In the past, the realm of artificial intelligence was largely navigated by major technology firms. Healthcare organizations cautiously observed from the sidelines. These healthcare leaders, prioritizing patient well-being, traditionally leaned on retrospective analytics, making crucial care management decisions based on hindsight, analyzing data reflecting past events. However, a seismic shift has occurred in recent years. The healthcare industry, now grappling with an aging population, a surge in chronic diseases, and escalating costs, is simultaneously inundated and gifted with an explosion of data from diverse sources, such as unstructured text, fitness watches, and virtual care visits. This confluence of challenges and technological advances has necessitated a pivot towards innovative, accurate strategies for risk identification, health outcome improvement, and cost reduction. Consequently, healthcare leaders can no longer remain passive observers of the AI revolution but instead must shift towards being actively engaged participants. Partnerships with data and predictive analytics experts are becoming indispensable, leveraging deep learning and generative AI techniques to transform raw data into a potent analytical asset. This not only amplifies their clinical outreach strategies but also fortifies successful financial outcomes by infusing their approach with a forward-looking perspective, especially when it comes to population health and value-based care. Let’s explore the three pivotal ways in which AI is reshaping the approaches of healthcare leaders to value-based care: 1. Evolving Beyond Standard Notion of Risk: Traditional risk stratification often unfolds in a predictable manner: frequent emergency room visits, particularly those related to ongoing health issues like diabetes, trigger retrospective action. Strategies aimed at curbing ER over-utilization are formulated, often when annual key performance metrics spotlight a member. Despite these interventions, healthcare costs have surged and clinical outreach programs have been under-exploited. The contemporary solution? AI-driven risk stratification. Here’s a detailed exploration: Holistic Health Views Through Comprehensive Data Integration: All-inclusive data from various sources (medical, pharmacy, dental, etc.) converge on a predictive analytics platform, providing a multifaceted view of a member’s health beyond mere historical events. This panorama encompasses influencers of health inside and outside clinical settings, including social barriers and medication adherence. Early Disease Prevalence Detection with Enriched Analytics: Enhanced predictive analytics empower leaders to identify disease prevalence, such as cancer and diabetes, much earlier. This prescience enables strategic, timely intervention and informed healthcare planning. Future-focused Care Management Through Predictive Insights: Care strategies are crafted with an eye to the future, utilizing insights to forecast specific conditions and potential avoidable costs. The approach is anchored in both the individual and broader population landscapes, ensuring interventions are as preventative as they are reactive. Employing these AI-driven strategies, healthcare leaders can craft, adapt, and implement plans that are not just responsive but anticipatory, ensuring robust health outcomes and organizational efficacy. Here’s a snapshot of what healthcare leaders see when assessing future risk in their population from Certilytics’ platform, which also takes risk assessment a step further by showing healthcare partners the financial savings possible with each clinical outcome intervention. 2. Proactive and Targeted Outreach: Managing the voluminous and multifaceted nature of healthcare data demands a proficient, AI-driven analytics partner. Certilytics, having crafted our deep learning platform from the ground up—condensing over 250,000 medical codes into 250 deep features—enables healthcare plans and employers to discern and act upon otherwise hidden opportunities for intervention. But it’s not just about foreseeing high-risk events. It’s about precisely directing resources to maximize impact and engagement. A straightforward example: while heart disease may represent a significant portion of healthcare spend, AI-illuminated insights might reveal an already-compliant heart disease patient population with limited opportunity. But another prevalent condition like diabetes might present more substantial opportunities for impactful intervention and cost avoidance. Armed with this intelligence, leaders can confidently invest in areas—such as a diabetes management program—that promise tangible savings and enhanced outcomes. 3. Program Measurement and ROI Analysis: Historically, isolating and quantifying the impact of a single healthcare initiative has been a herculean task for leaders. Even with a data-centric approach, discerning genuine impact amidst a sea of influencing factors remained elusive. Advanced AI platforms, however, have altered this landscape. Deep learning capabilities allow for the isolation and accurate evaluation of a program’s impact, granting leaders a crystal-clear view into the efficacy of wellness programs or the performance of a potential vendor solution. Certilytics’ Program Performance Analytics Solution delivers more than data to healthcare leaders, but clarity, precision, and actionable insights for evaluating program success. Take a look here:   Are you ready to use AI-powered predictive analytics to transform your value-based care strategies? Schedule a meeting with our team.

You’ve heard a lot about generative AI, machine learning, and large language models lately. But these concepts are part of our DNA. Check out these five facts to learn a little about our history and see what happened when we leveraged the power of AI to improve health and lower costs. 1. 2017 These headlines about generative AI and deep learning are fresh, but our roots with these concepts run deep. Let’s go back to 2017. Our founding partnerships with large and complex healthcare companies necessitated AI-driven predictive analytic solutions. We saw firsthand how these large data sets of medical and pharmacy claims had the potential to transform their clinical and financial outcomes – if they were supercharged with predictive insights. So that’s what we built: market-leading, scalable, and unified predictive models that have uncovered billions in savings and improved health outcomes. And we’ve continued building on that foundation ever since. Since our first deployment in 2017, we’ve been applying various AI and machine learning techniques in designing, deploying and calibrating a full library of 1000+ predictive healthcare models.   2. 83 Million Patients Many data science solutions to healthcare challenges have difficulty putting those solutions in action. Predictive model results may be published in academic papers, but in reality, those predictive models require a lot of manual intervention and maintenance to get from conception to deployment to reproduction. Occasionally, on the ground, this means a simpler model approach is favored over a state-of-the-art approach. That’s why we built a healthcare-first, deep-learning platform from scratch. Think 250,000+ medical codes compressed to 250 deep features. We can represent complete member histories across time and data sets to provide the richest possible view of a member for making new predictions. We’ve generated over 67 billion scores for over 83 million patients. Source: Certilytics   3. 1000s+ Models and Growing The news and rapid advancements in AI and large language models are a matter of scale – training ever-larger networks on ever-larger data sets. Most commercial, application agnostic machine learning software focuses narrowly on algorithm fitting, meaning it takes a longer time to get a predictive application up and running. Our platform encompasses the entire model lifecycle, with a particular focus on scaling and reliability for healthcare. The result? Supercharging the predictive model development cycle so it can be completed in a single two week sprint. This allows our team to maintain and expand a model library of thousands of predictive models plus deliver customized predictive models to customers.   4. $10B in Savings Identified We’ve continually tested our deep learning applications against internal and external machine learning models. Our consistent finding? They produce more accurate predictions – with greater agility and speed. For example, using our platform, customers can quantify the social determinants of health risk to understand what factors are driving negative health outcomes. They can answer not just the “who” and “where” of SDoH risk, but also the “why” and “how,” prioritizing outreach and driving meaningful and personalized interventions to at-risk patients. It’s how we helped uncover over $10B in savings for large and complex healthcare organizations.   5. 30+ Unique Data Domains Because of our AI and deep learning approach, our models are more accurate, more robust to missing or imperfect healthcare data, and they can make use of new data with greater speed. Now, the platform we built from scratch supports billions of data points and hundreds of millions of lives, including 30+ unique data domains from medical claims to disability benefits to income. Our models accomplish everything from predicting member financial, clinical, and behavioral outcomes to validating claims data to measuring the ROI of clinical programs.   Ready to start using AI-driven predictive insights to improve health outcomes and lower costs? Learn about our AI-centered solutions by downloading this brochure or scheduling a meeting with our team.

As a digital health solution, you know the market continues to get more crowded every day. Your customers and prospects are overwhelmed with pitches for the next great fitness tracker, disease management program, engagement strategy, mindfulness app, and more. Now, more than ever before, you need more than just great marketing and a good sales pitch to stand out. You need to prove your solution can deliver on its clinical and financial promises. The solution? More vendors are starting to use advanced, AI-driven analytics to demonstrate their positive impact on health and cost. How do they do it? Check out these three ways data-backed insights help solution vendors back up their performance claims, deliver on their promises, and gain confidence from their clients. 1. Program Validation to Support Sales Discussions Telling a compelling value story can be a major challenge. Healthcare interventions are incredibly complex, each population is unique, and the number of variables influencing one member makes it difficult to quantify the clinical and financial impact of your solution. But effective data analytics platforms are built to handle this complexity. After connecting disparate data sources, a data and analytics partner should be able to detangle these variables so that digital health solutions are able to transparently track the progress of their solutions and demonstrate a positive return on investment. For example, using Certilytics’ platform, one solution vendor who previously struggled to demonstrate their progress was able to validate a $200PMPY savings for their medical management program. Check out this snapshot of what  solution vendors are able to see when they measure their performance on Certilytics’ platform: When it comes time to approach a new client, vendors who’ve leveraged advanced data analytics earn customer trust with metrics showing how their solutions helped improve health outcomes and generate cost savings for other customers. 2. Leverage Predictive Analytics to Direct Resources for Maximum Impact Demonstrating how your solution has performed with other populations will go far in client discussions. But what if you could go even further? Data analytics help digital health solutions prove their value to clients, but they can also be used to show how you can uncover hidden opportunity and cost savings for them. With AI-powered insights, you can measure a member’s likelihood to engage with your solution. This helps determine the targeted outreach that would make the most impact for your client. As a result, you can ensure that your solution targets those most likely to benefit, matching them to the right programs at the right time to capture the maximum health and financial upside. For example, Certilytics’ AI-driven solutions can pinpoint specific members who are most likely to respond to education around taking their prescribed medication, and what the potential ROI is adhering to their medication regimen. Once you’ve implemented your solution, you can use risk stratification to continue to optimize your strategies by identifying and directing resources not just to the riskiest members, but to those who will benefit most from your solution’s targeted resources. Here’s a snapshot of the opportunities solution vendors see when analyzing and building a strategy for  a customer’s population: 3. Measure Program Performance and ROI It’s no secret that measuring the ROI of a solution is tough. With so many variables affecting cost, program engagement, and health outcomes, it feels impossible to prove that your solution alone drove positive change. But with data-backed advanced analysis, such a compelling value story is possible. Certilytics’ machine learning models are built to detangle these many variables and clearly track and visualize the performance of your solution. Take a look at what solution vendors see when utilizing our Program Performance Analytics to measure program success. With this insight, clinical point solutions more than justify the cost of their programs. They gain support from existing customers to continue and expand their solutions as well as gather more success metrics to show potential customers. Are you ready to tell a compelling value story that measures the ROI of your solution? Schedule a meeting with our team or check out our case study to see how one solution vendor demonstrated $200PMPY in savings.