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.

Impact of Visit Volume on the Effectiveness of Electronic Tools to Improve Heart Failure Care.

BACKGROUND: Electronic health record (EHR) tools can improve prescribing of guideline-recommended therapies for heart failure with reduced ejection fraction (HFrEF), but their effectiveness may vary by physician workload. OBJECTIVES: This paper aims to assess whether physician workload modifies the effectiveness of EHR tools for HFrEF. METHODS: This was a prespecified subgroup analysis of the BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure) cluster-randomized trial, which compared effectiveness of an alert vs message vs usual care on prescribing of mineralocorticoid antagonists (MRAs). The trial included adults with HFrEF seen in cardiology offices who were eligible for and not prescribed MRAs. Visit volume was defined at the cardiologist-level as number of visits per 6-month study period (high = upper tertile vs non-high = remaining). Analysis at the patient-level used likelihood ratio test for interaction with log-binomial models. RESULTS: Among 2,211 patients seen by 174 cardiologists, 932 (42.2%) were seen by high-volume cardiologists (median: 1,853; Q1-Q3: 1,637-2,225 visits/6 mo; and median: 10; Q1-Q3: 9-12 visits/half-day). MRA was prescribed to 5.5% in the high-volume vs 14.8% in the non-high-volume groups in the usual care arm, 10.3% vs 19.6% in the message arm, and 31.2% vs 28.2% in the alert arm, respectively. Visit volume modified treatment effect (P for interaction = 0.02) such that the alert was more effective in the high-volume group (relative risk: 5.16; 95% CI: 2.57-10.4) than the non-high-volume group (relative risk: 1.93; 95% CI: 1.29-2.90). CONCLUSIONS: An EHR-embedded alert increased prescribing by >5-fold among patients seen by high-volume cardiologists. Our findings support use of EHR alerts, especially in busy practice settings. (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure [BETTER CARE-HF]; NCT05275920).

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.

Cluster-Randomized Trial Comparing Ambulatory Decision Support Tools to Improve Heart Failure Care.

BACKGROUND: Mineralocorticoid receptor antagonists (MRAs) are underprescribed for patients with heart failure with reduced ejection fraction (HFrEF). OBJECTIVES: This study sought to compare effectiveness of 2 automated, electronic health record-embedded tools vs usual care on MRA prescribing in eligible patients with HFrEF. METHODS: BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations for Heart Failure) was a 3-arm, pragmatic, cluster-randomized trial comparing the effectiveness of an alert during individual patient encounters vs a message about multiple patients between encounters vs usual care on MRA prescribing. This study included adult patients with HFrEF, no active MRA prescription, no contraindication to MRAs, and an outpatient cardiologist in a large health system. Patients were cluster-randomized by cardiologist (60 per arm). RESULTS: The study included 2,211 patients (alert: 755, message: 812, usual care [control]: 644), with average age 72.2 years, average ejection fraction 33%, who were predominantly male (71.4%) and White (68.9%). New MRA prescribing occurred in 29.6% of patients in the alert arm, 15.6% in the message arm, and 11.7% in the control arm. The alert more than doubled MRA prescribing compared to usual care (relative risk: 2.53; 95% CI: 1.77-3.62; P < 0.0001) and improved MRA prescribing compared to the message (relative risk: 1.67; 95% CI: 1.21-2.29; P = 0.002). The number of patients with alert needed to result in an additional MRA prescription was 5.6. CONCLUSIONS: An automated, patient-specific, electronic health record-embedded alert increased MRA prescribing compared to both a message and usual care. These findings highlight the potential for electronic health record-embedded tools to substantially increase prescription of life-saving therapies for HFrEF. (Building Electronic Tools to Enhance and Reinforce Cardiovascular Recommendations-Heart Failure [BETTER CARE-HF]; NCT05275920).

Design and pilot implementation for the BETTER CARE-HF trial: A pragmatic cluster-randomized controlled trial comparing two targeted approaches to ambulatory clinical decision support for cardiologists.

BACKGROUND: Beart failure with reduced ejection fraction (HFrEF) is a leading cause of morbidity and mortality. However, shortfalls in prescribing of proven therapies, particularly mineralocorticoid receptor antagonist (MRA) therapy, account for several thousand preventable deaths per year nationwide. Electronic clinical decision support (CDS) is a potential low-cost and scalable solution to improve prescribing of therapies. However, the optimal timing and format of CDS tools is unknown. METHODS AND RESULTS: We developed two targeted CDS tools to inform cardiologists of gaps in MRA therapy for patients with HFrEF and without contraindication to MRA therapy: (1) an alert that notifies cardiologists at the time of patient visit, and (2) an automated electronic message that allows for review between visits. We designed these tools using an established CDS framework and findings from semistructured interviews with cardiologists. We then pilot tested both CDS tools (n = 596 patients) and further enhanced them based on additional semistructured interviews (n = 11 cardiologists). The message was modified to reduce the number of patients listed, include future visits, and list date of next visit. The alert was modified to improve noticeability, reduce extraneous information on guidelines, and include key information on contraindications. CONCLUSIONS: The BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce CArdiovascular REcommendations for Heart Failure) trial aims to compare the effectiveness of the alert vs. the automated message vs. usual care on the primary outcome of MRA prescribing. To our knowledge, no study has directly compared the efficacy of these two different types of electronic CDS interventions. If effective, our findings can be rapidly disseminated to improve morbidity and mortality for patients with HFrEF, and can also inform the development of future CDS interventions for other disease states. (Trial registration: Clinicaltrials.gov NCT05275920).

Effects of Real-time Prescription Benefit Recommendations on Patient Out-of-Pocket Costs: A Cluster Randomized Clinical Trial.

Importance: Rising drug costs contribute to medication nonadherence and adverse health outcomes. Real-time prescription benefit (RTPB) systems present prescribers with patient-specific out-of-pocket cost estimates and recommend lower-cost, clinically appropriate alternatives at the point of prescribing. Objective: To investigate whether RTPB recommendations lead to reduced patient out-of-pocket costs for medications. Design, Setting, and Participants: In this cluster randomized trial, medical practices in a large, urban academic health system were randomly assigned to RTPB recommendations from January 13 to July 31, 2021. Participants were adult patients receiving outpatient prescriptions during the study period. The analysis was limited to prescriptions for which RTPB could recommend an available alternative. Electronic health record data were used to analyze the intervention's effects on prescribing. Data analyses were performed from August 20, 2021, to June 8, 2022. Interventions: When a prescription was initiated in the electronic health record, the RTPB system recommended available lower-cost, clinically appropriate alternatives for a different medication, length of prescription, and/or choice of pharmacy. The prescriber could select either the initiated order or one of the recommended options. Main Outcomes and Measures: Patient out-of-pocket cost for a prescription. Secondary outcomes were whether a mail-order prescription and a 90-day supply were ordered. Results: Of 867 757 outpatient prescriptions at randomized practices, 36 419 (4.2%) met the inclusion criteria of having an available alternative. Out-of-pocket costs were $39.90 for a 30-day supply in the intervention group and $67.80 for a 30-day supply in the control group. The intervention led to an adjusted 11.2%; (95% CI, -15.7% to -6.4%) reduction in out-of-pocket costs. Mail-order pharmacy use was 9.6% and 7.6% in the intervention and control groups, respectively (adjusted 1.9 percentage point increase; 95% CI, 0.9 to 3.0). Rates of 90-day supply were not different. In high-cost drug classes, the intervention reduced out-of-pocket costs by 38.9%; 95% CI, -47.6% to -28.7%. Conclusions and Relevance: This cluster randomized clinical trial showed that RTPB recommendations led to lower patient out-of-pocket costs, with the largest savings occurring for high-cost medications. However, RTPB recommendations were made for only a small percentage of prescriptions. Trial Registration: ClinicalTrials.gov Identifier: NCT04940988; American Economic Association Registry: AEARCTR-0006909.

Implementing the physical activity vital sign in an academic preventive cardiology clinic.

The aims were to implement physical activity (PA) screening as part of the electronic kiosk check-in process in an adult preventive cardiology clinic and assess factors related to patients' self-reported PA. The 3-question physical activity vital sign (PAVS) was embedded in the Epic electronic medical record and included how many days, minutes and intensity (light, moderate, vigorous) of PA patients conducted on average. This is a data analysis of PAVS data over a 60-day period. We conducted multivariable logistic regression to identify factors associated with not meeting current PA recommendations. Over 60 days, a total of 1322 patients checked into the clinic using the kiosk and 72% (n = 951) completed the PAVS at the kiosk. The majority of those patients were male (58%) and White (71%) with a mean age of 64 ± 15 years. Of the 951 patients completing the PAVS, 10% reported no PA, 55% reported some PA, and 35% reported achieving at least 150 min moderate or 75 min vigorous PA/week. In the logistic model, females (AOR = 1.4, 95%CI: 1.002-1.8, p = .049) vs. males, being Black (AOR = 2.0, 95%CI: 1.04-3.7, p = .038) or 'Other' race (AOR = 1.5, 95%CI: 1.02-2.3, p = .035) vs. White, unknown or other types of relationships (AOR = 0.0.26, 95%CI: 0.10-0.68, p = .006) vs. being married/partnered, and those who were retired (AOR = 1.9, 95% CI: 1.4-2.8, p < .001) or unemployed (AOR = 2.2, 95%CI: 1.3-3.7, p = .002) vs. full-time workers were associated with not achieving recommended levels of PA. The PAVS is a feasible electronic tool for quickly assessing PA and may prompt providers to counsel on this CVD risk factor.

Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials.

BACKGROUND: Clinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools. OBJECTIVE: This paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care. METHODS: A multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior. RESULTS: To demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid (~6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images. CONCLUSIONS: These experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS. TRIAL REGISTRATION: Flu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191.

Giving Your Electronic Health Record a Checkup After COVID-19: A Practical Framework for Reviewing Clinical Decision Support in Light of the Telemedicine Expansion.

BACKGROUND: The transformation of health care during COVID-19, with the rapid expansion of telemedicine visits, presents new challenges to chronic care and preventive health providers. Clinical decision support (CDS) is critically important to chronic care providers, and CDS malfunction is common during times of change. It is essential to regularly reassess an organization's ambulatory CDS program to maintain care quality. This is especially true after an immense change, like the COVID-19 telemedicine expansion. OBJECTIVE: Our objective is to reassess the ambulatory CDS program at a large academic medical center in light of telemedicine's expansion in response to the COVID-19 pandemic. METHODS: Our clinical informatics team devised a practical framework for an intrapandemic ambulatory CDS assessment focused on the impact of the telemedicine expansion. This assessment began with a quantitative analysis comparing CDS alert performance in the context of in-person and telemedicine visits. Board-certified physician informaticists then completed a formal workflow review of alerts with inferior performance in telemedicine visits. Informaticists then reported on themes and optimization opportunities through the existing CDS governance structure. RESULTS: Our assessment revealed that 10 of our top 40 alerts by volume were not firing as expected in telemedicine visits. In 3 of the top 5 alerts, providers were significantly less likely to take action in telemedicine when compared to office visits. Cumulatively, alerts in telemedicine encounters had an action taken rate of 5.3% (3257/64,938) compared to 8.3% (19,427/233,636) for office visits. Observations from a clinical informaticist workflow review included the following: (1) Telemedicine visits have different workflows than office visits. Some alerts developed for the office were not appearing at the optimal time in the telemedicine workflow. (2) Missing clinical data is a common reason for the decreased alert firing seen in telemedicine visits. (3) Remote patient monitoring and patient-reported clinical data entered through the portal could replace data collection usually completed in the office by a medical assistant or registered nurse. CONCLUSIONS: In a large academic medical center at the pandemic epicenter, an intrapandemic ambulatory CDS assessment revealed clinically significant CDS malfunctions that highlight the importance of reassessing ambulatory CDS performance after the telemedicine expansion.

Patient Experience With Notification of Radiology Results: A Comparison of Direct Communication and Patient Portal Use.

OBJECTIVE: Patients increasingly access radiology results through digital portals. We compared patient satisfaction and understanding of radiology results when received through an electronic patient portal versus direct communication from providers. METHODS: Patients were invited to participate in an online survey within 7 days of undergoing a radiology examination. Participants received one of two survey versions, based on whether or not they viewed results in the patient portal. The associations between method of result notification and satisfaction with notification timing and self-reported understanding of results were evaluated using χ2 tests and logistic regression. RESULTS: Of 1,005 survey respondents, 87.8% (882 of 1,005) reported having received their imaging test results, with 486 (48.4%) first being notified through the patient portal and 396 (39.4%) via direct provider communication. Patients reported high levels of satisfaction with timing regardless of whether they first received the results through the patient portal or through direct provider communication (88.8%-89.9%). Patients who first received their results through the patient portal reported a lesser degree of perceived understanding than those who first received their results through direct provider communication (26.7% versus 47.8%; P < .001). Patients were less likely to report clear understanding for advanced imaging (CT or MRI) than ultrasound or x-rays (29.3% versus 40.3% versus 38.2%, respectively; P = .02). Patient characteristics showed no association with understanding in multivariable analysis. CONCLUSION: As online portal release of radiology results to patients becomes commonplace, efforts may be warranted to improve patient experience when first receiving their radiology results online.

User-Centered Development of a Behavioral Economics Inspired Electronic Health Record Clinical Decision Support Module.

Changing physician behaviors is difficult. Electronic health record (EHR) clinical decision support (CDS) offers an opportunity to promote guideline adherence. Behavioral economics (BE) has shown success as an approach to supporting evidence-based decision-making with little additional cognitive burden. We applied a user-centered approach to incorporate BE "nudges" into a CDS module in two "vanguard" sites utilizing: (1) semi-structured interviews with key informants (n = 8); (2) a design thinking workshop; and (3) semi-structured group interviews with clinicians. In the 133 day development phase at two clinics, the navigator section fired 299 times for 27 unique clinicians. The inbasket refill alert fired 124 times for 22 clinicians. Fifteen prescriptions for metformin were written by 11 clinicians. Our user-centered approach yielded a BE-driven CDS module with relatively high utilization by clinicians. Next steps include the addition of two modules and continued tracking of utilization, and assessment of clinical impact of the module.

Use of an electronic patient portal among disadvantaged populations.

BACKGROUND: Electronic patient portals give patients access to information from their electronic health record and the ability to message their providers. These tools are becoming more widely used and are expected to promote patient engagement with health care. OBJECTIVE: To quantify portal usage and explore potential differences in adoption and use according to patients' socioeconomic and clinical characteristics in a network of federally qualified health centers serving New York City and neighboring counties. DESIGN: Retrospective analysis of data from portal and electronic health records. PARTICIPANTS: 74,368 adult patients seen between April 2008 and April 2010. MAIN MEASURES: Odds of receiving an access code to the portal, activating the account, and using the portal more than once KEY RESULTS: Over the 2 years of the study, 16% of patients (n = 11,903) received an access code. Of these, 60% (n = 7138) activated the account, and 49% (n = 5791) used the account two or more times. Patients with chronic conditions were more likely to receive an access code and to become repeat users. In addition, the odds of receiving an access code were significantly higher for whites, women, younger patients, English speakers, and the insured. The odds of repeat portal use, among those with activated accounts, increased with white race, English language, and private insurance or Medicaid compared to no insurance. Racial disparities were small but persisted in models that controlled for language, insurance, and health status. CONCLUSIONS: We found good early rates of adoption and use of an electronic patient portal during the first 2 years of its deployment among a predominantly low-income population, especially among patients with chronic diseases. Disparities in access to and usage of the portal were evident but were smaller than those reported recently in other populations. Continued efforts will be needed to ensure that portals are usable for and used by disadvantaged groups so that all patients benefit equally from these technologies.