As we strive to improve clinical decison making and patient outcomes – predictive analytics are a wonderful tool to help us in that goal. But you can’t just decide to throw predictive analytics into your organization; it’s a tool that needs a lot of support. We reached out to our beautiful Healthcare IT Today Community to ask — how can healthcare IT systems support predictive analytics to improve clinical decision making and patient outcomes? The following is what they had to share.
Sandra Johnson, Senior Vice President, Client Services at CliniComp
Predictive analytics have the power to move healthcare from being reactive to proactive. Organizations that prioritize real-time interoperability with clean, normalized, and accessible data will be best positioned to harness that data and AI effectively. When clinical teams have access to real-time data and AI-driven predictions, they can anticipate complications, optimize treatment plans, and ultimately improve patient outcomes.
Justin Schrager, MD, Founder and Chief Medical Officer at Vital
Healthcare IT should not only serve clinicians, it should and can help patients, too. Predictive analytics can personalize patient journeys and fill the communication gaps that exist between doctor and nurse visits by explaining test results, assigning education, and simplifying documentation.
Patient-centered AI solutions should focus on the real problems that patients face: depersonalization, confusion, and being left out of medical decision-making. The current crop of AI technologies, LLMs, agentic systems, etc., has tremendous potential to democratize the healthcare experience for patients; let’s lean into that instead of operational AI, CDS, and scheduling chatbots.
Abhi Gupta, Co-Founder and CEO at Fold Health
Predictive analytics and insights matter most when impacting care in real time. With AI, capabilities once reserved for mega-systems now reach small practices and first-time risk bearers. The mandate for healthcare IT is to close the loop from prediction to execution: unify signals (EHR, claims, pharmacy, patient-reported), generate explainable risk insights, and route them as protocol-driven next-best actions at the point of care and across the team. Those insights should trigger work, scheduling, orders/referrals, outreach, tasking, automatically, with humans in the loop for exceptions and every step instrumented for learning. That’s the leap healthcare IT needs to enable, from ‘data to insights’ to ‘signal to service’, improving decisions and outcomes while right-sizing the operational burden for organizations of any size.
John Weir, Managing Director at BluePath Health
Predictive analytics relies on the integration of various data sources, from EHRs to patient-reported data from wearables. This makes data accuracy, cleanliness, and the ability to match data across those data sets effectively and consistently a critical step in the process. Healthcare IT systems supporting the normalization of data are an unseen yet vital part in making the rendered data useful and trustworthy when applying machine learning and AI algorithms against it to ensure that clinical applications are complete, appropriate, and can be trusted as support in decision-making.
As the industry continues to evolve, likely faster than ever before, processes such as data governance and the build-out of predictive models that together offer accuracy, consider health care equity, and can be trusted by clinicians and patients as they address their patient population, will continue to be a linchpin of the health care system.
Virginia Pfeifer, Senior Director of Analytics at Optum
Predictive analytics allows us to look forward and make better decisions, whether it’s estimating the number of readmissions from weekend discharges or the impact of improved sepsis protocols. These insights empower clinicians to adjust workflows, intervene earlier, and ultimately improve outcomes. And smarter data means clinicians can focus on what matters most: delivering care that truly makes a difference.
Ritesh Ramesh, Chief Executive Officer at MDaudit
Predictive analytics depends on the ability of IT systems to integrate and process diverse datasets (clinical, financial, and third-party) in a way that enables real-time insights. Modern cloud-based platforms allow organizations to run advanced algorithms and large language models at scale. Importantly, this includes unstructured data, which holds the key to much of the untapped value in healthcare.
This ability creates opportunities not only to anticipate clinical risks but also to predict denials, identify revenue leakage, and optimize resource allocation. By combining clinical and financial perspectives, healthcare IT systems enable decision-makers to act earlier and ultimately improve patient outcomes while promoting a healthy bottom line.
Tommy Thompson, Senior Manager, Technical Product Development at Solventum Health Information Solutions
Healthcare IT companies can support predictive analytics by providing and supporting the data needed for these efforts. A key example is reducing patient readmissions, which can not only save time and resources but also improve patient outcomes. To achieve this, we first need to proactively analyze data to identify high-risk patients. This includes both clinical data, such as diagnoses, length of stay, past medical history, and lab results, and data related to items such as Social Determinants of Health (SDoH). By using this information to calculate a readmission risk score for each patient, we can move from reactive to proactive care. The readmission risk score can be used to:
- Tailor Discharge Plans: Customize patient discharge plans and provide additional education to patients and their families on follow-up care and appointment scheduling
- Coordinate Care: Proactively notify social workers, care coordinators, or home health agencies to ensure the patient has necessary support, such as transportation assistance or home care
- Implement Remote Monitoring: Identify patients who could benefit from remote monitoring devices for vital signs like blood pressure and heart rate
These devices can also alert care providers to intervene before a readmission becomes necessary. Data governance is an essential, often misunderstood strategy for managing data. It provides a framework of rules, policies, and processes to ensure your data is clean, consistent, and useful. To implement an effective data governance strategy, you should focus on: five key steps:
- Establish a Framework: Start by defining a clear data governance framework. This includes establishing specific roles and responsibilities for data ownership, management, and quality. For example, determine who is responsible for managing data related to a specific area and set rules for accessing sensitive information.
- Align with Business Goals: Ensure your data strategy aligns with your business goals. You need to know what you want to achieve, whether it’s predicting readmissions, improving patient outcomes, increasing operational efficiency, or something entirely different. This ensures that the data you collect directly contributes to solving real-world business problems.
- Educate Stakeholders: Educate your team, including providers, leaders, staff, and others on proper data documentation. They need to understand not only how to enter data accurately but also why this accuracy is crucial for achieving your goals. This step is vital for improving data quality at its source.
- Ensure Data Quality: Maintain clean and consistent data by standardizing formats and terminology. This is especially important for inconsistent data, such as physician names, where you have multiple Dr. John Smiths; you need to make each one separately identifiable in the data. Clean up this data to ensure you can accurately identify who performed a procedure or was the attending physician, similar to the reconciliation activities for a Master Patient Index.
- Secure Your Infrastructure: Finally, secure your data infrastructure. Put the necessary security and privacy rules and measures in place to protect your data. This ensures your data is not only usable but also safe from unauthorized access or breaches.
Dean Slawson, Vice President, Advanced Technology at PointClickCare
The power of predictive analytics in healthcare is that it can help providers anticipate patient needs before they arise. When healthcare IT systems bring data together in a connected and structured way, providers are able to use that data to improve patient outcomes. The goal is to build solutions that provide care teams with early visibility, so they can reduce hospital readmissions and better manage patient transitions across different care settings.
Mohan Giridharadas, Founder and CEO at LeanTaaS
Health systems today are stuck in a rush-hour-like gridlock every single day. Adding capacity used to be an effective way of easing the gridlock; however, tighter financial margins and staff shortages have made it more difficult to continue adding capacity. Therefore, healthcare has to optimize the capacity that it already has in place in order to improve the flow of patients. Predictive analytics can help anticipate bottlenecks and generate recommendations to reroute patient flow or move resources to where they are most needed. By unlocking capacity and increasing velocity, staff know what’s coming, patients are seen and treated sooner, delays are reduced, and clinicians can focus their time on delivering care, all of which supports better provider and patient experiences and improved outcomes.
Jordan Ruch, Chief Information Officer at AtlantiCare
Predictive analytics becomes truly transformative when it is seamlessly embedded into clinical workflows, surfacing the right insight at the right moment to guide clinical decisions, reduce risk, and ultimately improve patient outcomes. The real value lies in making complex data feel simple, usable, and timely for the people delivering care.
Simos Kedikoglou, President and COO at Anumana
Healthcare IT systems provide the infrastructure that can either enable or hinder the implementation of predictive analytics. With existing standards and connections already in place, the opportunity now to integrate AI tools with simplicity in mind to focus on optimal patient outcomes with personalized clinical workflows. By moving beyond routine maintenance and upgrades to actively adopting technologies, there is an opportunity to leverage established workflows that can unlock earlier detection, guide personalized treatment decisions, optimize care coordination, and patient outcomes. This approach ensures insights are both reliable and actionable, ultimately improving patient outcomes while enabling more effective resource allocation and long-term innovation.
Salvatore Viscomi, Co-Founder and CEO at Carna Health
Emerging predictive technologies and advanced data capabilities are shaping the future of more personalized treatments for the patients that need them most. Access to diverse patient data not only enhances health equity but it enables more precise interventions and expands access to tailored care across different demographics and regions around the globe.
The growth of remote monitoring technologies and wearable devices that capture real-time health data can empower patients, especially in underserved areas, to actively manage their health, while simultaneously easing clinician workloads. Enhanced predictive analytics are improving clinical decision-making and patient outcomes, as seen in recent chronic kidney disease (CKD) screening programs combined with preventative screenings. By harnessing comprehensive patient data through these platforms and remote patient monitoring technologies, we can generate valuable insights in regions, such as Bermuda and Cameroon, where reliable patient data has historically been scarce. AI-driven upskilling platforms help extend healthcare capacity by supporting primary care clinicians and nurses in managing complex cases, especially in regions with a shortage of specialists, where specialist referrals may not be readily available.
Once captured, the data needs to be stored and managed in a centralized system that spans the entire care continuum, which can be especially challenging in rural areas or under-resourced communities. In these communities, healthcare organizations often struggle with data fragmentation caused by inconsistent formats, varying standards, and siloed systems, making EHR integration complicated. Ensuring seamless interoperability with existing EHRs and health systems while prioritizing connectivity and ease of use for both clinicians and patients supports real-time diagnostics and treatment implementation, bringing care closer to patients.
So many great answers to think about 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 healthcare IT systems can support predictive analytics to improve clinical decision making and patient outcomes? Let us know over on social media, we’d love to hear from all of you!
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