The Impact of Digital Health Solutions on Bridging the Health Care Gap in Rural Areas: A Scoping Review.

Digital health tools can improve health care access and outcomes for individuals with limited access to health care, particularly those residing in rural areas. This scoping review examines the existing literature on using digital tools in patients with limited access to health care in rural areas. It assesses their effectiveness in improving health outcomes. The review adopts a comprehensive search strategy to identify relevant studies from electronic databases, and the selected studies are analyzed descriptively. The findings highlight the advantages and barriers of digital health interventions in rural populations. The advantages include increased access to health care practitioners through teleconsultations, improved health care outcomes through remote monitoring, better disease management through mobile health applications and wearable devices, and enhanced access to specialized care and preventive programs. However, limited internet connectivity and a lack of familiarity with digital tools are barriers that must be addressed to ensure equitable access to digital health interventions in rural areas. Overall, digital tools improve health outcomes for individuals with limited health care access in rural areas.

A narrative review of telemedicine and its adoption across specialties.

Background and Objective: Telemedicine and video consultation are crucial advancements in healthcare, allowing remote delivery of care. Telemedicine, encompassing various technologies like wearable devices, mobile health, and telemedicine, plays a significant role in managing illnesses and promoting wellness. The corona virus disease 2019 (COVID-19) pandemic accelerated the adoption of telemedicine, ensuring convenient access to medical services while maintaining physical distance. Legislation has supported its integration into clinical practice and addressed compensation issues. However, ensuring clinical appropriateness and sustainability of telemedicine post-expansion has gained attention. We south to identify the most friendly and resistant specialties to telemedicine and to understand areas of interest within those specialties to grasp potential barriers to its use. Methods: We aimed to identify articles that incorporated telemedicine in any medical or surgical specialty and determine the adoption rate and intent of this new form of care. Additionally, a secondary search within these databases was conducted to analyze the advantages, disadvantages, and implementation of telemedicine in the healthcare system. Non-English articles and those without full text were excluded. The study selection and data collection process involved using search terms such as "medicine", "surgery", "specialties", "telemedicine", and "telemedicine". Key Content and Findings: Telemedicine adoption varies among specialties. The pandemic led to increased usage, with telemedicine consultations comprising 30.1% of all visits, but specialties like mental health, gastroenterology, and endocrinology showed higher rates of adoption compared to optometry, physical therapy, and orthopedic surgery. Conclusions: The data shows that telemedicine uptake varies by specialty and condition due to the need for physical exams. In-person visits still dominate new patient visits despite increased telemedicine use. Telemedicine cannot fully replace in-person care but has increased visit volume and is secure. The adoption of telemedicine is higher in medical practices than in surgical practices, with neurosurgery and urology leading. Further research is needed to assess telemedicine's suitability and effectiveness in different specialties and conditions.

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications.

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare's path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

Patient Satisfaction With a Multisite, Multiregional Remote Patient Monitoring Program for Acute and Chronic Condition Management: Survey-Based Analysis.

BACKGROUND: Remote patient monitoring (RPM) is an option for continuously managing the care of patients in the comfort of their homes or locations outside hospitals and clinics. Patient engagement with RPM programs is essential for achieving successful outcomes and high quality of care. When relying on technology to facilitate monitoring and shifting disease management to the home environment, it is important to understand the patients' experiences to enable quality improvement. OBJECTIVE: This study aimed to describe patients' experiences and overall satisfaction with an RPM program for acute and chronic conditions in a multisite, multiregional health care system. METHODS: Between January 1, 2021, and August 31, 2022, a patient experience survey was delivered via email to all patients enrolled in the RPM program. The survey encompassed 19 questions across 4 categories regarding comfort, equipment, communication, and overall experience, as well as 2 open-ended questions. Descriptive analysis of the survey response data was performed using frequency distribution and percentages. RESULTS: Surveys were sent to 8535 patients. The survey response rate was 37.16% (3172/8535) and the completion rate was 95.23% (3172/3331). Survey results indicated that 88.97% (2783/3128) of participants agreed or strongly agreed that the program helped them feel comfortable managing their health from home. Furthermore, 93.58% (2873/3070) were satisfied with the RPM program and ready to graduate when meeting the program goals. In addition, patient confidence in this model of care was confirmed by 92.76% (2846/3068) of the participants who would recommend RPM to people with similar conditions. There were no differences in ease of technology use according to age. Those with high school or less education were more likely to agree that the equipment and educational materials helped them feel more informed about their care plans than those with higher education levels. CONCLUSIONS: This multisite, multiregional RPM program has become a reliable health care delivery model for the management of acute and chronic conditions outside hospitals and clinics. Program participants reported an excellent overall experience and a high level of satisfaction in managing their health from the comfort of their home environment.

Payment and Coverage Parity for Virtual Care and In-Person Care: How Do We Get There?

Background: A steep increase in the use of delivery of virtual care occurred during the COVID-19 public health emergency (PHE) because of easing up of payment and coverage restrictions. With the end of PHE, there is uncertainty regarding continued coverage and payment parity for the virtual care services. Methods: On November 8, 2022, The Mass General Brigham held the Third Annual Virtual Care Symposium: Demystifying Clinical Appropriateness in Virtual Care and What's Ahead for Pay Parity. Results: In one of the panels, experts from Mayo Clinic led by Dr. Bart Demaerschalk discussed key issues related to "Payment and Coverage Parity for Virtual Care and In-Person Care: How Do We Get There?" The discussions centered around current policies around payment and coverage parity for virtual care, including state licensure laws for virtual care delivery and the current evidence base regarding outcomes, costs, and resource utilization associated with virtual care. The panel discussion ended with highlighting next steps targeting policymakers, payers, and industry groups to help strengthen the case for parity. Conclusions: To ensure the continued viability of virtual care delivery, legislators and insurers must address the coverage and payment parity between telehealth and in-person visits. This will require a renewed focus on research on clinical appropriateness, parity, equity and access, and economics of virtual care.

Quality Frameworks for Virtual Care: Expert Panel Recommendations.

Given the significant advance of virtual care in the past year and half, it seems timely to focus on quality frameworks and how they have evolved collaboratively across health care organizations. Massachusetts General Hospital's (MGH) Center for TeleHealth and Mass General Brigham's (MGB) Virtual Care Program are committed to hosting annual symposia on key topics related to virtual care. Subject matter experts across the country, health care organizations, and academic medical centers are invited to participate. The inaugural MGH/MGB Virtual Care Symposium, which focused on rethinking curriculum, competency, and culture in the virtual care era, was held on September 2, 2020. The second MGH/MGB Virtual Care Symposium was held on November 2, 2021, and focused on virtual care quality frameworks. Resultant topics were (1) guiding principles necessary for the future of virtual care measurement; (2) best practices deployed to measure quality of virtual care and how they compare and align with in-person frameworks; (3) evolution of quality frameworks over time; (4) how increased adoption of virtual care has impacted patient access and experience and how it has been measured; (5) the pitfalls and barriers which have been encountered by organizations in developing virtual care quality frameworks; and (6) examples of how quality frameworks have been applied in various use cases. Common elements of a quality framework for virtual care programs among symposium participants included improving the patient and provider experience, a focus on achieving health equity, monitoring success rates and uptime of the technical elements of virtual care, financial stewardship, and clinical outcomes. Virtual care represents an evolution in the access to care paradigm that helps keep health care aligned with other modern industries in digital technology and systems adoption. With advances in health care delivery models, it is vitally important that the quality measurement systems be adapted to include virtual care encounters. New methods may be necessary for asynchronous transactions, but synchronous virtual visits and consults can likely be accommodated in traditional quality frameworks with minimal adjustments. Ultimately, quality frameworks for health care will adapt to hybrid in-person and virtual care practices.

Impact of a High-Risk, Ambulatory COVID-19 Remote Patient Monitoring Program on Utilization, Cost, and Mortality.

OBJECTIVE: To evaluate care utilization, cost, and mortality among high-risk patients enrolled in a coronavirus disease 2019 (COVID-19) remote patient monitoring (RPM) program. METHODS: This retrospective analysis included patients diagnosed with COVID-19 at risk for severe disease who enrolled in the RPM program between March 2020 and October 2021. The program included in-home technology for symptom and physiologic data monitoring with centralized care management. Propensity score matching established matched cohorts of RPM-engaged (defined as ≥1 RPM technology interactions) and non-engaged patients using a logistic regression model of 59 baseline characteristics. Billing codes and the electronic death certificate system were used for data abstraction from the electronic health record and reporting of care utilization and mortality endpoints. RESULTS: Among 5796 RPM-enrolled patients, 80.0% engaged with the technology. Following matching, 1128 pairs of RPM-engaged and non-engaged patients comprised the analysis cohorts. Mean patient age was 63.3 years, 50.9% of patients were female, and 81.9% were non-Hispanic White. Patients who were RPM-engaged experienced significantly lower rates of 30-day, all-cause hospitalization (13.7% vs 18.0%, P=.01), prolonged hospitalization (3.5% vs 6.7%, P=.001), intensive care unit admission (2.3% vs 4.2%, P=.01), and mortality (0.5% vs 1.7%; odds ratio, 0.31; 95% CI, 0.12 to 0.78; P=.01), as well as cost of care ($2306.33 USD vs $3565.97 USD, P=0.04), than those enrolled in RPM but non-engaged. CONCLUSION: High-risk COVID-19 patients enrolled and engaged in an RPM program experienced lower rates of hospitalization, intensive care unit admission, mortality, and cost than those enrolled and non-engaged. These findings translate to improved hospital bed access and patient outcomes.

Assessment of Clinician Diagnostic Concordance With Video Telemedicine in the Integrated Multispecialty Practice at Mayo Clinic During the Beginning of COVID-19 Pandemic From March to June 2020.

Importance: There was a shift in patient volume from in-person to video telemedicine visits during the COVID-19 pandemic. Objective: To determine the concordance of provisional diagnoses established at a video telemedicine visit with diagnoses established at an in-person visit for patients presenting with a new clinical problem. Design, Setting, and Participants: This is a diagnostic study of patients who underwent a video telemedicine consultation followed by an in-person outpatient visit for the same clinical problem in the same specialty within a 90-day window. The provisional diagnosis made during the video telemedicine visit was compared with the reference standard diagnosis by 2 blinded, independent medical reviewers. A multivariate logistic regression model was used to determine factors significantly related to diagnostic concordance. The study was conducted at a large academic integrated multispecialty health care institution (Mayo Clinic locations in Rochester, Minnesota; Scottsdale and Phoenix, Arizona; and Jacksonville, Florida; and Mayo Clinic Health System locations in Iowa, Wisconsin, and Minnesota) between March 24 and June 24, 2020. Participants included Mayo Clinic patients residing in the US without age restriction. Data analysis was performed from December 2020 to June 2021. Exposures: New clinical problem assessed via video telemedicine visit to home using Zoom Care Anyplace integrated into Epic. Main Outcomes and Measures: Concordance of provisional diagnoses established over video telemedicine visits compared against a reference standard diagnosis. Results: There were 2393 participants in the analysis. The median (IQR) age of patients was 53 (37-64) years; 1381 (57.7%) identified as female, and 1012 (42.3%) identified as male. Overall, the provisional diagnosis established over video telemedicine visit was concordant with the in-person reference standard diagnosis in 2080 of 2393 cases (86.9%; 95% CI, 85.6%-88.3%). Diagnostic concordance by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision chapter ranged from 64.7% (95% CI, 42.0%-87.4%) for diseases of the ear and mastoid process to 96.8% (95% CI, 94.7%-98.8%) for neoplasms. Diagnostic concordance by medical specialty ranged from 77.3% (95% CI, 64.9%-89.7%) for otorhinolaryngology to 96.0% (92.1%-99.8%) for psychiatry. Specialty care was found to be significantly more likely than primary care to result in video telemedicine diagnoses concordant with a subsequent in-person visit (odds ratio, 1.69; 95% CI, 1.24-2.30; P < .001). Conclusions and Relevance: This diagnostic study of video telemedicine visits yielded a high degree of diagnostic concordance compared with in-person visits for most new clinical concerns. Some specific clinical circumstances over video telemedicine were associated with a lower diagnostic concordance, and these patients may benefit from timely in-person follow-up.

Development and implementation of a nurse-based remote patient monitoring program for ambulatory disease management.

Introduction: Numerous factors are intersecting in healthcare resulting in an increased focus on new tools and methods for managing care in patients' homes. Remote patient monitoring (RPM) is an option to provide care at home and maintain a connection between patients and providers to address ongoing medical issues. Methods: Mayo Clinic developed a nurse-led RPM program for disease and post-procedural management to improve patient experience, clinical outcomes, and reduce health care utilization by more directly engaging patients in their health care. Enrolled patients are sent a technology package that includes a digital tablet and peripheral devices for the collection of symptoms and vital signs. The data are transmitted from to a hub integrated within the electronic health record. Care team members coordinate patient needs, respond to vital sign alerts, and utilize the data to inform and provide individualized patient assessment, patient education, medication management, goal setting, and clinical care planning. Results: Since its inception, the RPM program has supported nearly 22,000 patients across 17 programs. Patients who engaged in the COVID-19 RPM program experienced a significantly lower rate of 30-day, all-cause hospitalization (13.7% vs. 18.0%, P = 0.01), prolonged hospitalization >7 days (3.5% vs. 6.7%, P = 0.001), intensive care unit (ICU) admission (2.3% vs. 4.2%, P = 0.01), and mortality (0.5% vs. 1.7%, P = 0.01) when compared with those enrolled and unengaged with the technology. Patients with chronic conditions who were monitored with RPM upon hospital discharge were significantly less likely to experience 30-day readmissions (18.2% vs. 23.7%, P = 0.03) compared with those unmonitored. Ninety-five percent of patients strongly agreed or agreed they were likely to recommend RPM to a friend or family member. Conclusions: The Mayo Clinic RPM program has generated positive clinical outcomes and is satisfying for patients. As technology advances, there are greater opportunities to enhance this clinical care model and it should be extended and expanded to support patients across a broader spectrum of needs. This report can serve as a framework for health care organizations to implement and enhance their RPM programs in addition to identifying areas for further evolution and exploration in developing RPM programs of the future.

Making sense of diabetes medication decisions: a mixed methods cluster randomized trial using a conversation aid intervention.

PURPOSE: To determine the effectiveness of a shared decision-making (SDM) tool versus guideline-informed usual care in translating evidence into primary care, and to explore how use of the tool changed patient perspectives about diabetes medication decision making. METHODS: In this mixed methods multicenter cluster randomized trial, we included patients with type 2 diabetes mellitus and their primary care clinicians. We compared usual care with or without a within-encounter SDM conversation aid. We assessed participant-reported decisions made and quality of SDM (knowledge, satisfaction, and decisional conflict), clinical outcomes, adherence, and observer-based patient involvement in decision-making (OPTION12-scale). We used semi-structured interviews with patients to understand their perspectives. RESULTS: We enrolled 350 patients and 99 clinicians from 20 practices and interviewed 26 patients. Use of the conversation aid increased post-encounter patient knowledge (correct answers, 52% vs. 45%, p = 0.02) and clinician involvement of patients (Mean between-arm difference in OPTION12, 7.3 (95% CI 3, 12); p = 0.003). There were no between-arm differences in treatment choice, patient or clinician satisfaction, encounter length, medication adherence, or glycemic control. Qualitative analyses highlighted differences in how clinicians involved patients in decision making, with intervention patients noting how clinicians guided them through conversations using factors important to them. CONCLUSIONS: Using an SDM conversation aid improved patient knowledge and involvement in SDM without impacting treatment choice, encounter length, medication adherence or improved diabetes control in patients with type 2 diabetes. Future interventions may need to focus specifically on patients with signs of poor treatment fit. CLINICAL TRIAL REGISTRATION: ClinicalTrial.gov: NCT01502891.

Implementation of a multisite, interdisciplinary remote patient monitoring program for ambulatory management of patients with COVID-19.

Established technology, operational infrastructure, and nursing resources were leveraged to develop a remote patient monitoring (RPM) program for ambulatory management of patients with COVID-19. The program included two care-delivery models with different monitoring capabilities supporting variable levels of patient risk for severe illness. The primary objective of this study was to determine the feasibility and safety of a multisite RPM program for management of acute COVID-19 illness. We report an evaluation of 7074 patients served by the program across 41 US states. Among all patients, the RPM technology engagement rate was 78.9%. Rates of emergency department visit and hospitalization within 30 days of enrollment were 11.4% and 9.4%, respectively, and the 30-day mortality rate was 0.4%. A multisite RPM program for management of acute COVID-19 illness is feasible, safe, and associated with a low mortality rate. Further research and expansion of RPM programs for ambulatory management of other acute illnesses are warranted.

Association of a Remote Patient Monitoring (RPM) Program With Reduced Hospitalizations in Cancer Patients With COVID-19.

PURPOSE: The goal of this study was to assess the impact of an interdisciplinary remote patient monitoring (RPM) program on clinical outcomes and acute care utilization in cancer patients with COVID-19. METHODS: This is a cross-sectional analysis following a prospective observational study performed at Mayo Clinic Cancer Center. Adult patients receiving cancer-directed therapy or in recent remission on active surveillance with polymerase chain reaction-confirmed SARS-CoV-2 infection between March 18 and July 31, 2020, were included. RPM was composed of in-home technology to assess symptoms and physiologic data with centralized nursing and physician oversight. RESULTS: During the study timeframe, 224 patients with cancer were diagnosed with COVID-19. Of the 187 patients (83%) initially managed in the outpatient setting, those who did not receive RPM were significantly more likely to experience hospitalization than those receiving RPM. Following balancing of patient characteristics by inverse propensity score weighting, rates of hospitalization for RPM and non-RPM patients were 2.8% and 13%, respectively, implying that the use of RPM was associated with a 78% relative risk reduction in hospital admission rate (95% CI, 54 to 102; P = .002). Furthermore, when hospitalized, these patients experienced a shorter length of stay and fewer prolonged hospitalizations, intensive care unit admissions, and deaths, although these trends did not reach statistical significance. CONCLUSION: The use of RPM and a centralized virtual care team was associated with a reduction in hospital admission rate and lower overall acute care resource utilization among cancer patients with COVID-19.

A Scalable Framework for Telehealth: The Mayo Clinic Center for Connected Care Response to the COVID-19 Pandemic.

Background: The Mayo Clinic Center for Connected Care has an established organizational framework for telehealth care delivery. It provides patients, consumers, care teams, and referring providers access to clinical knowledge through technologies and integrated practice models. Central to the framework are teams that support product management and operational functions. They work together across the asynchronous, synchronous video telemedicine, remote patient monitoring (RPM), and mobile core service lines. Methods: The organizational framework of the Center for Connected Care and Mayo Clinic telehealth response to the COVID-19 pandemic is described. Barriers to telehealth delivery that were addressed by the public health emergency are also reported. This report was deemed exempt from full review by the Mayo Clinic IRB. Results: After declaration of the COVID-19 pandemic, there was rapid growth in established telehealth offerings, including patient online services account creation, secure messaging, inpatient eConsults, express care online utilization, and video visits to home. Census for the RPM program for patients with chronic conditions remained stable; however, its framework was rapidly adapted to develop and implement a COVID-19 RPM service. In addition to this, other new telehealth and virtual care services were created to support the unique needs of patients with COVID-19 symptoms or disease and the health care workforce, including a digital COVID-19 self-assessment tool and video telemedicine solutions for ambulances, emergency departments, intensive care units, and designated medical-surgical units. Conclusion: Rapid growth, adoption, and sustainability of telehealth services through the COVID-19 pandemic were made possible by a scalable framework for telehealth and alignment of regulatory and reimbursement models.

Synthesis and characterization of a deuterium labeled stercobilin: A potential biomarker for autism.

Stercobilin is an end-stage metabolite of hemoglobin, a component of red blood cells. It has been found that there is a significantly lower concentration of stercobilin in the urine of people diagnosed with autism spectrum disorders, suggesting potential use as a biomarker. In vitro, we have synthesized stercobilin from its precursor bilirubin through a reduction reaction proceeded by an oxidation reaction. In addition, we have isotopically labeled the stercobilin product with deuterium using this protocol. Nuclear magnetic resonance investigations show the products of the unlabeled stercobilin (Rxn 1) and the deuterated stercobilin (Rxn 2) both had a loss of signals in the 5.0- to 7.0-ppm range indicating proper conversion to stercobilin. Changes in the multiplicity of the sp3 region of the proton nuclear magnetic resonance suggest proper deuterium incorporation. Mass spectrometry studies of Rxn 1 show a difference in fragmentation patterns than that of Rxn 2 proposing potential locations for deuterium incorporation. This isotopologue of stercobilin is stable (>6 mo), and further analysis permits investigation for its use as a biomarker and potential quantitative diagnostic probe for autism spectrum disorders.