By Michael Solomon, PhD, MBA, Practice Lead, eCare Management
AI is transforming medication management
Over the past few years, artificial intelligence (AI) has been touted as a transformational force in health care. The AI health market has experienced explosive growth, with public- and private-sector investments estimated to reach $6.6 billion in 2021.
Health care is embracing AI for a range of applications, from robotic surgery to drug development to clinical research. In addition, AI is being adopted as a practical tool to reduce costs, improve outcomes and replace labor-intensive, repetitive tasks that are prone to error. Those applications make AI poised to transform medication management. This article takes a brief look at how that is happening today.
What Is AI? Before we look at how AI may be used, let’s level-set. Real-world AI technology may be defined as specialized software being deployed today to improve health care. Two types of AI technology are gaining real traction in health care:
Other emerging AI technologies (e.g., artificial neural networks, deep learning) show promise. However, they are probably a few years away from significant, real applications in health care, including in the area of medication management.
Use of AI in Medication Management. We often hear about the use of AI to collect and analyze complex health data and provide previously undiscovered insights through deep learning. Reflecting on persistent problem areas in medication use and therapy, AI is being used in a number of ways to improve medication management. Six examples come to mind. AI is being used today to:
How do ePA programs ascertain which data are required? This is where evidence-based algorithms and machine learning come into play. With AI, the adjudication process is transformed. ePA becomes a decision support tool for the prescriber. The evidence-based algorithmic programs factor in the therapy policy and restrictions (including formulary) and gather patient-specific data (e.g., the patient’s health status and medical history) and outcomes of patient populations with similar characteristics to present the prescriber with therapy recommendations. Selection of one of the recommendations triggers automatic approval of the prior authorization (PA) request in real time.
This “next generation” ePA will be prevalent in the not-too-distant future. In fact, it’s here today. One company has implemented an AI engine that uses NLP to extract data needed to adjudicate a PA. Another is working on increasing the level of automatic adjudication of PAs by using algorithms to predict which PAs should be approved. As a result, we’ll see more consistent and accurate PA adjudications, faster turnaround for patients to get onto therapy and reduced administrative costs for both payers and providers.
As a result, medication compliance is of great interest to payers. A Medicare Advantage plan identified members at risk of medication nonadherence who are receptive to such digital interventions as personalized text messaging about refills and remote patient monitoring instead of follow-up by letters or phone. By focusing on patients at greatest risk and communicating in the most effective way, adherence rates are rising.
This strategy could be extended to predict specific difficulties a patient will likely experience in adhering to a prescribed medication regimen and determine the most appropriate intervention (i.e., right level of intensity and approach for patient compliance, optimal effectiveness and lowest cost). Drawing on the patient’s EHR (both structured and unstructured data) and interactions with a virtual assistant, multiple sets of factors can continuously be monitored by analytics that both predict risks of failure and prescribe actions. For example, factors such as the number of medications a patient must take, involvement of multiple prescribing physicians, whether a spouse or caretaker resides in the home, and travel distance to a drugstore can all be considered simultaneously when determining the need for and frequency of follow-up consultations.
Looking ahead. In just a few short years, machine learning and NLP will have profound and positive impacts on the delivery of health care and medication management. Point-of-Care Partners is tracking innovations in AI. We monitor and are actively involved in advances in standards, EHRs, electronic prescribing and other applications used at the point of care. Let us help you to navigate the rapidly changing world of health IT. Contact me at michael.solomon@pocp.com.