The Difference in Practice Expense Costs Between Telehealth and In-Office Care Could Serve as the Basis for Differential Reimbursement Structures.

Background: There has been much recent discussion about the reimbursement of telehealth virtual visits. Advocates argue strongly for payment parity with in-person encounters, whereas payers insist that telehealth visits should be reimbursed at a lower value. Methods: Using the Resource-Based Relative Value Scale structure as a guideline (where physician compensation is divided into categories: time/medical decision making/malpractice expense and practice expense), we developed a framework to examine the difference in practice expense of an in-person practice compared with a scaled virtual practice. Results: We found that for current procedural terminology (CPT) code 99213, the total relative value unit (RVU) for a virtual visit would be 1.62. The in-office RVU for CPT code 99213 is 2.09. This difference could serve as the basis for a rational discussion on differential reimbursement for virtual visits.

Use of teledermatology by dermatology hospitalists is effective in the diagnosis and management of inpatient disease.

BACKGROUND: Patient outcomes are improved when dermatologists provide inpatient consultations. Inpatient access to dermatologists is limited, illustrating an opportunity to use teledermatology. Little is known about the ability of dermatologists to accurately diagnose disease and manage inpatients with teledermatology, particularly when using nondermatologist-generated clinical data. METHODS: This prospective study assessed the ability of teledermatology to diagnose disease and manage 41 dermatology consultations from a large urban tertiary care center, using internal medicine referral documentation and photographs. Twenty-seven dermatology hospitalists were surveyed. Interrater agreement was assessed by the κ statistic. RESULTS: There was substantial agreement between in-person and teledermatology assessment of the diagnosis with differential diagnosis (median κ = 0.83), substantial agreement in laboratory evaluation decisions (median κ = 0.67), almost perfect agreement in imaging decisions (median κ = 1.0), and moderate agreement in biopsy decisions (median κ = 0.43). There was almost perfect agreement in treatment (median κ = 1.0), but no agreement in follow-up planning (median κ = 0.0). There was no association between raw photograph quality and the primary plus differential diagnosis or primary diagnosis alone. LIMITATIONS: Selection bias and single-center nature. CONCLUSIONS: Teledermatology may be effective in the inpatient setting, with concordant diagnosis, evaluation, and management decisions.

Pediatric dermatology eConsults: Reduced wait times and dermatology office visits.

BACKGROUND/OBJECTIVES: Store-and-forward teledermatology provides pediatricians with specialist guidance in managing skin disease. This study evaluates wait times and face-to-face (FTF) dermatology visit avoidance associated with a pediatric dermatology eConsult program at an urban academic medical center. METHODS: In this retrospective cohort study, electronic medical records were reviewed for patients under age 18 for whom a dermatology eConsult was completed between November 1, 2014, and December 31, 2017. Wait times for eConsult completion and initial FTF dermatology appointments were calculated and compared to average wait times for new patient dermatology office appointments from 2016 to 2017. Recommendations for FTF dermatology visits were assessed, along with FTF visit attendance and potential cost savings. RESULTS: One hundred eighty pediatric patients with 188 unrelated skin conditions ("cases") were referred to the program. Of 188 cases, FTF dermatology visits were recommended for 60 (31.9%). Actual FTF dermatology visit avoidance was 53.7% of total cases (n = 101 for whom FTF visit was not recommended and no dermatology visit occurred within 90 days after eConsult submission). The program generated potential savings of $24 059 ($9840 out-of-pocket) in 2016 dollars. Average turnaround for eConsult completion was 1.8 calendar days (median: 1 calendar day, target: 2 business days). Average wait time to initial FTF dermatology evaluation was 37.3 calendar days (versus 54.1 days for pediatric patients referred directly to dermatology clinic between 2016 and 2017). CONCLUSION: Pediatric dermatology eConsults reduce wait times for specialist care, triage cases for in-office evaluation, reduce need for FTF dermatology visits, and offer potential cost savings for payers and patients.

Beyond the COVID Pandemic, Telemedicine, and Health Care.

This article reviews the current experience and the flaws encountered in the rush to deploy telemedicine as a substitute for in-person care in response to the raging coronavirus (COVID-19) pandemic; the preceding fault lines in the U.S. health care system that exacerbated the problem; and the importance of emerging from this calamity with a clear vision for necessary health care reforms. It starts with the premise that the precursors of catastrophes of this magnitude provide a valid basis for planning corrective measures, improved preparedness, and ultimately serious health reform. Such reform should include standardized protocols for proper deployment of telemedicine to triage patients to the appropriate level and source of care at the point of need, proper use of relevant technological innovations to deliver precision medicine, and the development of regional networks to coordinate and improve access to care while streamlining the care process. The other essential element is a universal payment system that puts the United States at par with the rest of the industrialized countries, regardless of variation among them. The ultimate goal is creating an efficient, effective, accessible, and equitable system of care. Although timing is uncertain, the pandemic will be brought under control. The path to a better future after the pandemic offers some consolation for the massive loss of life and treasure during this pandemic.

Corrigendum and Editorial Warning Regarding Use of the MMAS-8 Scale (A Remote Medication Monitoring System for Chronic Heart Failure Patients to Reduce Readmissions: A Two-Arm Randomized Pilot Study).

[This corrects the article DOI: .].

A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.

BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission. METHODS: We used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system. RESULTS: Data from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital. CONCLUSIONS: Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes.

Healthcare utilization in older patients using personal emergency response systems: an analysis of electronic health records and medical alert data : Brief Description: A Longitudinal Retrospective Analyses of healthcare utilization rates in older patients using Personal Emergency Response Systems from 2011 to 2015.

BACKGROUND: Personal Emergency Response Systems (PERS) are traditionally used as fall alert systems for older adults, a population that contributes an overwhelming proportion of healthcare costs in the United States. Previous studies focused mainly on qualitative evaluations of PERS without a longitudinal quantitative evaluation of healthcare utilization in users. To address this gap and better understand the needs of older patients on PERS, we analyzed longitudinal healthcare utilization trends in patients using PERS through the home care management service of a large healthcare organization. METHODS: Retrospective, longitudinal analyses of healthcare and PERS utilization records of older patients over a 5-years period from 2011-2015. The primary outcome was to characterize the healthcare utilization of PERS patients. This outcome was assessed by 30-, 90-, and 180-day readmission rates, frequency of principal admitting diagnoses, and prevalence of conditions leading to potentially avoidable admissions based on Centers for Medicare and Medicaid Services classification criteria. RESULTS: The overall 30-day readmission rate was 14.2%, 90-days readmission rate was 34.4%, and 180-days readmission rate was 42.2%. While 30-day readmission rates did not increase significantly (p = 0.16) over the study period, 90-days (p = 0.03) and 180-days (p = 0.04) readmission rates did increase significantly. The top 5 most frequent principal diagnoses for inpatient admissions included congestive heart failure (5.7%), chronic obstructive pulmonary disease (4.6%), dysrhythmias (4.3%), septicemia (4.1%), and pneumonia (4.1%). Additionally, 21% of all admissions were due to conditions leading to potentially avoidable admissions in either institutional or non-institutional settings (16% in institutional settings only). CONCLUSIONS: Chronic medical conditions account for the majority of healthcare utilization in older patients using PERS. Results suggest that PERS data combined with electronic medical records data can provide useful insights that can be used to improve health outcomes in older patients.

Text to Move: A Randomized Controlled Trial of a Text-Messaging Program to Improve Physical Activity Behaviors in Patients With Type 2 Diabetes Mellitus.

BACKGROUND: Text messages are increasingly being used because of the low cost and the ubiquitous nature of mobile phones to engage patients in self-care behaviors. Self-care is particularly important in achieving treatment outcomes in type 2 diabetes mellitus (T2DM). OBJECTIVE: This study examined the effect of personalized text messages on physical activity, as measured by a pedometer, and clinical outcomes in a diverse population of patients with T2DM. METHODS: Text to Move (TTM) incorporates physical activity monitoring and coaching to provide automated and personalized text messages to help patients with T2DM achieve their physical activity goals. A total of 126 English- or Spanish-speaking patients with glycated hemoglobin A1c (HbA1c) >7 were enrolled in-person to participate in the study for 6 months and were randomized into either the intervention arm that received the full complement of the intervention or a control arm that received only pedometers. The primary outcome was change in physical activity. We also assessed the effect of the intervention on HbA1c, weight, and participant engagement. RESULTS: All participants (intervention: n=64; control: n=62) were included in the analyses. The intervention group had significantly higher monthly step counts in the third (risk ratio [RR] 4.89, 95% CI 1.20 to 19.92, P=.03) and fourth (RR 6.88, 95% CI 1.21 to 39.00, P=.03) months of the study compared to the control group. However, over the 6-month follow-up period, monthly step counts did not differ statistically by group (intervention group: 9092 steps; control group: 3722 steps; RR 2.44, 95% CI 0.68 to 8.74, P=.17). HbA1c decreased by 0.07% (95% CI -0.47 to 0.34, P=.75) in the TTM group compared to the control group. Within groups, HbA1c decreased significantly from baseline in the TTM group by -0.43% (95% CI -0.75 to -0.12, P=.01), but nonsignificantly in the control group by -0.21% (95% CI -0.49 to 0.06, P=.13). Similar changes were observed for other secondary outcomes. CONCLUSION: Personalized text messaging can be used to improve outcomes in patients with T2DM by employing optimal patient engagement measures.

A Remote Medication Monitoring System for Chronic Heart Failure Patients to Reduce Readmissions: A Two-Arm Randomized Pilot Study.

BACKGROUND: Heart failure (HF) is a chronic condition affecting nearly 5.7 million Americans and is a leading cause of morbidity and mortality. With an aging population, the cost associated with managing HF is expected to more than double from US $31 billion in 2012 to US $70 billion by 2030. Readmission rates for HF patients are high-25% are readmitted at 30 days and nearly 50% at 6 months. Low medication adherence contributes to poor HF management and higher readmission rates. Remote telehealth monitoring programs aimed at improved medication management and adherence may improve HF management and reduce readmissions. OBJECTIVE: The primary goal of this randomized controlled pilot study is to compare the MedSentry remote medication monitoring system versus usual care in older HF adult patients who recently completed a HF telemonitoring program. We hypothesized that remote medication monitoring would be associated with fewer unplanned hospitalizations and emergency department (ED) visits, increased medication adherence, and improved health-related quality of life (HRQoL) compared to usual care. METHODS: Participants were randomized to usual care or use of the remote medication monitoring system for 90 days. Twenty-nine participants were enrolled and the final analytic sample consisted of 25 participants. Participants completed questionnaires at enrollment and closeout to gather data on medication adherence, health status, and HRQoL. Electronic medical records were reviewed for data on baseline classification of heart function and the number of unplanned hospitalizations and ED visits during the study period. RESULTS: Use of the medication monitoring system was associated with an 80% reduction in the risk of all-cause hospitalization and a significant decrease in the number of all-cause hospitalization length of stay in the intervention arm compared to usual care. Objective device data indicated high adherence rates (95%-99%) among intervention group participants despite finding no significant difference in self-reported adherence between study arms. The intervention group had poorer heart function and HRQoL at baseline, and HRQoL declined significantly in the intervention group compared to controls. CONCLUSIONS: The MedSentry medication monitoring system is a promising technology that merits continued development and evaluation. The MedSentry medication monitoring system may be useful both as a standalone system for patients with complex medication regimens or used to complement existing HF telemonitoring interventions. We found significant reductions in risk of all-cause hospitalization and the number of all-cause length of stay in the intervention group compared to controls. Although HRQoL deteriorated significantly in the intervention group, this may have been due to the poorer HF-functioning at baseline in the intervention group compared to controls. Telehealth medication adherence technologies, such as the MedSentry medication monitoring system, are a promising method to improve patient self-management,the quality of patient care, and reduce health care utilization and expenditure for patients with HF and other chronic diseases that require complex medication regimens. TRIAL REGISTRATION: ClinicalTrials.gov NCT01814696; https://clinicaltrials.gov/ct2/show/study/NCT01814696 (Archived by WebCite® at http://www.webcitation.org/6giqAVhno).

Practice Guidelines for Teledermatology.

Previous American Telemedicine Association (ATA) Teledermatology Practice Guidelines were issued in 2007. This updated version reflects new knowledge in the field, new technologies, and the need to incorporate teledermatology practice in a variety of settings, including hospitals, urgent care centers, Federally Qualified Health Centers, school-based clinics, public health facilities, and patient homes.

Teledermatology: from historical perspective to emerging techniques of the modern era: part I: History, rationale, and current practice.

Telemedicine is the use of telecommunications technology to support health care at a distance. Technological advances have progressively increased the ability of clinicians to care for diverse patient populations in need of skin expertise. Dermatology relies on visual cues that are easily captured by imaging technologies, making it ideally suited for this care model. Moreover, there is a shortage of medical dermatologists in the United States, where skin disorders account for 1 in 8 primary care visits and specialists tend to congregate in urban areas. Even in regions where dermatologic expertise is readily accessible, teledermatology may serve as an alternative that streamlines health care delivery by triaging chief complaints and reducing unnecessary in-person visits. In addition, many patients in the developing world have no access to dermatologic expertise, rendering it possible for teledermatologists to make a significant contribution to patient health outcomes. Teledermatology also affords educational benefits to primary care providers and dermatologists, and enables patients to play a more active role in the health care process by promoting direct communication with dermatologists.

Teledermatology: from historical perspective to emerging techniques of the modern era: part II: Emerging technologies in teledermatology, limitations and future directions.

Telemedicine is the use of telecommunications technology to support health care at a distance. Dermatology relies on visual cues that are easily captured by imaging technologies, making it ideally suited for this care model. Advances in telecommunications technology have made it possible to deliver high-quality skin care when patient and provider are separated by both time and space. Most recently, mobile devices that connect users through cellular data networks have enabled teledermatologists to instantly communicate with primary care providers throughout the world. The availability of teledermoscopy provides an additional layer of visual information to enhance the quality of teleconsultations. Teledermatopathology has become increasingly feasible because of advances in digitization of entire microscopic slides and robot-assisted microscopy. Barriers to additional expansion of these services include underdeveloped infrastructure in remote regions, fragmented electronic medical records, and varying degrees of reimbursement. Teleconsultants also confront special legal and ethical challenges as they work toward building a global network of practicing physicians.

Heart failure remote monitoring: evidence from the retrospective evaluation of a real-world remote monitoring program.

BACKGROUND: Given the magnitude of increasing heart failure mortality, multidisciplinary approaches, in the form of disease management programs and other integrative models of care, are recommended to optimize treatment outcomes. Remote monitoring, either as structured telephone support or telemonitoring or a combination of both, is fast becoming an integral part of many disease management programs. However, studies reporting on the evaluation of real-world heart failure remote monitoring programs are scarce. OBJECTIVE: This study aims to evaluate the effect of a heart failure telemonitoring program, Connected Cardiac Care Program (CCCP), on hospitalization and mortality in a retrospective database review of medical records of patients with heart failure receiving care at the Massachusetts General Hospital. METHODS: Patients enrolled in the CCCP heart failure monitoring program at the Massachusetts General Hospital were matched 1:1 with usual care patients. Control patients received care from similar clinical settings as CCCP patients and were identified from a large clinical data registry. The primary endpoint was all-cause mortality and hospitalizations assessed during the 4-month program duration. Secondary outcomes included hospitalization and mortality rates (obtained by following up on patients over an additional 8 months after program completion for a total duration of 1 year), risk for multiple hospitalizations and length of stay. The Cox proportional hazard model, stratified on the matched pairs, was used to assess primary outcomes. RESULTS: A total of 348 patients were included in the time-to-event analyses. The baseline rates of hospitalizations prior to program enrollment did not differ significantly by group. Compared with controls, hospitalization rates decreased within the first 30 days of program enrollment: hazard ratio (HR)=0.52, 95% CI 0.31-0.86, P=.01). The differential effect on hospitalization rates remained consistent until the end of the 4-month program (HR=0.74, 95% CI 0.54-1.02, P=.06). The program was also associated with lower mortality rates at the end of the 4-month program: relative risk (RR)=0.33, 95% 0.11-0.97, P=.04). Additional 8-months follow-up following program completion did not show residual beneficial effects of the CCCP program on mortality (HR=0.64, 95% 0.34-1.21, P=.17) or hospitalizations (HR=1.12, 95% 0.90-1.41, P=.31). CONCLUSIONS: CCCP was associated with significantly lower hospitalization rates up to 90 days and significantly lower mortality rates over 120 days of the program. However, these effects did not persist beyond the 120-day program duration.

The effect of technology-based interventions on pain, depression, and quality of life in patients with cancer: a systematic review of randomized controlled trials.

BACKGROUND: The burden of cancer is increasing; projections over the next 2 decades suggest that the annual cases of cancer will rise from 14 million in 2012 to 22 million. However, cancer patients in the 21st century are living longer due to the availability of novel therapeutic regimens, which has prompted a growing focus on maintaining patients' health-related quality of life. Telehealth is increasingly being used to connect with patients outside of traditional clinical settings, and early work has shown its importance in improving quality of life and other clinical outcomes in cancer care. OBJECTIVE: The aim of this study was to systematically assess the literature for the effect of supportive telehealth interventions on pain, depression, and quality of life in cancer patients via a systematic review of clinical trials. METHODS: We searched PubMed, EMBASE, Google Scholar, CINAHL, and PsycINFO in July 2013 and updated the literature search again in January 2015 for prospective randomized trials evaluating the effect of telehealth interventions in cancer care with pain, depression, and quality of life as main outcomes. Two of the authors independently reviewed and extracted data from eligible randomized controlled trials, based on pre-determined selection criteria. Methodological quality of studies was assessed by the Cochrane Collaboration risk of bias tool. RESULTS: Of the 4929 articles retrieved from databases and relevant bibliographies, a total of 20 RCTs were included in the final review. The studies were largely heterogeneous in the type and duration of the intervention as well as in outcome assessments. A majority of the studies were telephone-based interventions that remotely connected patients with their health care provider or health coach. The intervention times ranged from 1 week to 12 months. In general, most of the studies had low risk of bias across the domains of the Cochrane Collaboration risk of bias tool, but most of the studies had insufficient information about the allocation concealment domain. Two of the three studies focused on pain control reported significant effects of the intervention; four of the nine studies focus on depression reported significant effects, while only the studies that were focused on quality of life reported significant effects. CONCLUSIONS: This systematic review demonstrates the potential of telehealth interventions in improving outcomes in cancer care. However, more high-quality large-sized trials are needed to demonstrate cogent evidence of its effectiveness.

The Empirical Foundations of Teledermatology: A Review of the Research Evidence.

INTRODUCTION: This article presents the scientific evidence for the merit of telemedicine interventions in the diagnosis and management of skin disorders (teledermatology) in the published literature. The impetus for this work derives from the high prevalence of skin disorders, the high cost, the limited availability of dermatologists in certain areas, and the promise of teledermatology to address unmet needs in this area. MATERIALS AND METHODS: The findings are based on a targeted review of scientific studies published from January 2005 through April 2015. The initial search yielded some 5,020 articles in Google Scholar and 428 in PubMed. A review of the abstracts yielded 71 publications that met the inclusion criteria for this analysis. Evidence is organized according to the following: feasibility and acceptance; intermediate outcomes (use of service, compliance, and diagnostic and treatment concordance and accuracy); outcomes (health improvement and problem resolution); and cost savings. A special section is devoted to studies conducted at the Veterans Health Administration. RESULTS: Definitions of teledermatology varied across a wide spectrum of skin disorders, technologies, diagnostic tools, provider types, settings, and patient populations. Outcome measures included diagnostic concordance, treatment plans, and health. CONCLUSIONS: Despite these complexities, sufficient evidence was observed consistently supporting the effectiveness of teledermatology in improving accessibility to specialty care, diagnostic and treatment concordance, and skin care provided by primary care physicians, while also reducing cost. One study reported suboptimal clinical results from teledermatology for patients with pigmented skin lesions. On the other hand, confocal microscopy and advanced dermoscopy improved diagnostic accuracy, especially when rendered by experienced teledermatologists.