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How Does AI Improve Care Coordination?

Healthcare AI analyzes and interprets large and often disparate sets of patient data to help healthcare professionals optimally manage patient health, including anticipatory actions to circumvent adverse events. Healthcare AI systems review hundreds of thousands of pieces of data to determine patients at risk of unplanned hospital readmissions.

Certain AI systems can review data and identify patients at risk of adverse events, for example, patient no-shows, change in a patient’s condition or health status, change in a patient’s treatment or discharge from a hospital.

The primary objective of AI in healthcare is to provide healthcare workers, in real-time, the most pertinent information regarding a patient, and to enable the healthcare worker to interact with the patient in a timely manner.

Why Readmissions Remain a Challenge

A patient might feel better after being discharged, but recovery continues. Gaps in follow-ups, missed doses, and communication issues can result in another stay in the hospital.

 The Hospital Readmissions Reduction Program by CMS in 2026 continues to collect data on heart failure, COPD, and pneumonia readmissions. Hospitals with high readmissions will receive drops of up to 3% in their Medicare payments.

 CMS – Hospital Readmissions Reduction Program AI helps to fill those gaps by connecting data on patients and anticipating risks, as well as prompting follow-up visits. This helps health services to shift from providing care that is merely responsive, to care that is proactive.

Connecting Care Teams with Real-Time Data

Healthcare teams already have a lot of data on their patients, but the main issue is the time it takes to connect and transform that data into actionable insights. AI helps bring traditionally siloed data into a framework that healthcare teams can action faster and in a more intelligent manner.

What AI Can Connect

• EHR Information: Documentation such as patient history, clinical notes, diagnoses, and treatment plans.
• Medication Changes: Prescription, dosage, and medication risk changes.
• Lab Results: Significant changes in the results of important tests that require emerging care.
• Appointment History: Missed, upcoming, and follow up appointments that are overdue.
• Previous Admissions: Past hospitalizations and admissions leading to persistent readmissions.
• Patient Symptoms: Changes reported that may imply a deterioration of the patient’s condition.
• Remote Monitoring: Data collected from connected devices and remote tools used to monitor a patient at home.
• Claims & Utilization: Healthcare services utilized and the behavioral patterns of patients.

Why Real-Time Data Matters

ONC reported that 76% of U.S. hospitals engaged in all four major interoperability activities in In 2025, ONC reported that 76% of the U.S. hospitals were engaged in electronic information exchange during each of the four major interoperability activities. ONC also reported improvements in connected healthcare information.

AI can locate risks and changes, and direct information to the best care team.

The end result is a better patient care experience. There is less searching for data, and care coordination is faster and more efficient.

AI for Follow-Up and Patient Monitoring

Leaving the hospital is just the first step of the recovery process. AI could support healthcare staff in staying in touch with patients during the most critical and vulnerable time of recovery.

How AI Makes Follow-Up Smarter

• Continuous Patient Monitoring: Using AI in between appointments lets systems constantly track symptoms and health.
• Early Risk Detection: AI can predict which patients are at an increased risk of developing complications.
• Medication Tracking: AI is capable of detecting missed doses, suspicious dose changes, and potential non-adherence.
• Remote Monitoring: Data collected from smart devices can provide valuable insights to a care team beyond the discharge of a patient.
• Smart Alerts: AI can significantly reduce the workforce by eliminating the need for clinicians to review tons of data.
• Personalized Follow-Up: AI can detect patient risk and the level of risk due to their individual conditions and prioritize follow up.

The Human + AI Advantage

Clinical teams must remain in control over patient care. AI should strengthen the way clinical teams work, not automate their work with human oversight. Alerts must be reviewed, clinical decisions should be made, and communication should be performed where needed to provide care.The opportunity gap exists for technology companies that begin to design solutions with prediction, monitoring, workflow, and human action in mind–not just another dashboard.The objective is straightforward: to detect and respond to anomalies along the patient care pathway post-discharge more effectively and efficiently to keep patients on their therapeutic journey.

Improving Care Transitions

Transitioning to the next phase of care can be challenging for patients. There are gaps in care when a patient is discharged to another location, such as home, rehabilitation, or primary care.

AI has the potential to help overcome these challenges by assisting care teams in managing unaddressed tasks by tracking medications, medical concerns, and following up on the patient. AI can even help recommend when care managers should reach out to patients following discharge.

Transitional care in 2026 is starting to use AI to assist providers with discharge planning and follow up care, addressing gaps in care through interoperability, and helping to navigate patients among disparate healthcare systems. 2026 AI Transitional Care Review

The aim is to get care teams to think of discharge not as the end of the relationship with their patient, but as the start of a journey of ongoing, seamlessly integrated care.

Challenges in Coordinated Care

AI can make care coordination smarter, but it isn’t a plug-and-play solution. Healthcare organizations need the right data, systems, workflows, and safeguards to make it work effectively.

• Data Quality: Incorrect or old information on patients leads to incorrect assumptions for AI. The quality of the data is of utmost importance. 
• Interoperability: AI has to communicate with different EHRs and other software. In 2025, 76% of hospitals in the U.S. participated in the four defined interoperability activities. 
• System Complexity: Due to the lack of interconnectedness of various systems, in 2025, an average of 4.1 methods were used to request patient records from outside the hospital. 
• AI Adoption: The use of predictive AI in U.S. hospitals increased from 66% in 2023 to 71% in 2024. Rural and smaller hospitals lag behind. 
• Alert Fatigue: TA system may generate a multitude of alerts, and while covering all alerts may seem prudent, it is more beneficial to alert on important risks. 
• Privacy & Security: Healthcare organizations must protect sensitive patient information. In April 2026, HHS reported four ransomware settlements affecting more than 427,000 individuals, highlighting the importance of strong security.

When incorporating AI into different areas of healthcare, focus should be on designing a integrated and secure system that allows staff to proactally and effectively engage with patients.

Benefits for Patients and Providers

AI-powered care coordination helps make healthcare faster, smarter, and more connected, creating benefits for both patients and care teams.

For patients, AI can support clearer discharge instructions, timely follow-ups, continuous monitoring, and earlier detection of potential health concerns.

For providers, AI reduces manual data review and helps teams quickly identify patients who need attention, allowing them to spend more time on meaningful patient care.

For hospital leaders, better coordination can improve workflows, support readmission reduction, and help teams use limited clinical resources more effectively.

For healthcare technology companies, AI opens opportunities to build solutions around predictive analytics, remote monitoring, patient engagement, interoperability, and workflow automation.

The bigger benefit is simple: better-connected teams can deliver better-connected care

Future of Intelligent Care Management

Healthcare is moving from reactive treatment to proactive, AI-powered care management. Instead of waiting for complications, AI can help teams identify risks earlier and respond faster.

The shift is already visible. According to ONC, 71% of U.S. hospitals used predictive AI integrated into EHRs in 2024, up from 66% in 2023. (healthit.gov)

Future care platforms can combine predictive analytics, remote monitoring, EHR data, and workflow automation to create more personalized patient journeys.

AI can also support smarter follow-ups, prioritize high-risk patients, track recovery, and automate routine coordination tasks while clinicians remain responsible for important decisions.

The goal is simple: predict earlier, coordinate smarter, and keep patients connected throughout their care journey.

Key Takeaways

Using AI for care coordination enables organizations to structure their workflows to recognize patient risks, and provide support and care beyond the conventional setting.

• Predict Risks Earlier: AI technologies help recognize high-risk patients to prevent unnecessary readmissions. 
• Connect Care Teams: Relevant patient information help care teams, both internal and external, coordinate and collaborate toward a unified goal of patient care. 
• Smarter Follow-Ups: AI has the potential to support patients in recovering by helping them comply with follow-up care. 
• Monitor Patient Recovery: Remediate situational care gaps by using technology to assess a patient’s condition. 
• Act Before Problems Grow: Alert and notify care teams to proactively resolve potentially worsening conditions for patients. 
• Improve Healthcare Efficiency: Automation helps solve care coordination’s most persistent challenges. 
• Deliver Personalized Care: AI can support care plans based on individual patient risks and needs.
• Keep Humans in Control: AI provides insights while healthcare professionals remain responsible for clinical decisions.

Ultimately, AI turns fragmented patient information into coordinated action—helping healthcare teams deliver better care at the right time.

Conclusion: Better Coordination Leads to Better Outcomes

Readmissions to hospitals occur as a result of a variety of issues related to the coordination of a patient’s care after they are discharged. Common issues such as missed follow up appointments, and break downs in communication can lead to issues in the timely and accurate delivery of a patient’s medications. Care teams often have limited tools to prepare for these risks, and as a result are often disadvantaged in their efforts to facilitate seamless care for their patients.

One potential solution to this problem is the increased use of artificial intelligence (AI) within healthcare. AI can help care teams to identify breakdowns in care. This is especially useful for providing care and support to patients outside of the hospital. This technology can help care teams to prepare for the delivery of care beyond the walls of the hospital.

The future of healthcare will focus on the prevention of adverse events for patients, as opposed to simply reacting to events once they occur. This will require a shift in how the healthcare system views risk. Healthcare will need to shift its efforts toward greater integration and the delivery of seamless care.