Leveraging Generative AI Tools to Support the Development of Digital Solutions in Health Care Research: Case Study.

BACKGROUND: Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. OBJECTIVE: This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. METHODS: We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. RESULTS: Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies. CONCLUSIONS: ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. TRIAL REGISTRATION: ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.

Quantifying the impact of telemedicine and patient medical advice request messages on physicians' work-outside-work.

The COVID-19 pandemic has boosted digital health utilization, raising concerns about increased physicians' after-hours clinical work ("work-outside-work"). The surge in patients' digital messages and additional time spent on work-outside-work by telemedicine providers underscores the need to evaluate the connection between digital health utilization and physicians' after-hours commitments. We examined the impact on physicians' workload from two types of digital demands - patients' messages requesting medical advice (PMARs) sent to physicians' inbox (inbasket), and telemedicine. Our study included 1716 ambulatory-care physicians in New York City regularly practicing between November 2022 and March 2023. Regression analyses assessed primary and interaction effects of (PMARs) and telemedicine on work-outside-work. The study revealed a significant effect of PMARs on physicians' work-outside-work and that this relationship is moderated by physicians' specialties. Non-primary care physicians or specialists experienced a more pronounced effect than their primary care peers. Analysis of their telemedicine load revealed that primary care physicians received fewer PMARs and spent less time in work-outside-work with more telemedicine. Specialists faced increased PMARs and did more work-outside-work as telemedicine visits increased which could be due to the difference in patient panels. Reducing PMAR volumes and efficient inbasket management strategies needed to reduce physicians' work-outside-work. Policymakers need to be cognizant of potential disruptions in physicians carefully balanced workload caused by the digital health services.

Implementing a Clinical Decision Support Tool to Improve Physical Activity.

BACKGROUND: Currently, only about half of U.S. adults achieve current physical activity guidelines. Routine physical activity is not regularly assessed, nor are patients routinely counseled by their healthcare provider on achieving recommended levels. The three-question physical activity vital sign (PAVS) was developed to assess physical activity duration and intensity and identify adults not meeting physical activity guidelines. Clinical decision support provided via a best practice advisory in an electronic health record (EHR) system can be triggered as a prompt, reminding healthcare providers to implement the best practice intervention when appropriate. Remote patient monitoring of physical activity can provide objective data in the EHR. OBJECTIVES: This study aimed to evaluate the feasibility and clinical utility of embedding the PAVS and a triggered best practice advisor into the EHR in an ambulatory preventive cardiology practice setting to alert providers to patients reporting low physical activity and prompt healthcare providers to counsel these patients as needed. METHODS: Three components based in the EHR were integrated for the purpose of this study: Patients completed the PAVS through their electronic patient portal prior to an office visit, a best practice advisory was created to prompt providers to counsel patients who reported low levels of physical activity, and remote patient monitoring via Fitbit synced to the EHR provided objective physical activity data. The intervention was pilot-tested in the Epic EHR for 1 year (July 1, 2021 to June 30, 2022). Qualitative feedback on the intervention from both providers and patients was obtained at the completion of the study. RESULTS: Monthly assessments of the use of the PAVS and best practice advisory and remote patient monitoring were completed. Patients' completion of the PAVS varied from 35% to 48% per month. The best practice advisory was signed by providers between 2% and 65% and was acknowledged by 2%-22% per month. The majority (58%) of patients were able to sync a Fitbit device to their EHR for remote monitoring. DISCUSSION: Although uptake of each component needs improvement, this pilot demonstrated the feasibility of incorporating a physical activity promotion intervention into the EHR. Qualitative feedback provided guidance for future implementation.

Reducing prescribing of antibiotics for acute respiratory infections using a frontline nurse-led EHR-Integrated clinical decision support tool: protocol for a stepped wedge randomized control trial.

BACKGROUND: Overprescribing of antibiotics for acute respiratory infections (ARIs) remains a major issue in outpatient settings. Use of clinical prediction rules (CPRs) can reduce inappropriate antibiotic prescribing but they remain underutilized by physicians and advanced practice providers. A registered nurse (RN)-led model of an electronic health record-integrated CPR (iCPR) for low-acuity ARIs may be an effective alternative to address the barriers to a physician-driven model. METHODS: Following qualitative usability testing, we will conduct a stepped-wedge practice-level cluster randomized controlled trial (RCT) examining the effect of iCPR-guided RN care for low acuity patients with ARI. The primary hypothesis to be tested is: Implementation of RN-led iCPR tools will reduce antibiotic prescribing across diverse primary care settings. Specifically, this study aims to: (1) determine the impact of iCPRs on rapid strep test and chest x-ray ordering and antibiotic prescribing rates when used by RNs; (2) examine resource use patterns and cost-effectiveness of RN visits across diverse clinical settings; (3) determine the impact of iCPR-guided care on patient satisfaction; and (4) ascertain the effect of the intervention on RN and physician burnout. DISCUSSION: This study represents an innovative approach to using an iCPR model led by RNs and specifically designed to address inappropriate antibiotic prescribing. This study has the potential to provide guidance on the effectiveness of delegating care of low-acuity patients with ARIs to RNs to increase use of iCPRs and reduce antibiotic overprescribing for ARIs in outpatient settings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04255303, Registered February 5 2020, https://clinicaltrials.gov/ct2/show/NCT04255303 .

Considerations for using predictive models that include race as an input variable: The case study of lung cancer screening.

Indiscriminate use of predictive models incorporating race can reinforce biases present in source data and lead to an exacerbation of health disparities. In some countries, such as the United States, there is therefore a push to remove race from prediction models; however, there are still many prediction models that use race as an input. Biomedical informaticists who are given the responsibility of using these predictive models in healthcare environments are likely to be faced with questions like how to deal with race covariates in these models. Thus, there is a need for a pragmatic framework to help model users think through how to include race in their chosen model so as to avoid inadvertently exacerbating disparities. In this paper, we use the case study of lung cancer screening to propose a simple framework to guide how model users can approach the use (or non-use) of race inputs in the predictive models they are tasked with leveraging in electronic health records and clinical workflows.

Implementing Remote Patient Monitoring of Physical Activity in Clinical Practice.

PURPOSE: Remote patient monitoring (RPM) is a tool for patients to share data collected outside of office visits. RPM uses technology and the digital transmission of data to inform clinician decision-making in patient care. Using RPM to track routine physical activity is feasible to operationalize, given contemporary consumer-grade devices that can sync to the electronic health record. Objective monitoring through RPM can be more reliable than patient self-reporting for physical activity. DESIGN AND METHODS: This article reports on four pilot studies that highlight the utility and practicality of RPM for physical activity monitoring in outpatient clinical care. Settings include endocrinology, cardiology, neurology, and pulmonology settings. RESULTS: The four pilot use cases discussed demonstrate how RPM is utilized to monitor physical activity, a shift that has broad implications for prediction, prevention, diagnosis, and management of chronic disease and rehabilitation progress. CLINICAL RELEVANCE: If RPM for physical activity is to be expanded, it will be important to consider that certain populations may face challenges when accessing digital health services. CONCLUSION: RPM technology provides an opportunity for clinicians to obtain objective feedback for monitoring progress of patients in rehabilitation settings. Nurses working in rehabilitation settings may need to provide additional patient education and support to improve uptake.

Opening the Black Box of an mHealth Patient-Reported Outcome Tool for Diabetes Self-Management: Interview Study Among Patients With Type 2 Diabetes.

BACKGROUND: Mobile health (mHealth) tools are used to collect data on patient-reported outcomes (PROs) and facilitate the assessment of patients' self-management behaviors outside the clinic environment. Despite the high availability of mHealth diabetes tools, there is a lack of understanding regarding the underlying reasons why these mHealth PRO tools succeed or fail in terms of changing patients' self-management behaviors. OBJECTIVE: This study aims to identify the factors that drive engagement with an mHealth PRO tool and facilitate patients' adoption of self-management behaviors, as well as elicit suggestions for improvement. METHODS: This qualitative study was conducted within the context of a randomized controlled trial designed to evaluate the efficacy of an mHealth PRO tool (known as i-Matter) versus usual care regarding reduction in glycated hemoglobin (HbA1c) levels and adherence to self-management behaviors at 12 months among patients with uncontrolled type 2 diabetes. Patients randomized to i-Matter participated in semistructured interviews about their experiences at the 3-, 6-, 9-, and 12-month study visits. A qualitative analysis of the interviews was conducted by 2 experienced qualitative researchers using conventional qualitative content analysis. RESULTS: The sample comprised 71 patients, of whom 67 (94%) completed at least one interview (n=48, 72% female patients; n=25, 37% identified as African American or Black; mean age 56.65 [SD 9.79] years). We identified 4 overarching themes and 6 subthemes. Theme 1 showed that the patients' reasons for engagement with i-Matter were multifactorial. Patients were driven by internal motivating factors that bolstered their engagement and helped them feel accountable for their diabetes (subtheme 1) and external motivating factors that helped to serve as reminders to be consistent with their self-management behaviors (subtheme 2). Theme 2 revealed that the use of i-Matter changed patients' attitudes toward their disease and their health behaviors in 2 ways: patients developed more positive attitudes about their condition and their ability to effectively self-manage it (subtheme 3), and they also developed a better awareness of their current behaviors, which motivated them to adopt healthier lifestyle behaviors (subtheme 4). Theme 3 showed that patients felt more committed to their health as a result of using i-Matter. Theme 4 highlighted the limitations of i-Matter, which included its technical design (subtheme 5) and the need for more resources to support the PRO data collected and shared through the tool (subtheme 6). CONCLUSIONS: This study isolated internal and external factors that prompted patients to change their views about their diabetes, become more engaged with the intervention and their health, and adopt healthy behaviors. These behavioral mechanisms provide important insights to drive future development of mHealth interventions that could lead to sustained behavior change.

Operational Implementation of Remote Patient Monitoring Within a Large Ambulatory Health System: Multimethod Qualitative Case Study.

BACKGROUND: Remote patient monitoring (RPM) technologies can support patients living with chronic conditions through self-monitoring of physiological measures and enhance clinicians' diagnostic and treatment decisions. However, to date, large-scale pragmatic RPM implementation within health systems has been limited, and understanding of the impacts of RPM technologies on clinical workflows and care experience is lacking. OBJECTIVE: In this study, we evaluate the early implementation of operational RPM initiatives for chronic disease management within the ambulatory network of an academic medical center in New York City, focusing on the experiences of "early adopter" clinicians and patients. METHODS: Using a multimethod qualitative approach, we conducted (1) interviews with 13 clinicians across 9 specialties considered as early adopters and supporters of RPM and (2) speculative design sessions exploring the future of RPM in clinical care with 21 patients and patient representatives, to better understand experiences, preferences, and expectations of pragmatic RPM use for health care delivery. RESULTS: We identified themes relevant to RPM implementation within the following areas: (1) data collection and practices, including impacts of taking real-world measures and issues of data sharing, security, and privacy; (2) proactive and preventive care, including proactive and preventive monitoring, and proactive interventions and support; and (3) health disparities and equity, including tailored and flexible care and implicit bias. We also identified evidence for mitigation and support to address challenges in each of these areas. CONCLUSIONS: This study highlights the unique contexts, perceptions, and challenges regarding the deployment of RPM in clinical practice, including its potential implications for clinical workflows and work experiences. Based on these findings, we offer implementation and design recommendations for health systems interested in deploying RPM-enabled health care.

Putting ChatGPT's Medical Advice to the (Turing) Test: Survey Study.

BACKGROUND: Chatbots are being piloted to draft responses to patient questions, but patients' ability to distinguish between provider and chatbot responses and patients' trust in chatbots' functions are not well established. OBJECTIVE: This study aimed to assess the feasibility of using ChatGPT (Chat Generative Pre-trained Transformer) or a similar artificial intelligence-based chatbot for patient-provider communication. METHODS: A survey study was conducted in January 2023. Ten representative, nonadministrative patient-provider interactions were extracted from the electronic health record. Patients' questions were entered into ChatGPT with a request for the chatbot to respond using approximately the same word count as the human provider's response. In the survey, each patient question was followed by a provider- or ChatGPT-generated response. Participants were informed that 5 responses were provider generated and 5 were chatbot generated. Participants were asked-and incentivized financially-to correctly identify the response source. Participants were also asked about their trust in chatbots' functions in patient-provider communication, using a Likert scale from 1-5. RESULTS: A US-representative sample of 430 study participants aged 18 and older were recruited on Prolific, a crowdsourcing platform for academic studies. In all, 426 participants filled out the full survey. After removing participants who spent less than 3 minutes on the survey, 392 respondents remained. Overall, 53.3% (209/392) of respondents analyzed were women, and the average age was 47.1 (range 18-91) years. The correct classification of responses ranged between 49% (192/392) to 85.7% (336/392) for different questions. On average, chatbot responses were identified correctly in 65.5% (1284/1960) of the cases, and human provider responses were identified correctly in 65.1% (1276/1960) of the cases. On average, responses toward patients' trust in chatbots' functions were weakly positive (mean Likert score 3.4 out of 5), with lower trust as the health-related complexity of the task in the questions increased. CONCLUSIONS: ChatGPT responses to patient questions were weakly distinguishable from provider responses. Laypeople appear to trust the use of chatbots to answer lower-risk health questions. It is important to continue studying patient-chatbot interaction as chatbots move from administrative to more clinical roles in health care.

Leveraging Electronic Health Record Technology and Team Care to Address Medication Adherence: Protocol for a Cluster Randomized Controlled Trial.

BACKGROUND: Low medication adherence is a common cause of high blood pressure but is often unrecognized in clinical practice. Electronic data linkages between electronic health records (EHRs) and pharmacies offer the opportunity to identify low medication adherence, which can be used for interventions at the point of care. We developed a multicomponent intervention that uses linked EHR and pharmacy data to automatically identify patients with elevated blood pressure and low medication adherence. The intervention then combines team-based care with EHR-based workflows to address medication nonadherence. OBJECTIVE: This study aims to describe the design of the Leveraging EHR Technology and Team Care to Address Medication Adherence (TEAMLET) trial, which tests the effectiveness of a multicomponent intervention that leverages EHR-based data and team-based care on medication adherence among patients with hypertension. METHODS: TEAMLET is a pragmatic, cluster randomized controlled trial in which 10 primary care practices will be randomized 1:1 to the multicomponent intervention or usual care. We will include all patients with hypertension and low medication adherence who are seen at enrolled practices. The primary outcome is medication adherence, as measured by the proportion of days covered, and the secondary outcome is clinic systolic blood pressure. We will also assess intervention implementation, including adoption, acceptability, fidelity, cost, and sustainability. RESULTS: As of May 2023, we have randomized 10 primary care practices into the study, with 5 practices assigned to each arm of the trial. The enrollment for the study commenced on October 5, 2022, and the trial is currently ongoing. We anticipate patient recruitment to go through the fall of 2023 and the primary outcomes to be assessed in the fall of 2024. CONCLUSIONS: The TEAMLET trial will evaluate the effectiveness of a multicomponent intervention that leverages EHR-based data and team-based care on medication adherence. If successful, the intervention could offer a scalable approach to address inadequate blood pressure control among millions of patients with hypertension. TRIAL REGISTRATION: ClinicalTrials.gov NCT05349422; https://clinicaltrials.gov/ct2/show/NCT05349422. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47930.

The Impact of an Electronic Best Practice Advisory on Patients' Physical Activity and Cardiovascular Risk Profile.

BACKGROUND: Regular physical activity (PA) is a component of cardiovascular health and is associated with a lower risk of cardiovascular disease (CVD). However, only about half of US adults achieved the current PA recommendations. OBJECTIVE: The study purpose was to implement PA counseling using a clinical decision support tool in a preventive cardiology clinic and to assess changes in CVD risk factors in a sample of patients enrolled over 12 weeks of PA monitoring. METHODS: This intervention, piloted for 1 year, had 3 components embedded in the electronic health record: assessment of patients' PA, an electronic prompt for providers to counsel patients reporting low PA, and patient monitoring using a Fitbit. Cardiovascular disease risk factors included PA (self-report and Fitbit), body mass index, blood pressure, lipids, and cardiorespiratory fitness assessed with the 6-minute walk test. Depression and quality of life were also assessed. Paired t tests assessed changes in CVD risk. RESULTS: The sample who enrolled in the remote patient monitoring (n = 59) were primarily female (51%), White adults (76%) with a mean age of 61.13 ± 11.6 years. Self-reported PA significantly improved over 12 weeks (P = .005), but not Fitbit steps (P = .07). There was a significant improvement in cardiorespiratory fitness (469 ± 108 vs 494 ± 132 m, P = .0034), and 23 participants (42%) improved at least 25 m, signifying a clinically meaningful improvement. Only 4 participants were lost to follow-up over 12 weeks of monitoring. CONCLUSIONS: Patients may need more frequent reminders to be active after an initial counseling session, perhaps getting automated messages based on their step counts syncing to their electronic health record.

Self-reported adherence and reasons for nonadherence among patients with low proportion of days covered for antihypertension medications.

BACKGROUND: Incorporation of pharmacy fill data into the electronic health record has enabled calculations of medication adherence, as measured by proportion of days covered (PDC), to be displayed to clinicians. Although PDC values help identify patients who may be nonadherent to their medications, it does not provide information on the reasons for medication-taking behaviors. OBJECTIVE: To characterize self-reported adherence status to antihypertensive medications among patients with low refill medication adherence. Our secondary objective was to identify the most common reasons for nonadherence and examine the patient sociodemographic characteristics associated with these barriers. METHODS: Participants were adult patients seen in primary care clinics of a large, urban health system and on antihypertensive therapy with a PDC of less than 80% based on 6-month linked electronic health record-pharmacy fill data. We administered a validated medication adherence screener and a survey assessing reasons for antihypertensive medication nonadherence. We used descriptive statistics to characterize these data and logistic and Poisson regression models to assess the relationship between sociodemographic characteristics and adherence barriers. RESULTS: The survey was completed by 242 patients (57% female; 61.2% White; 79.8% not Latino/a or Hispanic). Of these patients, 45% reported missing doses of their medications in the last 7 days. In addition, 48% endorsed having at least 1 barrier to adherence and 38.4% endorsed 2 or more barriers. The most common barriers were being busy and having difficulty remembering to take medications. Compared with White participants, Black participants (incident rate ratio = 2.49; 95% CI = 1.93-3.22) and participants of other races (incident rate ratio = 2.16; 95% CI = 1.62-2.89) experienced a greater number of barriers. CONCLUSIONS: Nearly half of patients with low PDC reported nonadherence in the prior week, suggesting PDC can be used as a screening tool. Augmenting PDC with brief self-report tools can provide insights into the reasons for nonadherence. DISCLOSURES: Dr Kharmats, Ms Martinez, Dr Belli, Ms Zhao, Dr Mann, Dr Schoenthaler, and Dr Blecker received grants from the National Institute of Health/National Heart, Lung, Blood Institute. Dr Voils holds a license by Duke University for the DOSE-Nonadherence measure and is a consultant for New York University Grossman School of Medicine. This research was supported by the NIH (R01HL156355). Dr Kharmats received a postdoctoral training grant from the National Institutes of Health (5T32HL129953-04). Dr Voils was supported by a Research Career Scientist award from the Health Services Research & Development Service of the Department of Veterans Affairs (RCS 14-443). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the United States Government.

Remote patient monitoring for management of diabetes mellitus in pregnancy is associated with improved maternal and neonatal outcomes.

BACKGROUND: Diabetes mellitus is a common medical complication of pregnancy, and its treatment is complex. Recent years have seen an increase in the application of mobile health tools and advanced technologies, such as remote patient monitoring, with the aim of improving care for diabetes mellitus in pregnancy. Previous studies of these technologies for the treatment of diabetes in pregnancy have been small and have not clearly shown clinical benefit with implementation. OBJECTIVE: Remote patient monitoring allows clinicians to monitor patients' health data (such as glucose values) in near real-time, between office visits, to make timely adjustments to care. Our objective was to determine if using remote patient monitoring for the management of diabetes in pregnancy leads to an improvement in maternal and neonatal outcomes. STUDY DESIGN: This was a retrospective cohort study of pregnant patients with diabetes mellitus managed by the maternal-fetal medicine practice at one academic institution between October 2019 and April 2021. This practice transitioned from paper-based blood glucose logs to remote patient monitoring in February 2020. Remote patient monitoring options included (1) device integration with Bluetooth glucometers that automatically uploaded measured glucose values to the patient's Epic MyChart application or (2) manual entry in which patients manually logged their glucose readings into their MyChart application. Values in the MyChart application directly transferred to the patient's electronic health record for review and management by clinicians. In total, 533 patients were studied. We compared 173 patients managed with paper logs to 360 patients managed with remote patient monitoring (176 device integration and 184 manual entry). Our primary outcomes were composite maternal morbidity (which included third- and fourth-degree lacerations, chorioamnionitis, postpartum hemorrhage requiring transfusion, postpartum hysterectomy, wound infection or separation, venous thromboembolism, and maternal admission to the intensive care unit) and composite neonatal morbidity (which included umbilical cord pH <7.00, 5 minute Apgar score <7, respiratory morbidity, hyperbilirubinemia, meconium aspiration, intraventricular hemorrhage, necrotizing enterocolitis, sepsis, pneumonia, seizures, hypoxic ischemic encephalopathy, shoulder dystocia, trauma, brain or body cooling, and neonatal intensive care unit admission). Secondary outcomes were measures of glycemic control and the individual components of the primary composite outcomes. We also performed a secondary analysis in which the patients who used the two different remote patient monitoring options (device integration vs manual entry) were compared. Chi-square, Fisher's exact, 2-sample t, and Mann-Whitney tests were used to compare the groups. A result was considered statistically significant at P<.05. RESULTS: Maternal baseline characteristics were not significantly different between the remote patient monitoring and paper groups aside from a slightly higher baseline rate of chronic hypertension in the remote patient monitoring group (6.1% vs 1.2%; P=.011). The primary outcomes of composite maternal and composite neonatal morbidity were not significantly different between the groups. However, remote patient monitoring patients submitted more glucose values (177 vs 146; P=.008), were more likely to achieve glycemic control in target range (79.2% vs 52.0%; P<.0001), and achieved the target range sooner (median, 3.3 vs 4.1 weeks; P=.025) than patients managed with paper logs. This was achieved without increasing in-person visits. Remote patient monitoring patients had lower rates of preeclampsia (5.8% vs 15.0%; P=.0006) and their infants had lower rates of neonatal hypoglycemia in the first 24 hours of life (29.8% vs 51.7%; P<.0001). CONCLUSION: Remote patient monitoring for the management of diabetes mellitus in pregnancy is superior to a traditional paper-based approach in achieving glycemic control and is associated with improved maternal and neonatal outcomes.

Nudging Health Care Providers' Adoption of Clinical Decision Support: Protocol for the User-Centered Development of a Behavioral Economics-Inspired Electronic Health Record Tool.

BACKGROUND: The improvements in care resulting from clinical decision support (CDS) have been significantly limited by consistently low health care provider adoption. Health care provider attitudes toward CDS, specifically psychological and behavioral barriers, are not typically addressed during any stage of CDS development, although they represent an important barrier to adoption. Emerging evidence has shown the surprising power of using insights from the field of behavioral economics to address psychological and behavioral barriers. Nudges are formal applications of behavioral economics, defined as positive reinforcement and indirect suggestions that have a nonforced effect on decision-making. OBJECTIVE: Our goal is to employ a user-centered design process to develop a CDS tool-the pulmonary embolism (PE) risk calculator-for PE risk stratification in the emergency department that incorporates a behavior theory-informed nudge to address identified behavioral barriers to use. METHODS: All study activities took place at a large academic health system in the New York City metropolitan area. Our study used a user-centered and behavior theory-based approach to achieve the following two aims: (1) use mixed methods to identify health care provider barriers to the use of an active CDS tool for PE risk stratification and (2) develop a new CDS tool-the PE risk calculator-that addresses behavioral barriers to health care providers' adoption of CDS by incorporating nudges into the user interface. These aims were guided by the revised Observational Research Behavioral Information Technology model. A total of 50 clinicians who used the original version of the tool were surveyed with a quantitative instrument that we developed based on a behavior theory framework-the Capability-Opportunity-Motivation-Behavior framework. A semistructured interview guide was developed based on the survey responses. Inductive methods were used to analyze interview session notes and audio recordings from 12 interviews. Revised versions of the tool were developed that incorporated nudges. RESULTS: Functional prototypes were developed by using Axure PRO (Axure Software Solutions) software and usability tested with end users in an iterative agile process (n=10). The tool was redesigned to address 4 identified major barriers to tool use; we included 2 nudges and a default. The 6-month pilot trial for the tool was launched on October 1, 2021. CONCLUSIONS: Clinicians highlighted several important psychological and behavioral barriers to CDS use. Addressing these barriers, along with conducting traditional usability testing, facilitated the development of a tool with greater potential to transform clinical care. The tool will be tested in a prospective pilot trial. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42653.

Analyzing User Engagement Within a Patient-Reported Outcomes Texting Tool for Diabetes Management: Engagement Phenotype Study.

BACKGROUND: Patient-reported outcomes (PROs) capture patients' views on their health conditions and its management, and are increasingly used in clinical trials, including those targeting type 2 diabetes (T2D). Mobile health (mHealth) tools offer novel solutions for collecting PRO data in real time. Although patients are at the center of any PRO-based intervention, few studies have examined user engagement with PRO mHealth tools. OBJECTIVE: This study aimed to evaluate user engagement with a PRO mHealth tool for T2D management, identify patterns of user engagement and similarities and differences between the patients, and identify the characteristics of patients who are likely to drop out or be less engaged with a PRO mHealth tool. METHODS: We extracted user engagement data from an ongoing clinical trial that tested the efficacy of a PRO mHealth tool designed to improve hemoglobin A1c levels in patients with uncontrolled T2D. To date, 61 patients have been randomized to the intervention, where they are sent 6 PRO text messages a day that are relevant to T2D self-management (healthy eating and medication adherence) over the 12-month study. To analyze user engagement, we first compared the response rate (RR) and response time between patients who completed the 12-month intervention and those who dropped out early (noncompleters). Next, we leveraged latent class trajectory modeling to classify patients from the completer group into 3 subgroups based on similarity in the longitudinal engagement data. Finally, we investigated the differences between the subgroups of completers from various cross-sections (time of the day and day of the week) and PRO types. We also explored the patient demographics and their distribution among the subgroups. RESULTS: Overall, 19 noncompleters had a lower RR to PRO questions and took longer to respond to PRO questions than 42 completers. Among completers, the longitudinal RRs demonstrated differences in engagement patterns over time. The completers with the lowest engagement showed peak engagement during month 5, almost at the midstage of the program. The remaining subgroups showed peak engagement at the beginning of the intervention, followed by either a steady decline or sustained high engagement. Comparisons of the demographic characteristics showed significant differences between the high engaged and low engaged subgroups. The high engaged completers were predominantly older, of Hispanic descent, bilingual, and had a graduate degree. In comparison, the low engaged subgroup was composed mostly of African American patients who reported the lowest annual income, with one of every 3 patients earning less than US $20,000 annually. CONCLUSIONS: There are discernible engagement phenotypes based on individual PRO responses, and their patterns vary in the timing of peak engagement and demographics. Future studies could use these findings to predict engagement categories and tailor interventions to promote longitudinal engagement. TRIAL REGISTRATION: Clinicaltrials.gov NCT03652389; https://clinicaltrials.gov/ct2/show/NCT03652389. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/18554.

Association of Disparities in Family History and Family Cancer History in the Electronic Health Record With Sex, Race, Hispanic or Latino Ethnicity, and Language Preference in 2 Large US Health Care Systems.

Importance: Clinical decision support (CDS) algorithms are increasingly being implemented in health care systems to identify patients for specialty care. However, systematic differences in missingness of electronic health record (EHR) data may lead to disparities in identification by CDS algorithms. Objective: To examine the availability and comprehensiveness of cancer family history information (FHI) in patients' EHRs by sex, race, Hispanic or Latino ethnicity, and language preference in 2 large health care systems in 2021. Design, Setting, and Participants: This retrospective EHR quality improvement study used EHR data from 2 health care systems: University of Utah Health (UHealth) and NYU Langone Health (NYULH). Participants included patients aged 25 to 60 years who had a primary care appointment in the previous 3 years. Data were collected or abstracted from the EHR from December 10, 2020, to October 31, 2021, and analyzed from June 15 to October 31, 2021. Exposures: Prior collection of cancer FHI in primary care settings. Main Outcomes and Measures: Availability was defined as having any FHI and any cancer FHI in the EHR and was examined at the patient level. Comprehensiveness was defined as whether a cancer family history observation in the EHR specified the type of cancer diagnosed in a family member, the relationship of the family member to the patient, and the age at onset for the family member and was examined at the observation level. Results: Among 144 484 patients in the UHealth system, 53.6% were women; 74.4% were non-Hispanic or non-Latino and 67.6% were White; and 83.0% had an English language preference. Among 377 621 patients in the NYULH system, 55.3% were women; 63.2% were non-Hispanic or non-Latino, and 55.3% were White; and 89.9% had an English language preference. Patients from historically medically undeserved groups-specifically, Black vs White patients (UHealth: 17.3% [95% CI, 16.1%-18.6%] vs 42.8% [95% CI, 42.5%-43.1%]; NYULH: 24.4% [95% CI, 24.0%-24.8%] vs 33.8% [95% CI, 33.6%-34.0%]), Hispanic or Latino vs non-Hispanic or non-Latino patients (UHealth: 27.2% [95% CI, 26.5%-27.8%] vs 40.2% [95% CI, 39.9%-40.5%]; NYULH: 24.4% [95% CI, 24.1%-24.7%] vs 31.6% [95% CI, 31.4%-31.8%]), Spanish-speaking vs English-speaking patients (UHealth: 18.4% [95% CI, 17.2%-19.1%] vs 40.0% [95% CI, 39.7%-40.3%]; NYULH: 15.1% [95% CI, 14.6%-15.6%] vs 31.1% [95% CI, 30.9%-31.2%), and men vs women (UHealth: 30.8% [95% CI, 30.4%-31.2%] vs 43.0% [95% CI, 42.6%-43.3%]; NYULH: 23.1% [95% CI, 22.9%-23.3%] vs 34.9% [95% CI, 34.7%-35.1%])-had significantly lower availability and comprehensiveness of cancer FHI (P < .001). Conclusions and Relevance: These findings suggest that systematic differences in the availability and comprehensiveness of FHI in the EHR may introduce informative presence bias as inputs to CDS algorithms. The observed differences may also exacerbate disparities for medically underserved groups. System-, clinician-, and patient-level efforts are needed to improve the collection of FHI.

Evidence for Telemedicine's Ongoing Transformation of Health Care Delivery Since the Onset of COVID-19: Retrospective Observational Study.

BACKGROUND: The surge of telemedicine use during the early stages of the COVID-19 pandemic has been well documented. However, scarce evidence considers the use of telemedicine in the subsequent period. OBJECTIVE: This study aims to evaluate use patterns of video-based telemedicine visits for ambulatory care and urgent care provision over the course of recurring pandemic waves in 1 large health system in New York City (NYC) and what this means for health care delivery. METHODS: Retrospective electronic health record (EHR) data of patients from January 1, 2020, to February 28, 2022, were used to longitudinally track and analyze telemedicine and in-person visit volumes across ambulatory care specialties and urgent care, as well as compare them to a prepandemic baseline (June-November 2019). Diagnosis codes to differentiate suspected COVID-19 visits from non-COVID-19 visits, as well as evaluating COVID-19-based telemedicine use over time, were compared to the total number of COVID-19-positive cases in the same geographic region (city level). The time series data were segmented based on change-point analysis, and variances in visit trends were compared between the segments. RESULTS: The emergence of COVID-19 prompted an early increase in the number of telemedicine visits across the urgent care and ambulatory care settings. This use continued throughout the pandemic at a much higher level than the prepandemic baseline for both COVID-19 and non-COVID-19 suspected visits, despite the fluctuation in COVID-19 cases throughout the pandemic and the resumption of in-person clinical services. The use of telemedicine-based urgent care services for COVID-19 suspected visits showed more variance in response to each pandemic wave, but telemedicine visits for ambulatory care have remained relatively steady after the initial crisis period. During the Omicron wave, the use of all visit types, including in-person activities, decreased. Patients between 25 and 34 years of age were the largest users of telemedicine-based urgent care. Patient satisfaction with telemedicine-based urgent care remained high despite the rapid scaling of services to meet increased demand. CONCLUSIONS: The trend of the increased use of telemedicine as a means of health care delivery relative to the pre-COVID-19 baseline has been maintained throughout the later pandemic periods despite fluctuating COVID-19 cases and the resumption of in-person care delivery. Overall satisfaction with telemedicine-based care is also high. The trends in telemedicine use suggest that telemedicine-based health care delivery has become a mainstream and sustained supplement to in-person-based ambulatory care, particularly for younger patients, for both urgent and nonurgent care needs. These findings have implications for the health care delivery system, including practice leaders, insurers, and policymakers. Further investigation is needed to evaluate telemedicine adoption by key demographics, identify ongoing barriers to adoption, and explore the impacts of sustained use of telemedicine on health care outcomes and experience.