A national survey of digital health company experiences with electronic health record application programming interfaces.

OBJECTIVES: This study sought to capture current digital health company experiences integrating with electronic health records (EHRs), given new federally regulated standards-based application programming interface (API) policies. MATERIALS AND METHODS: We developed and fielded a survey among companies that develop solutions enabling human interaction with an EHR API. The survey was developed by the University of California San Francisco in collaboration with the Office of the National Coordinator for Health Information Technology, the California Health Care Foundation, and ScaleHealth. The instrument contained questions pertaining to experiences with API integrations, barriers faced during API integrations, and API-relevant policy efforts. RESULTS: About 73% of companies reported current or previous use of a standards-based EHR API in production. About 57% of respondents indicated using both standards-based and proprietary APIs to integrate with an EHR, and 24% worked about equally with both APIs. Most companies reported use of the Fast Healthcare Interoperability Resources standard. Companies reported that standards-based APIs required on average less burden than proprietary APIs to establish and maintain. However, companies face barriers to adopting standards-based APIs, including high fees, lack of realistic clinical testing data, and lack of data elements of interest or value. DISCUSSION: The industry is moving toward the use of standardized APIs to streamline data exchange, with a majority of digital health companies using standards-based APIs to integrate with EHRs. However, barriers persist. CONCLUSION: A large portion of digital health companies use standards-based APIs to interoperate with EHRs. Continuing to improve the resources for digital health companies to find, test, connect, and use these APIs "without special effort" will be crucial to ensure future technology robustness and durability.

On-Call Junior Neurosurgery Residents Spend 9 hours of Their On-Call Shift Actively Using the Electronic Health Record.

BACKGROUND: The electronic health record (EHR) is central to clinical workflow, yet few studies to date have explored EHR usage patterns among neurosurgery trainees. OBJECTIVE: To describe the amount of EHR time spent by postgraduate year (PGY)-2 and PGY-3 neurosurgery residents during on-call days and the distribution of EHR activities in which they engage. METHODS: This cohort study used the EHR audit logs, time-stamped records of user activities, to review EHR usage of PGY-2 and PGY-3 neurosurgery residents scheduled for 1 or more on-call days across 2 calendar years at the University of California San Francisco. We focused on the PGY-2 and PGY-3, which, in our training program, represent the primary participants in the in-house on-call pool. RESULTS: Over 723 call days, 12 different residents took at least one on-call shift. The median (IQR) number of minutes that residents spent per on-call shift actively using the EHR was 536.8 (203.5), while interacting with an average (SD) of 68.1 (14.7) patient charts. There was no significant difference between Active EHR Time between residents as PGY-2 and PGY-3 on paired t -tests. Residents spent the most time on the following EHR activities: patient reports, notes, order management, patient list, and chart review. CONCLUSION: Residents spent, on average, 9 hours of their on-call shift actively using the EHR, and there was no improved efficiency as residents gained experience. We noted several areas of administrative EHR burden, which could be reduced.

Clinical Assessment of Residents: A Survey of Clinician Educators Regarding Resident Assessment Burden and Modifiable Factors.

Background: A fundamental role of the clinician educator is to provide thoughtful assessments for resident development. A gap in the literature exists about whether the completion of assessments contributes to clinician educator burden. Objective: We sought to understand the degree to which completing resident assessments contributes to clinician educator burden, the drivers behind such perception, and whether modifiable factors exist. Methods: In October 2020, we conducted a cross-sectional study of adult hospital medicine clinician educators to explore burden associated with resident assessment. The authors developed a 10-item electronic survey (Likert type and sliding scale responses), asking about demographics, context, frequency and degree of burden, burdensome aspects of assessments, estimated time for assessments, and percentage of assessments turned in late or never. We conducted subgroup analyses for differences in responses based on sex and number of years practicing, and regression analyses for predictors of burden degree. Results: Fifty of 81 (62%) surveyed faculty responded. Two percent (1 of 50) reported no burden, while 42% (21 of 50) reported infrequent ("never," "rarely," "sometimes") and 56% (28 of 50) reported frequent ("often," "always") burden. Of those experiencing burden, 67% (33 of 49) reported slight or moderate, and 33% (16 of 49) reported significant or extreme burden. Potentially modifiable causes included assessment request boluses, lag time between resident service and assessment requests, and technology involved. Female clinician educators estimated submitting a higher percentage of late assessments than males (65% vs 41%, P=.02). Number of years practicing was inversely associated with assessment time (ß=-0.28, P=.01). Conclusions: Our findings suggest that resident assessments are a source of burden among adult hospital medicine clinician educators and that several potentially modifiable factors may underlie this burden.

Where Is the Digitally Silent Provider? Development and Validation of a Team-Centered Electronic Health Record Attribution Model for Supervising Residents.

PROBLEM: Providing trainees with data and benchmarks on their own patient populations is an Accreditation Council for Graduate Medical Education core residency requirement. Leveraging electronic health records (EHRs) for this purpose relies on correctly attributing patients to the trainees responsible for their care. EHR activity logs are useful for attributing interns to inpatients but not for attributing supervising residents, who often have no inpatient EHR usage obligations, and therefore may generate no digital "footprints" on a given patient-day from which to ascertain attribution. APPROACH: The authors developed and tested a novel team-centered binary logistic regression model leveraging EHR activity logs from July 1, 2018, to June 30, 2019, for pediatric hospital medicine (PHM) supervising residents at the University of California, San Francisco. Unlike patient-centered models that determine daily attribution according to the trainee generating the greatest relative activity in individual patients' charts, the team-centered approach predicts daily attribution based on the trainee generating EHR activity across the greatest proportion of a team's patients. To assess generalizability, the authors similarly modeled supervising resident attribution in adult hospital medicine (AHM) and orthopedic surgery (OS). OUTCOMES: For PHM, AHM, and OS, 1,100, 1,399, and 803 unique patient encounters and 29, 62, and 10 unique supervising residents were included, respectively. Team-centered models outperformed patient-centered models for the 3 specialties, with respective accuracies of 85.4% versus 72.4% (PHM), 88.7% versus 75.4% (AHM), and 69.3% versus 51.6% (OS; P < .001 for all). AHM and PHM models demonstrated relative generalizability to one another while OS did not. NEXT STEPS: Validation at other institutions will be essential to understanding the potential for generalizability of this approach. Accurately attributed data are likely to be trusted more by trainees, enabling programs to operationalize feedback for use cases including performance measurement, case mix assessment, and postdischarge opportunities for follow-up learning.

Imagining the future of diagnostic performance feedback.

Diagnostic performance is uniquely challenging to measure, and providing feedback on diagnostic performance to catalyze diagnostic recalibration remains the exception to the rule in healthcare. Diagnostic accuracy, timeliness, and explanation to the patient are essential dimensions of diagnostic performance that each intersect with a variety of technical, contextual, cultural, and policy barriers. Setting aside assumptions about current constraints, we explore the future of diagnostic performance feedback by describing the "minimum viable products" and the "ideal state" solutions that can be envisioned for each of several important barriers. Only through deliberate and iterative approaches to breaking down these barriers can we improve recalibration and continuously drive the healthcare ecosystem towards diagnostic excellence.

Patient-generated health data earn a seat at the table: clinical adoption during the COVID-19 transition to telemedicine.

Patient-generated health data (PGHD) have not achieved widespread clinical adoption. However, the COVID-induced shift to telemedicine may have created opportunities for PGHD as surrogates for vital signs collected in person. We assessed whether this shift was associated with greater ambulatory care PGHD use. We conducted an interrupted time series analysis of physician enrollment in, and patient-initiated vital sign transmission of non-COVID-associated PGHD through, a national PGHD platform (Validic). Ten health systems, 4695 physicians, and 51 320 patients were included. We found a significant increase in physician enrollment (slope change of 0.86/week, P = .02). Platform application programming interface calls continued their pre-COVID upward trend, despite large reductions in overall encounters. These findings suggest significantly greater pandemic-associated clinical demand for PGHD, and patient supply disproportionate to encounter rates. Increasing clinical use and ongoing efforts to reduce barriers, could help seize current adoption momentum to realize PGHD's potential value.

Quality of Life after Radioembolization for Hepatocellular Carcinoma Using a Digital Patient-Reported Outcome Tool.

Health-related quality of life (HRQoL) measurements are important for patient care, and emerging bundles in value-based care are placing an increasing emphasis on tying reimbursement to patient surveys. A multicenter pilot study was carried out to assess the efficacy of an automated digital patient engagement (DPE) platform for collecting HRQoL measurements at baseline and at 2- to 4-week intervals through 90 days after transarterial radioembolization (TARE) with yttrium-90 (90Y) treatments for hepatocellular carcinoma (HCC). The results revealed a survey completion of 78.4% and demonstrated only 4 of 35 individual symptom instances across all time points of transient worsening relative to baseline. Most importantly, the DPE platform provided an effective means for deploying and collecting patient-reported outcome measures.

Navigating the Digital Health Ecosystem to Bridge the Gap from Innovation to Transformation: A NODE. Health Perspective on Digital Evidence.

This article examines the challenges of broad adoption of digital solutions within the healthcare industry and why evidence is so critical for advancement of digital health technologies. It then illustrates how emerging nonprofits are positioned to help the ecosystem overcome these challenges and to catalyze the process of matching the right evidence-based solution to the right clinical challenge.

Accuracy of Internet-Based Patient Self-Report of Postdischarge Health Care Utilization and Complications Following Orthopedic Procedures: Observational Cohort Study.

BACKGROUND: The accuracy of patient self-report of health care utilization and complications has yet to be determined. If patients are accurate and engaged self-reporters, collecting this information in a manner that is temporally proximate to the health care utilization events themselves may prove valuable to health care organizations undertaking quality improvement initiatives for which such data are often unavailable. OBJECTIVE: The objective of this study was to measure the accuracy of patient self-report of health care utilization and complications in the 90 days following orthopedic procedures using an automated digital patient engagement platform. METHODS: We conducted a multicenter real-world observational cohort study across 10 orthopedic practices in California and Nevada. A total of 371 Anthem members with claims data meeting inclusion criteria who had undergone orthopedic procedures between March 1, 2015, and July 1, 2016, at participating practices already routinely using an automated digital patient engagement platform for asynchronous remote guidance and telemonitoring were sent surveys through the platform (in addition to the other materials being provided to them through the platform) regarding 90-day postencounter health care utilization and complications. Their self-reports to structured survey questions of health care utilization and complications were compared to claims data as a reference. RESULTS: The mean age of the 371 survey recipients was 56.5 (SD 15.7) years, 48.8% (181/371) of whom were female; 285 individuals who responded to 1 or more survey questions had a mean age of 56.9 (SD 15.4) years and a 49.5% (141/285) female distribution. There were no significant differences in demographics or event prevalence rates between responders and nonresponders. With an overall survey completion rate of 76.8% (285/371), patients were found to have accuracy of self-report characterized by a kappa of 0.80 and agreement of 0.99 and a kappa of 1.00 and agreement of 1.00 for 90-day hospital admissions and pulmonary embolism, respectively. Accuracy of self-report of 90-day emergency room/urgent care visits and of surgical site infection were characterized by a kappa of 0.45 and agreement of 0.96 and a kappa of 0.53 and agreement of 0.97, respectively. Accuracy for other complications such as deep vein thrombosis, hemorrhage, severe constipation, and fracture/dislocation was lower, influenced by low event prevalence rates within our sample. CONCLUSIONS: In this multicenter observational cohort study using an automated internet-based digital patient engagement platform, we found that patients were most accurate self-reporters of 90-day hospital admissions and pulmonary embolism, followed by 90-day surgical site infection and emergency room/urgent care visits. They were less accurate for deep vein thrombosis and least accurate for hemorrhage, severe constipation, and fracture/dislocation. A total of 76.8% (285/371) of patients completed surveys without the need for clinical staff to collect responses, suggesting the acceptability to patients of internet-based survey dissemination from and collection by clinical teams. While our methods enabled detection of events outside of index institutions, assessment of accuracy of self-report for presence and absence of events and nonresponse bias analysis, low event prevalence rates, particularly for several of the complications, limit the conclusions that may be drawn for some of the findings. Nevertheless, this investigation suggests the potential that engaging patients in self-report through such survey modalities may offer for the timely and accurate measurement of matters germane to health care organizations engaged in quality improvement efforts post discharge.

Effectiveness of an Automated Digital Remote Guidance and Telemonitoring Platform on Costs, Readmissions, and Complications After Hip and Knee Arthroplasties.

BACKGROUND: The impact of a new class of automated digital patient engagement (DPE) platforms on potentially avoidable costs, hospital admissions, and complications after discharge following hip and knee arthroplasties has not been established. METHODS: We conducted a multicenter observational cohort study comparing claims data for potentially avoidable costs, hospital admissions, and complications for 90 days after discharge following hip and knee arthroplasties at 10 practice sites in CA and NV. One hundred eighty-six patients, enrolled between 2014 and 2016 on an automated DPE platform receiving guidance and remote monitoring perioperatively, were compared with 372 patients who underwent the same procedures from the same physicians within 3 years immediately preceding platform implementation. The primary end point was the proportion of patients with $0.00 in 90-day target costs because of potentially avoidable utilization within the platform's influence. Secondary end points included rates of potentially avoidable 90-day hospital admissions and composite complications. RESULTS: Ninety-three percent and 84.7% of the study and baseline cohorts, respectively, had $0.00 in target costs (P = .004), with a mean savings of $656.52/patient (P = .006). The baseline and study cohorts had 3.0% and 1.6% 90-day hospital admission rates (relative risk 0.545; 0.154, 1.931, P = .40), and 15.3% and 7.0% composite complication rates, respectively (relative risk 0.456; 0.256, 0.812, P = .004). CONCLUSION: Patients enrolled on an automated DPE platform after hip and knee arthroplasties demonstrated a significant reduction in potentially avoidable 90-day costs, a 45.4% nonsignificant relative reduction in 90-day hospital admissions, and a 54.4% significant relative reduction in 90-day complications.

Physicians' actions and influence, such as aggressive blood pressure control, greatly improve the health of diabetes patients.

Managing diabetes and preventing its associated morbidities require active partnerships between physicians and patients. Studies to date lack the level of detail to quantify the degree to which interventions that are more controlled by physicians influence outcomes versus those that are more controlled by patients. Using the Archimedes model, we simulated a thirty-year clinical trial and compared the effects of three sets of interventions over which physicians have progressively less control: compliance with process-of-care standards, such as conducting foot and retinal exams and screening for signs of early kidney disease; control of biomarkers, such as hemoglobin A1c and blood pressure; and lifestyle modifications, such as patients' switching to healthier diets and losing weight. We found that if all US adults diagnosed with type 2 diabetes met quality targets in all of these areas, they would experience a nearly 16 percent increase in quality-adjusted life-years and a nearly 23 percent reduction in fifteen-year mortality over the thirty-year simulation period. Meeting aggressive biomarker targets yielded the most benefit. Meeting conservative biomarker targets came next, followed closely by meeting process-of-care standards. The incremental benefits of complying fully with diet and smoking cessation yielded the least benefit. Thus, through measures more readily within their control, and through collaboration with their patients, physicians have a substantial opportunity to improve outcomes. These findings can inform policy makers' rational resource allocation decisions and the design of programs to improve diabetes care.

Health benefits and cost-effectiveness of primary genetic screening for Lynch syndrome in the general population.

In current clinical practice, genetic testing to detect Lynch syndrome mutations ideally begins with diagnostic testing of an individual affected with cancer before offering predictive testing to at-risk relatives. An alternative strategy that warrants exploration involves screening unaffected individuals via demographic and family histories, and offering genetic testing to those individuals whose risks for carrying a mutation exceed a selected threshold. Whether this approach would improve health outcomes in a manner that is cost-effective relative to current standards of care has yet to be demonstrated. To do so, we developed a simulation framework that integrated models of colorectal and endometrial cancers with a 5-generation family history model to predict health and economic outcomes of 20 primary screening strategies (at a wide range of compliance levels) aimed at detecting individuals with mismatch repair gene mutations and their at-risk relatives. These strategies were characterized by (i) different screening ages for starting risk assessment and (ii) different risk thresholds above which to implement genetic testing. For each strategy, 100,000 simulated individuals, representative of the U.S. population, were followed from the age of 20, and the outcomes were compared with current practice. Findings indicated that risk assessment starting at ages 25, 30, or 35, followed by genetic testing of those with mutation risks exceeding 5%, reduced colorectal and endometrial cancer incidence in mutation carriers by approximately 12.4% and 8.8%, respectively. For a population of 100,000 individuals containing 392 mutation carriers, this strategy increased quality-adjusted life-years (QALY) by approximately 135 with an average cost-effectiveness ratio of $26,000 per QALY. The cost-effectiveness of screening for mismatch repair gene mutations is comparable to that of accepted cancer screening activities in the general population such as colorectal cancer screening, cervical cancer screening, and breast cancer screening. These results suggest that primary screening of individuals for mismatch repair gene mutations, starting with risk assessment between the ages of 25 and 35, followed by genetic testing of those whose risk exceeds 5%, is a strategy that could improve health outcomes in a cost-effective manner relative to current practice.