient encounters at CWC from January 2015-2017 (5772 visits) were obtained through Stanford STRIDE. For the 20 most common visit categories, including 17 specific diagnoses, we compared the frequency of prescriptions, labs, procedures, and images ordered, as well as rates of repeat visits. RESULTS: For the 17 specific diagnoses, there are no differences in labs ordered. Two diagnoses show differences in images ordered, and four differences in prescriptions. Overall, there are more labs (0.16 virtual, 0.33 in-person p < 0.0001) and images ordered (0.07 virtual, 0.16 in-person, p < 0.0001) for in-person visits - due mainly to general medical exam visits. Repeat visits were more likely after in-person visits (19% virtual, 38% in-person, p < 0.0001), 10 out of 17 specific diagnoses showed differences in visit frequency between visit modalities. Visits for both anxiety (5.3x, p < 0.0001) and depression (5.1x, p < 0.0001) were much more frequent in the virtual setting. CONCLUSIONS: Prescriptions, labs, and images ordered were similar between in-person and virtual visits for most diagnoses. Overall however, for in-person visits we find increased orders for labs and images, primarily from general medical exams. Finally, for anxiety and depression patients show clear preferences for virtual visits.

A 15-year review of the Stanford Internal Medicine Residency Program: predictors of resident satisfaction and dissatisfaction.

INTRODUCTION: Satisfaction with training and with educational experiences represents important internal medicine (IM) programmatic goals. Graduates from IM residency programs are uniquely poised to provide insights into their educational and training experiences and to assess whether these experiences were satisfactory and relevant to their current employment. METHODS: We surveyed former IM residents from the training program held during the years 2000-2015 at the Department of Medicine, Stanford University. The first part of the survey reviewed the IM residency program and the second part sought identifying data regarding gender, race, ethnicity, work, relationships, and financial matters. The primary outcome was satisfaction with the residency experience. RESULTS: Of the 405 individuals who completed the Stanford IM residency program in the study period, we identified 384 (95%) former residents with a known email address. Two hundred and one (52%) former residents responded to the first part and 185 (48%) answered both the parts of the survey. The mean age of the respondents was 36.9 years; 44% were female and the mean time from IM residency was 6.1 (±4.3) years. Fifty-eight percent reported extreme satisfaction with their IM residency experience. Predictors associated with being less than extremely satisfied included insufficient outpatient experience, insufficient international experience, insufficient clinical research experience, and insufficient time spent with family and peers. CONCLUSION: The residents expressed an overall high satisfaction rate with their IM training. The survey results provided insights for improving satisfaction with IM residency training that includes diversifying and broadening IM training experiences.