23Apr

The 2024 Customer Service Transformation: Generative AI’s Pivotal Role

The 2024 Customer Service Transformation:

Generative AI's Pivotal Role

In 2024, a significant evolution in customer service is anticipated, driven by advancements in Generative AI (GenAI). This shift is expected to notably improve customer experience, a first in three years. Through this article, we’ll explore the implications of this prediction, its pros and cons, and how Curate’s AI Advisory services can help businesses harness this technology effectively.

The GenAI Revolution in Customer Service

Generative AI is set to redefine how customer service operates, equipping agents with advanced tools that enhance their problem-solving abilities and interaction quality.

Pros

Enhanced Efficiency: AI-driven tools will significantly speed up response times and improve accuracy in handling customer inquiries.

Personalized Interactions: GenAI can provide tailored responses, creating a more personalized experience for customers.

Cons

Depersonalization Risk: Over-reliance on AI might lead to a lack of personal touch in customer interactions.

Technical Complexity: Implementing and maintaining GenAI solutions requires technical expertise and resources.

Impact: Elevating Customer Experiences

The integration of GenAI in customer service is expected to elevate the customer experience, offering quicker and more effective solutions.

Analogies and Examples:

AI as a Skilled Concierge: Just like a concierge knows guests’ preferences, GenAI can anticipate customer needs and offer personalized assistance.

The AI-Assisted Physician: Imagine a doctor (customer service agent) with an AI assistant providing instant information to diagnose and treat patients (solve customer issues) more effectively.

Impact: Elevating Customer Experiences

The integration of GenAI in customer service is expected to elevate the customer experience, offering quicker and more effective solutions.

Analogies and Examples:

AI as a Skilled Concierge: Just like a concierge knows guests’ preferences, GenAI can anticipate customer needs and offer personalized assistance.

The AI-Assisted Physician: Imagine a doctor (customer service agent) with an AI assistant providing instant information to diagnose and treat patients (solve customer issues) more effectively.

Curate's Role: Navigating the GenAI Wave

Curate’s AI Advisory services are crucial in guiding businesses through the adoption and optimization of GenAI in customer service.

Conclusion: A New Era for Customer Service

The year 2024 marks a turning point for customer service, with GenAI significantly improving how businesses interact with their customers. Curate’s AI Advisory expertise is pivotal in helping companies adapt to this change, ensuring they leverage GenAI to enhance customer satisfaction while maintaining a human touch.
The material and information contained in this resource is for general interest purposes only and is based on our experience; it does not constitute financial, legal, or investment advice.
18Apr

Unlocking Efficiency: A Guide to Automation Technologies for Healthcare Payers

Unlocking Efficiency:

A Guide to Automation Technologies for Healthcare Payers

Executive Summary:

Embracing automation is becoming a standard for operational excellence in the healthcare payer landscape. In this primer for payers, we break down the five core automation technologies: robotic process automation (RPA), smart workflows, computer vision, natural language processing (NLP), and cognitive agents. As healthcare payers navigate digital transformations and a competitive market, embracing this suite of automation technologies becomes not merely a technological evolution but a strategic imperative for sustained growth and operational sustainability.

There’s no way around it. The automation revolution has come for healthcare—and payers are no exception.

The more you look around, the fewer payers remain who haven’t implemented some form of AI-enabled automation in their business. These technologies are helping next-generation healthcare payers operate more efficiently and offer effortless white-glove service to their members and network.

However, much like healthcare, automation is not one-size-fits-all. Which automation technology you implement—and where in your business it fits best—is a very individual choice. To understand what automation upgrades would work best for your payer business, you need to feel confident with your knowledge of the automation landscape.

Whether you’re just getting started with automation for payers or looking to refine your toolkit, you’ve come to the right place.

At Curate Partners, we’ve worked with a range of payer organizations to refine a wide variety of business operations using automation. Now, we’re boiling down our automation experience for you, all in one accessible place. Consider this your crash course on the core automation technologies payers need to know.

The core automation technologies for payers:

  • Robotic process automation (RPA)
  • Smart workflows
  • Computer vision
  • Natural language processing (NLP)
  • Cognitive agents

RPA for payers: Start your automation journey here

Robotic process automation (RPA) is one of the most powerful and relevant automation technologies for healthcare payers. It’s a game-changer for organizations seeking to streamline routine, rule-based tasks.

At a fundamental level, RPA works like this: Software robots mimic human interactions with digital systems. The result is a win-win—staff can redirect their attention to areas where their input is more necessary, and the business lowers their risk of administrative errors. Plus, because RPA relies on existing interfaces, implementing it doesn’t require a costly IT infrastructure overhaul.

In healthcare, RPA improves functions ranging from billing accuracy to regulatory compliance and credentialing. RPA can be so nimble because of how it can work with or without supervision. Attended RPA bots are activated by employees on workstations or private servers, giving organizations a high level of control over how the bot functions in collaboration. In comparison, unattended RPA bots ease automation at scale, working independently by relying on a preset schedule and meticulously constructed logic.

Expert estimates predict that up to 60 percent of the healthcare value chain can be automated with attended and unattended RPA.

McKinsey: Where machines could replace humans—and where they can’t (yet) UiPath: Robotic Process Automation (RPA) in the Healthcare Sector

UiPath also projects that automation with both attended and unattended bots gives healthcare organizations, on average, a 5.3-month ROI after a 5.2-week implementation time.

Supercharging claims processing with RPA

For healthcare payers, RPA can especially help speed up claims processing. Software robots can efficiently extract relevant information from incoming claims, validate data accuracy, and update records without human intervention.
This accelerates the claims adjudication process and frees up human resources to focus on more complex tasks that require critical thinking and decision-making.

When paired with other automation—like smart workflows—payers can leverage RPA to significantly increase work intake while decreasing errors and adjudication time. For instance, a national U.S. payer saved $30 million in administrative costs while decreasing manual downstream processing. This further opened a door to digitizing 15 additional business operations.

From our experience, we’ve found RPA is the best place for payers interested in automation upgrades to start. We’ve worked with many payers implementing RPA across different business areas. If you’re wondering where RPA might improve your payer organization, we’d love to weigh in. Let’s start the automation conversation.

Smart workflows for payers: Creating integrated experiences

Imagine you’re a healthcare plan member. You’ve gotten used to having your digital health experiences operate like many of your favorite apps and lifestyle subscription services. Think sleek interfaces and seamless personalization. Why wouldn’t you expect your interactions with your health plan to be the same?

Creating an elevated digital experience takes more than one step of automation. Don’t miss our specialized recommendations for digital transformation.

One of the first steps toward creating that seamless digital experience is implementing smart workflows. Smart workflows leverage intelligent automation to streamline end-to-end processes.

As a payer, you can take advantage of smart workflows to optimize the entire healthcare management lifecycle—from member enrollment to claims processing. For instance, when a member submits a claim, a smart workflow can automatically trigger the necessary approvals, track the progress, and update stakeholders in real-time. Your overall member experience elevates with reduced processing times and increased transparency.

When it comes to updating business workflows, your payer business needs a rigorous and sensitive approach. That’s why our approach to change management pairs smart workflow design with stakeholder alignment and comprehensive change management education.

Computer vision: Leveraging payers’ unstructured data


From our years of working with payer businesses, we understand that one of the biggest headaches is coordinating and integrating unstructured data.

Healthcare as an industry manages so many forms of unstructured data—from discharge summaries to medical imaging. And that data is only growing. In fact, the rate of data generated across the industry has been rising at a rate of 47 percent per year.

Payers are no exception when it comes to the pains of handling all of that unstructured data. That unstructured data especially comes into play with document processing and fraud detection.

That’s where computer vision becomes a healthcare payer’s secret weapon.

If you’re already using this form of visual information processing automation, you’re likely using optical character recognition (OCR). This application of computer vision is most useful for tasks like verifying member identity documents. The days of manually inputting names from driver’s licenses are long behind us.

If you’re not yet using computer vision and OCR, now is the best time to get on board, as advances in artificial intelligence have made this automation technology much more accurate at pulling information from visual media.

And as natural language processing (NLP) techniques work with computer vision to improve OCR—especially when it comes to handwriting and lower image quality—the technology will continue to power how payers integrate unstructured data into their business operations.

NLP for enhanced healthcare payer communication

As we’ve covered, NLP pairs well with computer vision to help payers manage their unstructured data. 

Of course, NLP is about more than powering the word-detection aspect of OCR.

NLP consists of the following automated tasks:

  • Text classification
  • Sentiment analysis
  • Language translation
  • Speech recognition

To put it most broadly: NLP is a form of AI that helps computers to interact with human language by breaking it down into its foundational parts.

Payers can take advantage of NLP to, for example, automate and error-proof diagnosis coding reviews and loop unstructured, free-text data into claims decision support.

In healthcare, organizations also use NLP to revolutionize how they communicate—with each other and with consumers. This is, of course, where generative AI chatbots come into play.

For payers, the customer-facing application of NLP is where we see the most momentum. An effective chatbot can lower the pressure on a payer’s customer service department by answering members’ common questions, guiding them through the enrollment process, or providing information on coverage options. After all, IBM has found that three out of four times, answers to customer service questions can be easily found on the company’s website. Even the most rudimentary NLP algorithm could remedy this source of waste.

Plus, when leveraging sentiment analysis, NLP could help payers better understand prospective and current members’ common frustrations, turning customer service into valuable customer experience (CX) research.

The result: A more focused customer service department and a more seamless, personalized digital experience for prospective and existing members alike.

Cognitive agents for personalized payer decision-making

As we’ve been mentioning, automation can be useful to organizations looking to dedicate their human resources to more critical evaluations. Even so, not every complex decision necessarily requires an entirely human touch.

This is where cognitive agents come in. This automation technology bridges NLP with machine learning, creating virtual entities—agents—that perform tasks typically requiring human intelligence.

In healthcare, we commonly see this automation technology in AI-enabled clinical decision software. For healthcare payers, cognitive agents can especially come in handy for eligibility assessments and prior authorization.

Cognitive agents can analyze historical data, take into account the member’s medical history, and provide recommendations based on predefined eligibility criteria. Automating this process not only speeds up decision-making but also ensures that recommendations are consistent and aligned with clinical guidelines.

The difference in efficiency can be staggering. When Elevance Health began employing cognitive agents in its preauthorization processes, they were able to cut down urgent care authorizations from 72 hours to a few seconds. When it came to elective procedures, preauthorizations that previously took three to five days also came down to a few seconds with the cognitive technology.

Takeaways: How your payer business can get up to speed with automation technologies

The choice to automate processes in your payer business is a personal one. Your operation is unique in its mission, workflows, populations, and business plan.

The automation technology that is right for your business may not be what has brought other payers success. At the same time, with the rise of intelligent automation and advancements in AI, even the simplest of automation technologies—RPA—is growing further intertwined with the other core automation technologies. The most automation-savvy healthcare businesses are embracing this multi-faceted approach.

Overall, you must keep in mind that the journey toward greater automation is not just a technological upgrade—or a fad. It’s a strategic move aimed at keeping up and excelling in an increasingly digital and personalized healthcare landscape.

Embracing these automation technologies will not only drive operational excellence but also empower you to focus on your core mission of providing quality care to your members.

Let’s discuss how automation can future-proof your payer business. Get in touch.

The material and information contained in this resource is for general interest purposes only and is based on our experience; it does not constitute financial, legal, or investment advice.
01Apr

The Future of Medicare: Advancing Interoperability and Streamlining Prior Authorization 

The Future of Medicare:

Advancing Interoperability and Streamlining Prior Authorization

Executive Summary:

We delve into the evolving and intersecting landscapes of prior authorization and healthcare interoperability for payers. Amidst increasing scrutiny related to AI-enabled denial decisions, CMS continues to waver on prior authorization rulings. We urge payers to stay ahead, navigating regulatory changes, optimizing workflows through automation, and investing in interoperability for enhanced trust and efficiency in the healthcare ecosystem.

Prior authorization (PA) has been in the news a lot lately. And more often than not, the coverage has been unflattering.

The cost-saving measure that helps plans save on unnecessary care has come under scrutiny. Partially due to the government’s slow-moving regulation process regarding PA—more on that in a moment.

However, the most damning coverage has been about Medicare Advantage (MA) plans using AI to deny coverage.

Of course, the use of algorithms in itself is not a problem. When used carefully, we think automation can improve authorization processes for plans and patients. The problem is that investigations of the denials found widespread issues and inconsistencies.

Given these headlines, payers need to be especially confident and mindful of how they proceed with their updates and messaging around PA. Of course, compliance is always important, but heightened media scrutiny increases the pressure.

At Curate Partners, we’re used to guiding our payer clients through business transformations for a variety of strategic reasons—regulatory compliance included. In this evolving regulatory landscape, we want to ensure payers stay up to date with best practices around making the most of the latest regulatory moves.

Today, we’re diving into what payers need to know about the future of Medicare policy, including:

  • A timeline of recent CMS rulemaking regarding PA
  • A check-in on how payers fit into the healthcare interoperability landscape
  • Action steps for payers ready to level-up their PA processes and interoperability

What is the new CMS rule on prior authorization?

Prior authorization is increasingly common. A KFF analysis of Medicare Advantage plans found 35 million requests in 2021 alone.

Over the past few years, the federal government has been studying and ruling on the future of PA. They’ve been doing the same with healthcare interoperability. We wouldn’t blame you if the slew of PA and interoperability rulings from the past few years have made your head spin.

A timeline of the latest CMS moves regarding Prior Authorization and/or interoperability:

Policymakers hope that the proposed and final regulation will shorten PA wait times for Medicare Advantage, Medicaid managed care, and Affordable Care Act exchange plan patients. They also hope to quell tension regarding Medicare Advantage and traditional Medicare patients receiving different standards of care due to PA.

In the meantime, several states have passed or proposed legislation to give providers “gold cards” to bypass prior authorization. Statements by figures like Surgeon General Vivek Murthy blame PA for provider burden, adding pressure to get the rule finalized.

What payers can anticipate from CMS on these issues soon:

Another CMS move on the horizon is the expected 2024 release of the HHS Medicare managed care organization compliance audits. The Office of Inspector General (OIG) has conducted audits regarding denials of requested care that required PA.

We’re also awaiting rulemaking regarding MA plans’ use of algorithms to reject PA requests. In Congress, House Democrats are especially pushing for further regulation specifically regarding this issue.

The Ecosystem: Payers’ role in healthcare interoperability

As intended, electronic PA intends to improve patients’ access to care, keeping the flow of information moving between facilities, payers, and patients.

The ideal of interoperability is similar—it evokes an ethos of a harmonious healthcare ecosystem. In this ideal, providers and payers each equitably share data—and health information is always available when it’s needed, where it’s needed.

In reality, there’s a bit more friction. These delineated payer and provider roles have blurred as payer-provider relationships grow closer with more emphasis on value-based care. And communication between different sources of health data has improved with HL7 and FHIR standards—but there is still room for improvement.

The CMS requirement for a patient access, provider access, and payer-to-payer APIs and metrics reporting increases the urgency for payer interoperability improvements. But there’s a long way to go.

While payers have made great strides with ADT and CCDA data ingestion, FHIR data exchange is still “in its infancy,” per Gartner’s 2Q23 research report on U.S. Healthcare Payer Interoperability Benchmarks.

Of course, in a competitive payer marketplace, working toward interoperability is not just about compliance. It’s about providing greater value to providers and retaining a high-quality network.

What are the benefits of automating Prior Authorization?

In case you missed it, read our primer on automation technologies for healthcare payers.

Despite the challenges in these early forays into PA automation, we still see opportunity in pursuing these innovations. PA will benefit from automation—especially given the requirements around PA status reporting in the FHIR API.

The typical PA workflow is ripe for disruption with AI-enabled automation. Per McKinsey’s analysis, we foresee the most AI disruption along the initial three steps of the eight-step standard PA workflow.

However, to even be able to implement these kinds of automations, payers first need to make their PA process electronic.

Ernst & Young estimates that even just digitization can significantly improve PA processes, bringing the time from submission to decision down to seven days for standard requests and 72 hours for expedited.

With the addition of automated PA triage, the CAQH estimates the entire healthcare industry could save $437 million a year from the benefits of digitized, near-real-time PA processes.

As we await movement from the government on possible regulation of PA algorithms, we urge impacted payers to familiarize themselves with how they may optimize their PA workflows—whether it’s sooner or later.

Building trust through data availability

Proposed CMS policy is forcing impacted payers to up their interoperability game, creating compliant FHIR APIs for patient, provider, and payers-to-payer access.

At the moment the interoperability ROI is simple: Compliance helps payers avoid penalties and negative payment adjustments. However, the financial opportunity does not end there.

Investment in FHIR APIs and associated data integration processes will also further reduce administrative friction for payers with respect to prior authorization. Seamless claims and encounter data flow will reduce time spent searching for relevant data and improve payers’ ability to meet regulatory requirements.

And in the end, giving providers greater access to real-time data—especially during transitions of care—helps reduce the utilization of high-cost care and increases the use of in-network providers. Plus, quick care gap identification boosts plan quality scores (such as MA Star ratings).

More than just meeting compliance thresholds, investing in interoperability builds goodwill in the broader healthcare ecosystem. As you seek to maintain a high-quality provider network and maintain a stellar reputation, your ability to “play nice” with the greater healthcare landscape will make your plans a more attractive choice.

Perhaps nothing will be more of a marker of this reputation than how well your patient and provider access APIs work. We recommend intentional investment in these data resources.

Takeaways: Teamwork for improved interoperability and prior authorization

Given our guidance on this page, we don’t recommend payers go about upgrading their PA and data-sharing processes and systems themselves. One of the best reasons to consult experts on these issues is that legacy system mindsets will hold you back from seeing the potential of strategic change.

Whether we’re building a comprehensive data strategy or mapping out how to align stakeholders through a period of organizational change, working with Curate Partners reduces time and headaches for payers in getting solutions like these operational.

Stay informed with our expertly curated healthcare updates. Sign up for our newsletter.

The material and information contained in this resource is for general interest purposes only and is based on our experience; it does not constitute financial, legal, or investment advice.
12Mar

Embracing Decentralization: The Power of Peer-to-Peer Networks in Modern Technology 

Embracing Decentralization:

The Power of Peer-to-Peer Networks in Modern Technology

Curate Consulting Services: Pioneering P2P Solutions in Healthcare and Tech Modernization

In an era dominated by the internet and digital transformations, the Peer-to-Peer (P2P) architectural pattern stands out for its robustness, decentralization, and innovative approach to resource sharing and communication. P2P networks empower devices, termed as ‘peers,’ to collaborate, share resources, and communicate directly, embodying a truly decentralized and egalitarian digital landscape. This article explores the intricacies of P2P technology, its transformative impact across sectors, and how Curate Consulting Services leverages this architecture to revolutionize healthcare, technology modernization, and specialized talent deployment.

The Essence of Peer-to-Peer Architecture

Decentralization and Equality: At its core, P2P is about decentralization. Unlike traditional client-server models, P2P networks eliminate the need for a central authority, allowing peers to interact directly with each other. Each peer in a P2P network is both a client and a server, playing an equal role in sharing and consuming resources.

Resource Sharing: P2P networks thrive on the collective power of peers, each contributing and consuming resources such as files, processing power, or bandwidth. This not only optimizes the use of resources but also ensures that the network becomes more robust and capable as more peers join.

Dynamic and Scalable: P2P networks are inherently scalable and adaptable. As peers join or leave, the network reconfigures itself, ensuring uninterrupted service and optimal resource utilization. The dynamic nature of P2P networks makes them ideal for handling varying loads and a large number of simultaneous users.

Applications and Impact of P2P Networks

File Sharing and Content Distribution: P2P networks revolutionized file sharing, allowing users to distribute and access content directly. Platforms like BitTorrent exemplify the efficiency and speed of P2P file distribution, significantly impacting media consumption worldwide.

Decentralized Finance (DeFi) and Blockchain: P2P technology is a cornerstone of blockchain and decentralized finance, enabling direct, secure transactions without intermediaries. This not only enhances security and privacy but also opens up new avenues for financial innovation.

IoT and Collaborative Computing: In the realm of the Internet of Things (IoT), P2P networks facilitate direct device-to-device communication, enabling smarter, more efficient systems. Similarly, in collaborative projects like SETI@home, P2P allows individuals to contribute their computing power to complex, distributed computations.

Curate Consulting Services: Harnessing P2P for Advanced Solutions

At Curate Consulting Services, we recognize the transformative potential of P2P networks. Our expertise in deploying P2P architectures allows us to offer superior solutions in:

Healthcare: We leverage P2P networks to create robust, scalable, and secure healthcare systems. From patient data exchange to telemedicine, our P2P solutions ensure that healthcare providers can deliver timely and efficient care.

Technology Modernization: We help businesses modernize their technology stack with P2P solutions, ensuring greater resilience, flexibility, and efficiency. Whether it’s upgrading legacy systems or deploying cutting-edge applications, our P2P expertise is at the forefront.

Specialized Talent Support: Our team of experts is skilled in designing, implementing, and managing P2P networks. We provide specialized talent support to businesses looking to harness the power of P2P for their digital strategies.

Embracing the Future with P2P

Peer-to-Peer networks mark a significant shift from centralized to decentralized digital ecosystems. As businesses and industries evolve, the adoption of P2P architectures will play a critical role in shaping a more resilient, efficient, and democratic digital future. At Curate Consulting Services, we are committed to being at the forefront of this transformation, empowering our clients with advanced P2P solutions that drive growth, innovation, and excellence.

Disclaimer: This article is for informational purposes only and does not constitute professional advice. Curate Consulting Services is dedicated to providing innovative solutions tailored to the unique needs of our clients. For more information or to discuss how our expertise can benefit your organization, please contact us.

05Mar

Identifying Early Win Scenarios for AI in Healthcare

Identifying Early Win Scenarios for AI in Healthcare

The healthcare industry is uniquely positioned to benefit from the advent of artificial intelligence (AI), particularly in its generative form. As healthcare organizations cautiously navigate the landscape of this innovative technology, identifying scenarios where AI can quickly deliver value — or “early wins” — is crucial. These scenarios act as beacons, guiding the incremental adoption of AI, building stakeholder trust, and setting the stage for broader transformation. This article delves into how healthcare leaders can pinpoint these opportunities, showcasing Curate’s expertise in guiding healthcare consulting and technology modernization.

Early Wins Scene

Understanding Generative AI in Healthcare

Before identifying early win scenarios, it’s important to understand what generative AI means in the context of healthcare. Generative AI can design new molecules for drug development, create realistic and diverse medical images for training, or predict patient outcomes with high accuracy. These capabilities, among others, offer numerous opportunities for early adoption and significant impact.

Setting the Scene for Early Wins

Early win scenarios in AI adoption share common characteristics: they address immediate and significant pain points, have a clearly measurable impact, and can be implemented with relatively low risk and complexity. Importantly, they also set a precedent for AI’s role in driving innovation and efficiency in healthcare settings.

Identifying Early Win Scenarios

  1. Diagnostic Accuracy and Speed: AI can rapidly analyze images, pathology slides, or patient records to assist in diagnosis, often with higher accuracy and speed than humans alone. Implementing AI in diagnostic procedures, particularly in radiology and pathology, can be a powerful early win.
  2. Patient Flow Optimization: AI can predict patient admission rates, optimize scheduling, and manage resource allocation effectively. By implementing AI in operational areas, healthcare facilities can see immediate improvements in efficiency and patient satisfaction.
  3. Personalized Treatment Plans: AI’s ability to analyze vast datasets allows for the personalization of treatment plans at an individual level. Early adoption in areas such as oncology, where treatment can be tailored based on genetic information, can significantly improve patient outcomes.
  4. Administrative Automation: AI can automate time-consuming administrative tasks, such as billing, scheduling, or maintaining patient records. Implementing AI in these areas can reduce costs and allow healthcare professionals to focus more on patient care.
  5. Enhanced Patient Engagement and Monitoring: AI-powered apps and devices can provide patients with personalized health monitoring and recommendations. Early implementation of these technologies can empower patients, reduce readmission rates, and improve overall health outcomes.
Early Wins Steps

Steps to Implement Early Wins

  1. Assessment and Alignment: 
    Assess the current state of technology, data infrastructure, and workforce readiness. Ensure that the chosen early win scenarios align with the organization’s broader goals and capabilities.
  2. Stakeholder Engagement: Engage with clinicians, staff, and patients to understand their needs and concerns. Stakeholder buy-in is crucial for the successful implementation and adoption of AI technologies.
  3. Pilot Projects: Start with pilot projects to test and refine the AI applications. Monitor these projects closely for performance, user satisfaction, and ROI.
  4. Risk Management: Even with early wins, it’s vital to manage risks related to data privacy, security, and ethical considerations. Ensure all AI implementations are compliant with relevant regulations and ethical guidelines.
  5. Evaluation and Scaling: After successful pilot projects, evaluate the outcomes and plan for scaling the AI solutions across the organization. Continuous monitoring and adaptation are key to maintaining the value of AI over time.

Curate's Role in Early Wins

Curate Partners brings a wealth of experience in healthcare consulting and technology modernization to the table. With a deep understanding of the healthcare industry and the capabilities of AI, Curate can guide organizations in identifying and implementing early win scenarios. Our approach is collaborative and strategic, ensuring that AI solutions are tailored to meet the unique needs and challenges of each healthcare organization.

Conclusion

Identifying and capitalizing on early win scenarios is a strategic approach to the incremental adoption of AI in healthcare. By focusing on areas where AI can deliver immediate value, healthcare organizations can build momentum, demonstrate the benefits of AI, and pave the way for broader, more transformative applications. As the healthcare industry continues to evolve, embracing AI early and strategically will be key to enhancing patient care, improving operational efficiency, and staying competitive in a rapidly changing landscape. With the guidance of experienced partners like Curate, healthcare leaders can navigate this journey successfully, achieving early wins and setting the stage for ongoing innovation and improvement.
Early Wins winning
The material and information contained in this resource is for general interest purposes only and is based on our experience; it does not constitute financial, legal, or investment advice.
20Feb
patient relationship

Embracing the Silver Tsunami

Embracing the Silver Tsunami:

CRM Tools and their role in helping transform Healthcare Organization-Patient Relationships

As the clock ticks and candles on the birthday cake add up, we’re all part of a journey called aging. It’s a journey that comes with its share of challenges, not just for individuals but also for Healthcare Organizations navigating the so-called “Silver Tsunami.” This tidal wave of aging Baby Boomers is not just a demographic inevitability; it’s an emotional transition filled with complex healthcare needs. As Healthcare Organizations grapple with escalating demands, can CRM solutions serve as a lighthouse guiding us through the storm?

Aging is an emotional process, especially when it comes to healthcare. There’s a psychological transition as one leaves a full-time job, becomes more susceptible to chronic illnesses, and loses some level of independence. These aren’t just patients; they’re our parents, our mentors, ourselves. The healthcare decisions made during this period aren’t merely transactional; they’re deeply personal and impactful.

The Healthcare Organization’s Challenge

Healthcare Organizations find themselves in a unique bind. On one hand, they need to manage an ever-increasing volume of aging members with more complex healthcare needs. On the other, they need to be a trusted advisor, a source of solace and support. How can they balance the scales of efficiency and empathy?

CRM: A Compass in Complexity

Enter CRM: a customer relationship management (CRM) solution that has proven transformative in various industries. For example, Salesforce CRM has a suite of healthcare solutions designed to manage the complex web of Healthcare Organization interactions and streamline processes, but its power extends beyond mere functionality.

Human-Centric Design

With Salesforce-like CRM systems that are based on Human-Centered Design, Healthcare Organizations can customize interactions to address the individual emotional and healthcare needs of each member. For instance, automated reminders for medication or highly personalized healthcare tips can offer a form of virtual companionship to the elderly.

Predictive Analytics

Imagine if Healthcare Organizations could predict the needs of their members before they even have to voice them. CRM data analytics capabilities make this almost intuitive level of service possible, capturing not just data but the nuances of human needs.

Interoperability and Communication

Salesforce-like CRM tools allow seamless interoperability between systems. This ensures that everyone from your general practitioner to your specialist is on the same page, an essential factor for the elderly who are often caught in a complex web of healthcare services.
predictive analytics

Outcomes and Impact

By employing CRM strategies and tools effectively, Healthcare Organizations can humanize a system often criticized for its lack of personal touch. CRM tools help maintain a balance between operational efficiency and empathetic customer service. This is not just good business practice; it’s a transformative shift in how healthcare can and should be delivered.

Conclusion

The Silver Tsunami is inevitable, but sinking under its weight isn’t. As we all ride the wave of aging, healthcare Healthcare Organizations have the opportunity to redefine what it means to grow old. With CRM leaders like Salesforce, Healthcare Organizations can transform the narrative from a challenge to an opportunity, making the journey not just manageable but emotionally fulfilling.

Delve further into part two of three in this series for deeper insights, Navigating the Medicare Maze: How Curate Partners and CRM Tools are Revolutionizing Healthcare.

The material and information contained in this resource is for general interest purposes only and is based on our experience; it does not constitute financial, legal, or investment advice.
13Feb

Balancing Innovation and Risk: Gradual AI Integration in Healthcare

Balancing Innovation and Risk:

Gradual AI Integration in Healthcare

Understanding the Landscape of Generative AI in Healthcare ​

Generative AI refers to the subset of AI technologies that can generate new data or patterns based on the learned information. In healthcare, this could mean creating synthetic medical images for training radiologists, generating realistic patient data for simulation purposes, or personalizing patient treatment plans. The potential is vast, but so are the questions of ethics, accuracy, and safety.
Setting the Stage

Setting the Stage: Vision and Strategy Alignment ​​

Before embarking on integrating AI into healthcare systems, leaders must align the technology with the organization’s broader vision and strategic goals. Are you looking to improve diagnostic precision, patient engagement, operational efficiency, or all the above? Defining clear objectives will steer the course of implementation and set benchmarks for success.

Incremental Adoption: A Phased Approach

  1. Pilot Testing: Begin with small-scale pilot programs to understand the capabilities and limitations of AI in your specific context. Select projects that have a direct impact on patient outcomes or operational efficiency and monitor the results closely.
  2. Feedback Loops: Incorporate feedback mechanisms to learn from each stage of implementation. This includes feedback from clinicians, IT staff, patients, and other stakeholders who interact with AI tools.
  3. Scalability Considerations: As you validate the results and utility of initial AI applications, plan for their scalability across different departments or locations. Ensure that the infrastructure, both technical and human, is in place to support a wider rollout.
Test Pilot Programs

Navigating the Risk Landscape ​

  1. Data Privacy and Security: With AI’s heavy reliance on data, ensuring the privacy and security of patient information is paramount. Adherence to regulations like HIPAA in the U.S. or GDPR in Europe is not just about compliance but about maintaining trust and integrity in healthcare services.
  2. Ethical Considerations: The use of AI must align with ethical principles, particularly around issues like bias, transparency, and accountability. Establishing an ethical framework for AI use is crucial to guide decision-making and maintain public trust.
  3. Clinical Validation and Regulation: Any AI tool intended for clinical use must go through rigorous validation and regulatory approval processes. These ensure that the technology is safe, effective, and ready for real-world healthcare settings.
Training Staff

Building the Foundation: Infrastructure and Skills

  1. Robust IT Infrastructure: AI applications require significant computational power and data storage capabilities. Assessing and upgrading your IT infrastructure is a prerequisite for successful AI integration.
  2. Workforce Development: Equipping your workforce with the necessary skills to work alongside AI is as important as the technology itself. This might involve training existing staff, hiring new talent, or both.

Engaging Stakeholders: Transparency and Communication ​

  1. Clinician Involvement: Clinicians should be involved from the early stages of AI projects to ensure that the tools developed meet their needs and fit seamlessly into their workflow.
  2. Patient Engagement: Patients must be informed about how AI might be used in their care, including the benefits and any potential risks. Clear communication can alleviate concerns and foster acceptance.

Measuring Success: Impact and ROI ​

Establish clear metrics to measure the impact of AI on healthcare outcomes, patient satisfaction, cost savings, and other relevant areas. Continuous measurement and analysis will help in fine-tuning the AI strategy and demonstrating return on investment.

Future-Proofing: Keeping Pace with Innovation ​

The field of AI is rapidly evolving, and keeping pace requires a commitment to continuous learning and flexibility. Stay informed about the latest developments in AI technology and be ready to adapt your strategies as needed.

Conclusion ​

The journey to AI integration in healthcare is a complex but ultimately rewarding endeavor. By taking an incremental approach, healthcare leaders can balance the need for innovation with the imperative of risk management. Careful planning, stakeholder engagement, and continuous learning are key to navigating this journey successfully. As healthcare continues to evolve, AI will undoubtedly play a pivotal role in shaping its future, offering promising new ways to enhance patient care, streamline operations, and drive forward the mission of healthcare organizations.

Curate Partners, with its deep expertise in healthcare consulting and technology modernization, stands ready to guide and support healthcare organizations at every step of this journey. Whether it’s through strategic planning, technology implementation, or organizational change management, we’re here to ensure that the transition to AI-enhanced healthcare is as smooth and beneficial as possible.

The material and information contained in this resource is for general interest purposes only and is based on our experience; it does not constitute financial, legal, or investment advice.
06Feb

The Importance of Healthcare Payment Integrity

The Importance of Healthcare Payment Integrity

In the complex world of healthcare, maintaining payment integrity is of utmost importance for payers. Understanding what healthcare payment integrity entails and why it is essential allows payers to navigate the challenges and ensure accurate and efficient payment processes. 

What is Healthcare Payment Integrity?

Healthcare payment integrity refers to the accuracy, efficiency, and compliance of the payment process within the healthcare industry. It encompasses the various activities and strategies aimed at preventing fraud, waste, and abuse, as well as addressing billing errors and inefficient claims processing. Achieving payment integrity involves implementing measures to validate the accuracy of claims, detect fraudulent activities, and ensure appropriate reimbursement.

Ensuring payment integrity is a collaborative effort involving payers, healthcare providers, and other stakeholders in the ecosystem. By maintaining payment integrity programs, payers can safeguard financial resources and ensure healthcare dollars are utilized appropriately, benefiting both the payer and the patients. 

Why Payment Integrity is Essential for Payers

Payment integrity is of paramount importance for health plans, serving as a critical safeguard against financial losses arising from fraudulent activities, billing errors, and inefficient claims processing. It plays a pivotal role in healthcare cost containment, aiding in the accurate assessment of claims and the prevention of overpayments or inappropriate payments. This proactive approach allows payers to manage costs efficiently, especially in an environment where healthcare expenses are on a continuous upward trajectory.

In addition, payment integrity enhances trust and transparency between payers and healthcare providers. By implementing robust processes and technologies to validate claims, payers can build credibility and foster stronger relationships with providers. Efficient payment processes and accurate reimbursement also contribute to provider satisfaction, reducing administrative burdens and streamlining operations. 

Challenges in Maintaining Payment Integrity

Ensuring payment integrity in the healthcare industry is not without its challenges. Payers face various obstacles that can compromise the accuracy and efficiency of payment processes. Let’s explore some of the key challenges in maintaining payment integrity: fraud, waste, and abuse; billing errors and inaccurate claims; and inefficiencies in claims processing. 

Fraud, Waste, and Abuse

One of the significant challenges in maintaining payment integrity is combating fraud, waste, and abuse within the healthcare system.

These activities can have a detrimental impact on payers and lead to significant financial losses. Payers employ various strategies to address these issues such as implementing advanced data analytics and predictive modeling techniques to identify suspicious patterns and anomalies in claims data. By leveraging these technologies, payers can detect and prevent fraudulent activities and safeguard the integrity of their payment systems. 

Billing Errors and Inaccurate Claims

health plan billing invoiceAnother challenge in maintaining payment integrity is the occurrence of billing errors and inaccurate claims. These errors can arise due to coding mistakes, documentation discrepancies, or misunderstandings of reimbursement guidelines. Inaccurate claims can result in overpayment or underpayment of healthcare providers, leading to financial inaccuracies and disputes.

To address this challenge, payers implement robust healthcare claims auditing processes. These processes involve reviewing claims for accuracy, compliance, and adherence to reimbursement policies. By conducting regular audits, payers can identify and rectify billing errors and ensure payments are made accurately and fairly.

Inefficiencies in Claims Processing

Inefficiencies in claims processing can pose a significant challenge to payment integrity. Manual and paper-based processes, lack of standardized protocols, and complex reimbursement systems can contribute to delays, errors, and increased administrative costs. These inefficiencies not only impact payment accuracy but also affect the overall efficiency and effectiveness of the healthcare payment ecosystem.

Payers are addressing challenges by embracing robotic process automation (RPA) and artificial intelligence (AI). RPA automates repetitive tasks, reducing errors, while AI analyzes claims data to optimize processing workflows. The integration of these technologies, alongside effective auditing processes, is crucial for upholding payment integrity and sustaining a healthy healthcare payment ecosystem

Strategies for Achieving Payment Integrity

To ensure healthcare payment integrity, payers employ various strategies and technologies to identify and prevent fraud, waste, and abuse, as well as reduce billing errors and inefficiencies in claims processing. Here are four key strategies that play a vital role in achieving payment integrity:

Advanced Data Analytics

Advanced Analytics Healthcare

Advanced data analytics is a powerful tool in the quest for payment integrity. By leveraging large volumes of healthcare data, payers can detect patterns, anomalies, and potential instances of fraud or billing errors. Through sophisticated algorithms and statistical models, payers can analyze claims data to identify unusual billing patterns, high-risk providers, and suspicious activities. 

Data analytics also enables payers to gain insights into cost containment strategies and optimize payment accuracy. By uncovering trends and patterns, payers can proactively identify areas of improvement and implement targeted interventions to enhance the overall efficiency of claims processing. 

Predictive Modeling and Machine Learning

Predictive modeling and machine learning are invaluable tools in predicting and preventing potential payment integrity issues. By training models on historical claims data, payers can develop algorithms that identify patterns, predict outliers, and flag claims likely to be fraudulent or inaccurate. These models continuously learn and adapt to new data, improving their accuracy over time.

Machine learning algorithms can also assist in automating the claims auditing process, saving time and resources. By employing algorithms to review claims, payers can prioritize audits based on the likelihood of errors or fraud and ensure a more efficient and targeted approach.

Artificial Intelligence and Automation 

Artificial intelligence (AI) and automation are revolutionizing the healthcare industry, including payment integrity. AI algorithms can analyze vast amounts of data quickly and accurately, identifying patterns and anomalies that may indicate fraud, waste, and abuse. By automating routine and repetitive tasks, AI-powered systems can enhance the efficiency of claims processing and reduce the risk of human error.

One of the significant advantages of AI and automation in payment integrity is their ability to continuously learn and improve. These technologies can adapt to changing trends and new fraud patterns, making them highly effective in detecting suspicious activities. By incorporating AI and automation into payment processes, payers can enhance their ability to identify and prevent fraudulent claims and ensure accurate and compliant payments.

Provider Education and Collaboration

Provider education and collaboration are essential components of achieving payment integrity. By fostering open lines of communication and providing educational resources, payers can help providers understand the complexities of billing and coding regulations. This collaboration encourages accurate claims submissions, reduces overall errors, and improves reimbursement outcomes.

Payers can offer training programs, webinars, and documentation to educate providers on coding guidelines, documentation requirements, and common billing errors to avoid. By working together, payers and providers can align their efforts to ensure accurate claims submissions and minimize payment integrity issues.

The Benefits of Payment Integrity

Implementing and maintaining healthcare payment integrity offers several key benefits for payers. From significant cost savings to improved compliance, payment integrity initiatives have a positive impact on the overall healthcare ecosystem. 

Cost Savings

Healthcare Billing and Claims

One of the primary benefits of payment integrity is the potential for substantial cost savings. By identifying and preventing fraudulent claims, billing errors, and wasteful practices, payers can significantly reduce unnecessary healthcare expenditures by 8%-10% or more. This ensures resources are allocated efficiently and funds are directed towards providing high-quality care to patients who truly need it.

According to a study conducted by the National Health Care Anti-Fraud Association, healthcare fraud alone costs the United States tens of billions of dollars each year. By leveraging advanced data analytics, predictive modeling, and other technologies, payers can proactively detect and prevent fraudulent activities leading to significant financial savings. 

Improved Compliance

Payment integrity initiatives also contribute to improved compliance with regulatory guidelines and industry standards. By ensuring claims and billing practices adhere to established rules and regulations, payers minimize the risk of non-compliance penalties and legal repercussions.

When payment integrity is prioritized, payers can implement robust processes and systems to monitor and validate claims for accuracy, appropriateness, and adherence to coding and documentation guidelines. This not only reduces the likelihood of improper payments but also enhances the overall integrity and transparency of the healthcare payment system. 

Enhanced Provider-Payer Relationships

Effective payment integrity strategies can foster stronger relationships between payers and healthcare providers. By providing clear guidelines and educational resources to providers, payers can facilitate better communication and cooperation. This collaboration helps to streamline claims processing, reduce payment errors, and improve overall efficiency. When providers have a clear understanding of the payment integrity process, they are better equipped to submit accurate claims and documentation. This reduces the need for claims audits and appeals, leading to faster and more reliable reimbursement to providers. Enhanced collaboration between payers and providers ultimately benefits patients by ensuring timely access to quality care.

Conclusion

The benefits of payment integrity extend beyond cost savings and compliance. By embracing payment integrity initiatives and leveraging advanced technologies, payers can foster a more efficient, transparent, and sustainable healthcare system. For more insights on payment integrity in healthcare, please contact us and speak with our Payment Integrity Consulting Group.

The material and information contained in this resource is for general interest purposes only and is based on our experience; it does not constitute financial, legal, or investment advice.
30Jan

Incremental Adoption of Generative AI in Healthcare: Steps to Introduce Generative AI in Healthcare Settings

Incremental Adoption of Generative AI in Healthcare:

Steps to Introduce Generative AI in Healthcare Settings

The healthcare industry stands on the cusp of a transformative era with the integration of Generative Artificial Intelligence (AI). This technology, when incrementally adopted, promises to revolutionize patient care, administrative efficiency, and medical research. However, the path to its integration is fraught with challenges, including ethical considerations, data security, and the need for robust infrastructure. As leaders in healthcare, understanding the nuances of this technology and strategically implementing it is critical. Here’s a general guide that might help with this journey.

1. Understanding Generative AI in Healthcare

Before diving into implementation, it’s imperative for healthcare executives to grasp what Generative AI is and its potential impact. Generative AI refers to algorithms that can learn from data and generate new, similar data. In healthcare, this can mean creating synthetic patient records for research, aiding in drug discovery, or even predicting patient outcomes.

2. Establishing the Vision and Strategy​

Leaders must define what they aim to achieve with Generative AI. Whether it’s improving diagnostic accuracy, enhancing personalized treatment plans, or streamlining operational efficiency, having a clear vision will guide the entire implementation process. This vision should align with broader organizational goals and patient care objectives.

3. Assessing Readiness and Capability​

Evaluate the current IT infrastructure, data management capabilities, and workforce readiness. Understanding the gaps in technology, skills, and processes is crucial to planning an incremental adoption. This assessment should also consider data governance and the ethical implications of using AI in healthcare settings. 

4. Regulatory Compliance and Ethical Considerations​

Navigating the complex landscape of healthcare regulations is vital. Leaders must ensure that the use of Generative AI complies with HIPAA, GDPR, and other relevant regulations. Equally, establishing ethical guidelines for AI usage, including transparency, accountability, and patient consent, is essential. 

5. Pilot Projects and Phased Implementation ​

Start small with pilot projects targeting specific problems or processes. These initial projects can provide valuable insights into the technology’s effectiveness and help gauge staff and patient reactions. A phased approach allows for iterative learning and adjustments before wide-scale implementation.

6. Building a Skilled Team ​

The successful implementation of Generative AI requires a team with diverse skills, including data science, healthcare expertise, and IT security. Invest in training existing staff and consider hiring or partnering with AI specialists. A multidisciplinary team will be instrumental in navigating the technical and ethical complexities of AI. 

7. Integrating with Existing Systems ​

Generative AI should complement and enhance existing healthcare systems, not replace them. Ensuring seamless integration with EHRs, diagnostic tools, and administrative systems is crucial. This integration requires careful planning and robust data interoperability standards. 

8. Continuous Learning and Adaptation ​

AI technologies evolve rapidly, and healthcare applications are no exception. Establish mechanisms for continuous learning, feedback, and adaptation. This includes updating models with new data, refining algorithms, and staying abreast of the latest AI research and ethical discussions. 

9. Patient Engagement and Transparency ​

Patients should be at the center of AI initiatives. Communicate transparently about how AI is used, the benefits it brings, and any risks involved. Involving patients and the public in the conversation helps build trust and ensures that AI solutions are aligned with patient needs and values. 

10. Measuring Impact and ROI ​

Define clear metrics to evaluate the success of AI initiatives. These might include improvements in diagnostic accuracy, patient satisfaction, cost savings, or workflow efficiencies. Regularly reviewing these metrics will provide insights into the value of AI investments and inform future strategies. 

11. Scalability and Future-Proofing​

As initial projects succeed, plan for scaling AI solutions across the organization. This requires a scalable IT infrastructure, ongoing investment in staff training, and a strategic approach to expanding AI applications. Future-proofing also means staying flexible and responsive to new AI developments and healthcare needs. 

12. Creating a Culture of Innovation ​

Finally, fostering a culture that embraces innovation, continuous improvement, and learning is vital. Encourage staff to experiment, provide feedback, and contribute ideas. A supportive culture will help overcome resistance and ensure that the benefits of AI are fully realized. 

In conclusion, the incremental adoption of Generative AI in healthcare holds immense promise for improving patient care, operational efficiency, and clinical research. By following these steps, healthcare leaders can navigate the complex landscape of AI implementation, ensuring that technology serves the best interests of patients and healthcare providers. As a leader in healthcare consulting and technology modernization, Curate Partners is committed to guiding healthcare organizations through this journey, leveraging our expertise to foster innovation, improve outcomes, and drive the future of healthcare. 

The material and information contained in this resource is for general interest purposes only and is based on our experience; it does not constitute financial, legal, or investment advice.
29Jan
Medicine doctor team meeting and analysis. Diagnose checking brain testing result with modern virtual screen interface on laptop with stethoscope in hand, Medical technology network connection concept.

2024 Innovative Predictive Analytics Trends for Healthcare Payers

Revolutionary Predictive Analytics Trends Shaping Healthcare Payers’ Future in 2024

Executive Summary:

As the healthcare sector braces for more advanced algorithms from the AI revolution, we’re underscoring the strategic importance of predictive analytics in shaping the reputation and future viability of payer businesses. The key predictive analytics trends payers must take advantage of this year include: leveraging claims analytics for cost reduction and health improvement, employing clinical alerts to manage high-risk populations, optimizing provider networks through data-informed intervention, prioritizing consumer experience personalization, and enhancing cost transparency with data visualization—a collective approach that ensures payers not only survive but thrive in an industry increasingly defined by data-driven decisions.

Over the past few years, payers have been going through a digital metamorphosis. This digital transformation, like any industry-wide shift, is about staying ahead of the curve.

Short of having a crystal ball, harnessing predictive analytics is the best way payers can strategically outpace the competition and cultivate a sustainable, seamless business.

Yet, even in 2023, reports suggested that healthcare organizations—even payers—have been underutilizing their data for forward-thinking business decisions.

ARCADIA: Healthcare Data Utilization

In 2024, the stakes of putting that data to work have never been higher.

  • Consumer expectations continue to push toward sleek digital and personalized experiences.
  • The push toward value-based care further emphasizes closer relationships between providers and payers.
  • Shifting Medicare regulations emphasize how payers can and must wield their data and step up efficiency and transparency.
  • A contracting workforce demands more clear-cut delegation and management.

In response to these pressures, we expect payers to up their predictive analytics game in a few key ways. To get you up to speed, here are five of the most impactful ways we’re advising our payer clients to implement predictive analytics in their payer organizations.

This year’s top predictive analytics trends for payers:

  • Claims analytics
  • Care gap alerts
  • Provider network optimization
  • Customer experience personalization
  • Cost transparency visualization

Trend #1: Reducing costs and improving health with claims analytics

Applying predictive data analytics to claims can expedite claims processing, cutting out inefficiencies and identifying opportunities for automation.

But there’s an even more compelling use case for analyzing past claims. Hidden in that data are clues as to which patients are likely to incur high-cost medical care in the future. Identifying these patients and intervening with preventive health measures improves these patients’ chances to avoid worsening conditions—and it saves payers money.

McKinsey machine learning analysis used Medicare fee-for-service data to demonstrate how predictive analytics can accurately identify readmission risk. They found that, for the five percent of patients identified with the highest readmission risk, 75 percent were readmitted.

Analytics-enabled targeted intervention could allow payers to coordinate with provider teams and keep at-risk patients out of the hospital. One such intervention program from a New York-based health plan resulted in a 21% reduction in readmissions.

Plus, with advances in artificial intelligence, predictive analytics allows for more real-time insights, helping payers make smarter decisions.

Trend #2: Staying on top of high-risk populations with clinical alerts

If you’ve been in the health IT world as long as we have, clinical alerts may sound like old news to you.

Yes, EHR-based ADT (admission, discharge, transfer) alerts have been helping providers better intervene on their high-risk patients’ behalf since the rise of EHR adoption in the last decade.

EHR adoption

Now, recent advances in machine learning have increased how specific we can get with clinical alerts. As a result, more health data solutions have begun offering comprehensive care gap analysis for high-risk patients.

This analysis makes it easier for healthcare payers to stay on top of their HEDIS scores and other quality measures. Optimizing those measures required payers and providers to carefully coordinate care—especially when it comes to chronic care management.

Plus, with increasing the availability of care gap data to providers, payers have an opportunity to offer provider networks more value and deepen those relationships. Healthcare payers are perfectly poised to offer their providers these kinds of care gap alerts based on their wealth of claims information.

Trend #3: Building and keeping high-quality provider networks

Building and keeping high-quality provider networks

We hear this from our payer clients often: In such a competitive market, payers are scrambling to minimize provider churn.

Attracting and retaining a high-caliber provider network doesn’t have to be complicated. For one, offering data analytics services makes providers likelier to stay loyal to a payer by increasing satisfaction.

But ultimately, payers do need to think bigger when it comes to keeping and entering new markets. Analyzing provider performance can help payers stay on top of waste, care variability, and network leakage.

As an example, we would advise a proactive leakage mitigation strategy of minutely tracking provider performance. Specialty physician billing is behind about a quarter of healthcare spending. And studies estimate that inappropriate referrals across all conditions happen between 12.4 to 30 percent of the time.

Noting the culprits behind network leakage can help payers smoothly intervene—whether by enticing new providers to join the network or offering incentives to in-network providers to minimize outside referrals. One plan’s data-driven interventions led to an 18% increase in high-value referrals by primary care providers.

Predictive analytics can also allow payers to more confidently enter new markets. Payers can forecast the effects of network changes using available data and provider performance benchmarks. However, processing, cleaning, and normalizing provider performance data across such a wide span can be incredibly time-consuming. We’d recommend predictive analytics solutions to cut down on the costs of this market research.

Trend #4: Getting inside the consumer’s mind

In today’s personalized, consumer-centric landscape, generic messaging and experiences don’t cut it anymore. Effective digital transformation means prioritizing customer experience (CX).

prioritizing customer experience

Predictive analytics is a key tool for optimizing your consumer-facing front end. When applied to member interaction data, payers can use predictive trends to create a roadmap for hyper-personalized and razor-sharp CX.

When we’re helping our payer clients build out digital CX and marketing campaigns, data-driven experimentation is core to our process. Through one such project, we helped a client enhance patient adherence and engagement metrics using a data-driven campaign personalization strategy.

When working with another payer client, we wielded our collected customer interaction data to generate new use cases for building out their integrated digital experience for members. For this client, our primary focus was on the ‘Member 360 Record,’ facilitating smooth transitions from Commercial Health Plans to Medicare for those aged 60+. Our analytics-driven decisions helped us create a consistent CX tailored for members going through this transition.

Trend #5: Visualizing cost transparency

Healthcare is a trust-based industry. Members face an increasing power of choice in plan selection. Payer-provider relationships are moving toward value. Now more than ever, building that trust is sink-or-swim for payers.

data visualization platforms

Whether it comes to member or provider relationships, much of the trust payers can build is grounded in cost transparency. Plus, with CMS regulation, cost transparency is no longer optional. More and more payers are upping their compliance.

Predictive analysis makes cost transparency easier to deliver with custom data visualization platforms.

We’ve worked with one of our payer clients to create one such cost transparency data-as-a-product platform. The resulting product and updated FinOps processes helped our client transition to a data product mindset, offering greater transparency and value within their native platform.

Takeaways: How payers use data determines their reputation

A payer’s reputation is one of its most important assets. Predictive analytics allows payers to wield data more effectively—both providing more value to patients and providers and running a smoother operation internally. Both of these factors contribute to a future-proof reputation. Looking forward, the importance of predictive analytics in the healthcare industry will only continue to grow. More advanced algorithms coming out of the AI revolution are coming—it’s not a matter of if, but when. With AI regulation continuing to be limited, the way individual payers choose to use these tools will set standards for the rest—and the broader healthcare world. We see sustainable payer businesses as those that choose to be trailblazers at this inflection point—not the ones that underestimate the power of what predictive algorithms can do.

To discover more about how we transform payer data into actionable growth strategies for our clients, click here.

The material and information contained in this resource is for general interest purposes only and is based on our experience; it does not constitute financial, legal, or investment advice.
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