Telehealth in US hospitals: State-level reimbursement policies no longer influence adoption rates.

OBJECTIVES: Prior to COVID-19, levels of adoption of telehealth were low in the U.S., though they exploded during the pandemic. Following the pandemic, it will be critical to identify the characteristics that were associated with adoption of telehealth prior to the pandemic as key drivers of adoption and outside of a public health emergency. MATERIALS AND METHODS: We examined three data sources: The American Telemedicine Association's 2019 state telehealth analysis, the American Hospital Association's 2018 annual survey of acute care hospitals and its Information Technology Supplement. Telehealth adoption was measured through five telehealth categories. Independent variables included seven hospital characteristics and five reimbursement policies. After bivariate comparisons, we developed a multivariable model using logistic regression to assess characteristics associated with telehealth adoption. RESULTS: Among 2923 US hospitals, 73% had at least one telehealth capability. More than half of these hospitals invested in telehealth consultation services and stroke care. Non-profit hospitals, affiliated hospitals, major teaching hospitals, and hospitals located in micropolitan areas (those with 10-50,000 people) were more likely to adopt telehealth. In contrast, hospitals that lacked electronic clinical documentation, were unaffiliated with a hospital system, or were investor-owned had lower odds of adopting telehealth. None of the statewide policies were associated with adoption of telehealth. CONCLUSIONS: Telehealth policy requires major revisions soon, and we suggest that these policies should be national rather than at the state level. Further steps as incentivizing rural hospitals for adopting interoperable systems and expanding RPM billing opportunities will help drive adoption, and promote equity.

Artificial intelligence in oncology: Path to implementation.

In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer-related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high-value oncology care. AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user-design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data. Concrete actions that major stakeholders can take to overcome barriers to AI implementation in oncology include training and educating the oncology workforce in AI; standardizing data, model validation methods, and legal and safety regulations; funding and conducting future research; and developing, studying, and deploying AI tools through multidisciplinary collaboration.

Proximity-Based Emergency Response Communities for Patients With Allergies Who Are at Risk of Anaphylaxis: Clustering Analysis and Scenario-Based Survey Study.

BACKGROUND: Anaphylaxis is a potentially fatal allergic reaction. However, many patients at risk of anaphylaxis who should permanently carry a life-saving epinephrine auto injector (EAI) do not carry one at the moment of allergen exposure. The proximity-based emergency response communities (ERC) strategy suggests speeding EAI delivery by alerting patient-peers carrying EAI to respond and give their EAI to a nearby patient in need. OBJECTIVES: This study had two objectives: (1) to analyze 10,000 anaphylactic events from the European Anaphylaxis Registry (EAR) by elicitor and location in order to determine typical anaphylactic scenarios and (2) to identify patients' behavioral and spatial factors influencing their response to ERC emergency requests through a scenario-based survey. METHODS: Data were collected and analyzed in two phases: (1) clustering 10,000 EAR records by elicitor and incident location and (2) conducting a two-center scenario-based survey of adults and parents of minors with severe allergy who were prescribed EAI, in Israel and Germany. Each group received a four-part survey that examined the effect of two behavioral constructs-shared identity and diffusion of responsibility-and two spatial factors-emergency time and emergency location-in addition to sociodemographic data. We performed descriptive, linear correlation, analysis of variance, and t tests to identify patients' decision factors in responding to ERC alerts. RESULTS: A total of 53.1% of EAR cases were triggered by food at patients' home, and 46.9% of them were triggered by venom at parks. Further, 126 Israeli and 121 German participants completed the survey and met the inclusion criteria. Of the Israeli participants, 80% were parents of minor patients with a risk of anaphylaxis due to food allergy; their mean age was 32 years, and 67% were women. In addition, 20% were adult patients with a mean age of 21 years, and 48% were female. Among the German patients, 121 were adults, with an average age of 47 years, and 63% were women. In addition, 21% were allergic to food, 75% were allergic to venom, and 2% had drug allergies. The overall willingness to respond to ERC events was high. Shared identity and the willingness to respond were positively correlated (r=0.51, P<.001) in the parent group. Parents had a stronger sense of shared identity than adult patients (t243= -9.077, P<.001). The bystander effect decreased the willingness of all patients, except the parent group, to respond (F1,269=28.27, P<.001). An interaction between location and time of emergency (F1,473=77.304, P<.001) revealed lower levels of willingness to respond in strange locations during nighttime. CONCLUSIONS: An ERC allergy app has the potential to improve outcomes in case of anaphylactic events, but this is dependent on patient-peers' willingness to respond. Through a two-stage process, our study identified the behavioral and spatial factors that could influence the willingness to respond, providing a basis for future research of proximity-based mental health communities.