**Patient Portal Use is Growing, But Physician Billing in Medicare Hasn’t Caught Up**

A study reveals an increase in Medicare beneficiaries using patient portals for communication with physicians, yet only a small fraction (~0.05%) of these communications result in a bill for Medicare, primarily for high blood pressure or diabetes management. Despite CMS's Medicare compensation for virtual services necessitating medical decision-making over five minutes during the COVID-19 pandemic, billed e-visits have plateaued since an initial surge. The study, headed by the University of Michigan, suggests providers might be wary of billing for portal messages due to concerns over patient costs, highlighting a need for clearer guidance on billing for digital patient interactions.

**More than a Third of Americans Have Skipped a Healthcare Visit Due to Inability to Pay**

According to a Payzen report surveying over a thousand Americans who had a hospital visit or surgery in the last two years, over a third of insured Americans and 83% of uninsured individuals have forgone healthcare visits due to cost concerns. The average American can only afford up to $97 a month out-of-pocket for healthcare expenses, with significant disparities across racial groups. The most commonly skipped services include preventative visits, prescription refills, mental health care, and diagnostic testing, underscoring the pressing issue of healthcare affordability in the United States.

Patient Portal Use is Growing, But Physician Billing in Medicare Hasn’t Caught Up Managed Healthcare Executive

In the article "Like Herding Cats": Making a Case for AI Assurance Labs, John Halamka, President of the Mayo Clinic Platform, highlights the collaborative efforts of academic medical centers, technology companies, and federal agencies in establishing AI assurance labs. These labs aim to bring structure and standardization to the development and deployment of AI algorithms in healthcare, responding to concerns over their reliability and safety. Following President Joe Biden's executive order on AI algorithm guidelines, this initiative seeks to create a network of both private and public assurance labs for the technical and ethical evaluation of AI technologies. This effort addresses the challenges of a fragmented approach to AI development, aiming to ensure that algorithms are beneficial and non-biased across diverse patient groups and healthcare settings.

"Like Herding Cats": Making a Case for AI Assurance Labs | healthsystemcio.com healthsystemcio.com

In Q1 2024, the digital health sector moved fully into a new funding cycle, securing $2.7 billion across 133 deals despite a challenging climate leading to smaller average deal sizes. The shift to creative financing and an emphasize on startups showing strong outcomes over high growth potential marked this period. AI investments dominated, capturing significant funding as attention to outcome-based evidence grew among stakeholders. Public market dynamics also evolved, with notable delistings signaling a recalibration of expectations around exits, emphasizing the need for digital health companies to adapt to more prudent growth forecasts and financial planning. The overall landscape suggests a maturing digital health sector with recalibrated success metrics, focusing more on sustainable growth, robust outcomes, and strategic exits, signaling a more scrutinizing yet promising future for innovators in the space.

Q1 2024 digital health funding: Great (reset) expectations Rock Health

This study, published in JAMA Internal Medicine on March 25, 2024, examines the effectiveness of an AI-enabled intervention aimed at detecting clinical deterioration among hospitalized patients to reduce escalations in care, such as rapid response team activation, ICU transfers, or cardiopulmonary arrests. Conducting a cohort study of 9,938 patients at an academic center using a regression discontinuity design for analysis, the researchers found that exposure to the intervention led to a 10.4 percentage point reduction in the risk of such clinical escalations. This significant finding suggests that integrating AI deterioration models into patient care can decrease the likelihood of emergency interventions, supporting the potential for broader application and further research in different healthcare settings.

Effectiveness of an AI–Enabled Intervention for Detecting Clinical Deterioration JAMA Internal Medicine