Summa Health in Ohio tested an AI tool to predict sepsis in emergency departments, aiming to improve early detection amid overwhelmed staff and excessive false alerts from traditional systems, highlighting the potential and challenges of AI in clinical settings.
Patients in Northern Ireland's emergency departments are experiencing extreme delays, with some waiting over a week, due to overcrowding and insufficient discharge planning, prompting calls for better investment and management of healthcare resources.
The Senate Homeland Security and Governmental Affairs Committee is investigating the impact of private-equity firms on patient care in hospital emergency departments, focusing on three major firms: Apollo Global Management, the Blackstone Group, and KKR. Concerns include patient safety, improper billing, and anti-competitive activities. Private-equity firms' cost-saving measures and debt burdens are under scrutiny, with academic studies linking their involvement in healthcare to cost increases and lower quality of care. The inquiry also involves companies backed by the private-equity firms, and the Federal Trade Commission is examining potential anti-competitive activities in healthcare deals.
Suicidal patients without health insurance are often turned away by private psychiatric hospitals, leading to long waits in emergency departments. A recent investigation found that Tristar Centennial Medical Center in Nashville had discharged or held patients in the emergency department for up to six days until space became available at a state-run psychiatric hospital. The lack of capacity for uninsured patients in Tennessee is a widespread issue, with mental health patients experiencing long wait times for admission. The situation highlights the challenges faced by healthcare providers in addressing the increasing need for mental healthcare services.
Researchers from the University of Edinburgh have developed an AI algorithm called CoDE-ACS that can rule out a heart attack in more than double the number of patients than current methods with an accuracy of 99.6%. The algorithm uses routinely-collected patient information, such as age, sex, ECG findings, medical history, and troponin levels, to predict the probability that an individual has had a heart attack. The tool could greatly reduce hospital admissions and rapidly identify patients that are safe to go home, reducing pressure on overcrowded emergency departments. Clinical trials are now underway in Scotland to assess the AI tool's effectiveness.