Coordinating care for patients with chronic conditions can be a complex undertaking. Primary care providers, specialists, care managers, and family caregivers often juggle fragmented information and competing priorities. Traditional chronic care management (CCM) programs aim to bring order to this complexity but they may rely on fragmented systems and manual communication workflows that leave room for missed follow-ups and delayed interventions.
Artificial intelligence (AI) offers a better way to manage and overcome these challenges. By supporting care teams with better data and smarter workflows, AI is helping providers deliver more connected and timely care.
The Challenge of Fragmented Care
In many CCM programs, care coordination can be slowed down by outdated communication methods and siloed data. A care manager may rely on phone calls and spreadsheets to track appointments and follow-ups, often with incomplete or outdated information. The resulting delays and oversights can lead to missed interventions or duplicative outreach.
AI-supported platforms help streamline the care coordination process by centralizing data and communication in a single, intuitive interface. These platforms aggregate patient information from EHRs, remote patient monitoring (RPM), pharmacy data, and more, offering care teams a shared source of truth. The AI then continuously scans for potential gaps, such as overdue labs, missed visits, or unusual readings, and suggests timely actions and the next steps for the right team member.
How AI Enables Smarter Teamwork
AI helps care teams work together more effectively by streamlining workflows and keeping everyone aligned on priorities.
As Daniel Tashnek, founder and CEO of Prevounce, writes in Healthcare IT Today, “AI can also be used during the care management aspect of RPM to flag missed opportunities, such as topics that have not been addressed in recent visits and discussions but should be prioritized in future encounters. By automating these checks, AI not only supports accurate and defensible billing but also lowers administrative costs by reducing the need for manual chart audits and follow-ups. The result is a more efficient workflow that keeps programs compliant and more financially sustainable.”
AI also learns from historical care team interactions, helping reduce duplication of effort and optimize outreach schedules. This results in smoother collaboration and ensures that the most at-risk patients receive timely attention.
Beyond automating checks, AI also assigns and prioritizes tasks based on urgency and role.
For example:
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If a chronic obstructive pulmonary disease (COPD) patient hasn’t completed a symptom survey and shows a drop in oxygen levels, the platform can:
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Send the case to a nurse for review
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Prompt a care coordinator to follow up with the patient
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If a hypertension patient reports consistently elevated blood pressure readings despite recent medication adjustments, the platform can:
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Flag the case for the prescribing provider to review treatment options
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Trigger patient-specific education materials about lifestyle changes and medication adherence
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Over time, AI learns from historical team interactions, helping reduce duplicated work and improving outreach schedules.
Improving Communication With Patients
AI doesn't just improve internal workflows — it also strengthens the patient experience. Natural language processing (NLP) allows platforms to tailor communication to each patient's needs, literacy level, and preferred communication method. AI tracks which messages lead to better responses and adjusts future outreach automatically. This is particularly useful for multilingual populations or those with complex care needs. The result is more readable, relevant outreach that encourages patients to respond and stay engaged.
AI can also deliver subtle prompts or “nudges”
that encourage patients to take specific health actions. These cues are informed by behavioral insights and continuously refined by algorithms that learn what works best for each individual. When these nudges are aligned with a patient’s goals and past behavior, patients are more likely to stay engaged and feel supported in managing their care.
Real-World Impact of AI-Enhanced Coordination
The outcomes from AI-supported care coordination are promising. Studies have found that these platforms reduce missed follow-ups and improve the consistency of communication across care teams.
A recent perspective in the Journal of Medical Internet Research emphasizes that strategically leveraging AI allows healthcare systems to build a more connected and responsive digital information ecosystem. By improving data sharing and interoperability, AI helps care teams coordinate more effectively and deliver better outcomes.
Similarly, research highlights the need for continued investment in AI technologies to strengthen patient care pathways. The study advocates for a thoughtful integration of AI into healthcare practices, noting that its full potential lies in supporting smarter care coordination and improving patient outcomes through better data-driven decision-making.
The current literature shows that AI-supported care coordination is a path to better outcomes, clearer communication, and more efficient chronic care management. As healthcare organizations adopt AI and integrate it into existing workflows, care teams will be better equipped to close care gaps and deliver higher-quality, data-driven care.
Best Practices for Implementing AI in Care Coordination
To successfully implement AI in care coordination, practices must prioritize interoperability and user training. It’s important that platforms integrate seamlessly with existing EHRs and give staff the tools to monitor and adjust workflows. Teams should also routinely evaluate performance metrics to ensure that AI recommendations align with clinical goals.
Importantly, human oversight must remain central. AI can prioritize and suggest actions, but it’s the care team that interprets insights and decides on next steps. Care teams need visibility and final judgment, especially for high-acuity patients. AI nudges can then help fill gaps where constant monitoring isn’t feasible. As Tashnek explains in Healthcare IT Today, “... for medium- and lower-risk patients who couldn’t feasibly receive intensive human monitoring given the availability of personnel and costs, the question shifts from ‘Is AI better than a human?’ to ‘Is AI better than nothing?’ At a population scale, having some intelligent monitoring is clearly preferable to no monitoring at all.”
The Future of AI for Chronic Care Coordination
AI-supported care coordination is changing how chronic care teams manage patients and close care gaps. By integrating AI into existing CCM workflows, practices can collaborate more effectively and use patient data more efficiently.
Healthcare organizations that adopt AI thoughtfully today will be better positioned to deliver connected, patient-centered care and scale chronic care management programs in the future.
Want to see how AI can improve care coordination in your CCM program? Contact Prevounce Health for a demo of our AI-integrated chronic care platform.