Deploying Patient-Facing Application Programming Interfaces: Thematic Analysis of Health System Experiences.

BACKGROUND: Health systems have recently started to activate patient-facing application programming interfaces (APIs) to facilitate patient access to health data and other interactions. OBJECTIVE: This study sought to ascertain health systems' understanding, strategies, governance, and organizational infrastructure around patient-facing APIs, as well as their business drivers and barriers, to facilitate national learning, policy, and progress toward adoption. METHODS: We performed a content analysis of semistructured interviews with a convenience sample of 10 health systems known to be leading adopters of health technology, having either implemented or planning to implement patient-facing APIs. RESULTS: Of the 10 health systems, eight had operational patient-facing APIs, with organizational strategy driven most by federal policy, the emergence of Health Records on iPhone, and feelings of ethical obligation. The two priority use cases identified were enablement of a patient's longitudinal health record and digital interactions with the health system. The themes most frequently cited as barriers to the increased use of patient-facing APIs were security concerns, an immature app ecosystem that does not currently offer superior functionality compared with widely adopted electronic health record (EHR)-tethered portals, a lack of business drivers, EHR vendor hesitation toward data sharing, and immature technology and standards. CONCLUSIONS: Our findings reveal heterogeneity in health system understanding and approaches to the implementation and use of patient-facing APIs. Ongoing study, targeted policy interventions, and sharing of best practices appear necessary to achieve successful national implementation.

Diabetes Specialists Value Continuous Glucose Monitoring Despite Challenges in Prescribing and Data Review Process.

BACKGROUND: Diabetes clinicians are key facilitators of continuous glucose monitoring (CGM) provision, but data on provider behavior related to CGM use and CGM generated data are limited. METHODS: We conducted a national survey of providers caring for people with diabetes on CGM-related opinions, facilitators and barriers to prescription, and data review practices. RESULTS: Of 182 survey respondents, 73.2% worked at academic centers, 70.6% were endocrinologists, and 70.7% practiced in urban settings. Nearly 70% of providers reported CGM use in the majority of their patients with type 1 diabetes. Half of the providers reported CGM use in 10% to 50% of their patients with type 2 diabetes. All respondents believed CGM improved quality of life and could optimize diabetes control. We found no differences in reported rates of CGM use based on providers' years of experience, patient volume, practice setting, or clinic type. Most providers reviewed CGM data each visit (97.7%) and actively involved patients in the data interpretation (98.8%). Only 14.1% of clinicians reported reviewing CGM data without any prompting from patients or their family members outside of visits. Most providers (80.7%) reported their CGM data review was valued by patients although only half reported having adequate time (45.1%) or an efficient process (56.1%) to do so. CONCLUSIONS: Despite uniform support for CGM by providers, ongoing challenges related to cost, insurance coverage, and difficulties with prescription were major barriers to CGM use. Increased use of CGM in appropriate populations will necessitate improvements in data access and integration, clearly defined workflows, and decreased administrative burden to obtain CGM.

Large Language Models and Medical Education: Preparing for a Rapid Transformation in How Trainees Will Learn to Be Doctors.

Artificial intelligence has the potential to revolutionize health care but has yet to be widely implemented. In part, this may be because, to date, we have focused on easily predicted rather than easily actionable problems. Large language models (LLMs) represent a paradigm shift in our approach to artificial intelligence because they are easily accessible and already being tested by frontline clinicians, who are rapidly identifying possible use cases. LLMs in health care have the potential to reduce clerical work, bridge gaps in patient education, and more. As we enter this era of healthcare delivery, LLMs will present both opportunities and challenges in medical education. Future models should be developed to support trainees to develop skills in clinical reasoning, encourage evidence-based medicine, and offer case-based training opportunities. LLMs may also change what we continue teaching trainees with regard to clinical documentation. Finally, trainees can help us train and develop the LLMs of the future as we consider the best ways to incorporate LLMs into medical education. Ready or not, LLMs will soon be integrated into various aspects of clinical practice, and we must work closely with students and educators to make sure these models are also built with trainees in mind to responsibly chaperone medical education into the next era.

Digital Health at Enterprise Scale: Evaluation Framework for Selecting Patient-Facing Software in a Digital-First Health System.

The digital transformation of our health care system will require not only digitization of existing tools but also a redesign of our care delivery system and collaboration with digital partners. Traditional patient journeys are reactive to symptom presentation and delayed by health care system-centric scheduling, leading to poor experience and avoidable adverse outcomes. Patient journeys will be reimagined to a digital health pathway that seamlessly integrates various care experiences from telemedicine, remote monitoring, to in-person clinic visits. Through centering the care delivery around the patients, they can have more delightful experiences and enjoy the quality of standardized condition pathways and outcomes. To design and implement digital health pathways at scale, enterprise health care systems need to develop capabilities and partnerships in human-centered design, operational workflow, clinical content management, communication channels and mechanisms, reporting and analytics, standards-based integration, security and data management, and scalability. Using a human-centered design methodology, care pathways will be built upon an understanding of the unmet needs of the patients to have a more enjoyable experience of care with improved clinical outcomes. To power this digital care pathway, enterprises will choose to build or partner for clinical content management to operationalize up-to-date, best-in-class pathways. With this clinical engine, this digital solution will engage with patients through multimodal communication modalities, including written, audio, photo, or video, throughout the patient journey. Leadership teams will review reporting and analytics functions to track that the digital care pathways will be iterated to improve patient experience, clinical metrics, and operational efficiency. On the backend, standards-based integration will allow this system to be built in conjunction with the electronic medical record and other data systems to provide safe and efficient use of the digital care solution. For protecting patient information and compliance, a security and data management strategy is critical to derisking breeches and preserving privacy. Finally, a framework of technical scalability will allow digital care pathways to proliferate throughout the enterprise and support the entire patient population. This framework empowers enterprise health care systems to avoid collecting a fragmented series of one-off solutions but develop a sustainable concerted roadmap to the future of proactive intelligent patient care.

Behavioral "nudges" in the electronic health record to reduce waste and misuse: 3 interventions.

Electronic health records (EHRs) offer decision support in the form of alerts, which are often though not always interruptive. These alerts, though sometimes effective, can come at the cost of high cognitive burden and workflow disruption. Less well studied is the design of the EHR itself-the ordering provider's "choice architecture"-which "nudges" users toward alternatives, sometimes unintentionally toward waste and misuse, but ideally intentionally toward better practice. We studied 3 different workflows at our institution where the existing choice architecture was potentially nudging providers toward erroneous decisions, waste, and misuse in the form of inappropriate laboratory work, incorrectly specified computerized tomographic imaging, and excessive benzodiazepine dosing for imaging-related sedation. We changed the architecture to nudge providers toward better practice and found that the 3 nudges were successful to varying degrees in reducing erroneous decision-making and mitigating waste and misuse.

Design and implementation of a digital health home spirometry intervention for remote monitoring of lung transplant function.

BACKGROUND: We developed an automated, chat-based, digital health intervention using Bluetooth-enabled home spirometers to monitor for complications of lung transplantation in a real-world application. METHODS: A chat-based application prompted patients to perform home spirometry, enter their forced expiratory volume in 1 second (FEV1), answer symptom queries, and provided patient education. The program alerted patients and providers to substantial FEV1 decreases and concerning symptoms. Data was integrated into the electronic health record (EHR) system and dashboards were developed for program monitoring. RESULT: Between May 2020 and December 2021, 544 patients were invited to enroll, of whom 427 were invited remotely and 117 were enrolled in-person. 371 (68%) participated by submitting ≥1 FEV1 values. Overall engagement was high, with an average of 197 unique patients submitting FEV1 data per month. In-person enrollees submitted an average of 4.6 FEV1 values per month and responded to 55% of scheduled chats. Home and laboratory FEV1 values correlated closely (rho = 0.93). There was an average of 133 ± 59 FEV1 decline alerts and 59 ± 23 symptom alerts per month. 72% of patients accessed education modules, and the program had a high net promoter score (53) amongst users. CONCLUSIONS: We demonstrate that a novel, automated, chat-based, and EHR-integrated home spirometry intervention is well accepted, generates reliable assessments of graft function, and can deliver automated feedback and education resulting in moderately-high adherence rates. We found that in-person onboarding yields better engagement and adherence. Future work will aim to demonstrate the impact of remote care monitoring on early detection of lung transplant complications.

Smart Insulin Pens: Advancing Digital Transformation and a Connected Diabetes Care Ecosystem.

With the first commercially available smart insulin pens, the predominant insulin delivery device for millions of people living with diabetes is now coming into the digital age. Smart insulin pens (SIPs) have the potential to reshape a connected diabetes care ecosystem for patients, providers, and health systems. Existing SIPs are enhanced with real-time wireless connectivity, digital dose capture, and integration with personalized dosing decision support. Automatic dose capture can promote effective retrospective review of insulin dose data, particularly when paired with glucose data. Patients, providers, and diabetes care teams will be able to make increasingly data-driven decisions and recommendations, in real time, during scheduled visits, and in a more continuous, asynchronous care model. As SIPs continue to progress along the path of digital transformation, we can expect additional benefits: iteratively improving software, machine learning, and advanced decision support. Both these technological advances, and future care delivery models with asynchronous interactions, will depend on easy, open, and continuous data exchange between the growing number of diabetes devices. SIPs have a key role in modernizing diabetes care for a large population of people living with diabetes.

Analysis of "Accuracy of a 14-Day Factory Calibrated Continuous Glucose Monitoring System With Advanced Algorithm in Pediatric and Adult Population With Diabetes".

In this study by Alva et al, accuracy of a second-generation factory calibrated continuous glucose monitoring system is evaluated. Compared to the first-generation FreeStyle Libre 14-day system (FSL), accuracy was improved throughout the 14-day wear period, including improved accuracy in hypoglycemia for adults and youth. The addition of optional real-time alerts for hypoglycemia and hyperglycemia as well as an integrated continuous glucose monitor (iCGM) designation by the FDA may further enable users to benefit from using CGM in real time, including in future automated insulin delivery systems. As CGM accuracy, affordability, and accessibility improve, we anticipate increased uptake of CGM by people on intensive insulin therapy, and also potential benefits and expansion into a broader patient population. There are growing opportunities to leverage cloud-connected CGM devices in the increasingly virtual, continuous telehealth-driven diabetes care model, which will require more focus on development and use of data interoperability standards.

Patient Experience and Feedback After Using an Electronic Health Record-Integrated COVID-19 Symptom Checker: Survey Study.

BACKGROUND: Symptom checkers have been widely used during the COVID-19 pandemic to alleviate strain on health systems and offer patients a 24-7 self-service triage option. Although studies suggest that users may positively perceive web-based symptom checkers, no studies have quantified user feedback after use of an electronic health record-integrated COVID-19 symptom checker with self-scheduling functionality. OBJECTIVE: In this paper, we aimed to understand user experience, user satisfaction, and user-reported alternatives to the use of a COVID-19 symptom checker with self-triage and self-scheduling functionality. METHODS: We launched a patient-portal-based self-triage and self-scheduling tool in March 2020 for patients with COVID-19 symptoms, exposures, or questions. We made an optional, anonymous Qualtrics survey available to patients immediately after they completed the symptom checker. RESULTS: Between December 16, 2021, and March 28, 2022, there were 395 unique responses to the survey. Overall, the respondents reported high satisfaction across all demographics, with a median rating of 8 out of 10 and 288/395 (47.6%) of the respondents giving a rating of 9 or 10 out of 10. User satisfaction scores were not associated with any demographic factors. The most common user-reported alternatives had the web-based tool not been available were calling the COVID-19 telephone hotline and sending a patient-portal message to their physician for advice. The ability to schedule a test online was the most important symptom checker feature for the respondents. The most common categories of user feedback were regarding other COVID-19 services (eg, telephone hotline), policies, or procedures, and requesting additional features or functionality. CONCLUSIONS: This analysis suggests that COVID-19 symptom checkers with self-triage and self-scheduling functionality may have high overall user satisfaction, regardless of user demographics. By allowing users to self-triage and self-schedule tests and visits, tools such as this may prevent unnecessary calls and messages to clinicians. Individual feedback suggested that the user experience for this type of tool is highly dependent on the organization's operational workflows for COVID-19 testing and care. This study provides insight for the implementation and improvement of COVID-19 symptom checkers to ensure high user satisfaction.

Utilization patterns and efficiency gains from use of a fully EHR-integrated COVID-19 self-triage and self-scheduling tool: a retrospective analysis.

OBJECTIVE: Symptom checkers can help address high demand for SARS-CoV2 (COVID-19) testing and care by providing patients with self-service access to triage recommendations. However, health systems may be hesitant to invest in these tools, as their associated efficiency gains have not been studied. We aimed to quantify the operational efficiency gains associated with use of an online COVID-19 symptom checker as an alternative to a telephone hotline. METHODS: In our health system, ambulatory patients can either use an online symptom checker or a telephone hotline to be triaged and connected to COVID-19 care. We performed a retrospective analysis of adults who used either method between October 20, 2021 and January 10, 2022, using call logs, electronic health record data, and local wages to calculate labor costs. RESULTS: Of the 15 549 total COVID-19 triage encounters, 1820 (11.7%) used only the telephone hotline and 13 729 (88.3%) used the symptom checker. Only 271 (2%) of the patients who used the symptom checker also called the hotline. Hotline encounters required more clinician time compared to those involving the symptom checker (17.8 vs 0.4 min/encounter), resulting in higher average labor costs ($24.21 vs $0.55 per encounter). The symptom checker resulted in over 4200 clinician labor hours saved. CONCLUSION: When given the option, most patients completed COVID-19 triage and visit scheduling online, resulting in substantial efficiency gains. These benefits may encourage health system investment in such tools.

Telehealth in type 1 diabetes.

PURPOSE OF REVIEW: The role of telehealth in the care of people with type 1 diabetes (T1D) has expanded dramatically during the coronavirus pandemic, and is expected to remain a major care delivery modality going forward. This review explores the landscape of recent evidence for telehealth in T1D care. RECENT FINDINGS: Telemedicine for routine T1D care has shown equivalence to standard in-person care, with respect to glycemic control, while also increasing access, convenience, and satisfaction. Telehealth use promotes increased engagement of adolescents with T1D. Telehealth platforms have successfully been used in the care of microvascular complications and to support mental health related to diabetes. Machine learning and advanced decision support will increasingly be used to augment T1D care, as recent evidence suggests increasing capabilities to improve glycemic control. A spectrum of digital connected care services are emerging to support people with diabetes with daily management of diabetes. Finally, policy and systems are required that promote data interoperability, telemedicine provision, and reimbursement to support the ongoing growth of telehealth in T1D. SUMMARY: A developing field of evidence supports use of telehealth in T1D. As this care modality scales, it has the potential to increase access to high-quality diabetes care for many people with T1D.

Disparities in Telemedicine Use for Subspecialty Diabetes Care During COVID-19 Shelter-In-Place Orders.

BACKGROUND: During the COVID-19 pandemic, telemedicine use rapidly and dramatically increased for management of diabetes mellitus. It is unknown whether access to telemedicine care has been equitable during this time. This study aimed to identify patient-level factors associated with adoption of telemedicine for subspecialty diabetes care during the pandemic. METHODS: We conducted an explanatory sequential mixed-methods study using data from a single academic medical center. We used multivariate logistic regression to explore associations between telemedicine use and demographic factors for patients receiving subspecialty diabetes care between March 19 and June 30, 2020. We then surveyed a sample of patients who received in-person care to understand why these patients did not use telemedicine. RESULTS: Among 1292 patients who received subspecialty diabetes care during the study period, those over age 65 were less likely to use telemedicine (OR: 0.34, 95% CI: 0.22-0.52, P < .001), as were patients with a primary language other than English (OR: 0.53, 95% CI: 0.31-0.91, P = .02), and patients with public insurance (OR: 0.64, 95% CI: 0.49-0.84, P = .001). Perceived quality of care and technological barriers were the most common reasons cited for choosing in-person care during the pandemic. CONCLUSIONS: Our findings suggest that, amidst the COVID-19 pandemic, there have been disparities in telemedicine use by age, language, and insurance for patients with diabetes mellitus. We anticipate telemedicine will continue to be an important care modality for chronic conditions in the years ahead. Significant work must therefore be done to ensure that telemedicine services do not introduce or widen population health disparities.

Treating Alpelisib-Induced Hyperglycemia with Very Low Carbohydrate Diets and Sodium-Glucose Co-Transporter 2 Inhibitors: A Case Series.

Alpelisib is a α-selective phosphatidylinositol 3-kinase (PI3K) inhibitor approved for treatment of postmenopausal women, and men, with hormone receptor positive (HR+), human epidermal growth factor receptor 2 negative (HER2-), PIK3CA-mutated, advanced breast cancer (ABC). Hyperglycemia is a common, on-target adverse effect that impairs treatment efficacy and increases the rate of treatment delays, dose reductions, and discontinuation. Currently, there are no clear guidelines on how to manage hyperglycemia due to alpelisib when metformin is not effective. In this case series, we review 3 subjects with ABC that developed hyperglycemia during alpelisib-fulvestrant therapy and were successfully managed with dietary and pharmacologic interventions. These cases provide anecdotal evidence to support the use of sodium-glucose co-transporter-2 inhibitors (SGLT2i) and very low carbohydrate diets to minimize hyperglycemia during alpelisib therapy.

Top 10 Tips for Successfully Implementing a Diabetes Telehealth Program.

Diabetes management is well suited to use of telehealth, and recent improvements in both diabetes technology and telehealth policy make this an ideal time for diabetes providers to begin integrating telehealth into their practices. This article provides background information, specific recommendations for effective implementation, and a vision for the future landscape of telehealth within diabetes care to guide interested providers and practices on this topic. Note: This article was written prior to the COVID19 pandemic, and does not include information about recent telehealth policy changes that occurred during or as a result of this public health crisis.

Design and development of referrals automation, a SMART on FHIR solution to improve patient access to specialty care.

BACKGROUND: Referring patients to specialty care is an inefficient and error-prone process. Gaps in the referral process lead to delays in patients' access to care, negative patient experience, worse health outcomes, and increased operational costs. While implementation of standards-based electronic referral options can alleviate some of these inefficiencies, many referrals to tertiary and quaternary care centers continue to be sent via fax. OBJECTIVE: We describe the design process and architecture for a software application that has been developed and deployed to optimize the referrals intake process by automating the processing and digitization of incoming specialty referral faxes, extracting key data elements and integrating them into the electronic health record (EHR), and organizing referrals. METHODS: A human-centered design approach was used to identify and describe the inefficiencies in the external referral process at our large, urban tertiary care center. Referrals Automation, an application to convert referral faxes to digital referrals in the EHR, was conceptualized based on key stakeholder interviews and time and motion studies. This application was designed using Substitutable Medical Applications and Reusable Technologies (SMART) and Fast Healthcare Interoperability Resource (FHIR) platforms to allow for adaptability into other healthcare organizations. RESULTS: Referrals Automation software was developed as a healthcare information technology solution to streamline the fax to referral process. The application was implemented into several specialty clinics. Metrics were built-in to the applications to evaluate and guide the further iteration of these features. CONCLUSIONS: Referrals Automation will enhance the referrals process by further streamlining and organizing the patient referral process.

Implementation of a digital chatbot to screen health system employees during the COVID-19 pandemic.

The screening of healthcare workers for COVID-19 (coronavirus disease 2019) symptoms and exposures prior to every clinical shift is important for preventing nosocomial spread of infection but creates a major logistical challenge. To make the screening process simple and efficient, University of California, San Francisco Health designed and implemented a digital chatbot-based workflow. Within 1 week of forming a team, we conducted a product development sprint and deployed the digital screening process. In the first 2 months of use, over 270 000 digital screens have been conducted. This process has reduced wait times for employees entering our hospitals during shift changes, allowed for physical distancing at hospital entrances, prevented higher-risk individuals from coming to work, and provided our healthcare leaders with robust, real-time data for make staffing decisions.

Rapid design and implementation of an integrated patient self-triage and self-scheduling tool for COVID-19.

OBJECTIVE: To rapidly deploy a digital patient-facing self-triage and self-scheduling tool in a large academic health system to address the COVID-19 pandemic. MATERIALS AND METHODS: We created a patient portal-based COVID-19 self-triage and self-scheduling tool and made it available to all primary care patients at the University of California, San Francisco Health, a large academic health system. Asymptomatic patients were asked about exposure history and were then provided relevant information. Symptomatic patients were triaged into 1 of 4 categories-emergent, urgent, nonurgent, or self-care-and then connected with the appropriate level of care via direct scheduling or telephone hotline. RESULTS: This self-triage and self-scheduling tool was designed and implemented in under 2 weeks. During the first 16 days of use, it was completed 1129 times by 950 unique patients. Of completed sessions, 315 (28%) were by asymptomatic patients, and 814 (72%) were by symptomatic patients. Symptomatic patient triage dispositions were as follows: 193 emergent (24%), 193 urgent (24%), 99 nonurgent (12%), 329 self-care (40%). Sensitivity for detecting emergency-level care was 87.5% (95% CI 61.7-98.5%). DISCUSSION: This self-triage and self-scheduling tool has been widely used by patients and is being rapidly expanded to other populations and health systems. The tool has recommended emergency-level care with high sensitivity, and decreased triage time for patients with less severe illness. The data suggests it also prevents unnecessary triage messages, phone calls, and in-person visits. CONCLUSION: Patient self-triage tools integrated into electronic health record systems have the potential to greatly improve triage efficiency and prevent unnecessary visits during the COVID-19 pandemic.

A Pilot Study of Use of a Software Platform for the Collection, Integration, and Visualization of Diabetes Device Data by Health Care Providers in a Multidisciplinary Pediatric Setting.

BACKGROUND: Diabetes devices provide data for health care providers (HCPs) and people with type 1 diabetes to make management decisions. Extracting and viewing the data require separate, proprietary software applications for each device. In this pilot study, we examined the feasibility of using a single software platform (Tidepool) that integrates data from multiple devices. MATERIALS AND METHODS: Participating HCPs (n = 15) used the software with compatible devices in all patient visits for 6 months. Samples of registration desk activity and office visits were observed before and after introducing the software, and HCPs provided feedback by survey and focus groups. RESULTS: The time required to upload data and the length of the office visit did not change. However, the number of times the HCP referred to the device data with patients increased from a mean of 2.8 (±1.2) to 6.1 (±3.1) times per visit (P = 0.0002). A significantly larger proportion of children looked at the device data with the new application (baseline: 61% vs. study end: 94%, P = 0.015). HCPs liked the web-based user interface, integration of the data from multiple devices, the ability to remotely access data, and use of the application to initiate patient education. Challenges included the need for automated data upload and integration with electronic medical records. CONCLUSIONS: The software did not add to the time needed to upload data or the length of clinic visits and promoted discussions with patients about data. Future studies of HCP use of the application will evaluate clinical outcomes and effects on patient engagement and self-management.