Scaling care coordination through digital engagement: stepped-wedge trial assessing readmissions.

OBJECTIVES: Transitions of care are pivotal, vulnerable times as patients are discharged from the hospital. Telephonic care coordination is standard care, but labor intensive. We implemented a patient postdischarge digital engagement (PDDE) program to scale coordination. We hypothesized that PDDE could reduce readmissions for low-risk patients and supplement care coordination for medium- and high-risk patients. STUDY DESIGN: Pragmatic, stepped-wedge cluster randomization trial with 5 implementation waves based upon primary care clinic region. METHODS: All inpatient hospital discharges between March 2020 and November 2020 were stratified by readmission risk. Low-risk patients were offered access to PDDE, and moderate-risk and high-risk patients were offered access to PDDE and care coordination. Readmission was defined as an unplanned inpatient admission within 30 days from discharge. An intention-to-treat primary analysis was conducted using mixed-effects logistic regression clustering for wave; a treatment-on-the-treated analysis was also conducted to assess the impact among program users. RESULTS: A total of 5490 patient discharges were examined (2735 control; 2755 intervention); 1949 patients were high risk, 2032 were medium risk, and 1509 were low risk. PDDE intervention did not significantly affect readmission among low-risk (95% CI, -0.23 to 0.90; P = .23), medium-risk (95% CI, -0.14 to 0.60; P = .21), and high-risk (95% CI, -0.32 to 0.64; P = .48) groups after adjustment for time and patient factors. In a treatment-on-the-treated analysis, among patients who activated the PDDE program, readmission was also similar among the low-, medium-, and high-risk cohorts. CONCLUSIONS: Our study expanded resource-limited care coordination by offering low-risk patients a service they were unable to receive previously while having no impact on readmission. PDDE efficiently provided additional touch points between patients and providers.

Referral and Prescription Patterns for Female Patients With Urinary Incontinence.

IMPORTANCE: Although behavioral modifications, medications, and other interventions can improve urinary incontinence (UI), many women never receive them. OBJECTIVES: To better characterize UI treatment patterns in primary care, we examined prescriptions and referrals to pelvic floor physical therapy (PFPT) and specialist physicians within a large Midwestern academic health system. STUDY DESIGN: Electronic health records were queried to identify a cohort of adult female patients receiving a new UI diagnosis during outpatient primary care visits from 2016 to 2020. Urinary incontinence referrals and referral completion were examined for the overall cohort, and medication prescriptions were examined for women with urgency or mixed UI. Logistic regression was used to assess the association of prescriptions and/or referrals with patient demographics, comorbidities, and UI diagnosis dates. RESULTS: In the year after primary care UI diagnosis, 37.2% of patients in the overall cohort (n = 4,382) received guideline-concordant care. This included 20.6% of women who were referred for further management: 17.7% to urology/urogynecology and 3.2% to PFPT. Most women who were referred attended an initial appointment. Among those with urgency (n = 2,398) or mixed UI (n = 552), 17.1% were prescribed medication. Women with stress (odds ratio [OR], 3.10; 95% CI, 2.53-3.79) and mixed UI (OR, 6.17; 95% CI, 4.03-9.66) were more likely to be referred for further management, and women diagnosed during the COVID-19 pandemic were less likely to be referred for further care (OR, 0.39; 95% CI, 0.29, 0.48). CONCLUSION: Only slightly above 1 in 3 women with a new diagnosis of UI in primary care received guideline-based medications or referrals within 1 year, suggesting missed opportunities for timely care.

Effect of Implementing a Commercial Electronic Early Warning System on Outcomes of Hospitalized Patients.

Despite the widespread adoption of early warning systems (EWSs), it is uncertain if their implementation improves patient outcomes. The authors report a pre-post quasi-experimental evaluation of a commercially available EWS on patient outcomes at a 700-bed academic medical center. The EWS risk scores were visible in the electronic medical record by bedside clinicians. The EWS risk scores were also monitored remotely 24/7 by critical care trained nurses who actively contacted bedside nurses when a patient's risk levels increased. The primary outcome was inpatient mortality. Secondary outcomes were rapid response team calls and activation of cardiopulmonary arrest (code-4) response teams. The study team conducted a regression discontinuity analysis adjusting for age, gender, insurance, severity of illness, risk of mortality, and hospital occupancy at admission. The analysis included 53,229 hospitalizations. Adjusted analysis showed no significant change in inpatient mortality, rapid response team call, or code-4 activations after implementing the EWS. This study confirms the continued uncertainty in the effectiveness of EWSs and the need for further rigorous examinations of EWSs.

Patient and Provider Factors Associated With Successfully Addressing Medical Needs Using Telehealth: A Cross-Sectional Survey.

Few data exist that highlight areas where telemedicine shines or struggles from the patient perspective. We conducted a retrospective analysis of patient experience data from 19,465 visits using a logistic regression to model the odds a virtual visit addressed a patient's medical needs. Patient age (80 years: OR 0.58; 95% CI, 0.50-0.67 vs 40-64 years), race (Black: 0.68; 95% CI, 0.60-0.76 vs White), and connection (telephone conversion: OR 0.59; 95% CI, 0.53-0.66 vs video success) were associated with a lower likelihood of addressing medical needs; results varied modestly across specialties. These data suggest that while telehealth is generally well accepted by patients, differences are seen among patient factors and specialty.

Implementing real-time prescription benefit tools: Early experiences from 5 academic medical centers.

BACKGROUND: Medication price transparency tools are increasingly available, but data on their use, and their potential effects on prescribing behavior, patient out of pocket (OOP) costs, and clinician workflow integration, is limited. OBJECTIVE: To describe the implementation experiences with real-time prescription benefit (RTPB) tools at 5 large academic medical centers and their early impact on prescription ordering. DESIGN: and Participants: In this cross-sectional study, we systematically collected information on the characteristics of RTPB tools through discussions with key stakeholders at each of the five organizations. Quantitative encounter data, prescriptions written, and RTPB alerts/estimates and prescription adjustment rates were obtained at each organization in the first three months after "go-live" of the RTPB system(s) between 2019 and 2020. MAIN MEASURES: Implementation characteristics, prescription orders, cost estimate retrieval rates, and prescription adjustment rates. KEY RESULTS: Differences were noted with respect to implementation characteristics related to RTPB tools. All of the organizations with the exception of one chose to display OOP cost estimates and suggested alternative prescriptions automatically. Differences were also noted with respect to a patient cost threshold for automatic display. In the first three months after "go-live," RTPB estimate retrieval rates varied greatly across the five organizations, ranging from 8% to 60% of outpatient prescriptions. The prescription adjustment rate was lower, ranging from 0.1% to 4.9% of all prescriptions ordered. CONCLUSIONS: In this study reporting on the early experiences with RTPB tools across five academic medical centers, we found variability in implementation characteristics and population coverage. In addition RTPB estimate retrieval rates were highly variable across the five organizations, while rates of prescription adjustment ranged from low to modest.

Where Do Real-Time Prescription Benefit Tools Fit in the Landscape of High US Prescription Medication Costs? A Narrative Review.

The problem of unaffordable prescription medications in the United States is complex and can result in poor patient adherence to therapy, worse clinical outcomes, and high costs to the healthcare system. While providers are aware of the financial burden of healthcare for patients, there is a lack of actionable price transparency at the point of prescribing. Real-time prescription benefit (RTPB) tools are new electronic clinical decision support tools that retrieve patient- and medication-specific out-of-pocket cost information and display it to clinicians at the point of prescribing. The rise in US healthcare costs has been a major driver for efforts to increase medication price transparency, and mandates from the Centers for Medicare & Medicaid Services for Medicare Part D sponsors to adopt RTPB tools may spur integration of such tools into electronic health records. Although multiple factors affect the implementation of RTPB tools, there is limited evidence on outcomes. Further research will be needed to understand the impact of RTPB tools on end results such as prescribing behavior, out-of-pocket medication costs for patients, and adherence to pharmacologic treatment. We review the terminology and concepts essential in understanding the landscape of RTPB tools, implementation considerations, barriers to adoption, and directions for future research that will be important to patients, prescribers, health systems, and insurers.

Team principles for successful interdisciplinary research teams.

Interdisciplinary research teams can be extremely beneficial when addressing difficult clinical problems. The incorporation of conceptual and methodological strategies from a variety of research disciplines and health professions yields transformative results. In this setting, the long-term goal of team science is to improve patient care, with emphasis on population health outcomes. However, team principles necessary for effective research teams are rarely taught in health professional schools. To form successful interdisciplinary research teams in cardio-oncology and beyond, guiding principles and organizational recommendations are necessary. Cardiovascular disease results in annual direct costs of $220 billion (about $680 per person in the US) and is the leading cause of death for cancer survivors, including adult survivors of childhood cancers. Optimizing cardio-oncology research in interdisciplinary research teams has the potential to aid in the investigation of strategies for saving hundreds of thousands of lives each year in the United States and mitigating the annual cost of cardiovascular disease. Despite published reports on experiences developing research teams across organizations, specialties and settings, there is no single journal article that compiles principles for cardiology or cardio-oncology research teams. In this review, recurring threads linked to working as a team, as well as optimal methods, advantages, and problems that arise when managing teams are described in the context of career development and research. The worth and hurdles of a team approach, based on practical lessons learned from establishing our multidisciplinary research team and information gleaned from relevant specialties in the development of a successful team are presented.

Twenty-first century house calls: a survey of ambulatory care providers to inform organisational telehealth strategy.

OBJECTIVES: While patient interest in telehealth increases, clinicians' perspectives may influence longer-term adoption. We sought to identify facilitators and barriers to continued clinician incorporation of telehealth into practice. METHODS: A cross-sectional 24-item web-based survey was emailed to 491 providers with ≥50 video visits (VVs) within an academic health system between 1 March 2020 and 31 December 2020. We quantitatively summarised the characteristics and perceptions of respondents by using descriptive and test statistics. We used systematic content analysis to qualitatively code open-ended responses, double coding at least 25%. RESULTS: 247 providers (50.3%) responded to the survey. Seventy-nine per cent were confident in their ability to deliver excellent clinical care through VV. In comparison, 48% were confident in their ability to troubleshoot technical issues. Most clinicians (87%) expressed various concerns about VV. Providers across specialties generally agreed that VV reduced infection risk (71%) and transportation barriers (71%). Three overarching themes in the qualitative data included infrastructure and training, usefulness and expectation setting for patients and providers. DISCUSSION: As healthcare systems plan for future delivery directions, they must address the tension between patients' and providers' expectations of care within the digital space. Telehealth creates new friction, one where the healthcare system must fit into the patient's life rather than the usual dynamic of the patient fitting into the healthcare system. CONCLUSION: Telehealth infrastructure and patient and clinician technological acumen continue to evolve. Clinicians in this survey offered valuable insights into the directions healthcare organisations can take to right-size this healthcare delivery modality.

Telemedicine and health disparities: Association between patient characteristics and telemedicine, in-person, telephone and message-based care during the COVID-19 pandemic.

Telemedicine has been an essential form of care since the onset of the COVID-19 pandemic. However, telemedicine may exacerbate disparities for populations with limited digital literacy or access, such as older adults, racial minorities, patients of low income, rural residences, or limited English proficiency. From March 2020 to March 2022, this retrospective cohort study analyzed the use of in-person, phone/message, and telemedical care at a single tertiary care center in an oncology department. We investigated the association between economic, racial, ethnic, socioeconomic factors and forms of care, including in-person visits, telemedicine-based visits, and telephone/messages. The study results show that telemedicine utilization is lower among patients 65 and older, female patients, American Indian or Alaska Native patients, uninsured patients, and patients who require interpreters during clinical visits. As a result, it is unlikely that telemedicine will provide equal access to clinical care for all populations. On the other hand, in-person care utilization remains low in low-income and rural-living patients compared to the general population, while telephone and message use remains high in low-income and rural-living patients. We conclude that telemedicine is currently unable to close the utilization gap for populations of low socioeconomic status. Patients with low socioeconomic status use in-person care less frequently. For the disadvantaged, unusually high telephone or message utilization is unlikely to provide the same quality as in-person or telemedical care. Understanding the causes of disparity and promoting a solution to improve equal access to care for all patients is critical.

Hospitalization Outcomes Among Patients With COVID-19 Undergoing Remote Monitoring.

Importance: Health care systems have implemented remote patient monitoring (RPM) programs to manage patients with COVID-19 at home, but the associations between participation and outcomes or resource utilization are unclear. Objective: To assess whether an RPM program for COVID-19 is associated with lower or higher likelihood of hospitalization and whether patients who are admitted present earlier or later for hospital care. Design, Setting, and Participants: This retrospective, observational, cohort study of RPM was performed at Froedtert & Medical College of Wisconsin Health Network, an academic health system in southeastern Wisconsin. Participants included patients with internal primary care physicians and a positive SARS-CoV-2 test in the ambulatory setting between March 30, 2020, and December 15, 2020. Data analysis was performed from February 15, 2021, to February 2, 2022. Exposures: Activation of RPM program. Main Outcomes and Measures: Hospitalizations within 2 to 14 days of a positive test. Inverse propensity score weighting was used to account for differences between groups. Sensitivity analyses were performed looking at usage of the RPM among patients who activated the program. Results: A total of 10 660 COVID-19-positive ambulatory patients were eligible, and 9378 (88.0%) had email or mobile numbers on file and were invited into the RPM program; the mean (SD) age was 46.9 (16.3) years and 5448 patients (58.1%) were women. Patients who activated monitoring (5364 patients [57.2%]) had a mean (SD) of 35.3 (33.0) check-ins and a mean (SD) of 1.27 (2.79) (median [IQR], 0 [0-1]) free-text comments. A total of 878 patients (16.4%) experienced at least 1 alert; 128 of 5364 activated patients (2.4%) and 158 of 4014 inactivated patients (3.9%) were hospitalized (χ21 = 18.65; P < .001). In weighted regression analysis, activation of RPM was associated with a lower odds of hospitalization (odds ratio, 0.68; 95% CI, 0.54-0.86; P = .001) adjusted for demographics, comorbidities, and time period. Monitored patients had a longer mean (SD) time between test and hospitalization (6.67 [3.21] days vs 5.24 [3.03] days), a shorter length of stay (4.44 [4.43] days vs 7.14 [8.63] days), and less intensive care use (15 patients [0.3%] vs 44 patients [1.1%]). Conclusions and Relevance: These findings suggest that activation of an RPM program is associated with lower hospitalization, intensive care use, and length of stay among patients with COVID-19.

Real-world implementation evaluation of an electronic health record-integrated consumer informatics tool that collects patient-generated contextual data.

OBJECTIVE: Use the RE-AIM framework to examine the implementation of a patient contextual data (PCD) Tool designed to share patients' needs, values, and preferences with care teams ahead of clinical encounters. MATERIALS & METHODS: Observational study that follows initial PCD Tool scaling across primary care at a Midwestern academic health network. Program invitations, enrollment, patient submissions, and clinician views were tracked over a 1-year study period. Logistic regression modeled the likelihood of using the PCD Tool, accounting for patient covariates. RESULTS: Of 58,874 patients who could be contacted by email, 9,183 (15.6%) became PCD Tool users. Overall, 76% of primary care providers had patients who used the PCD Tool. Older age, female gender, non-minority race, patient portal activation, and Medicare coverage were significantly associated with increased likelihood of use. Number of office visits, medical issues, and behavioral health conditions also associated with use. Primary care staff viewed 18.7% of available PCD Tool summaries, 1.1% to 57.6% per clinic. DISCUSSION: The intervention mainly reached non-minority patients and patients who used more health services. Given the requirement for an email address on file, some patients may have been underrepresented. Overall, patient reach and adoption and clinician adoption, implementation, and maintenance of this Tool were modest but stable, consistent with a non-directive approach to fostering adoption by introducing the Tool in the absence of clear expectations for use. CONCLUSION: Healthcare organizations must implement effective methods to increase the reach, adoption, implementation, and maintenance of PCD tools across all patient populations. Assisting people, particularly racial minorities, with PCD Tool registration and actively supporting clinician use are critical steps in implementing technology that facilitates care.

Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity.

Study objective: A multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/ cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial. Design/setting/participants: Core team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology. Results: The team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects. Conclusion: Lessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.

Bugs in the Virtual Clinic: Confronting Telemedicine's Challenges Through Empathy and Support.

Although telemedicine has been an important conduit for clinical care during the COVID-19 pandemic, not all patients have been able to meaningfully participate in this mode of health care provision. Challenges with accessing telemedicine using consumer technology can interfere with the ability of patients and clinicians to meaningfully connect and lead to significant investments in time by clinicians and their staff. In this narrative case, we identify issues related to patients' use of technology, make comparisons between telehealth adoption and the deployment of electronic health records, and propose that building intuitive and supported digital care experiences for patients is required to make virtual care sustainable.

Nursing implications of an early warning system implemented to reduce adverse events: a qualitative study.

BACKGROUND: Unrecognised changes in a hospitalised patient's clinical course may lead to a preventable adverse event. Early warning systems (EWS) use patient data, such as vital signs, nursing assessments and laboratory values, to aid in the detection of early clinical deterioration. In 2018, an EWS programme was deployed at an academic hospital that consisted of a commercially available EWS algorithm and a centralised virtual nurse team to monitor alerts. Our objective was to understand the nursing perspective on the use of an EWS programme with centralised monitoring. METHODS: We conducted and audio-recorded semistructured focus groups during nurse staff meetings on six inpatient units, stratified by alert frequency (high: >100 alerts/month; medium: 50-100 alerts/month; low: <50 alerts/month). Discussion topics included EWS programme experiences, perception of EWS programme utility and EWS programme implementation. Investigators analysed the focus group transcripts using a grounded theory approach. RESULTS: We conducted 28 focus groups with 227 bedside nurses across all shifts. We identified six principal themes: (1) Alert timeliness, nurses reported being aware of the patient's deterioration before the EWS alert, (2) Lack of accuracy, nurses perceived most alerts as false positives, (3) Workflow interruptions caused by EWS alerts, (4) Questions of actionability of alerts, nurses were often uncertain about next steps, (5) Concerns around an underappreciation of core nursing skills via reliance on the EWS programme and (6) The opportunity cost of deploying the EWS programme. CONCLUSION: This qualitative study of nurses demonstrates the importance of earning user trust, ensuring timeliness and outlining actionable next steps when implementing an EWS. Careful attention to user workflow is required to maximise EWS impact on improving hospital quality and patient safety.

Analysis of Clinician and Patient Factors and Completion of Telemedicine Appointments Using Video.

Importance: Telemedicine provides patients access to episodic and longitudinal care. Policy discussions surrounding future support for telemedicine require an understanding of factors associated with successful video visits. Objective: To assess patient and clinician factors associated with successful and with failed video visits. Design, Setting, and Participants: This was a quality improvement study of 137 846 scheduled video visits at a single academic health system in southeastern Wisconsin between March 1 and December 31, 2020, supplemented with patient experience survey data. Patient information was gathered using demographic information abstracted from the electronic health record and linked with block-level socioeconomic data from the US Census Bureau. Data on perceived clinician experience with technology was obtained using the survey. Main Outcomes and Measures: The primary outcome of interest was the successful completion of a scheduled video visit or the conversion of the video visit to a telephone-based service. Visit types and administrative data were used to categorize visits. Mixed-effects modeling with pseudo R2 values was performed to compare the relative associations of patient and clinician factors with video visit failures. Results: In total, 75 947 patients and 1155 clinicians participated in 137 846 scheduled video encounters, 17 190 patients (23%) were 65 years or older, and 61 223 (81%) patients were of White race and ethnicity. Of the scheduled video encounters, 123 473 (90%) were successful, and 14 373 (10%) were converted to telephone services. A total of 16 776 patients (22%) completed a patient experience survey. Lower clinician comfort with technology (odds ratio [OR], 0.15; 95% CI, 0.08-0.28), advanced patient age (66-80 years: OR, 0.28; 95% CI, 0.26-0.30), lower patient socioeconomic status (including low high-speed internet availability) (OR, 0.85; 95% CI, 0.77-0.92), and patient racial and ethnic minority group status (Black or African American: OR, 0.75; 95% CI, 0.69-0.81) were associated with conversion to telephone visits. Patient characteristics accounted for systematic components for success; marginal pseudo R2 values decreased from 23% (95% CI, 21.1%-26.1%) to 7.8% (95% CI, 6.3%-9.4%) with exclusion of patient factors. Conclusions and Relevance: As policy makers consider expanding telehealth coverage and hospital systems focus on investments, consideration of patient support, equity, and friction should guide decisions. In particular, this quality improvement study suggests that underserved patients may become disproportionately vulnerable by cuts in coverage for telephone-based services.

Does a Centralized Scheduling Process Improve Referral Timeliness?

BACKGROUND: Timely, necessary specialist care is associated with better patient health outcomes and lower costs. This assessment looks at the effects of centralized scheduling, as well as patient and referral-level factors on referral completion rates. We hypothesized that centralized scheduling would increase access to specialty care, as evidenced by higher referral completion rates. METHODS: We analyzed data for specialty referrals to cardiology, nephrology, gastroenterology, and neurology from 6 months before to 6 months after implementation of a centralized scheduling system within a midwestern academic health system. We considered a referral complete if an appointment occurred within 3 months following an order for service. RESULTS: Overall, referral completion rates modestly increased (63.7% to 69.9%, P < 0.01), but this was driven by improvement within a single specialty (gastroenterology, 54.2% to 67.3%, P < 0.05). Other specialties saw either no significant change (neurology, nephrology) or a decrease (cardiology, 87.3% to 78.6%, P < 0.05). The time to schedule, or cycle time, improved overall from 21 days (SD 8-38) to 15 days (SD 8-30), P <0.05. CONCLUSIONS: Centralized scheduling had inconsistent effects on referral completion across specialties, though the process (cycle time) improved. Variable implementation fidelity and microenvironments likely contributed to uneven findings across specialties. Centralized scheduling may improve timely access but likely depends on implementation and buy-in.

Telemedicine Adoption during the COVID-19 Pandemic: Gaps and Inequalities.

BACKGROUND: The telemedicine industry has been experiencing fast growth in recent years. The outbreak of coronavirus disease 2019 (COVID-19) further accelerated the deployment and utilization of telemedicine services. An analysis of the socioeconomic characteristics of telemedicine users to understand potential socioeconomic gaps and disparities is critical for improving the adoption of telemedicine services among patients. OBJECTIVES: This study aims to measure the correlation of socioeconomic determinants with the use of telemedicine services in Milwaukee metropolitan area. METHODS: Electronic health record review of patients using telemedicine services compared with those not using telemedicine services within an academic-community health system: patient demographics (e.g., age, gender, race, and ethnicity), insurance status, and socioeconomic determinants obtained through block-level census data in Milwaukee area. The telemedicine users were compared with all other patients using regression analysis. The telemedicine adoption rates were calculated across regional ZIP codes to analyze the geographic patterns of telemedicine adoption. RESULTS: A total of 104,139 patients used telemedicine services during the study period. Patients who used video visits were younger (median age 48.12), more likely to be White (odds ratio [OR] 1.34; 95% confidence interval [CI], 1.31-1.37), and have private insurance (OR 1.43; CI, 1.41-1.46); patients who used telephone visits were older (median age 57.58), more likely to be Black (OR 1.31; CI 1.28-1.35), and have public insurance (OR 1.30; CI 1.27-1.32). In general, Latino and Asian populations were less likely to use telemedicine; women used more telemedicine services in general than men. In the multiple regression analysis of social determinant factors across 126 ZIP codes, college education (coefficient 1.41, p = 0.01) had a strong correlation to video telemedicine adoption rate. CONCLUSION: Adoption of telemedicine services was significantly impacted by the social determinant factors of health, such as income, education level, race, and insurance type. The study reveals the potential inequities and disparities in telemedicine adoption.

Glycemic relapse in a collaborative primary care-based type 2 diabetes management program.

OBJECTIVES: The purpose of this study is to evaluate the incidence of glycemic relapse in patients who attained their glycosylated hemoglobin (A1C) goal through a health system-wide collaborative primary care-based pharmacist- and Certified Diabetes Care and Education Specialist (CDCES)-led type 2 diabetes (T2D) management program and to identify relapse risk factors. METHODS: This retrospective cohort study examined patients with T2D in the diabetes management program with a baseline A1C of at least 9% who attained their A1C goal. The primary outcome was incidence of glycemic relapse. Time to relapse was estimated using Kaplan-Meier curve, and a cox proportional hazards model was fitted to identify the risk factors for glycemic relapse. RESULTS: Three hundred sixty-two patients were followed-up for a median of 10.5 (interquartile range 12.1) months after program completion; 38 patients (10.5%) experienced a glycemic relapse. Kaplan-Meier analysis estimated a 12-month relapse rate of 8.3%. The presence of a medication adherence barrier, presence of a higher number of chronic medications at baseline, presence of a baseline body mass index (BMI) of 30-39.9, and use of insulin at program completion increased risk for glycemic relapse in a univariate model. In multivariate regression, baseline BMI of 30-39.9 remained statistically significant. Older age at baseline was associated with a statistically significantly decreased relapse risk in both models. CONCLUSION: This study highlights a low incidence of glycemic relapse for patients with T2D who reach their A1C goal through a collaborative primary care-based pharmacist- and CDCES-led T2D management program. The presence of risk factors for glycemic relapse may indicate a need for ongoing intensive care despite achieving A1C goal.

How clinician-patient communication affects trust in health information sources: Temporal trends from a national cross-sectional survey.

BACKGROUND: Understanding patients' trust in health information sources is critical to designing work systems in healthcare. Patient-centered communication during the visit might be a major factor in shaping patients' trust in information sources. OBJECTIVE: The purpose of this paper is to explore relationships between patient ratings of clinician communication during the visit and patient trust in health information sources. METHODOLOGY: We conducted a secondary analysis of the nationally-representative Health Information National Trends Surveys; HINTS4 Cycle1 (2011), HINTS4 Cycle4 (2014), and HINTS5 Cycle1 (2017), and HINTS5 Cycle2 (2018). We created a composite score of patient-centered communication from five questions and dichotomized at the median. We created multivariable logistic regression models to see how patient-centered communication influenced trust in different information sources across cycles. Consecutively, we used hierarchical analysis for aggregated data. RESULTS: We analyzed data from 14,425 individuals. In the adjusted logistic models for each cycle and the hierarchical model, clinicians' perceived patient-centered communication skills were significantly associated with increased trust in the clinicians as an information source. CONCLUSION: Clinicians still represent an essential source of trustworthy information reinforced by patient-centered communication skills. Given that trust helps build healing relationships that lead to better healthcare outcomes, communication sets an essential foundation to establish necessary trust. Interpreting information from the internet sources for patients is likely to remain a vital clinician function.