In the ever-changing landscape of healthcare, two goals remain top priorities for all hospitals and health systems: workforce efficiency and clinical quality. Often, these goals are considered separate or even competing priorities when, in reality, both are the result of process optimization. In other words, the right process will not only minimize staffing requirements, it will also ensure high quality patient care by creating systematic repeatability. Unfortunately, we in the healthcare industry are often guilty of crafting a long list of key performance indicators (or lag measures) outlining exactly where we want to go without identifying the specific ways (lead measures) we’re going to get there. That’s where data-driven solutions come in.
Data-Driven Staffing to Better Understand Patient Needs
To understand the why’s, when’s, and how’s of healthcare processes, it’s important to leverage the extensive warehouse of data collected on patient encounters across your organization every day. And while the healthcare industry is increasingly rich in data, accessing and harnessing this wealth of information remains a challenge. But with just a few data points from each patient encounter in the Emergency Department, Imaging, the Operating Room, or Nursing Units, we can begin to build a detailed picture of patient flow through the largest healthcare channels.
Unveiling the Core Drivers of Inefficiency and Leveraging Patient-Level Data
To truly enhance healthcare efficiency, we must look beyond the rudimentary hours-to-volume ratios. Start by asking questions like: When and how do patients arrive? What specific care do they need? Which providers consistently deliver the best results? Where is the process delayed? This approach allows healthcare organizations to move beyond traditional metrics and high-level performance indicators to pinpoint specific lead measure actions and drive improvement. If we want to reduce the worked hours per Emergency Department visit, we may want to reduce the average length of stay in the department. To reduce the average length of stay, we’ll want to know which population is having the greatest impact by analyzing the characteristics of each visit, such as arrival time, chief complaint, discharge location, and more. If we know that patients admitted to the fourth floor have an average length of stay twice as long as others, we can establish a specific lead measure to improve length of stay for patients admitted to the 4th floor, ultimately reducing the worked hours required for each visit.
In an era where healthcare undergoes challenges of cost containment and quality care delivery, the role of data-driven solutions in staff optimization are at the forefront to ease these burdens. By focusing on the “when and how” of meeting patients’ needs through data analysis and visualization, healthcare organizations can find new avenues for improvement. As we embrace this data-driven paradigm, we pave the way for a more efficient and effective healthcare system that improves workforce productivity and the well-being of every patient.
