AI-Assisted Automation in Principal Care Management: Change or Break?

Principal Care Management (PCM) already requires strict record-keeping, coordination, and ongoing involvement. Practices are facing an increasing workload as they attempt to implement PCM for a larger patient base. Artificial intelligence (AI) and automation have the potential to revolutionize the field. But is it prepared? In this blog, we talk about how AI can help PCM, what risks and mistakes to avoid, and how to test these tools carefully.

The Promise of AI in PCM

Making and changing care plans

Generative models, like LLMs, can take structured data (like vitals, labs, and previous notes) and make draft plan updates (like interventions and next steps) for clinicians to look over.

This cuts down on repetitive writing and ensures consistency, especially when managing disease-specific plans through PCM CPT codes.

AI-Assisted Automation in Principal Care Management: Change or Break?


Putting together patient interactions

AI can make short summaries (like symptoms reported, actions taken, and red flags) to add to the record from phone calls, portal messages, and remote patient monitoring (RPM) alerts.

These summaries facilitate communication between doctors and care managers and improve coordination with other chronic care management (CCM) or transitional care management (TCM) programs.

Prioritizing alerts and finding risks

Automation can look through monitoring data or alerts and rank which patients need immediate care based on learned patterns (for example, a sudden weight gain or abnormal lab results).

This stops alert fatigue and helps enable proactive intervention — particularly when paired with remote blood pressure monitoring workflows that detect early risk trends.

Drafting a message or communication

AI can assist you in crafting customized messages for patients, including reminders, encouragement, education, and follow-up questions.

Another benefit is that the tone is always the same and the care plan is consistently followed.

Support for audits and compliance

Tools could flag missing timestamps, not enough documentation, or parts needed for PCM billing, such as changes to medications.

This lowers the chance of audits or denials and supports accuracy for CPT documentation under Medicare’s Advanced Primary Care Management (APCM) standards.

Main Risks and Problems

  • Accuracy and Clinical Safety: AI-generated content can make things up, get them wrong, or leave out important details. Errors in a care plan or a message could have an effect on the patient's health.

  • Audit/Compliance Risk: The paperwork for PCM billing must meet the requirements of the payer. Claims may be denied if AI drafts don't follow the required structure, such as having clear time logs and assigning responsibility.

  • Data Privacy and Security: Using AI tools with patient data brings up issues of privacy, HIPAA, and security.

  • Clinician Trust and Adoption: Providers may not want to use tools that seem unclear or untrustworthy. It is important to be able to change or override AI output, but doing so too much takes away from the benefit.

  • Liability and Oversight: Who is liable if AI makes a wrong suggestion? There must be clear lines and human supervision.

  • Integration with existing EHR/PCM systems: The AI/automation tool must work well with the systems that are already in place. Changing contexts makes efficiency gains less effective.

  • Bias and Fairness Issues: If AI models are trained on biased datasets, they might not serve some groups as well as they should or misinterpret context for marginalized groups.

How to Pilot AI in PCM: A Stepwise Approach with Key Safeguards

  1. Only Help with Drafts — AI can suggest changes to care plans or message drafts, but a person must verify and approve them. Always show where something came from, let people make changes, and keep track of who approved it.

  2. Help with Triaging Alerts — Let AI decide which alerts or trends are most important, but send the decision to a person. Monitoring performance involves keeping a close watch on instances of false positives and false negatives.

  3. Quality Assurance and Audit of Documentation Flagging — AI checks for completeness and missing items in notes or checklists against payer rules. Keep a rule engine overlay to verify against the most recent PCM billing requirements.

  4. Limited Scope of Gradual Autonomy — AI may push content with little human editing for low-risk tasks like routine reminders and non-critical check-ins. Maintain a limited scope, monitor results carefully, and revisit any areas where errors may occur.

A successful pilot would set clear goals such as error rates, time saved per patient, clinician satisfaction, billing denials, and patient impact.

Examples and New Uses

A PCM program in cardiology uses AI to make a monthly update to the CHF care plan. This includes weight trends, lab results, and symptoms, and it suggests changing the diuretic dose. The doctor reviews, makes changes, and signs off on it.

An AI tool keeps track of the vital signs of many PCM patients and alerts care coordinators to the top five who are at the highest risk. This lets them focus their efforts.

After a coaching call, the system writes down what was said and makes summary notes. The clinician then quickly checks and logs these notes.

Even though it's still in its early stages, early published work in related case management (like diabetes) shows that AI can predict complications with about 83% accuracy using multimodal data.
 (Source: arXiv.org)

Furthermore, generative models are being used more and more to write clinical notes and make administrative work easier.
 (Source: arXiv.org)

Advice for Businesses

  • Start with small, unimportant tasks, like writing messages instead of making care decisions.

  • Always keep a human in the loop; AI should help, not take over.

  • Make sure that every suggestion can be traced back to its source (what data source, what prompt).

  • Continuously assess and retrain the AI system to monitor performance, establish feedback loops, and identify potential biases.

  • Get clinicians involved early by co-designing templates, reviewing outputs, and building trust.

  • Tools need to be part of the EHR/PCM workflow, not just a separate “AI silo.”

  • Don't rely too much on it; keep the ability to think critically and manually override it.

Conclusion

AI and automation are excellent ways to make PCM more scalable, efficient, and long-lasting. But the way forward is fragile: mistakes in clinical accuracy, record-keeping, or trust can set back progress.

It will be crucial for pilots to be careful, for people to keep a close eye on them, for measurements to be accurate, and for everything to be in line with compliance rules. AI-augmented PCM could be what sets apart PCM programs that do well from those that never get off the ground after 2025.

For providers ready to bridge automation with value-based care, explore HealthArc’s Principal Care Management platform and discover how it seamlessly integrates AI, compliance, and patient engagement to future-proof your care workflows.

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