John M. McKeever Executive Vice President
John McKeever works with clients to develop market insight to deliver exceptional customer experiences, revitalize their brands, or enter new markets. He has worked with...
Physician burnout is increasingly problematic, and healthcare leaders recognize a need to balance organizational revenue requirements with accommodating physician preferences. This is particularly relevant when optimizing physician scheduling templates. Patients expect scheduling availability will go elsewhere if the lag time is too long, yet the challenge must be addressed in a manner that balances business requirements, informal scheduling rules, and physician burnout.
So how can you meet organizational revenue needs while addressing physician preferences? Endeavor has created an approach to creating an physician schedule optimization model that assembles and predicts the impact of schedule changes on patient revenue against a series of constraints and variables.
Our optimization model uses data science to align patient, organization, and physician interests for strategy and clinical operations decision-making. Through assembling and predicting the impact of schedule changes on patient revenue against a series of constraints and variables, it makes transparent many of the informal business rules in determining and managing physician utilization and defines the optimal scheduling template that will most affect desire outcomes.
In our approach, we first create alignment around the current process, including assumptions, hypotheses and constraints. This includes reviewing contracts, discussing systemic errors, and identifying data.
Next, we develop a data blueprint by identifying KPIs, evaluating their performance, and formatting and cleansing data to be used in the qualitative model that illustrates current definitions of revenue, patient needs, physician needs, and measured outcomes. Through data visualization, we assess what the data tells us about current performance, including information such as lag times, decisions to schedule the next patient or add capacity.
This leads to experimentation, where we evaluate what actions we can now to better meet patient needs while optimizing against constraints – and drill down by specifics such as service line, practice, physician, etc. The end output allows leadership to visualize the impact of changes to the scheduling template in both utilization and outcomes, and use a recommendations engine that continues to learn and adjust to physician availability.
View this presentation to learn more.