Council Post: Embracing AI For A Better… Leave a comment
Roshan Navagamuwa is Executive Vice President and Chief Information Officer (CIO) for CVS Health.
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The consumer healthcare experience looks a lot different today than it did 10 years ago. The industry has become more consumer-oriented, which means companies are striving to make it easier for people to understand and use their health benefits. This shift is helping consumers make more informed choices and more easily engage with their healthcare.
However, with so much information, and a dizzying array of care choices, in order to simplify the experience even further, technologies like artificial intelligence (AI) can help patients and their care teams.
How AI Is Meeting Consumers’ (And Practitioners’) Increasing Expectations
When it comes to AI’s utility, industry leaders are both optimistic about the future and impressed with what they’ve already seen. According to a 2021 survey from KPMG, 82% of healthcare and life sciences executives want to see their organizations more aggressively adopt AI technology.
The value, potential and relevance of AI in the healthcare industry are showing up in many ways, with some of the biggest advances and potential in the following areas.
1. Making healthcare administrative processes more efficient: Machine learning (ML) and AI are making once manual healthcare administrative processes simpler, easier and more cost-effective. For example, using computer perception, ML can make sense of written and faxed documents and then structure that information into smart workflows. Information is extracted and then used to drive actions or guide team members, making it easier for clinicians and other staff, while increasing speed and consistency.
When trained properly, ML and AI can also uncover insights and recommendations that clinicians and team members can leverage to improve care and make processes more efficient and effective.
2. Reimagining benefit design: The complexity of healthcare benefit plan designs can also benefit from AI. Today, benefit plan designs are designed using conventional practices and insights. Health plans can augment these plans with AI-driven models using their large data sets and plan sponsor objectives to design smarter benefit plans and products.
These models can help benefits managers make more informed design decisions that take into account the impact on membership, customer spending and outcomes. The advanced capabilities available today can optimize both individual outcomes and population health—including better health equity.
3. Providing a better consumer experience: While healthcare can be complex to navigate, AI can streamline the consumer experience in many ways. Predictive models can make interactions easier for people by personalizing and optimizing experiences. Intelligent agents can serve up answers, triage, and connect to resources and experts faster and more effectively than ever before. And in some cases, AI can fully automate decisions—like auto-approving pre-certification requests in certain contexts.
And when someone does need to speak with customer service, AI can help agents with intelligent search capabilities in order to make it easier for them to get the information needed. AI capabilities can also enhance how they interact, with real-time speech analytics and instructional call summaries that drive continuous learning and improvement.
At the pharmacy, AI can help consumers maximize their benefits and streamline managing multiple prescriptions. This level of personalized, easier and better experiences can contribute to greater patient engagement over the long term.
4. Enabling better outcomes: Healthcare is personal. It is important to have the support of a care team that knows an individual and can provide effective counsel to enable the best care. AI can support and enable these care teams at an unprecedented scale.
AI can leverage large clinical datasets and combine that with individual healthcare records to generate individualized recommendations at scale. Healthcare data interoperability standards make this easier today. These recommendations could include next-best actions like getting an A1C test, medication adherence reminders or counseling opportunities that reduce gaps in care. How and when these are delivered can also be tuned through ML to find the optimal approach.
More holistically, AI can generate insights that are important for care management and disease prevention—like flagging key risk factors of chronic kidney disease or catching a pre-diabetes diagnosis based on other health indicators. Care teams can leverage these insights for timely patient engagement and, over time, contribute to better individual health outcomes.
5. Making it easier for healthcare professionals: AI is also improving the experience and workload of healthcare professionals.
For example, retail pharmacies are among the busiest settings in healthcare. Pharmacists play a critical role across the care ecosystem, between patient, provider and payor—working in real time to help someone standing at the counter. AI can augment and assist the real-life pharmacist.
The AI “peer” can use computer vision and natural language processing to read and validate prescription information and the prescriber’s directions in seconds. It can apply ML models to detect errors (e.g., incorrect dosage or instructions) and use robotic process automation to automatically resolve these issues or make recommendations, like lower-cost alternatives.
In short, AI can help free up some time for busy healthcare professionals to focus more on the clinical aspects of their role and the needs of their patients.
Responsible AI Development
AI will undoubtedly continue to be one of healthcare’s biggest transformation levers going forward, with great potential.
And it must be developed responsibly.
Because AI and its applications are dynamic and always evolving, responsible AI governance policies and practices must always be evolving as well. Especially in healthcare, there have to be industry-wide practices that put patients first and ensure that safety, efficacy and equity can be assured, among other things. Professional standards must be embedded so that trust is preserved. Responsible development, oversight and transparency will be critical to ensure AI can achieve the benefits we can already see and guide what is yet to come.
The thoughtful and measured approach being taken in healthcare is therefore for very good reason. Starting with operational tasks and augmenting the professionals who are charged with the care and consideration of other people is appropriate for this stage of AI development and experience.
Companies that invest resources in AI to learn and gain experience now will help shape the future of healthcare.
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