NNews December 2023

32 December 2023 What Is Artificial Intelligence? Artificial Intelligence (AI) combines robust data, computers and machines to mimic the problem-solving and decision-making capabilities of the human mind. The benefits of AI include automation of repetitive tasks, improved decision-making, and a better customer experience. Machine learning can be used to process and analyze large sets of clinical data more quickly, efficiently and accurately than we can. We should look to machines to continually learn how to improve the human element in clinical work. The Value of Clinical Decision Support at the Point of Clinical Intervention According to AI Subject Matter Expert Grayson Kelso, Product Director at Qualifacts, we should not think of AI as “robo-clinicians”, set out to replace human clinical work, but instead as clinical suggestion and support tools, allowing clinicians more time to provide care and empathy to clients; think of it like “Clinical Decision Support (CDS) 2.0”. Over time, through research and learning, your care teams and leadership have adopted best practices and protocols that had been trained on and communicated agency wide for general adoption. An AI supported electronic health record (EHR) will aggregate and analyze the assessments, screenings and interventions used to recommend protocols for an enhanced intake process, based on its analysis of past processes and your population’s demographics, diagnoses, living ByMary Givens,MRA, CCBHC ProgramManager, Qualifacts Artificial Intelligence Supports Clinical Decision Making in Behavioral Health Care circumstances, social determinants of health (SDoH), outcomes, and other relevant criteria. The goal of the machine and human partnership is to create more personalized treatment plans faster, enhance care and drive better outcomes for clients. When AI is compared to the current CDS functionality, we see CDS as being manual while AI sees a CDS engine with aggregated data across all clients from all times on the EHR that is going to allow, in one click, to generate output of all suggested supports and interventions for a client with these specific characteristics. It is efficient and automated, enabling the provider to have more face-to-face time with each client — a powerful addition to value-based care. A next level AI feature would be integrated with a third- party, research-based, “evidence-based practice” (EBP) app that the EHR could automatically query for current research-based, recommended practices. It could then pull that information back into the EHR for it to be acted upon in real time. This would be a combination of CDS and the use of EBPs, but in real time and automated. AI data analysis can be used to show patterns of events and risks, based on past occurrences, including risk of suicide, substance use relapse and recurrence of depression, for example. Tracking whether an individual is on a path to crisis or a repeated inpatient hospitalization can alert a provider to mitigate risks, improving the quality of life of every person served. Mitigating risk is key in value-based care, from automating outreach calls after missed appointments to compiling outreach actions based on trending outcomes.