Article contributed by AAPACN Business Partner Profility
By Katelyn Caselli, VP of Customer Operations & Growth, MHA, OT, RAC-CT
Artificial intelligence is no longer a future concept reserved for large health systems or technology companies. It is already embedded in the daily operations of post-acute care, skilled nursing, and long-term care environments. The shift is happening now—and for clinicians, especially MDS coordinators, the question is no longer if AI will impact your role, but how.
For many of us, the initial reaction to AI is hesitation. That hesitation is understandable. Healthcare professionals are already navigating staffing shortages, regulatory pressure, reimbursement complexity, and increasing documentation demands. The idea of adding “another system” can feel overwhelming. Others worry about job security or whether technology will erode the human judgment and compassion that define clinical care.
But I challenge all of us, this moment presents a different opportunity—one that is often overlooked. Clinicians are not just end users of AI, but uniquely positioned to lead its adoption.
The concern that AI will replace clinical roles is one of the most common barriers to adoption. AI is not and should not be, designed to replace clinicians. It is designed to reduce the friction that prevents clinicians from working at the top of their license.
It supports work such as:
- Identifying documentation gaps before they become compliance issues
- Prioritizing daily workflows so critical tasks are not missed
- Surfacing reimbursement opportunities that are difficult to detect manually
- Organizing large volumes of clinical data into actionable insights
What AI cannot do—and is not intended to do—is replace clinical judgment, ethical decision-making, patient and family engagement, or interdisciplinary coordination. Even the Centers for Medicare & Medicaid Services reinforces that AI in healthcare requires human oversight. That means your role is not diminished—it is strengthened, provided you understand how to use the tools effectively.
The clinicians who embrace AI will not be replaced. They will become indispensable.
Among all roles in post-acute care, MDS coordinators are particularly well positioned to lead AI adoption. You already operate at the center of clinical documentation integrity, reimbursement accuracy, regulatory compliance, and interdisciplinary communication. You are constantly interpreting complex clinical information, managing time-sensitive assessments, identifying missing or incomplete documentation, and preparing for audits and reviews. These are exactly the areas where AI can provide immediate, tangible value.
Imagine starting your day with a system that prioritizes which residents require immediate attention, flags documentation inconsistencies before submission, highlights cases that may require earlier review, and reduces the manual burden of compiling audit materials. These capabilities are not hypothetical—they exist today. Which leads to a critical question: if you are not bringing these solutions forward, who will?
The clinicians best suited to lead AI adoption are not necessarily the most technical—they are the ones experiencing the most operational friction. If your day includes staying late to complete documentation, manually tracking deadlines and assessments, assembling records for audits under pressure, or worrying about missed opportunities or compliance gaps, then you are exactly the person who should be identifying and advocating for better tools.
Instead of waiting for leadership to introduce solutions, consider a different approach:
- Seek out AI tools that align with your workflow challenges
- Evaluate them through real clinical use—not theoretical features
- Pilot small changes within your team
- Bring structured, informed recommendations to leadership
Leadership does not always see the inefficiencies you manage daily. You do. And that insight is powerful.
For clinicians to successfully champion AI, it is essential to connect technology to measurable impact. Leadership teams are not evaluating platforms based on features—they are evaluating outcomes. When presenting AI solutions to your leadership consider their viewpoint and focus on:
- Operational efficiency: time saved on administrative and documentation tasks
- Compliance strength: more complete, accurate, and audit-ready records
- Financial performance: improved identification of reimbursement opportunities
- Workflow clarity: better prioritization and reduced missed follow-ups
- Workforce sustainability: less burnout, fewer after-hours tasks, improved job satisfaction
Equally important are the human outcomes—leaving work on time, reducing cognitive overload, creating time for patient interaction, and sustaining long-term career satisfaction. These are not soft benefits; they are critical to retention and performance.
One of the most effective ways clinicians can lead AI adoption is by defining success upfront. Before implementing any tool, ask what specific problem you are solving, how improvement will be measured, and what success looks like in your daily workflow. Meaningful metrics you already know are:
- Reduction in time spent preparing MDS or audit documentation from current
- Decrease in missed or late assessments, how many do we have now?
- Improvement in first-pass submission accuracy, what is our current percentage?
- Faster turnaround for ADR or audit responses, what is our current timeline?
Once improvements are achieved, share them. Stories combined with data are what drive broader adoption.
AI is not something happening to healthcare—it is something being shaped within it. The clinicians who step forward now will influence how these tools are used, integrated, and measured. Those who wait will eventually inherit systems they had no role in selecting or designing.
You have a choice: continue managing the same inefficiencies and hoping for change, or step forward and help define what better looks like. Be the clinician who identifies the problem, seeks out the solution, educates peers, and guides leadership decisions. Healthcare will always require human judgment, compassion, and expertise. AI does not replace those qualities—it creates the conditions for them to be used more effectively.
The future will not be led by those who resist change. It will be led by clinicians who engage with it—and bring others with them.
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