Using a digital tool to detect early changes in everyday functioning in older adults: A pilot study of the Assessment of Smartphone Everyday Tasks (ASSET).

INTRODUCTION: To investigate the utility of a new digital tool for measuring everyday functioning in preclinical Alzheimer's disease, we piloted the Assessment of Smartphone Everyday Tasks (ASSET) application. METHODS: Forty-six participants (50.3 ± 27.1 years; 67% female; 20 young unimpaired, 17 old unimpaired, 9 mildly cognitively impaired) completed ASSET 7 times. ASSET comprises two main tasks, simulating a Patient Portal and a Calendar. We assessed ASSET's internal consistency, test-retest reliability, and user experience. RESULTS: ASSET main tasks correlated with each other (r = 0.75, 95% confidence interval [CI] = [0.58, 0.86]). Performance on ASSET's Patient Portal related to cognition (r = 0.64, 95% CI = [0.42, 0.79]) and observer ratings of everyday functioning (r = 0.57, 95% CI = [0.24, 0.79]). Test-retest reliability was good (intraclass correlation coefficient = 0.87, 95% CI = [0.77, 0.93]). Most participants rated their experience with ASSET neutrally or positively. DISCUSSION: ASSET is a promising smartphone-based digital assessment of everyday functioning. Future studies may investigate its utility for early diagnosis and evaluation of treatment of Alzheimer's disease.

Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer.

OBJECTIVES: To develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset. METHODS: Baseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1-2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups: (1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval (2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation (3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length (4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC) Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97). RESULTS: After external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48-0.72) to predict complete response and 0.65 (95%CI=0.53-0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables. CONCLUSIONS: Overall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset). CLINICAL RELEVANCE STATEMENT: Predicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization. KEY POINTS: This multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer. Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates. No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.

Trends in 2-1-1 Calls During Public Health Emergencies, Overall and By Gender: Hurricane Irma and COVID-19 in Broward County, Florida.

Trends in 2-1-1 calls reflect evolving community needs during public health emergencies (PHEs). The study examined how changes in 2-1-1 call volume after 2 PHEs (Hurricane Irma and the coronavirus disease 2019 [COVID-19] pandemic declaration) in Broward County, Florida, varied by PHE type and whether variations differed by gender and over time. Examining 2-1-1 calls during June to December 2016, June to December 2017, and March 2019 to April 2021, this study measured changes in call volume post-PHEs using interrupted time series analysis. Hurricane Irma and the COVID-19 pandemic were associated with increases in call volume (+81 calls/d and +84 calls/d, respectively). Stratified by gender, these PHEs were associated with larger absolute increases for women (+66 and +57 calls/d vs +15 and +27 calls/d for men) but larger percent increases above their baseline for men (+143% and +174% vs +119% and +138% for women). Calls by women remained elevated longer after Hurricane Irma (5 wk vs 1 wk), but the opposite pattern was observed after the pandemic declaration (8 vs 21 wk). PHEs reduce gender differences in help-seeking around health-related social needs. Findings demonstrate the utility of 2-1-1 call data for monitoring and responding to evolving community needs in the PHE context.

Changes in Poison Center Calls for Intentional Exposure During Public Health Emergencies: COVID-19 and Winter Storm Uri in Dallas County, Texas.

OBJECTIVE: This study aimed to: (1) explore changes in the volume of calls to poison control centers (PCs) for intentional exposures (IEs) in Dallas County, Texas, overall and by gender and age, and (2) examine the association between 2 different public health emergencies (PHEs) and changes in IE call volume. METHODS: PCs categorize calls they receive by intentionality of the exposure, based on information from the caller. We analyzed data on PC calls categorized as intentional in Dallas County, Texas, from March 2019 - April 2021. This period includes the COVID-19 pandemic declaration (March 2020), a surge in COVID-19 cases (July 2020), and Winter Storm Uri (February 2021). Changes in IE call volume (overall and by age and gender), were explored, and interrupted time series analysis was used to examine call volume changes after PHE onset. RESULTS: The summer surge in COVID-19 cases was associated with 1.9 additional IE calls/day (95% CI 0.7 to 3.1), in the context of a baseline unadjusted mean of 6.2 calls per day (unadjusted) before November 3, 2020. Neither the pandemic declaration nor Winter Storm Uri was significantly associated with changes in call volume. Women, on average, made 1.2 more calls per day compared to men during the study period. IE calls for youth increased after the pandemic declaration, closing the longstanding gap between adults and youth by early 2021. CONCLUSIONS: Changes in IE call volume in Dallas County varied by gender and age. Calls increased during the local COVID-19 surge. Population-level behavioral health may be associated with local crisis severity.

Use Of And Willingness To Use Video Telehealth Through The COVID-19 Pandemic.

We examined use of and willingness to use video telehealth during the COVID-19 pandemic in a longitudinally followed cohort. Between February 2019 and March 2021, use and willingness to use increased among nearly all subgroups, with large increases among Black adults and adults with lower educational attainment. In March 2021 Black adults, adults ages 20-39, and high-income adults reported the greatest willingness to use video telehealth.

Type III Interferons, Viral Loads, Age, and Disease Severity in Young Children With Respiratory Syncytial Virus Infection.

BACKGROUND: The interplay among respiratory syncytial virus (RSV) loads, mucosal interferons (IFN), and disease severity in RSV-infected children is poorly understood. METHODS: Children <2 years of age with mild (outpatients) or severe (inpatients) RSV infection and healthy controls were enrolled, and nasopharyngeal samples obtained for RSV loads and innate cytokines quantification. Patients were stratified by age (0-6 and >6-24 months) and multivariable analyses performed to identify predictors of disease severity. RESULTS: In 2015-2019 we enrolled 219 RSV-infected children (78 outpatients; 141 inpatients) and 34 healthy controls. Type I, II, and III IFN concentrations were higher in children aged >6 versus 0-6 months and, like CXCL10, they were higher in outpatients than inpatients and correlated with RSV loads (P < .05). Higher IL6 concentrations increased the odds of hospitalization (odds ratio [OR], 2.30; 95% confidence interval [CI], 1.07-5.36) only in children >6 months, while higher IFN-λ2/3 concentrations had the opposite effect irrespective of age (OR, 0.38; 95% CI, .15-.86). Likewise, higher CXCL10 concentrations decreased the odds of hospitalization (OR, 0.21; 95% CI, .08-.48), oxygen administration (OR, 0.42; 95% CI, .21-.80),PICU admission (OR, 0.39; 95% CI, .20-.73), and prolonged hospitalization (OR, 0.57; 95% CI, .32-.98) irrespective of age. CONCLUSIONS: Children with milder RSV infection and those aged >6 months had higher concentrations of mucosal IFNs, suggesting that maturation of mucosal IFN responses are associated with protection against severe RSV disease.

Barriers to Price and Quality Transparency in Health Care Markets.

Consumers of health care in the United States often lack information on the actual prices of the care they receive and can also lack access to information about the quality of their care. RAND researchers gathered information on how health care prices are set, price variation in health care markets, barriers to price and quality transparency for consumers, and the extent to which price and quality information is used in marketing efforts. Public payers typically set prices for physicians and hospitals prospectively, and commercial health plans negotiate with physicians and hospitals to determine prices. Some research has shown substantial variation in negotiated prices, while other research suggests more moderate variation in some markets. Although the government does not directly affect prices paid by commercial health plans, commercial prices tend to be positively correlated with Medicare fee-for-service prices. Medicaid receives mandated rebates from drug manufacturers for dispensed prescriptions. Commercial health plans negotiate both the prices paid to pharmacies and any discounts and rebates received directly from drug manufacturers. Self-pay prices faced by consumers in pharmacies are set by individual pharmacies. The barriers to consumer price and quality transparency identified through this work generally represented limitations of existing tools. Consumer price transparency is being pursued by federal and state governments. Most commercial insurers have created price transparency tools to help members estimate the costs of various services. However, these tools can be difficult to navigate and do not always provide accurate pricing.

Standardized assessment of medication reconciliation in post-acute care.

BACKGROUND: Medication reconciliation (MR) facilitates safety during transitions of care, which occur frequently across post-acute care (PAC) settings. Under the intent of the IMPACT Act of 2014, the Centers for Medicare & Medicaid Services contracted with the RAND Corporation to develop and test standardized assessment data elements (SADEs) that assess the MR process. METHODS: We employed an iterative process that incorporated stakeholder input and three rounds of testing to identify, refine, and evaluate MR SADEs. Testing took place in 186 PAC sites (57 home health agencies, 28 inpatient rehabilitation facilities, 28 long-term care hospitals, and 73 skilled nursing facilities). There were 2951 patients in the final test. Novel MR SADEs, based on the Joint Commission's framework, were refined. The final SADEs assessed whether: patient was taking high-risk medications; an indication was noted for each medication class; discrepancies were identified; patient or family/caregiver was involved in addressing discrepancies; discrepancies were communicated to physician (or designee) within 24 h; recommended physician actions regarding discrepancies were implemented within 24 h after physician response; and the reconciled list was communicated to patient, prescriber, and/or pharmacy. Two assessors per facility collected data for each patient. Analyses described completion time, data missingness, and interrater reliability, as well as feedback on assessor burden. RESULTS: Time to complete the MR SADEs was 3.2 min. Missing data were <5%. Interrater reliability was moderate to high (κ: 0.42 [whether a reconciled list was communicated to prescribers] to 0.89 [identifying patients taking hypoglycemics]). For identifying high-risk medication classes, interrater reliability was high (κ: 0.72-0.89). There were minimal differences by setting. CONCLUSIONS: This is the first set of MR SADEs that have been assessed across the PAC settings. Results demonstrate feasibility, based on missing data and completion time, and moderate to strong reliability, based on interrater comparisons, of assessing MR.

Corrigendum to: 204. Mucosal Cytokine Profiles in Children with COVID-19.

[This corrects the article DOI: 10.1093/ofid/ofab466.000.].

Severe Acute Respiratory Syndrome Coronavirus 2 RNAemia and Clinical Outcomes in Children With Coronavirus Disease 2019.

The burden of coronavirus disease 2019 (COVID-19) in children represents a fraction of cases worldwide, yet a subset of those infected are at risk for severe disease. We measured plasma severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in a cohort of 103 children hospitalized with COVID-19 with diverse clinical manifestations. SARS-CoV-2 RNAemia was detected in 27 (26%) of these children, lasted for a median of 6 (interquartile range, 2-9) days, and was associated with higher rates of oxygen administration, admission to the intensive care unit, and longer hospitalization.

Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility.

OBJECTIVES: To investigate sources of variation in a multicenter rectal cancer MRI dataset focusing on hardware and image acquisition, segmentation methodology, and radiomics feature extraction software. METHODS: T2W and DWI/ADC MRIs from 649 rectal cancer patients were retrospectively acquired in 9 centers. Fifty-two imaging features (14 first-order/6 shape/32 higher-order) were extracted from each scan using whole-volume (expert/non-expert) and single-slice segmentations using two different software packages (PyRadiomics/CapTk). Influence of hardware, acquisition, and patient-intrinsic factors (age/gender/cTN-stage) on ADC was assessed using linear regression. Feature reproducibility was assessed between segmentation methods and software packages using the intraclass correlation coefficient. RESULTS: Image features differed significantly (p < 0.001) between centers with more substantial variations in ADC compared to T2W-MRI. In total, 64.3% of the variation in mean ADC was explained by differences in hardware and acquisition, compared to 0.4% by patient-intrinsic factors. Feature reproducibility between expert and non-expert segmentations was good to excellent (median ICC 0.89-0.90). Reproducibility for single-slice versus whole-volume segmentations was substantially poorer (median ICC 0.40-0.58). Between software packages, reproducibility was good to excellent (median ICC 0.99) for most features (first-order/shape/GLCM/GLRLM) but poor for higher-order (GLSZM/NGTDM) features (median ICC 0.00-0.41). CONCLUSIONS: Significant variations are present in multicenter MRI data, particularly related to differences in hardware and acquisition, which will likely negatively influence subsequent analysis if not corrected for. Segmentation variations had a minor impact when using whole volume segmentations. Between software packages, higher-order features were less reproducible and caution is warranted when implementing these in prediction models. KEY POINTS: • Features derived from T2W-MRI and in particular ADC differ significantly between centers when performing multicenter data analysis. • Variations in ADC are mainly (> 60%) caused by hardware and image acquisition differences and less so (< 1%) by patient- or tumor-intrinsic variations. • Features derived using different image segmentations (expert/non-expert) were reproducible, provided that whole-volume segmentations were used. When using different feature extraction software packages with similar settings, higher-order features were less reproducible.

Assessment of Patient Preferences for Telehealth in Post-COVID-19 Pandemic Health Care.

Importance: Telehealth use greatly increased in 2020 during the first year of the COVID-19 pandemic. Patient preferences for telehealth or in-person care are an important factor in defining the role of telehealth in the postpandemic world. Objective: To ascertain patient preferences for video visits after the ongoing COVID-19 public health emergency and to identify patient perceptions of the value of video visits and the role of out-of-pocket cost in changing patient preference for each visit modality. Design, Setting, and Participants: This survey study was conducted using a nationally representative sample of adult members of the RAND American Life Panel. The data were obtained from the American Life Panel Omnibus Survey, which was fielded between March 8 and 19, 2021. Main Outcomes and Measures: Preferences for video visits vs in-person care were analyzed in the survey. The first question was about participants' baseline preference for an in-person or a video visit for a nonemergency health issue. The second question entailed choosing between the preferred visit modality with a cost of $30 and another modality with a cost of $10. Questions also involved demographic characteristics, experience with video visits, willingness to use video visits, and preferences for the amount of telehealth use after the COVID-19 pandemic. Results: A total of 2080 of 3391 sampled panel members completed the survey (participation rate, 61.3%). Participants in the weighted sample had a mean (SE) age of 51.1 (0.67) years and were primarily women (1079 [51.9%]). Most participants (66.5%) preferred at least some video visits in the future, but when faced with a choice between an in-person or a video visit for a health care encounter that could be conducted either way, more than half of respondents (53.0%) preferred an in-person visit. Among those who initially preferred an in-person visit when out-of-pocket costs were not a factor, 49.8% still preferred in-person care and 23.5% switched to a video visit when confronted with higher relative costs for in-person care. In contrast, among those who initially preferred a video visit, only 18.9% still preferred a video visit and 61.7% switched to in-person visit when confronted with higher relative costs for video visits. Conclusions and Relevance: This survey study found that participants were generally willing to use video visits but preferred in-person care, and those who preferred video visits were more sensitive to paying out-of-pocket cost. These results suggest that understanding patient preferences will help identify telehealth's role in future health care delivery.

Barriers to using clinical decision support in ambulatory care: Do clinics in health systems fare better?

OBJECTIVE: We quantify the use of clinical decision support (CDS) and the specific barriers reported by ambulatory clinics and examine whether CDS utilization and barriers differed based on clinics' affiliation with health systems, providing a benchmark for future empirical research and policies related to this topic. MATERIALS AND METHODS: Despite much discussion at the theoretic level, the existing literature provides little empirical understanding of barriers to using CDS in ambulatory care. We analyze data from 821 clinics in 117 medical groups, based on in Minnesota Community Measurement's annual Health Information Technology Survey (2014-2016). We examine clinics' use of 7 CDS tools, along with 7 barriers in 3 areas (resource, user acceptance, and technology). Employing linear probability models, we examine factors associated with CDS barriers. RESULTS: Clinics in health systems used more CDS tools than did clinics not in systems (24 percentage points higher in automated reminders), but they also reported more barriers related to resources and user acceptance (26 percentage points higher in barriers to implementation and 33 points higher in disruptive alarms). Barriers related to workflow redesign increased in clinics affiliated with health systems (33 points higher). Rural clinics were more likely to report barriers to training. CONCLUSIONS: CDS barriers related to resources and user acceptance remained substantial. Health systems, while being effective in promoting CDS tools, may need to provide further assistance to their affiliated ambulatory clinics to overcome barriers, especially the requirement to redesign workflow. Rural clinics may need more resources for training.

Social isolation is closely linked to a marked reduction in physical activity in male mice.

The effects of social isolation on an individual's behavior is an important field of research, especially as public health officials encourage social distancing to prevent the spread of pandemic disease. In this study we evaluate the effects of social isolation on physical activity in mice. Utilizing a pixel-based tracking system, we continuously monitored the movement of isolated mice compared with paired cage mates in the home cage environment. We demonstrate that mice that are socially isolated dramatically decrease their movement when separated from their cage mate, and especially in the dark cycle, when mice are normally most active. When isolated mice are re-paired with their original cage mate, this effect is reversed, and mice return to their prior levels of activity. These findings suggest a close link between social isolation and physical activity, and are of particular interest in the wake of coronavirus disease 2019, when many are forced into isolation. Social isolation may affect an individual's overall activity levels in humans too, which may have unintended effects on health that deserve further consideration.

Reflections on the 7th international Jerusalem conference on health policy in the wake of the Covid-19 outbreak.

In 2019, a conference in Israel showcased new frontiers in technology in healthcare, highlighting research conducted in Israel as well as across the globe. At the time, no one realized how critical-and ubiquitous-some of these technologies would become. In the wake of a global pandemic, the ability to provide healthcare remotely has become ever more important. We explore some Israeli innovations and consider how healthcare may be permanently changed.

Optimizing health IT to improve health system performance: A work in progress.

BACKGROUND: Despite significant investments in health information technology (IT), the technology has not yielded the intended performance effects or transformational change. We describe activities that health systems are pursuing to better leverage health IT to improve performance. METHODS: We conducted semi-structured telephone interviews with C-suite executives from 24 U.S. health systems in four states during 2017-2019 and analyzed the data using a qualitative thematic approach. RESULTS: Health systems reported two broad categories of activities: laying the foundation to improve performance with IT and using IT to improve performance. Within these categories, health systems were engaged in similar activities but varied greatly in their progress. The most substantial effort was devoted to the first category, which enabled rather than directly improved performance, and included consolidating to a single electronic health record (EHR) platform and common data across the health system, standardizing data elements, and standardizing care processes before using the EHR to implement them. Only after accomplishing such foundational activities were health systems able to focus on using the technology to improve performance through activities such as using data and analytics to monitor and provide feedback, improving uptake of evidence-based medicine, addressing variation and overuse, improving system-wide prevention and population health management, and making care more convenient. CONCLUSIONS AND IMPLICATIONS: Leveraging IT to improve performance requires significant and sustained effort by health systems, in addition to significant investments in hardware and software. To accelerate change, better mechanisms for creating and disseminating best practices and providing advanced technical assistance are needed.

Prevalence and Characteristics of Telehealth Utilization in the United States.

Importance: Telehealth services, which allow patients to communicate with a remotely located clinician, are increasingly available; however, prevalence of telehealth use, including videoconferencing visits, remains unclear. Objective: To measure the use of and willingness to use telehealth modalities across the US population. Design, Setting, and Participants: This survey study, conducted between February 2019 and April 2019, asked participants about their use of different telehealth modalities, reasons for not using videoconferencing visits, and willingness to use videoconferencing visits. Questions were continuously posed to panel members and closed after 2555 responses were obtained, at which point 3932 panel members had been invited, for a 65.0% response rate. Exposures: Demographic characteristics (ie, age, sex, race, rural/urban residency, education level, and income). Main Outcomes and Measures: Self-reported use of specific telehealth modalities, reasons for nonuse, and willingness to use videoconferencing in the future. Results: A total of 2555 individuals completed the survey with a mean (SD) age of 57.2 (14.2) years; 1453 respondents (weighted percentage, 51.9%) were women, and 2043 (weighted percentage, 73.4%) were White individuals. Overall, 1343 respondents (weighted percentage, 50.8%) reported use of a nontelephone telehealth modality, ranging from 873 respondents (weighted percentage, 31.9%) for patient portals and 89 respondents (weighted percentage, 4.2%) for videoconferencing visits. Although 1309 respondents (weighted percentage, 49.2%) overall answered that they were willing or very willing to use videoconferencing visits, respondents who were Black individuals (OR, 0.58; 95% CI, 0.38-0.91), aged older than 65 years (OR, 0.51; 95% CI, 0.40-0.66), or had less education (high school or less vs advanced degrees: OR, 0.37; 95% CI, 0.25-0.56) were less likely to express willingness. Conclusions and Relevance: Despite the focused policy attention on videoconferencing visits, the results of this survey study suggest that other forms of telehealth were more dominant prior to 2020. Targeted efforts may be necessary for videoconferencing visits to reach patient groups who are older or have less education, and payer policies supporting other forms of telemedicine may be appropriate to enhance access.

Trends in the use of computerized physician order entry by health-system affiliated ambulatory clinics in the United States, 2014-2016.

BACKGROUND: Computerized provider order entry (CPOE) can help providers deliver better quality care. We aimed to understand recent trends in use of CPOE by health system-affiliated ambulatory clinics. METHODS: We analyzed longitudinal data (2014-2016) for 19,109 ambulatory clinics that participated in all 3 years of the Healthcare Information and Management Systems Society Analytics survey to assess use of CPOE and identify characteristics of clinics associated with CPOE use. We calculated descriptive statistics to examine overall trends in use, location of order entry (bedside vs. clinical station), and system-level use CPOE across all clinics. We used linear probability models to explore the association between clinic characteristics (practice size, practice type, and health system type) and two outcomes of interest: CPOE use at any point between 2014 and 2016, and CPOE use beginning in 2015 or 2016. RESULTS: Between 2014 and 2016, use of CPOE increased more than 9 percentage points from 58 to 67%. Larger clinics and those affiliated with multi-hospital health systems were more likely to have reported use of CPOE. We found no difference in CPOE use by primary care versus specialty care clinics. When used, most clinics reported using CPOE for most or all of their orders. Health systems that used CPOE usually did so for all system-affiliated clinics. CONCLUSIONS: Small practice size or not being part of a multi-hospital system are associated with lower use of CPOE between 2014 and 2016. Less than optimal use in these environments may be harming patient outcomes.

Experiences of Medicaid Programs and Health Centers in Implementing Telehealth.

Despite telehealth's potential to improve access to care, it is underutilized by safety-net providers, including Federally Qualified Health Centers (FQHCs), due to a range of policy, organizational, and logistical barriers. Research that facilitates state-to-state learning can inform both Medicaid and Medicare policies going forward and provide lessons learned for FQHCs interested in starting or expanding telehealth programs. The authors conducted telephone discussions with representatives of seven state Medicaid programs and 19 urban and rural FQHCs to address how FQHCs in selected states are using telehealth, how the delivery of telehealth services is structured, barriers and facilitators of telehealth, and how Medicaid policy influences telehealth implementation. Live video telehealth, typically telebehavioral health, was the most prevalent type of telehealth among FQHCs in the sample. Stakeholders highlighted several weaknesses of Medicaid policies in one or more states, including general lack of clarity regarding which services were allowed by Medicaid programs, ambiguity around telepresenter requirements, lack of authorization for FQHCs to serve as distant sites in the federal Medicare program and in select state Medicaid programs, and insufficient reimbursement. FQHC stakeholders also identified multiple barriers to telehealth implementation beyond reimbursement. Nonetheless, FQHC stakeholders generally believed they could overcome these various barriers to telehealth implementation, if reimbursement and the risk of losing revenue in offering telehealth services were improved. While diversity of experiences makes it difficult to generalize about implementation of telehealth in the safety net, the authors identified several common themes and associated considerations for policymakers, payers, and FQHCs.