Artificial Intelligence In Health Care
Each year, medical diagnosis errors affect the health of millions of Americans and cost billions of dollars. Machine learning technologies can help identify hidden or complex patterns in diagnostic data to detect diseases earlier and improve treatments. A report from the U.S. Government Accountability Office (GAO), the congressional watchdog agency, identified such technologies in use and development, including some that improve their own accuracy by learning from new data. Developing and adopting these technologies has challenges, however, such as the need to demonstrate real-world performance in diverse clinical settings. GAO policy options, like improving data access and collaboration, may help address challenges. Several machine learning (ML) technologies are available in the U.S. to assist with the diagnostic process. The resulting benefits include earlier detection of diseases; more consistent analysis of medical data; and increased access to care, particularly for underserved populations. GAO identified a variety of ML-based technologies for five selected diseases: certain cancers, diabetic retinopathy, Alzheimer’s disease, heart disease, and COVID-19 with most technologies relying on data from imaging such as x-rays or magnetic resonance imaging (MRI). These ML technologies have generally not been widely adopted. Academic, government, and private sector researchers are working to expand the capabilities of ML-based medical diagnostic technologies. The report can be obtained here.
National Strategy To Support Family Caregivers
A report that recently was released is entitled, the 2022 National Strategy to Support Family Caregivers. It provides detailed information about nearly 350 actions federal agencies will take over the next three years to support the nation’s 53 million family caregivers. The National Strategy includes more than 150 actions that state and local governments, public health departments, philanthropies, and community-based, faith-based, and nonprofit organizations should take, recognizing that all these groups have a fundamental role to play in supporting family caregivers. The National Strategy was created to support family caregivers of all ages, from youth to grandparents, and regardless of where they live or what caregiving looks like for them and their loved ones. It was developed jointly by the advisory councils created by the RAISE Family Caregiving Act (P.L. 115-119) and the Supporting Grandparents Raising Grandchildren Act (P.L. 115-196), with extensive input from the public, including family caregivers and the individuals they support. It will be updated in response to public comments and will evolve with the caregiving landscape. A 60-day comment period opened on October 1, 2022. The report and the opportunity to submit comments are here.
Accelerating The Use Of Findings From Patient-Centered Outcomes Research
Patient-centered outcomes research (PCOR) studies consider the questions and outcomes that are meaningful to patients to compare the effectiveness of different prevention, diagnostic, and treatment options. PCOR also increases patient involvement in their care by providing them an opportunity to evaluate the quality, outcomes, and effectiveness of health care treatments and interventions, especially in areas where there is poor existing clinical evidence. The National Academies of Sciences, Engineering, and Medicine Board on Healthcare Services hosted a series of public workshops to explore ways of accelerating the use of PCOR findings in clinical practice to improve health and health care. Workshop discussions touched on the role of community health workers in helping care providers see and understand the whole picture of patient lives, the need for community engagement to ensure research is conducted and applied to practice equitably, and ways of measuring the impact of efforts to disseminate and implement new practices based on PCOR. This report summarizes the discussions that took place at these workshops. The document can be obtained here.