Persistent staffing challenges in medical practice offices were the impetus for an objective, data-driven tool to adjust staffing recommendations at MU Health Care in Columbia, Mo., as detailed by Tyler Adams, MBA, Manager of Business Intelligence, and Athena Beaudoin, MHA, Director of Strategic Planning, in their session at the 2025 HIMSS Global Conference.
This initiative was designed to more accurately capture the breadth of clinical and administrative tasks in ambulatory settings, moving beyond traditional metrics that have historically proven inadequate.
Framing the staffing crisis
Beaudoin opened the presentation by highlighting a widespread issue affecting practices nationwide: the inadequacy of current staffing practices. The traditional staffing model, based largely on worked hours per patient visit, was criticized as severely limited.
“It only takes into account the number of patients coming in, not all the other work our nursing and clinical staff perform,” she explained. These tasks, often unseen or unacknowledged in staffing formulas, include managing patient portal messages, handling prescription refills, and performing triage — all vital yet time-consuming responsibilities that directly affect clinician workload and patient care quality.
Beaudoin further detailed the administrative burdens clinicians face, citing internal research indicating that physicians at MU Health Care spend roughly “0.24 FTE per 1.0 physician FTE” on tasks that should ideally be handled by other clinical staff. She emphasized the human element behind these figures by sharing candid clinician feedback from an internal survey:
“Could someone please help me answer all the questions patients just randomly fire off to me in the patient portal? … Truly not a physician function. Please help me provide excellent care by providing more support.”
Developing a staffing matrix tool
Recognizing these widespread inefficiencies, MU Health Care developed a comprehensive staffing tool aimed at accurately capturing and quantifying the actual work clinical staff perform. Adams explained the intentional process of creating the staffing matrix, emphasizing the importance of assembling a multidisciplinary team combining operational expertise with sophisticated data analytics.
The development team meticulously identified and quantified clinic tasks through direct observation and time studies, ensuring realistic and actionable data. The staffing model, powered by an advanced SQL-driven “galaxy schema,” provided dynamic, precise staffing recommendations based on specific clinical settings and tasks. Adams described this innovative approach as empowering clinic leaders.
“The strength of this tool is that it empowers clinic leaders to clearly articulate and justify staffing needs using objective data,” he explained.
Technical components
- SQL tables extract: Only including relevant data with dynamic date filtering for automated refresh
- Relational data model: Galaxy Schema model with location, date, and job code grouping as main dictionary tables
- DAX measure buildout: Standardized conversion of minutes to FTE and percentage selection to total FTE need
- Creation of UX: User-friendly experience with few clicks and easy comprehension
Implementation and pilot results
The staffing model was initially implemented in three primary care pilot clinics: Family medicine, internal medicine, and pediatrics. One standout example from the family medicine clinic involved adding dedicated triage nurses, additional clinical nurses, and patient service representatives (PSRs). These changes dramatically streamlined clinic workflows, including enhancing copay collection processes, reducing no-show rates through predictive analytics, and enabling nurses to lead essential clinical tasks such as transitional care management and annual wellness visits.
These interventions resulted in notable quality improvements, particularly reflected in HealtheRegistries metrics, which improved by more than 5% within a year. Beaudoin explained the significance of this achievement, saying, “By reallocating tasks to appropriate staff, we significantly improved our quality metrics.” Additionally, nurses reported increased satisfaction by working closer to the top of their license, resulting in reduced turnover and enhanced job satisfaction.
Financial outcomes were equally compelling. Adams provided detailed evidence of dramatic shifts in financial performance, transitioning clinics from financial liabilities into sustainable, profitable entities.
“Our clinic visit volume increased dramatically, our net revenue per visit rose, and, perhaps most importantly, our direct cost per visit decreased due to greater operational efficiencies,” Adams explained. This was a critical factor in securing continued support from senior leadership, who initially expressed concern over increased staffing costs.
A profound impact on physician workload was also documented. The implementation significantly reduced the volume of after-hours portal messages physicians had to manage — commonly referred to as “pajama time.” Adams shared specific data showing that these messages decreased from nearly 600 per month to consistently fewer than 200. This improvement directly influenced clinician well-being and work-life balance, further reinforcing the value of the staffing model.
Addressing executive concerns
Beaudoin emphasized the importance of addressing executive hesitations proactively and transparently, especially regarding financial impacts.
“Always address executive concerns upfront, particularly financial impacts, to demonstrate the value clearly and objectively,” she advised. Clear communication and data-driven justifications were instrumental in obtaining leadership buy-in and approval for ongoing implementation and expansion of the staffing model.
Looking forward, Adams and Beaudoin highlighted plans to scale the model beyond primary care settings, potentially extending into inpatient units and non-clinical roles. The goal is to build a robust, scalable approach to staffing across the entire organization, leveraging the proven benefits of data-driven decision-making.
The presentation concluded with a broader message for healthcare leaders: strategic, data-driven staffing is not only feasible but essential for modern healthcare environments. As healthcare organizations continue to confront staffing shortages, clinician burnout, and economic pressures, MU Health Care’s initiative offers a clear example of how integrating sophisticated analytics and operational insight can lead to significant improvements in staff satisfaction, patient outcomes, and organizational sustainability.
