Artificial intelligence has demonstrated promise in healthcare by being able to predict patients’ health trajectory based on clinical history, recommend treatments, and increase efficiencies by summarizing physician notes and automating laborious tasks.
AI tools for healthcare vary wildly in their stages of maturity and adoption, ranging from emerging to widespread, according to the US Government Accountability Office.
On the emerging end of the scale, generative AI and other machine learning algorithms hold tremendous promise to improve healthcare and administration of electronic medical records (EMRs). Using them also poses serious risks, including the inadvertent release of sensitive information and erroneous outputs.
Dr. Andrew Albano is the chief medical officer for Prisma Health, a South Carolina-based system of hospitals and physician practices that provide care for more than 1.5 million patients annually.
Albano’s job is mostly strategic as he oversees Prisma’s clinically integrated network connecting about 5,400 clinicians spread across two-thirds of the state. He must ensure the network remains currently viable for use and must address any potential threats coming down the road. He’s also focused on the pathways through which the healthcare system delivers patient care, as well as dealing with insurance companies and technology vendor contracts.
Albano is currently evaluating AI tools that could have a drastic impact on the quality of patient care, as well as massively reduce the amount of time physicians and other clinicians spend looking at monitors instead of their patients.
Prisma
Dr. Andrew Albano
Currently, Albano is evaluating a just-launched precision care platform from RhythmX AI, a subsidiary of SAIGroup. The AI-based platform produces patient-specific prescriptive actions and recommendations for clinicians who can further drill down using a genAI-enabled natural language interface and chatbot tools.
Albano spoke with Computerworld about the current challenges with the use of electronic healthcare systems and records and the potential AI has to solve those issues.
What are some of the issues you face from a medical records perspective? “One of the biggest problems we see with medical records is the “note float” — so what’s happening is the clinical documentation, which traditionally was being used to track progress over time and is still part of the purpose of the notes, has now become the instrument for billing and coding.
“So there are other things that need to be included in the notes with each patient encounter. That creates a larger volume of cumbersome notes to navigate for a patient. So what we see from a management standpoint is time is a very limited resource for our clinicians and our clinical teams, and so to try to wade through all the information in a given patient’s medical record can be really time consuming and not efficient.
“That’s something we’re working…
2023-10-31 16:00:04
Article from www.computerworld.com rnrn