Unpacking AI's Current & Future Role in Healthcare

When we think of artificial intelligence (AI), many of us are transported to the realms of science fiction, imagining machines that think, reason, and even feel like humans. While this concept is fascinating, it’s not the most practical lens through which to view AI, especially in the context of business applications. Instead, it's more useful to consider AI as a way for machines to learn specific skills or tasks, much like humans do.

AI isn’t a new concept. It has been around for decades, and its applications are vast, ranging from machine learning algorithms that predict outcomes or detect anomalies to computer vision that identifies objects or faces in images. Think of natural language processing, which powers chatbots and voice recognition systems, or robotics in self-driving vehicles. These are all examples of how AI learns to perform specific tasks, such as recognizing a face, generating human-like speech, or steering a car.

So, why are we talking about AI now, especially when it’s been around for so long? The buzz today is largely due to the rise of generative AI — systems that can create content, whether it be text, images, audio, or video. Tools like ChatGPT have brought generative AI into the spotlight, demonstrating its potential to revolutionize how we work with text in a business context. Whether it's drafting emails, generating reports, or even writing code, generative AI offers powerful new capabilities.

However, with great power comes great responsibility. While generative AI can be incredibly useful, it also comes with challenges. The data these models are trained on can be biased or limited, leading to unexpected or even harmful outputs. Additionally, generative AI doesn’t perform actions; it merely produces content. This limitation means that while it can inform and assist, it can't execute tasks on its own — yet.

Given these limitations, it’s important not to overestimate what tools like ChatGPT can do for businesses. Yes, they can enhance communication and improve efficiency, especially for those new to a role. But AI's true potential lies in more specific, concrete applications that address real business problems. This is where organizations should focus their efforts: understanding AI, identifying the right use cases, and effectively integrating AI to solve business challenges.

The Promise of AI in Addressing Healthcare Challenges

​​As organizations across industries grapple with evolving business challenges, the healthcare payer and provider space is no exception. Economic pressures are mounting, with higher-than-expected utilization of medical services, downward pressure on reimbursement rates, and increased costs for both payers and providers. These financial strains necessitate innovative solutions, and AI presents a promising avenue for organizations to navigate these turbulent times.

Recent research by McKinsey underscores the transformative potential of AI for payer organizations. By implementing AI solutions, payers can expect significant improvements across various dimensions, particularly in administrative and medical costs, as well as revenue enhancement. Notably, AI applications in marketing and sales are identified as the top contributors to revenue growth, especially in areas such as member retention and acquisition. Moreover, the research highlights that AI-driven solutions in technology, data management, and claims processing can substantially reduce administrative costs, while AI's role in care management offers the highest potential for reducing medical expenses.

Placing Emphasis on High-Impact AI Opportunities

However, achieving these outcomes requires a strategic approach. It's crucial to avoid the pitfalls of shiny object syndrome — the allure of adopting AI for flashy but low-impact applications that don't align with strategic goals. An example might be implementing ChatGPT on every desktop without a clear use case that ties back to the organization’s financial objectives. Similarly, organizations must steer clear of continuous churn, where endless strategizing and planning lead to paralysis rather than progress.

To derive real value from AI, healthcare organizations should focus on high-impact opportunities that address pressing business challenges. These opportunities can be broadly categorized into two areas: member experience and operational excellence.

Patient & Member Experience:

• Acquisition and Onboarding: AI can enhance digital-first, personalized member and new patient journeys, improving both marketing campaigns and onboarding workflows. By leveraging AI to analyze consumer data, organizations can create frictionless enrollment processes, driving both efficiency and member satisfaction.
• Retention and Engagement: AI-driven retention efforts can personalize communications, recommend targeted programming, and expedite the creation of new initiatives, thereby keeping members engaged and reducing churn.
• Customer Service and Support: AI can streamline customer service by predicting member needs and guiding them to efficient self-service options, such as mobile apps for ID card retrieval.
• Care Management: AI models can optimize outreach, triage clinical conditions, and prevent readmissions, enhancing overall member care and reducing costs.

Operational Excellence:

• Prior Authorization: AI-assisted evaluation of cases can expedite prior authorization processes, reducing the burden on healthcare organizations and improving patient service delivery.
• Risk and Fraud Detection: AI can shift the focus from detecting fraud to investigating it, enabling more proactive and efficient use of resources.
• Data Management: AI can accelerate data migration, improve data quality, and automate tedious tasks, allowing teams to focus on strategic initiatives.
• Content and Asset Management: AI can automate content creation and manage digital assets, reducing the time and effort required while ensuring compliance with legal requirements.
• Infrastructure Support: AI can assist in legacy code migration and support help desks, addressing infrastructure challenges with greater efficiency.

Several organizations are already harnessing the power of AI to transform their customer experiences and improve operational excellence. A case study from Blue Cross Blue Shield of Massachusetts showcases the use of AI to analyze claims data and flag suspicious billing patterns. The AI doesn't automatically deny payments but instead alerts human teams for further investigation. This approach has led to the identification of potential fraud in 65-70% of cases flagged by the AI, significantly enhancing the efficiency of fraud detection. 

Similarly, HCSC is utilizing AI to speed up prior authorization, which can lead to a diminished opinion of their relationship with our organization. They’ve developed a model that expedites this process by analyzing prior diagnoses and treatments, making determinations up to 1,400 times faster than humans. While the AI can automatically approve straightforward cases, more complex ones are flagged for human review. This not only speeds up the process, it ensures that healthcare providers can focus on cases that truly need their attention.

At Bounteous x Accolite, we’re leveraging generative AI with a tool called The Brain to enhance our RFP responses, showcasing how we practice what we preach. As a professional services organization accustomed to the repetitive and varied nature of RFPs, we’ve developed a tailored AI model trained on our own data. This AI doesn’t just use chat GPT to craft coherent replies; it enables our team to ask natural language questions and receive answers rooted in our past RFP submissions. This innovation simplifies the adaptation of previous content to meet the unique demands of each new RFP, streamlining the process and improving efficiency.

Successfully Adopting AI: Beyond Technology to Transform Healthcare

Incorporating AI into healthcare goes beyond just adopting new technology — it requires a holistic approach that includes strategic alignment, talent management, operating models, data utilization, and change management. This comprehensive approach ensures that AI delivers real business value without falling into common pitfalls like shiny object syndrome or continuous churn.

1. Strategic Alignment: Avoiding the Shiny Objects

   The journey starts with understanding where AI fits into your organization's strategic roadmap. The temptation to chase the latest AI trends can lead to misaligned investments that do not support core business goals. Instead, focus on integrating AI into areas where it can deliver measurable value. This alignment prevents wasted resources and keeps the organization focused on long-term objectives.

2. Talent and Upskilling: Enhancing Human Capabilities

   AI is not a replacement for human talent; it's a tool to enhance it. Organizations must focus on upskilling existing employees to work alongside AI technologies effectively. This involves providing training that equips teams with the necessary skills to leverage AI in their daily tasks, turning AI into an enabler rather than a threat to job security.

3. Operating Models: Transforming How Teams Work

   Adopting AI requires a reevaluation of the existing operating model. The goal is to optimize team productivity by integrating AI into workflows, which may involve redesigning roles, responsibilities, and processes. This ensures that AI contributes to operational efficiency and cost-effectiveness, ultimately driving business value.

4. Technology and Data: The Backbone of AI

   For AI to be effective, the underlying technology infrastructure must be robust, and data must be readily available and of high quality. Organizations should adopt a data-first strategy, ensuring that data is continuously enriched, accessible, and utilized across the organization. This is the foundation upon which AI can be successfully implemented and scaled.

5. Change Management: The Critical Success Factor

   Change management is where many organizations falter in their AI journey. It involves gaining buy-in from stakeholders, planning for adoption, and ensuring that the value of AI is maximized across the organization. This requires careful mapping of how AI will interact with different teams and processes, as well as addressing any resistance to change.

6. Ethical and Risk Considerations: A Unique Challenge in Healthcare

   Healthcare presents unique challenges when it comes to AI, particularly around ethics and risk. Privacy, security, and the potential impact on patient outcomes are critical factors that must be carefully managed. AI in healthcare should augment human decision-making, not replace it, ensuring that ethical standards are upheld and risks are minimized particularly in patient-facing applications.

Looking Forward: The Future of AI in Healthcare

As AI continues to advance, its role in healthcare will expand. From fully automated claims processing to disease prevention, AI presents opportunities to tackle some of the industry's most pressing challenges. However, the key to unlocking this potential lies in starting small, focusing on high-impact areas, and building from there. Organizations should adopt a test-and-learn approach, piloting AI in specific areas, demonstrating value, and then expanding.

As you embark on your AI journey, remember that success lies in doing better things and doing things better. By placing the customer at the center and augmenting human capabilities with AI, healthcare organizations can drive significant value and transform the industry.