For Artificial Intelligence In Healthcare, The Quadruple Aim Isn’t Just A Goal — It’s A Requirement
There is enormous excitement around using artificial intelligence in healthcare, and at the same time, there is growing consensus among healthcare organizations (HCOs) to focus on the “quadruple aim.” This includes improving the customer experience, driving better outcomes, lowering costs, and supporting the whole care team. However, AI adoption in healthcare has been slow relative to other industries, and relatively few AI solutions for healthcare have delivered on AI’s transformative potential.
The extraordinary data challenge in healthcare is arguably the largest barrier to tapping AI’s potential. Equally important: Many healthcare AI projects do not provide enough value as measured by the goals of the quadruple aim or, worse, actively work against one of the goals. AI solutions that do not have alignment to all four goals, particularly improving the experience of the care team, will face an uphill battle against achieving adoption.
Lack Of Data Challenges Many Organizations
Arguably the largest and most visible challenge for using AI in healthcare is the lack of data — we either don’t have the data we need, or it is too inconsistent to use for machine learning. For example, we typically do not have the health outcomes data, and clinical notes vary to such a degree that creating a system to make care recommendations is extremely difficult.
This is exacerbated by the lack of interoperability in healthcare. While we have standards, adoption has been slow among providers due to lack of technical resources. Providers and patients need to be empowered with solutions that enable sharing data across the care trajectory to accurately report on outcomes and enable AI to be effective.
However, many organizations are blinded by the poor-quality data and are ignoring the abundant opportunities to support the quadruple aim with data that is available today. Similarly, many AI solutions for healthcare are unrealistic even with perfect data because they are not based on an understanding of what AI technologies are designed to do.
How You Can Succeed With AI In Healthcare
We know that AI can drive significant improvement in healthcare, including empowering the care team to make better, faster decisions. Here are some of our recommendations for success with AI in healthcare today:
- Identify AI projects that leverage your existing data assets. Identify what data you have available or what data you can get your hands on. A large enough training set is key to creating accurate analysis and relevant output.
- Ensure alignment to the entire quadruple aim, and prioritize projects that will deliver significant progress against the goals of the quadruple aim. Many healthcare solutions have neglected the experience of the care team, placing additional burden on them to capture more data as part of their workflow vs. creating an intuitive experience that supports them.
- Ensure that key stakeholders — the care team, data management, IT, and your data science team — are part of the conversation early on and participants in the decision-making process.
- Create a virtuous cycle to drive investment in your data and AI capabilities. Build a portfolio of AI projects and leverage success from smaller, fast time-to-value projects to support investment in improving your data and expanding your AI capabilities. Leverage this investment to create an ongoing cycle of increasingly larger and transformative AI projects.
Want to learn more? Watch for our upcoming report, “Use Artificial Intelligence To Achieve Quadruple Aim In Healthcare.”
Think you are leading the charge on AI in healthcare? We want to hear your story. Request to schedule a briefing here.