Challenges and Facilitators in Implementing Remote Patient Monitoring Programs in Primary Care.

BACKGROUND: The COVID-19 pandemic resulted in greater use of remote patient monitoring (RPM). However, the use of RPM has been modest compared to other forms of telehealth. OBJECTIVE: To identify and describe barriers to the implementation of RPM among primary care physicians (PCPs) that may be constraining its growth. DESIGN: We conducted 20 semi-structured interviews with PCPs across the USA who adopted RPM. Interview questions focused on implementation facilitators and barriers and RPM's impact on quality. We conducted thematic analysis of semi-structured interviews using both inductive and deductive approaches. The analysis was informed by the NASSS (non-adoption and abandonment and challenges to scale-up, spread, and sustainability) framework. PARTICIPANTS: PCPs who practiced at least 10 h per week in an outpatient setting, served adults, and monitored blood pressure and/or blood glucose levels with automatic transmission of data with at least 3 patients. KEY RESULTS: While PCPs generally agreed that RPM improved quality of care for their patients, many identified barriers to adoption and maintenance of RPM programs. Challenges included difficulties handling the influx of data and establishing a manageable workflow, along with digital and health literacy barriers. In addition to these barriers, many PCPs did not believe RPM was profitable. CONCLUSIONS: To encourage ongoing growth of RPM, it will be necessary to address implementation barriers through changes in payment policy, training and education in digital and health literacy, improvements in staff roles and workflows, and new strategies to ensure equitable access.

Effects of Remote Patient Monitoring Use on Care Outcomes Among Medicare Patients With Hypertension : An Observational Study.

BACKGROUND: Remote patient monitoring (RPM) is a promising tool for improving chronic disease management. Use of RPM for hypertension monitoring is growing rapidly, raising concerns about increased spending. However, the effects of RPM are still unclear. OBJECTIVE: To estimate RPM's effect on hypertension care and spending. DESIGN: Matched observational study emulating a longitudinal, cluster randomized trial. After matching, effect estimates were derived from a regression analysis comparing changes in outcomes from 2019 to 2021 for patients with hypertension at high-RPM practices versus those at matched control practices with little RPM use. SETTING: Traditional Medicare. PATIENTS: Patients with hypertension. INTERVENTION: Receipt of care at a high-RPM practice. MEASUREMENTS: Primary outcomes included hypertension medication use (medication fills, adherence, and unique medications received), outpatient visit use, testing and imaging use, hypertension-related acute care use, and total hypertension-related spending. RESULTS: 192 high-RPM practices (with 19 978 patients with hypertension) were matched to 942 low-RPM control practices (with 95 029 patients with hypertension). Compared with patients with hypertension at matched low-RPM practices, patients with hypertension at high-RPM practices had a 3.3% (95% CI, 1.9% to 4.8%) relative increase in hypertension medication fills, a 1.6% (CI, 0.7% to 2.5%) increase in days' supply, and a 1.3% (CI, 0.2% to 2.4%) increase in unique medications received. Patients at high-RPM practices also had fewer hypertension-related acute care encounters (-9.3% [CI, -20.6% to 2.1%]) and reduced testing use (-5.9% [CI, -11.9% to 0.0%]). However, these patients also saw increases in primary care physician outpatient visits (7.2% [CI, -0.1% to 14.6%]) and a $274 [CI, $165 to $384]) increase in total hypertension-related spending. LIMITATION: Lacked blood pressure data; residual confounding. CONCLUSION: Patients in high-RPM practices had improved hypertension care outcomes but increased spending. PRIMARY FUNDING SOURCE: National Institute of Neurological Disorders and Stroke.

How Emerging Telehealth Models Challenge Policymaking.

Policy Points Current telehealth policy discussions are focused on synchronous video and audio telehealth visits delivered by traditional providers and have neglected the growing number of alternative telehealth offerings. These alternative telehealth offerings range from simply supporting traditional brick-and-mortar providers to telehealth-only companies that directly compete with them. We describe policy challenges across this range of alternative telehealth offerings in terms of using the appropriate payment model, determining the payment amount, and ensuring the quality of care.

INFERNO: inferring the molecular mechanisms of noncoding genetic variants.

The majority of variants identified by genome-wide association studies (GWAS) reside in the noncoding genome, affecting regulatory elements including transcriptional enhancers. However, characterizing their effects requires the integration of GWAS results with context-specific regulatory activity and linkage disequilibrium annotations to identify causal variants underlying noncoding association signals and the regulatory elements, tissue contexts, and target genes they affect. We propose INFERNO, a novel method which integrates hundreds of functional genomics datasets spanning enhancer activity, transcription factor binding sites, and expression quantitative trait loci with GWAS summary statistics. INFERNO includes novel statistical methods to quantify empirical enrichments of tissue-specific enhancer overlap and to identify co-regulatory networks of dysregulated long noncoding RNAs (lncRNAs). We applied INFERNO to two large GWAS studies. For schizophrenia (36,989 cases, 113,075 controls), INFERNO identified putatively causal variants affecting brain enhancers for known schizophrenia-related genes. For inflammatory bowel disease (IBD) (12,882 cases, 21,770 controls), INFERNO found enrichments of immune and digestive enhancers and lncRNAs involved in regulation of the adaptive immune response. In summary, INFERNO comprehensively infers the molecular mechanisms of causal noncoding variants, providing a sensitive hypothesis generation method for post-GWAS analysis. The software is available as an open source pipeline and a web server.

Differences in Care Team Response to Patient Portal Messages by Patient Race and Ethnicity.

Importance: The COVID-19 pandemic was associated with substantial growth in patient portal messaging. Higher message volumes have largely persisted, reflecting a new normal. Prior work has documented lower message use by patients who belong to minoritized racial and ethnic groups, but research has not examined differences in care team response to messages. Both have substantial ramifications on resource allocation and care access under a new care paradigm with portal messaging as a central channel for patient-care team communication. Objective: To examine differences in how care teams respond to patient portal messages sent by patients from different racial and ethnic groups. Design, Setting, and Participants: In a cross-sectional design in a large safety-net health system, response outcomes from medical advice message threads sent from January 1, 2021, through November 24, 2021, from Asian, Black, Hispanic, and White patients were compared, controlling for patient and message thread characteristics. Asian, Black, Hispanic, and White patients with 1 or more adult primary care visits at Boston Medical Center in calendar year 2020 were included. Data analysis was conducted from June 23, 2022, through December 21, 2023. Exposure: Patient race and ethnicity. Main Outcomes and Measures: Rates at which medical advice request messages were responded to by care teams and the types of health care professionals that responded. Results: A total of 39 043 patients were included in the sample: 2006 were Asian, 21 600 were Black, 7185 were Hispanic, and 8252 were White. A total of 22 744 (58.3%) patients were women and mean (SD) age was 50.4 (16.7) years. In 2021, these patients initiated 57 704 medical advice request message threads. When patients who belong to minoritized racial and ethnic groups sent these messages, the likelihood of receiving any care team response was similar, but the types of health care professionals that responded differed. Black patients were 3.95 percentage points (pp) less likely (95% CI, -5.34 to -2.57 pp; P < .001) to receive a response from an attending physician, and 3.01 pp more likely (95% CI, 1.76-4.27 pp; P < .001) to receive a response from a registered nurse, corresponding to a 17.4% lower attending response rate. Similar, but smaller, differences were observed for Asian and Hispanic patients. Conclusions and Relevance: The findings of this study suggest lower prioritization of patients who belong to minoritized racial and ethnic groups during triaging. Understanding and addressing these disparities will be important for improving care equity and informing health care delivery support algorithms.

Building the Business Case for an Inclusive Approach to Digital Health Measurement With a Web App (Market Opportunity Calculator): Instrument Development Study.

BACKGROUND: The use of digital health measurement tools has grown substantially in recent years. However, there are concerns that the promised benefits from these products will not be shared equitably. Underserved populations, such as those with lower education and income, racial and ethnic minorities, and those with disabilities, may find such tools poorly suited for their needs. Because underserved populations shoulder a disproportionate share of the US disease burden, they also represent a substantial share of digital health companies' target markets. Incorporating inclusive principles into the product development process can help ensure that the resulting tools are broadly accessible and effective. In this context, inclusivity not only maximizes societal benefit but also leads to greater commercial success. OBJECTIVE: A critical element in fostering inclusive product development is building the business case for why it is worthwhile. The Digital Health Measurement Collaborative Community (DATAcc) Market Opportunity Calculator was developed as an open-access resource to enable digital health measurement product developers to build a business case for incorporating inclusive practices into their research and development processes. METHODS: The DATAcc Market Opportunity Calculator combines data on population demographics and disease prevalence and health status from the US Census Bureau and the US Centers for Disease Control and Prevention (CDC). Together, these data are used to calculate the share of US adults with specific conditions (eg, diabetes) falling into various population segments along key "inclusion vectors" (eg, race and ethnicity). RESULTS: A free and open resource, the DATAcc Market Opportunity Calculator can be accessed from the DATAcc website. Users first select the target health condition addressed by their product, and then an inclusion vector to segment the patient population. The calculator displays each segment as a share of the overall US adult population and its share specifically among adults with the target condition, quantifying the importance of underserved patient segments to the target market. The calculator also estimates the value of improvements to product inclusivity by modeling the downstream impact on the accessible market size. For example, simplifying prompts on a hypertension-focused product to make it more accessible for adults with lower educational attainment is shown by the calculator to increase the target market by 2 million people and the total addressable market opportunity by US $200 million. CONCLUSIONS: Digital health measurement is still in its infancy. Now is the time to establish a precedent for inclusive product development to maximize societal benefit and build sustainable commercial returns. The Market Opportunity Calculator can help build the business case for "why"-showing how inclusivity can translate to financial opportunity. Once the decision has been made to pursue inclusive design, other components of the broader DATAcc toolkit for inclusive product development can support the "how."

Rapid Growth Of Remote Patient Monitoring Is Driven By A Small Number Of Primary Care Providers.

Growing enthusiasm for remote patient monitoring has been motivated by the hope that it can improve care for patients with poorly controlled chronic illness. In a national commercially insured population in the US, we found that billing for remote patient monitoring increased more than fourfold during the first year of the COVID-19 pandemic. Most of this growth was driven by a small number of primary care providers. Among the patients of these providers with a high volume of remote patient monitoring, we did not observe substantial targeting of remote patient monitoring to people with greater disease burden or worse disease control. Further research is needed to identify which patients benefit from remote patient monitoring, to inform evidence-based use and coverage decisions. In the meantime, payers and policy makers should closely monitor remote patient monitoring use and spending.

Assessing the impact of the COVID-19 pandemic on clinician ambulatory electronic health record use.

OBJECTIVE: The COVID-19 pandemic changed clinician electronic health record (EHR) work in a multitude of ways. To evaluate how, we measure ambulatory clinician EHR use in the United States throughout the COVID-19 pandemic. MATERIALS AND METHODS: We use EHR meta-data from ambulatory care clinicians in 366 health systems using the Epic EHR system in the United States from December 2019 to December 2020. We used descriptive statistics for clinician EHR use including active-use time across clinical activities, time after-hours, and messages received. Multivariable regression to evaluate total and after-hours EHR work adjusting for daily volume and organizational characteristics, and to evaluate the association between messages and EHR time. RESULTS: Clinician time spent in the EHR per day dropped at the onset of the pandemic but had recovered to higher than prepandemic levels by July 2020. Time spent actively working in the EHR after-hours showed similar trends. These differences persisted in multivariable models. In-Basket messages received increased compared with prepandemic levels, with the largest increase coming from messages from patients, which increased to 157% of the prepandemic average. Each additional patient message was associated with a 2.32-min increase in EHR time per day (P < .001). DISCUSSION: Clinicians spent more total and after-hours time in the EHR in the latter half of 2020 compared with the prepandemic period. This was partially driven by increased time in Clinical Review and In-Basket messaging. CONCLUSIONS: Reimbursement models and workflows for the post-COVID era should account for these demands on clinician time that occur outside the traditional visit.

Investigating the Association Between Telemedicine Use and Timely Follow-Up Care After Acute Cardiovascular Hospital Encounters.

Background: Telemedicine use increased dramatically during the COVID-19 pandemic; however, questions remain as to how telemedicine use impacts care. Objectives: The purpose of this study was to examine the association of increased telemedicine use on rates of timely follow-up and unplanned readmission after acute cardiovascular hospital encounters. Methods: We examined hospital encounters for acute coronary syndrome, arrhythmia disorders, heart failure (HF), and valvular heart disease from a large U.S., multisite, integrated academic health system among patients with established cardiovascular care within the system. We evaluated 14-day postdischarge follow-up and 30-day all-cause unplanned readmission rates for encounters from the pandemic "steady state" period from May 24, 2020 through December 31, 2020, when telemedicine use was high and compared them to those of encounters from the week-matched period in 2019 (May 26, 2019, through December 31, 2019), adjusting for patient and encounter characteristics. Results: The study population included 6,026 hospital encounters. In the pandemic steady-state period, 40% of follow-ups after these encounters were conducted via telemedicine vs 0% during the week-matched period in 2019. Overall, 14-day follow-up rates increased from 41.7% to 44.9% (adjusted difference: +2.0 percentage points [pp], 95% CI: -1.1 to +5.1 pp, P = 0.20). HF encounters experienced the largest improvement from 50.1% to 55.5% (adjusted difference: +6.5 pp, 95% CI: +0.5 to +12.4 pp, P = 0.03). Overall 30-day all-cause unplanned readmission rates fell slightly, from 18.3% to 16.9% (adjusted difference -1.6 pp; 95% CI: -4.0 to +0.8 pp, P = 0.20). Conclusions: Increased telemedicine use during the COVID-19 pandemic was associated with earlier follow-ups, particularly after HF encounters. Readmission rates did not increase, suggesting that the shift to telemedicine did not compromise care quality.

Inferring the Molecular Mechanisms of Noncoding Alzheimer's Disease-Associated Genetic Variants.

Most of the loci identified by genome-wide association studies (GWAS) for late-onset Alzheimer's disease (LOAD) are in strong linkage disequilibrium (LD) with nearby variants all of which could be the actual functional variants, often in non-protein-coding regions and implicating underlying gene regulatory mechanisms. We set out to characterize the causal variants, regulatory mechanisms, tissue contexts, and target genes underlying these associations. We applied our INFERNO algorithm to the top 19 non-APOE loci from the IGAP GWAS study. INFERNO annotated all LD-expanded variants at each locus with tissue-specific regulatory activity. Bayesian co-localization analysis of summary statistics and eQTL data was performed to identify tissue-specific target genes. INFERNO identified enhancer dysregulation in all 19 tag regions analyzed, significant enrichments of enhancer overlaps in the immune-related blood category, and co-localized eQTL signals overlapping enhancers from the matching tissue class in ten regions (ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, EPHA1, FERMT2, ZCWPW1). In several cases, we identified dysregulation of long noncoding RNA (lncRNA) transcripts and applied the lncRNA target identification algorithm from INFERNO to characterize their downstream biological effects. We also validated the allele-specific effects of several variants on enhancer function using luciferase expression assays. By integrating functional genomics with GWAS signals, our analysis yielded insights into the regulatory mechanisms, tissue contexts, genes, and biological processes affected by noncoding genetic variation associated with LOAD risk.