Clinical Workforce Analytics-1

As we interact with Health systems across the country, we see three specific market forces driving departmental and clinical leaders to think differently about staffing strategies. Everyone by now has at least a cursory understanding of the direct impact of value-based reimbursement on cost management. However, the other two challenges are more discrete. Firstly, the staffing mix at most health systems is changing. A greater percentage of salary expense is shifting to physician and specialized clinician labor as provider organizations consolidate and care migrates to the outpatient setting. Coupled with competition for specialized clinicians and increasing turnover rates, departmental leaders must strike a difficult balance. How do you make sure you have qualified, credentialed staff at a safe level without over-running on cost or creating staff dissatisfaction?

Striking this balance is a significant challenge especially for leaders in demand-driven departments like surgical services, radiology, hospital medicine and emergency medicine. It becomes increasingly problematic as patient volumes and physical locations of care grow. Leaders must ask the right questions to solve the problem effectively. How can we predict patient demand? What shifts, specific times of day, or locations drive cost overruns? Can we pinpoint which providers work most and least effectively? How can we use data to take action when needed?

The Solution: Creating Clarity Through Data

The leading organizations are attacking this issue proactively by bringing together data, analytics, and the operational tools and organizational discipline to drive change in real time. Success rests on amalgamating data from multiple disparate sources for example:

  • Electronic Health Record systems for case or patient schedule data,
  • Departmental staff scheduling data
  • Time and attendance data,
  • Payroll data, and
  • Production data to describe the output or, ideally, the revenue generated by the clinical effort.

In short, you should know the work performed, the time it took, what it cost, and how much it produced in one set of actionable views.

That said, we also see many organizations that have some level of data but struggle to interpret or leverage it to make operational changes. Many organizations create lots of data but it takes time to compile and as such becomes significantly retroactive. Even some of the most progressive institutions lack the data sources to truly view staffing data in real time across shifts, personnel, locations, and time. The ideal state allows both leaders and managers to review important data about staffing on an ongoing basis and act “on the spot” to mitigate cost impact without sacrificing quality or patient care. This also allows for granular activity-based costing – a financial best-practice across industries that can identify specific areas for remediation or change. Finally, organizations need the infrastructure, both in terms of processes and systems, to enact and test any changes on a regular basis. This includes the cultural platform to engage clinicians and help them understand – through the data – the importance of any suggested changes.

OpenTempo InsightsTM

At OpenTempo, our Insights platform pulls together data from across our advanced scheduling capabilities and synthesizes it with data from your disparate sources. We use that data to create actionable insights that pinpoint costly shifts, locations, or times of day, allow you to make real-time intra-day decisions, and highlight how to improve productivity without compromising patient care. Our core scheduling system then helps you put the necessary changes into action. For more information, visit:

The Thought Leadership Series is sponsored by OpenTempo – the market leader in solutions for Clinical Workforce Management and Optimization. The Thought Leadership Series highlights topics of relevance presented by industry resources or OpenTempo experts to expose innovative ideas that help Healthcare Organizations improve performance.