By Rich Miller
One of healthcare’s most vital tasks is historically one of its hardest: Making sure the right staff are always available at the right locations to meet patient care demands.
Currently, most hospitals and health systems take a segmented approach to clinical capacity management. Each individual department or service line oversees its own staffing needs. This method ensures adherence to crucial specialty-specific requirements, but it also creates disparate manual processes that limit greater visibility into staffing trends. Consequently, it does not lend itself to allowing staff resource optimization at the department level—let alone across the enterprise.
By leveraging scheduling data, however, health systems open new opportunities to maximize staff productivity while preserving staff satisfaction. Achieving this fundamental balance is possible because scheduling data is a treasure-trove of information that accurately reflects actual daily workflows. When it’s normalized and rolled into a semantically consistent enterprise view, it can spotlight both current trends and areas for improvement.
The value of advanced visibility
The key to aligning staff supply with clinical demand is advanced insight. While historical staffing data alone provides some benefits, it can’t adequately support proactive clinical capacity planning. Agile and informed staffing decisions require the ability to identify needs and address potential problems hours, days or months in advance.
Analyzing near real-time scheduling data in conjunction with historical staffing trends gives health systems the power to adjust and adapt in real-time. With data-driven visibility comes the potential for continuous change management that encourages strategic benefits including:
● Increasing clinical capacity. Evaluating staff resources at both the department and enterprise levels monthly, weekly or daily lets health systems plan further into the future and better match each clinical area’s demands to the available provider supply. Although every specialty has unique staffing requirements that benefit from local management, enterprise-wide views can serve much like a traffic control function that drives strategic accountability and ideal resource deployment.
For example, analyzing the breakdown of clinical activity by facility or location could keep staffing plans in harmony with facility management and expansion efforts. Likewise, analyzing the breakdown of clinical activity by specialty could pinpoint FTE trends and variability by department or subspecialty.
● Managing costs and revenue. In most health systems, effective staffing strategies have a direct impact on the bottom line. Not only do labor costs typically top the expense report, but clinical productivity still drives most revenue. That’s why it’s important to identify opportunities to improve clinical productivity—which includes understanding how existing staff can best assist in high-need areas. With a real-time, enterprise-level approach to scheduling analytics, organizations can track needs comprehensively, address them by optimizing resource utilization, and measure results.
Tactical analysis, strategic results
One Midwestern multi-hospital health system planned to consolidate all of its knee surgeries to a single facility. To ensure it had the staff to support this strategic move, the organization used analytics to build a staff resource plan a year in advance that maximized existing clinical resources. With the strategy in place, the organization anticipates achieving efficiencies and economies of scale that will enable a 25 percent increase in patient volume, a reduction in care costs and an increase in care quality as patient volumes rebound as organizations begin elective surgeries again.
● Boosting productivity and satisfaction. The recent disruptions caused by COVID-19 have only magnified the positive and negative effects of clinical productivity pressures. We have seen how essential it is to balance productivity in a way that speeds patient access to care, but also safeguards provider satisfaction and prevents burnout. There are several ways that workforce analytics can help raise productivity and satisfaction at the same time.
One way is to look at “time away” trends together with “target” and “actual” clinical time. Superimposing time away and clinical time onto the same dashboard view can reveal why someone is ahead or behind in their clinical time. Such insight can alert managers when staff need to make up time, as well as their potential for burnout. Thus, managers and schedulers can proactively address work-life balance while ensuring clinical resource availability.
Another way staffing analytics can spur productivity and satisfaction is by displaying employees’ clinical, administrative, research and teaching (CART) time, which permits proactive management of their progress toward goals. Academic medical centers that review monthly or weekly CART reports can see trends toward targets in real-time, delivering transparency and accountability to help providers stay on track to meet their contractual expectations.
The beauty of scheduling data is that it reflects workflows for all sorts of staff resources. That means health systems can apply data analysis to physician and non-physician staff—including mid-level providers, nurses, on-call providers, technicians, orderlies, housekeepers, and the many others necessary to keep healthcare operations moving safely and smoothly.
Enterprise-wide data visualizations and alerts can give health systems the visibility they need to improve staff utilization, provider satisfaction and revenue. With the awareness generated by enterprise scheduling data, health systems can optimize staff resources to meet patient care demands and achieve strategic objectives.
About the author: Rich Miller is chief strategy officer at QGenda.