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Revolutionizing physician scheduling: The power of AI in physician practices

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How health systems can leverage artificial intelligence to reduce patient no-shows, revolutionize physician scheduling and improve patient outcomes.

physician scheduling | © Юлия Лазебная - stock.adobe.com

© Юлия Лазебная - stock.adobe.com

Since the early 2000s, artificial intelligence (AI) has raised concerns regarding its use in healthcare to manage vast amounts of patient data, ensure proper handling, and maintain robust security measures. Nevertheless, contemporary healthcare organizations are exploring ways AI can safely enhance operational efficiency and support their patient populations, with patient data emerging as one of the most valuable assets within healthcare organizations today.

Phoebe Physician Group (PPG), an affiliate of the Phoebe Putney Health System in southwest Georgia, put three years of patient data to work to solve one of its most complicated problems: An above average patient no-show rate in a rural area impacted by social determinants of health.

PPG’s administrative and physician leaders partnered with Berkeley Research Group (BRG) in 2022 to implement an AI solution to enhance practice performance around patient access, referral management and revenue cycle, and witnessed significant improvement after only a year.

From January 2023 to February 2024, PPG saw an average increase of 168 encounters per week resulting in approximately 7,800 additional encounter numbers contributing to $1.4M in net patient revenue.

The rural healthcare perception: When follow up appointment texts aren’t enough

PPG operates in a predominantly rural, 41-county service area where, over time, the patient’s perception that missing a doctor’s appointment is acceptable has developed. Reminder texts, e-mails, and phone calls have helped but have not solved the problem. At the outset of this initiative, the overall no-show rate at PPG was 12 percent — more than double the national average of 5 percent (Medical Group Management Association 2024).

PPG’s size, its clinics’ expansive markets, and the difficulties of staffing typically found in rural areas have also added to the operational challenges. Constant turnover and frequently minimal staff experience have resulted in inconsistent scheduling and double-booking and led to variable appointment confirmation practices across the organization. In response, PPG leadership decided to explore how the efficiencies of AI technology could facilitate higher patient volumes while minimizing disruptions to providers and improve the patient experience.

The AI solution: Using data and probability to revamp physician scheduling

Tool Development and Piloting MelodyMD, an AI tool developed by BRG and Trajum ML, uses machine learning to analyze years of patient visit data and predict a patient’s no-show probability. As new patients are scheduled, MelodyMD communicates with PPG’s scheduling system to analyze the patient’s no-shows and automatically creates an adjacent appointment slot if the probability of a no-show exceeds set thresholds. MelodyMD continues to learn and refine its model as new patient visit data is added.

The tool’s developers examined data points to identify those with the strongest correlation to a patient’s probability of not showing up, including patient demographics, provider specialty, appointment scheduling lead time, past history and insurance.

A key piece of developing an effective algorithm for PPG also meant ensuring double-bookings were capped per day, only patients with high no-show probability were considered for double-bookings and exclusions apply to specific clinics and appointment types. As the model was rolled out and tested, there were adjustments to the reminder process to enhance patient contact and ensure sufficient time to fill vacant slots.

Evaluating performance

Aside from the automation scheduling process decreasing patient no-shows and increasing net revenue, the exercise enabled the organization to measure performance and implement improvements at the following levels:

  • Patient access: Provides regular monitoring for utilization, no-show volumes, completed visits, cancellations, cancellations within 24 hours, and rescheduled visits.
  • Referral management: Allows regular monitoring for referral volume, patient leakage and keepage rates, and splitter and competitor volumes.
  • Provider scorecard: Provides regular monitoring for work relative value units, visit types (i.e., new or follow-up), evaluation and management coding, average visits per session, median days to schedule for new and established patients, no-show rates, and payer mix by provider.
  • Physician/advanced practice provider productivity: Provides regular monitoring for work relative value units, visit type, evaluation and management coding, and Current Procedural Terminology details by provider.
  • Nonprovider staffing: Provides regular monitoring of paid full-time equivalent pay, productivity, and overtime to ensure staffing to demand.

Conclusion

Technology is bringing new promises of advancements in patient care and improvements in the patient journey. Guided in this evolution by evidence-based decision-making, PPG and BRG are using AI to optimize operations, sustain high-quality patient care, and increase patient volumes. The PPG and BRG partnership illustrates how the active involvement of leaders, physicians, and staff in this evolution will continue to be essential in identifying novel approaches and continuously measuring their results.

Stephen H. Liebowitz, DHA, is managing consultant at Berkeley Research Group in New York City.

Matthew Robertson is chief administrative officer of Phoebe Physician Group in Albany, Georgia.

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