Care Plan Management Market Map: Can AI cut cancelled procedures and readmissions?
This is part of Elion’s weekly market map series where we break down critical vendor categories and the key players in them. For more, become a member and sign up for our email here.
Nearly 8% of surgical cancellations are due to patient-related reasons like improper preparation, and those cancelled surgeries can result in losses of anywhere from $2,000-$10,000 each. Similarly, inadequate care coordination—such as care transition management—has been estimated to account for $27-$78 billion in annual wasteful spending. Medicare data suggest eliminating just 10% of avoidable readmissions could save roughly $1 billion in Medicare costs.
This creates a massive opportunity for health systems that can improve on these metrics. Care plan management solutions aim to assist patients in better following pre- and post-care instructions, resulting in fewer cancelled procedures, fewer readmissions, and better outcomes.
Defining Care Plan Management Tools
Care plan management tools enable providers to deliver personalized care plans for maternal care, pre- and post-operative care, and transitions of care. These platforms offer an array of tools including customizable templates for care plans, resources for patient education such as informative guides and instructional videos, and features for direct virtual engagement with patients. They facilitate routine check-ins and deploy surveys to monitor patient adherence and enhance overall patient engagement in care processes. Some also escalate care concerns to the broader care team as needed.
While these tools are specifically built for the design and management of individual care plans, there is often overlap with categories like care team coordination, patient education, patient communications, remote patient monitoring, and medication adherence. Care plan management solutions are differentiated from care management tools in that they help healthcare organizations build, assign, and maintain detailed plans of care, often from a clinical workflow standpoint, as opposed to broader and more longitudinal tooling and dashboards used by care managers, as in value-based care contexts.
Evolution of Care Plan Management Tools
In the past, care plan management was merely an intrinsic part of the patient-provider relationship. But as the rate of chronic disease has grown along with the burden on an already strained health system, the ability for individual providers to routinely check in with each patient has dwindled to near zero. Simultaneously, health records moved to EHRs and communications have increasingly gone digital, such that consumer expectations have shifted toward digital as the first and most convenient means of communication.
Contemporary care plan management tools offer several key features:
Creation of a personalized care plan based on a library of templates.
Patient-facing UI, allowing patients to access and engage with care plans directly.
Automated follow-up and tracking, which may include pre-designed follow-up messages, surveys, or AI agent engagement.
Patient triage and escalation in the event that follow-up workflows determine intervention is merited.
Vendor Landscape and Functional Differentiation
Care plan management products differentiate based on a variety of factors:
Communication: Vendors may use some combination of pre-built automated messaging, AI agents, and 1:1 human messaging. They differentiate based on the quality of communication with the patient as well as clinical reliability of AI (e.g. the ability to accurately answer questions and appropriately triage concerns).
Library: Most vendors’ pre-built care plan libraries focus on specific specialties, such as Adhera Health for pediatrics and BabyScripts for maternity care. Some, though, like Memora Health (acquired by Commure), aim to offer a breadth of care plans across a variety of use-cases, providing a “one stop shop” across the health system.
Customizability: While vendors typically offer a library of pre-built care plans, plans can also be customized to the patient to differing degrees.
Integration: Particularly as interoperability grows across healthcare more generally, integration with the EHR, patient devices, and care coordination tools may allow care plan management solutions to become an essential part of clinical workflows.
Future Directions in Care Plan Management
The future of care plan management will increasingly be shaped by AI. A few changes we foresee include:
Moving from static but customizable templates to wholly original care plans automatically designed through clinical AI. This involves shifting away from a limited menu of pre-set care plans toward dynamically generated plans that draw on the latest medical guidelines, real-world data, and evidence-based research. The AI would adjust interventions in real time based on a patient’s evolving condition or new clinical insights, ensuring each plan is personalized to factors like lifestyle, comorbidities, and social determinants of health. Clinicians would still oversee and validate these AI-driven recommendations, maintaining transparency and accountability throughout.
More input from patient devices and video monitoring tools to ensure patient care is done correctly. By leveraging wearable sensors and connected devices, providers could monitor vital signs, activity levels, or adherence to medication schedules around the clock. Video analysis could offer feedback on the correctness of rehabilitative exercises or daily tasks, alerting patients if they need to correct their form. This real-time feedback loop empowers patients to engage more actively in their care, while also enabling remote clinical oversight and early detection of issues before they escalate.
Improved clinical Q&A as GenAI tools become more sophisticated. Large language models, integrated securely with patient data (when proper consents are obtained), can provide immediate, context-aware responses to a variety of patient inquiries. This reduces the burden on clinical staff for routine questions and helps patients feel supported and informed. As these systems mature and gain clinician trust, they may take on more advanced triage functions, although clear guardrails and escalation pathways remain critical to avoid misinformation or missed diagnoses.
Improved data collection at scale to design better dynamic care plans. Aggregating anonymized data from patients across diverse demographics and treatment pathways would form a rich repository of real-world evidence, enabling continuous refinement of care protocols. Machine learning models could detect trends—such as which interventions achieve the best results for certain patient profiles—and automatically integrate these insights into future recommendations. By harnessing predictive analytics, healthcare teams could proactively identify patients at higher risk of complications or non-adherence, allowing for timely interventions that improve outcomes and reduce costs.