Enterprise staffing analytics: An untapped aid for achieving financial success
- Staffing analytics can play a role in the financial success of enterprise healthcare organizations, especially as they navigate the long-term financial challenges of COVID-19.
- Using a single data store to consolidate information across multiple scheduling installations provides visibility into current capacity and utilization trends from multiple views.
- Staffing analytics can be viewed at an enterprise level while simultaneously recognizing and honoring departmental autonomy.
Until the past year, most hospitals and health systems may have thought they knew how to manage staff capacity. As with other aspects of healthcare, however, the COVID-19 pandemic has quickly exacerbated underlying staffing challenges — with substantial consequences for the bottom line.
Even in usual circumstances, labor expenses typically take the lion’s share of a health system’s operating costs. So the ability to maximize physician and support staff resources directly impacts both an organization’s care delivery and its fiscal health. Now, more than ever, healthcare leaders should be asking themselves: How do we maximize staff resources in real time to meet patient demand, control costs and drive revenue?
The most strategic answer requires taking a fresh look at a very tactical system: scheduling.
Scheduling data is the best representation of what physicians and other staff are doing daily. It’s a valuable asset capable of generating positive financial outcomes, but only a few healthcare enterprises currently tap into its full potential.
The adage, “You can’t control what you can’t measure,” holds just as true for scheduling as for other healthcare functions. Scheduling analytics can help organizations efficiently and cost-effectively match the demand of each clinical area to the available physician supply. Taken one step further, a real-time, enterprise-level approach to scheduling analytics helps hospitals and health systems manage staff capacity to achieve strategic goals.
Support departmental autonomy and enterprise goals
One of the biggest reasons that healthcare organizations aren’t fully leveraging their scheduling data is because each department historically has had its own capacity management process. For years, schedulers in each department or clinic have used manual tools to ensure appropriate coverage just for their piece of the organization — and for good reasons, too.
There is enormous value in managing schedules at the department level. Each specialty or practice has unique logistical, regulatory and contract perspectives to handle. One size does not fit all. An anesthesia group, for example, must follow staffing rules that are entirely unrelated to those for a neurology department.
That said, there is no reason not to empower both local control and enterprise visibility. Organizations can view staffing analytics at an enterprise level while simultaneously recognizing and honoring the appropriate degree of departmental autonomy. It’s a delicate balance, but one that can pay off by promoting insights at a variety of levels to support both tactical and strategic decisions.
Gain greater visibility throughout the health system
What’s needed to address capacity challenges and improve the financial picture is a consistent and fair way to measure and understand effort across the health system. Ideally, that entails using a single data store to consolidate information across multiple scheduling installations and gaining visibility into current capacity and utilization trends from multiple views.
This approach calls for a high-quality scheduling database that capably serves all departments — emergency, cardiology, anesthesia, radiology, pathology, urgent care, etc. With access to high-quality data, health systems can enable semantically consistent visibility. In other words, organizations need the ability to accurately compile data even if some departments track “vaca” time while others track “vacation” time or just “PTO.”
Then, by incorporating data visualization tools, healthcare organizations can begin to run analytics at the enterprise level and by department, subspecialty, location, team member and other categories.
For example, one multihospital academic medical system recently decided to create network-wide departments for many of its specialties. The organization will use scheduling data to view staff capacity:
- At the department level at each facility
- At the department level across multiple facilities
- At the enterprise level across specialties
Such real-time visibility opens up opportunities to manage resource allocation more proactively. Looking at a dashboard that shows “time away count versus planned,” for instance, lets health systems identify abnormal occurrences of time away, drill down into the data to pinpoint root causes and then adjust schedules well in advance. As an added benefit, analysis of “time away” trends can help organizations encourage vacations at optimal times, so they can adjust resource allocation and prevent an excess accumulation of time away at the end of the year.
A data-driven approach also permits proactive management of employee progress goals. An academic medical center can begin to do weekly or monthly reviews of employees’ clinical, administrative, research and teaching (CART) time, for example. By generating CART reports more frequently, organizations can see trends toward targets in real time, allowing providers to pivot as necessary to stay on track to meet their time commitments.
Enterprise staffing analytics don’t have to be limited to physician staffing, either. Similar types of data can be collected and analyzed for on-call providers, mid-level providers, nurses, technicians, orderlies, housekeepers and other support staff — all of whom play a role in ensuring patient care quality and safety.
Win strategic benefits through staffing analytics
COVID-19 triggered massive staffing shifts in health systems across the country. In some locations, there simply weren’t enough staff to meet ICU demand, while non-acute care settings turned into ghost towns. Because staff capacity typically has been managed with little visibility throughout the enterprise, staff capable of assisting in high-need areas may have been furloughed or laid off instead of being redeployed.
Furthermore, when shutdowns halted elective surgeries, many health systems lost more than half of their revenue. The pandemic also disrupted the pipeline of patients flowing from clinics to diagnostics to procedures. This interruption in the care cycle is expected to continue to produce financial shortfalls in the near term.
In this environment, optimizing staff capacity is as essential to health systems’ bottom lines as it is to clinical quality and access. COVID-19 demonstrates why hospitals and health systems must be able to leverage enterprise scheduling data in real time to make data-driven decisions that support effective staffing strategies and yield measurable financial improvements.
Hospitals and health systems are already sitting on a rich source of data capable of generating positive financial outcomes. Moving away from a manual, department-only view of staff capacity allows analytics to play a role in the economic success of healthcare enterprises as they navigate the long-term challenges of COVID-19.