The world of healthcare is flush with data – something that is vital for us to continue to improve the healthcare experience. But having all of this data doesn’t mean that it is all being used to its full potential, if at all. Much of the data that we have is in very complex datasets, which, by its very title, is very complex to break down, comprehend, and utilize. Rather than giving more work to your already overwhelmed staff – advanced analytics and AI tools can be very helpful in this area. For a closer insight into how advanced analytics and AI tools can be leveraged to derive actionable insights from complex healthcare datasets, we reached out to our brilliant Healthcare IT Today Community! The following are their answers.
George Dealy, VP of Healthcare Applications at Dimensional Insight
The key to unlocking insights from complex healthcare datasets starts with well-governed, meticulously curated data enriched with integrated metadata for context. From that foundation, AI tools can reveal patterns and measure them against benchmarks, targets, and best practices. Generative AI, when fueled by credible content, goes further, suggesting concrete actions to improve performance and spread best practices. Just as importantly, GenAI copilots and chatbot assistants can guide information consumers, ensuring they engage with the data in the most productive and meaningful ways.
Chris Aulbach, Senior Product Executive at ArcheHealth
AI elevates what’s possible in transforming raw, fragmented, dirty healthcare data into clear actionable insights that can drive decisions and impact. Through a mix of predictive models, NLP, and process analytics, the unseen can not only be seen but also fully understood. In healthcare operations, this brilliant clarity can be leveraged to drive out supply chain inefficiencies like cost volatility, performance variances, or recurring bottlenecks in workflows, which minimizes waste and unnecessary spend. Data readiness is the key to success, which requires overcoming the issues of data quality, fragmentation, and semantic alignment such to have a unified, reliable source of truth for unlocking data’s real potential.
Brian Kenah, Chief Technology Officer at EnableComp
Healthcare leaders face mounting pressure to protect revenue and deliver sustainable performance. In revenue cycle management, advanced analytics and AI can turn complexity into clarity, transforming claim and payer data into actionable insights that identify root issues, prevent denials, and highlight new opportunities for improvement. When data-driven insights are combined with the judgment and experience of people on the front lines, everyday workflows become smarter and more effective. This shift helps organizations stay ahead of industry changes, protect their revenue, and create the financial stability that allows them to focus on what matters most: caring for patients.
David Schweppe, Chief Analytics Officer at MedeAnalytics
When it comes to actionable healthcare insights, payers and providers are drinking from a firehose of data through a straw. These organizations aren’t just flooded with data; they’re overwhelmed by multiple datasets: lab results, family history, social determinants of health, claims data, clinical data, and more. Artificial intelligence and advanced analytics help surface the most relevant information at the point of care, enabling smarter decisions both at the individual patient level and across the organization. With a unified, AI-powered view of data, payers and providers can evaluate the effectiveness of initiatives, identify what’s working, and pivot when needed. AI doesn’t just help them drink from the right hose; it ensures they’re drawing from the same source, aligned in their efforts to improve outcomes.
Daniel Vreeman, DPT, Chief Standards Development Officer and Chief AI Officer at HL7 International
AI and advanced analytics do have the potential to transform healthcare by turning complex data into actionable insights that improve patient care and operational efficiency. But that promise depends on reliable, consistent, and interoperable data. Standards like FHIR, CDS Hooks, and CQL provide the essential infrastructure to integrate diverse data sources, support predictive analytics, and ensure AI tools deliver trustworthy, scalable, and equitable outcomes across the healthcare system. Without that foundation, even the most advanced AI tools can’t deliver reliable or trustworthy results.
Jeanne Cohen, Founder and CEO at Motive Medical Intelligence
The future of healthcare depends on how well we manage and apply our data. For too long, data has been siloed and opaque, giving us reports about populations but not real guidance for individualized decisions at the point of care. To truly improve patient outcomes, we need systems that make complex datasets transparent, traceable, and actionable, especially at the level where care decisions are made: by the physician. Advanced analytics and AI should not be black boxes; they should be evidence-based transparent tools that physicians can trust and use to refine their own practice patterns. When data is credible and actionable, it becomes a catalyst for measurable change in quality and cost across the healthcare system — driving the shift toward value-based care.
Benjamin Beadle-Ryby, Co-Founder of AKASA
Analytics in healthcare only matter if they translate into decisions that improve patient care and organizational performance. The real opportunity with AI isn’t just crunching more data, it’s revealing the blind spots that humans miss, patterns across thousands of encounters, and rare one-off errors alike. Nowhere is that more critical than in documentation and coding. What may look like an administrative detail actually determines whether patients face denied claims, whether hospitals get reimbursed fairly, and how quality scores reflect the true acuity of care delivered.
The industry’s long-accepted bar of “95% coding accuracy” may sound high, but at enterprise scale, those misses compound into millions of dollars lost and quality metrics that understate patient complexity. AI shines when it can sift through that noise, elevate both systemic trends and edge cases, and give health systems the confidence that their financial and quality performance accurately reflects the care they actually provide.
Charlie Lougheed, Founder and CEO at Axuall
Combining big data and AI is the only way to effectively address healthcare’s workforce shortage. Since health systems cannot simply create new clinicians out of thin air, they must use the tools at their fingertips to address this workforce supply chain gap, and AI is by far one of their most powerful tools. By leveraging AI and advanced analytics fueled by a comprehensive data network that encompasses years of detailed practice data on providers, such as their credentials, practice patterns, procedural volume statistics, patient panel characteristics, and more, health systems can fundamentally improve the way they recruit, onboard, retain, and optimize their clinical workforce. AI leveraging these data sets allows healthcare organizations to identify the best-fit based on openness to work, productivity levels, speciality, and more. Once hired, this data network leverages AI to ensure credentials meet the stringent requirements.
Mike Rousselle, Senior Vice President, Artificial Intelligence at OptimizeRx
AI and advanced analytics allow life sciences organizations to create more effective and actionable physician and consumer engagement strategies. Identifying the right audience is critical for life science marketers, yet manually uncovering the insights necessary for effective patient targeting is cumbersome and inefficient. By applying AI to integrate and analyze complex clinical datasets such as claims data, EHR data, medical histories, engagement behaviors, and social determinants of health, life science organizations can find or predict patient eligibility for therapies, as well as determine how best to reach and engage them. These tools enable life sciences organizations to obtain the actionable insights they need to communicate to patients in need across their media channels.
By leveraging AI to predict clinical trends and identify which patients will be eligible for certain pharmaceuticals (for drugs & medical devices), life sciences organizations can better understand evolving patient needs throughout the entire care journey, making them better equipped to deliver targeted messages to physicians and patients alike. This predictive, tailored approach to marketing enables life sciences organizations to more effectively educate consumers and patients about potential treatment options during key care windows in their care journey, ideally improving health outcomes and supporting brands’ bottom lines.
In the studies we’ve conducted on coordinated HCP & DTC tactics centered around these predictive patient eligibility windows, we’ve observed that there’s a multiplicative effect occurring, where the campaigns in market each have extra synergies attached to them; the HCP tactic helps the DTC tactic, the DTC tactic helps the HCP tactic, and we see in the data that 1+1 equals even more than 2! This data shows that it’s not only powerful to base campaigns around predictive patient eligibility, but that these campaigns have the power to amplify Commercial success beyond what we thought possible.
Frederico Braga, Head of Digital and IT at Debiopharm
Healthcare is moving beyond merely collecting data to empowering everyone to activate and use it. Advanced data and analytics are breaking down data silos, allowing researchers, patients, and clinicians to explore complex datasets, uncover patterns, and make evidence-based decisions. By integrating information across treatments, outcomes, and populations, these tools enable predictive, personalized care and support operational efficiency at scale.
Analytics are no longer just about dashboards or reporting; they are about generating actionable intelligence that informs strategy, guides innovation, and ultimately improves patient care. Initiatives in precision medicine and AI-driven insights demonstrate how turning raw data into meaningful knowledge can directly impact outcomes, helping healthcare organizations become more proactive, data-driven, and agile in an increasingly complex system.
Coleman Young, Senior Product Manager, AI & Regulatory at RXNT
AI and advanced analytics turn raw data into something providers can act on right away. Instead of static reports, practices get live views of patient trends and clinical insights. That means a provider can identify important warning signs and adjust treatment plans that same day. When data becomes this clear and immediate, it strengthens decisions and helps deliver better care.
Courtney Yeakel, Chief Product Officer, Payer at Veradigm
Payers have never had more data at their fingertips, yet the real opportunity lies in transforming data into actionable insights. By applying advanced analytics and AI to rich clinical and claims information, we can illuminate gaps in care, identify emerging risks, and improve processes like prior authorization. Layering predictive analytics directly on top of this data can generate forward-looking insights that support clinical decision-making and anticipate patient needs. For example, natural language processing and large language models can uncover contextual details like social determinants of health, test results, and patient-reported outcomes buried in unstructured notes, giving payers and providers a far more complete picture of each patient’s health journey.
This enables payers to monitor and manage risk adjustment, close HEDIS and Star Ratings gaps faster, and coordinate more effectively with providers. Importantly, analytics can be embedded directly into provider workflows, so insights aren’t just reports, but triggers for timely interventions. As interoperability rules evolve, these capabilities also help payers stay compliant and future-ready for regulatory and policy changes. The ultimate goal is simple but powerful: turn complex datasets into actionable intelligence that reduces cost, enhances quality, and improves patient outcomes and experiences.
Casey Williams, SVP of Patient Engagement at RevSpring
AI and advanced analytics are most valuable when they make patient interactions more personal. By using data about patient preferences and financial needs, we can help staff communicate with empathy, guide patients toward self-service when it fits, and make the process easier for both patients and providers.
Matthew Blosl, CEO at DexCare
Healthcare generates nearly a third of the world’s data, yet too much of it remains trapped in silos or buried in formats that can’t be integrated. The challenge is making that information usable in real time for patients trying to find care and for providers trying to deliver it. AI helps by taking in the messy data, existing policies, workflows, and records across EMRs and third-party tools, and turning it into intelligence that a health system can act on. That creates the digital awareness to route a low-acuity patient to a nurse practitioner or send a family to an urgent care clinic with more availability. For patients, it means less frustration, and for providers, it means more time to focus on care.
Such wonderful points to consider here! Huge thank you to everyone who took the time out of their day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.
How do you think advanced analytics and AI tools can be leveraged to derive actionable insights from complex healthcare datasets? Let us know over on social media, we’d love to hear from all of you!
