Background
Physician burnout, characterized by emotional exhaustion and a diminished sense of personal accomplishment, affects nearly 25% to 60% of primary care physicians (PCPs). With the ever- increasing burden of non-clinical tasks that compete with direct patient care, burnout rates are only projected to increase. Machine learning (ML), a subset of artificial intelligence where computers learn and make decisions from data, has been previously applied to healthcare to optimize processes and enhance decision-making. We hypothesized that ML-based natural language processing (NLP) could streamline non-clinical tasks by automating patient assessment form (PAF) completion for outpatient PCPs.

Methods
We developed and internally validated a ML-enabled workflow that combined note extraction from the EMR, protected health information de-identification, and NLP and federated learning to automate PAF completion. PAFs were auto-populated with ICD-10 codes matched directly to patient clinic notes. We externally validated our workflow in a real-world setting by applying it to a primary care clinic serving patients who are Medicare Advantage beneficiaries, following their annual wellness visits. PCPs then reviewed and approved completed PAFs with one click, triggering the workflow to auto-generate screening orders for at-risk patients.

Results
Our ML-enabled workflow was trained on data from 6,000 synthetic notes and achieved a ten-fold cross-validation accuracy of 96%. We further externally validated our workflow in a real-world, suburban outpatient clinic. Over five months, PAFs were completed for 179 patients, requiring only six hours of provider review time with a 99.4% approval rate. Compared to the previous year, the ML workflow decreased manual work from 30 hours across three staff to six hours for one provider, yielding overhead savings of $1,680, reimbursement incentives of $8,365, and an additional $8,480 from increased patient engagement. The system also led to a 3.2x increase in preventative and diagnostic order submissions for at-risk patients who have historically failed to complete necessary screenings.

Conclusion
Our study demonstrates the application of AI and ML in improving healthcare providers' operational efficiency and fiscal accountability, as well as enhancing patient care management, particularly for at-risk individuals needing preventative and diagnostic screenings. While future studies could expand on this preliminary data to investigate impacts on metrics like CMS Star and risk adjustment factor (RAF), the current results underline the potential for time savings, reduced healthcare costs, and improved provider satisfaction and rates of burnout. Given these promising findings, further exploration and development of ML-based workflows in healthcare are warranted.

Utilizing a ML-Enabled EMR Provider Workflow to Improve Non-Clinical Tasks

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