It’s the main attraction at every healthcare conference, a starring feature in every industry publication, and the subject of endless, curious speculation: AI. 

2024 is certainly shaping up to be a banner year for artificial intelligence. After decades of comparatively quiet innovation and research, AI has commandeered the spotlight in nearly every industry, with particular scrutiny stemming from the healthcare and life sciences sector. Today, the global AI market for healthcare payers stands at a whopping $1.95 billion (USD) and is expected to grow by a compound annual growth rate of 9.4% between now and 2030. 

Investor interest in healthcare at large is even more staggering. So far this year, over $2.8 billion has been invested in AI healthcare companies, with investment totals anticipated to hit $11.1 billion by the end of 2024. A few companies have taken very public steps to innovate, too; in February, Highmark Health announced that it would be partnering with Google Cloud and Epic to leverage data and AI to personalize consumer experiences and improve members’ health outcomes.

But despite this widespread enthusiasm, uncertainty around the technology’s practical applications and implementation requirements still looms large. According to a recent survey conducted by Datos Insights, “almost no” polled insurers feel “well-prepared” to invest in AI (specifically, Generative AI) despite wanting to do so. 

The technology’s lukewarm reception in Washington may also be chilling investment enthusiasm. In June, a bipartisan group of lawmakers penned a letter to CMS, calling for the agency to establish more regulations around how AI can be used during the prior authorization process. These lawmakers worry that payers who adopt faulty AI algorithms may inadvertently block patients from receiving medically-necessary care — and payers worry that by adopting AI, they may fall under scrutiny or out of compliance. 

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While these challenges are intimidating, they aren’t insurmountable — AI is an opportunity that organizations need to capitalize on now. According to a Salesforce survey of healthcare and life science organizations, 67% of business leaders are in the process or have already implemented AI. Holding back out of fear will only result in becoming outmoded and outperformed by bolder, more innovative competitors. 

Payers need to implement AI strategically, in a way that provides operational value and leverages member data without breaking compliance or compromising confidentiality. But how should they go about doing so?

One potential approach for payers would be to leverage a trusted environment, such as Salesforce, to take advantage of scalable AI enablement solutions like Data Cloud. 

• What is Data Cloud? A best-in-class platform that empowers businesses in every industry to put their data to work, make more informed strategic decisions, and elevate customer and member experiences. 

With Data Cloud, payers can gain unprecedented visibility into their data, bridge siloed systems, and start leveraging best-in-class AI solutions within Salesforce — no swivel-chairing required. This utility unlocks a wide variety of game-changing use cases for payers, including:

• Driving member acquisition through targeted marketing
• Enhancing member experiences and satisfaction
• Improving payer-provider contracting
• Increasing service center efficiency

At Gerent, we apply a practical approach to AI advancement by helping businesses identify and act on target use cases that will provide significant value to your organization, team, and members. In this article, we’ll provide context on how payers can use Data Cloud to establish a solid foundation for AI innovation, then consider how payers can use artificial intelligence to drive value for their members, partner providers, and businesses as a whole. 

(Want a more personalized perspective? Book a conversation with our practice leaders!) 

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Getting Payers “AI Ready” for Investment with Data Cloud

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Ensuring a high level of data quality is a necessary prerequisite for any AI investment. AI and machine learning algorithms require clean, consistent, and well-structured data to function effectively; ideally, an AI tool should be able to draw on all of an organization’s data to identify patterns, draw conclusions, generate content, and deliver informed recommendations. When data is siloed, inconsistent, or poorly structured, it becomes difficult — if not impossible — for AI to perform effectively. 

However, most payers today haven’t achieved the level of data cleanliness required to become “AI-ready.” Per a recent report from Salesforce, only 30% of surveyed healthcare and life science leaders claim to be fully ready for AI, and just 27% have proper data governance in place. Of those surveyed, 64% cited “inadequate data” as a top barrier to AI adoption for their organization. 

Let’s put this problem into perspective. Picture a payer’s organizational data as a vast library, and AI as a researcher. In an organization that prioritizes data quality, specific information (“books”) may be filed in specific sections, but all are legible and accessible. 

When an organization becomes siloed, its metaphorical library falls into a state of disarray; books may be locked in side rooms, organized in improper sections, or written illegibly. The AI “researcher” would naturally be limited to drawing conclusions based on the minimal information they have access to — and its analyses could be incomplete, unhelpful, biased, or even wrong.   

For payers, data cleanliness is often undermined or prevented by three common factors:

1. Siloed Legacy Systems. A single insurer may use several unconnected systems to support operations at the national, regional, and local divisions. This fragmentation prevents information-sharing within the organization, making it impossible for a payer to make full use of its AI tools. 
2. EHR Fragmentation. While interoperability standards are improving, there are hundreds of EHRs on the market today — and many are not amenable to easy integration. As a result, payers may be unable to effectively source information from provider networks. 
3. Data Inundation. Payers have a wealth of third and first-party data, but often lack the means to organize it in an accessible and AI-appropriate way.

To fully utilize AI, payers need to overcome these hurdles and ensure that their data is accurate, valid, traceable, complete, secure, and organized in a manner conducive to collection and analysis. This isn’t the easiest challenge to clear with conventional systems — but with Salesforce Data Cloud, payers can funnel all member data touchpoints, from claims systems to EHR records and 3rd-party vendor sites, into one hub.

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In doing so, Data Cloud allows payers to collect and use all of the information at their disposal — and, in some cases, external partner or vendor data — without needing to stand up a complex integration solution. 

This approach drastically reduces the risk of AI inaccuracy or hallucination, enabling a payer’s AI tools to draw complete, informed conclusions from the full breadth of the organization’s data. Organizations can further alleviate ethical and privacy concerns by configuring Salesforce to utilize private AI models and apply PII security across all data sources. This, in turn, allows organizations to maintain consent management for all information channels within Salesforce Data Center. 

In the following section, we’ll unpack a few use cases that payers can take advantage of once empowered with Data Cloud. However, it’s important to note that Data Cloud is not a turnkey solution; several hurdles identified by the Salesforce survey (e.g., uncertain ROI, lack of knowledge on how to implement, and a lack of defined budget) will continue to undermine investment unless a payer engages a trusted partner to support and guide their transformation. 

At Gerent, we take pride in maintaining dedicated practices not only for Data & AI consultation but also for every sector we serve. Our consultants have the extensive industry experience and deep technical expertise required to provide value-adding advice to payers regardless of where they stand in their implementation journey. We can help your organization:

• Identify value-adding AI use cases 
• Develop proof-of-concept(s) for potential use cases
• Make strategic, value-adding investments in Salesforce and AI
• Provide support during initiative planning; help craft short- and long-term project roadmaps
• Define and track progress to KPIs and ROI 
• Keep a continued pulse on emerging AI technology 

The upcoming section will explore a few high-potential Data Cloud and AI use cases that payers can make use of today. That said, AI initiatives are not one-size-fits-all; we highly recommend that any payers strongly considering AI investment engage a strategic partner to start crafting an action plan that aligns with a payer’s unique needs and goals.  

Data Cloud & AI Use Cases for Healthcare Payers

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AI generates quite a bit of buzz — but what value can it actually provide to payers today? Below, we’ve provided a snapshot of a few payer use cases for AI and Data Cloud. Please note that this is not an exhaustive list; if you want to learn more about Gerent’s AI approach and how our Salesforce Health Cloud consultants can enable your organization, please visit our AI & Data microsite or book a personalized conversation!

Drive Member Acquisition through Targeted Marketing

Leverage customer data to deliver personalized marketing communications that engage prospective members at scale. With Data Cloud and AI, payers can:

• Prompt outreach to prospects and/or plan members based on their activities on the payer’s website, sales portals (e.g., public exchange), and other relevant platforms. 
• Generate personalized plan recommendations based not only on an individual’s demographic but also their claims history and first-party data.
• Use members’ engagement data (with marketing campaigns, websites, advertisements, etc.) to develop tailored marketing campaigns. 

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Enhance Member Experiences & Boost Satisfaction

Design, develop, and deploy experiences based not only on a member's demographic but the member themselves. With Data Cloud and AI, payers can:

• Enhance collaboration with providers and use member data to deliver care that acknowledges a member’s situation, health requirements, and preferences. 
• Coalesce all data sources (e.g., from claims, social determinants of health, EHR, care management, CARIN IG, etc.) into a single, holistic view of the member.
• Use AI to conduct prerequisite checks during prior authorization request reviews, thus reduce time-to-decision turnarounds for providers and members. 
• Measure member satisfaction + engagement by compiling Net Promoter Scores, satisfaction metrics, engagement activity reports (relating to a member’s use of a service portal, engagement in wellness programs, etc.), and other key metrics into one profile. 
• Gauge a member’s renewal intentions by compiling direct feedback, sentiment scores, complaint resolution rates, etc. into a single, assessable summary.  

Improve Payer Contracting

Forge stronger and more constructive connections with providers; achieve critical insights into provider network performance and drive more efficient contracting negotiations. With Data Cloud and AI, payers can:

• Identify data trends and patterns that may influence contracting strategies, inform negotiations, and identify opportunities to strengthen provider-payer partnerships. 
• Use predictive analytics to forecast future trends in healthcare utilization, member demographics, and market dynamics, so payers can anticipate changes in demand and adjust their contracting strategies accordingly.
• Optimize contract terms by analyzing historical data on claims, reimbursements, and patient outcomes. 
• Reduce administrative burden and increase employees’ capacity for strategic work by automating manual contracting processes (e.g., data entry, document processing, contract management, etc).
• Analyze provider performance data to identify high-performing providers for recruitment and flag potential gaps in a payer’s network. 
• Get real-time support during contract negotiations; AI can help payer organizations identify potential trade-offs and anticipate the impact of different contract terms.

Increase Service Center Efficiency

Leverage AI to accelerate customers’ time-to-resolution on service concerns and equip your customer care team with the insights they need to deliver the informed, empathetic support members expect.  With Data Cloud and AI, payers can:

• Use chatbots to field basic inquiries regarding claim status, authorization decision rationale, ID requests, information updates, and more. 
• Surface rich contextual information about a member (i.e., regarding previous or ongoing claims, health conditions, pending authorizations, etc.) to customer care representatives during service calls.
• Surface plain-language information about plan coverages, limits, etc. to customer care representatives during service calls to reduce confusion, ensure accurate information-sharing, and accelerate time-to-resolution. 
• Enhance training and drive efficiency by identifying common service inquiries; payers can develop optimized strategies for each inquiry type to reduce handle time and improve call quality.

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Gerent Enables with AI & Salesforce

Let our team empower yours. At Gerent, we apply a cross-functional approach to digital transformation and enablement that weaves our consultants’ deep Salesforce expertise, AI acumen, and transformation experience into an empathetic and informed approach to partnership. 

You can trust our dedicated Data & AI practice — Enginable — to work with you and identify how your organization can leverage emerging AI to resolve specific business problems, unlock critical efficiencies, and enable your team with just-in-time information and recommendations.

Interested? Schedule a Discovery Workshop!

Take an hour to explore your organization’s AI potential. Our workshops are designed to help payers explore their current pain points, objectives, and improvement priorities. We’ll unpack how your data is currently being used — and identify how it could be applied to enhance operations + strengthen relationships with current or prospective customers and members. 

‍Schedule your workshop today or, for more information on our industry or technology-specific capabilities, review our Healthcare & Life Sciences and Data & AI microsites.