In Brief
A quick guide to evaluating the use of artificial intelligence in healthcare
- The use of generative AI in healthcare can revolutionize the industry and unlock new levels of productivity and problem-solving.
- When selecting AI use cases, it's important to consider whether it's feasible with the available resources/technology and why it's necessary to implement.
- Integrating AI into healthcare's core functions can remove costs, improve revenue growth, transform care, enhance consumer experience, and mitigate clinician burnout.
Generative artificial intelligence (AI) and traditional automation technologies are revolutionizing the workplace by transforming AI into a dynamic human assistant, creating new levels of innovation, productivity, and problem-solving across industries.
Unlocking maximum value hinges on the thoughtful selection of generative AI use cases, paving the path to success.
Prioritizing AI opportunities in healthcare
Opportunities exist to use generative AI in all areas of the health system, leaving leaders with the question of where to put what’s possible into practice. Key considerations for selecting AI use cases come down to two primary questions: should we do this, and can we do this?
| Key considerations for selecting AI use cases | |
|---|---|
| Should we do this? Why do you believe you need (or want) to implement generative AI? | Can we do this? Is the idea feasible with the assets/resourcing/technology available? |
Sequencing how and where AI is applied across the organization will require leaders to first think through the business problems they are trying to solve, the organization’s risk profile, and the availability of data. These three focus areas can help with determining and developing AI use cases.
- Prioritization and outcomes: Prioritizing AI activity based on the business and clinical outcomes the organization wants to achieve.
- Risk profile: Determining the risk tolerance of new technology introduction to help determine where the organization will focus first.
- Training the models: Understanding and selecting the data models that will be used to train an organization’s AI applications.
Integrating AI into healthcare's core functions
As excitement about the potential of AI in healthcare escalates, conversations can be grounded in a central outcomes-driven question: How will we use AI to operate more proactively by removing cost and improving experiences and outcomes for consumers and employees?
How work gets done with AI will vary by organization and across a spectrum, from administrative tasks to delivering patient care. The goal will be to utilize and architect AI to make it valuable and safe in the following core areas.
- Cost: The industry continues to be challenged with workforce shortages, inflation, supply challenges, and, as a symptom, high cost. Providers are leaning into increasingly advanced automation to alleviate pressures through automating tasks for high-cost resources responsible for administrative and clinical appeals and synthesizing critical takeaways from contracts.
- Revenue growth: Revenue growth stems from maximizing reimbursement of current services delivered, attracting new consumers, and developing new revenue streams. Generative AI allows providers to predict patient needs, proactively reach consumers, and utilize sophisticated voice bots and chatbots to schedule consumers for their care.
- Care transformation: Innovation in remote patient monitoring, disease prediction, predictive discharge planning, and capacity and demand planning lean into a broader set of intelligent automation tools. Generative AI allows systems to take care further by synthesizing relevant care information from many sources, enabling clinicians to make quick, better-informed care decisions. Conversational AI allows human-like interactions to generate, distribute, and address questions related to pre-and post-care instructions.
- Consumer experience: The engagement center of the future is coming into view. AI-powered chatbots, conversational AI platforms, and virtual assistants for automated scheduling, patient engagement, patient triage, and patient requests are evolving to be more human-like and provide a better consumer encounter. Additionally, AI copywriting tools are used across industries to generate consumer content. Writing and disseminating healthcare marketing material and patient communications can be streamlined through AI.
- Clinician burnout: AI is being touted for its ability to mitigate clinician burnout by helping physicians and nurses spend less time on administrative tasks and clinical documentation and more time engaging with patients. Integrations like GPT-4 in the EHR can facilitate quicker, easier, and more accurate patient data analysis, potentially leading to improved clinical outcomes.
The AI landscape will continue to accelerate and evolve. To avoid the pitfall of technology for the sake of technology, organizations will need to be grounded in a solid enterprisewide automation and AI strategy and focused on building an operating model that supports it.