Staffing OR to match demand

Hospitals and health systems are feeling the effects of the staffing shortage now more than ever. In a recent study, 90% of nurses considered leaving the nursing profession within a year. Even further, 71% of RNs with over 15 years of experience reported thinking about leaving as soon as possible. A related report from The Bureau of Labor Statistics indicated that the US healthcare sector lost nearly half a million workers between the 2020 onset of the COVID-19 pandemic and November 2021. As hospitals continue to navigate the persistent battle for adequate staffing, statistics like these make it more important than ever for hospital executives to be able to accurately predict and plan for nursing staff demands. One of the more challenging hospital environments to keep properly staffed and to optimally utilize is the OR.

Where many organizations have turned to interim directors and traveling nurses to fill in critical roles and supplement existing staff, others are partnering with local universities to attract nursing students to the OR, offering hiring bonuses, cross-training existing staff, and providing programs and resources to support nurses’ resilience and overall well-being. These solutions have proven helpful, but another avenue for unburdening staff is more efficient use of existing resources. Hospital systems that have adopted AI-based technology to improve OR utilization have gained efficiencies, reduced costs, and capitalized on a more predictable schedule to better match staffing to demand.

Reducing staff burnout

The pandemic placed unprecedented pressure on ORs. With many hospitals forced to delay surgical procedures because of surges in infections, backlogs of elective surgeries occurred and created new levels of asset and staff strain. This period was increasingly exhaustive to perioperative nurses. A 2021 AORN survey illustrates the impact of nursing shortages on surgery, with 58% of its respondents reporting that staffing issues led to delayed or canceled procedures (an increase of 16% in 2020). With rising demand for procedures colliding with limited staff, burnout among healthcare workers has only worsened since 2020.

Healthcare industry experts note that decreasing uncertainty and encouraging predictability is a key strategy for improving working conditions, particularly in the OR. Unpredictability directly affects staff performance and productivity because it leads to delayed cases and repeat instances of cases getting pushed or add-ons being scheduled late into the evening. With this in mind, accurately matching OR staff to demand is a crucial step in improving quality of life and reducing burnout, minimizing the need to ask staff to stay late or utilize on-call staff. Staff burnout not only happens when staff have to work overtime, but also when they have to contend with idle downtime in between cases that add up and prolong the day.

By aligning staff to a block schedule and planning ahead for predicted variances, OR managers are able to make more confident assignments. Should an OR block be released in a timely manner, the OR manager can make the decision to close down the OR because of staffing shortages or enable the schedulers to efficiently book into the staffed open time. Scheduling inaccuracy and unpredictability negatively impact staff morale, and while employing the necessary tools to increase accuracy and predictability are necessary, access to the appropriate data showcasing the upward trend is key to guaranteeing success. Ultimately, with more accurate insight into their shifts, staff report higher job satisfaction and are more likely to stay in a role for longer periods of time.

Accurately mining data

Data has become a buzzword, and the downfall for many organizations is to open access for their staff to data without purposeful thought and proper strategy. In order for data to be useful, it needs to serve the purpose of informing on the real problems that need to be solved and seeing the results post-implementation of the solutions. In the case of scheduling, for instance, it is easy to assume that the problem lies with underscheduling when the real issue might be overscheduling. OR managers need to learn to look at and mine data with intention behind it because eventually, they will have to turn to their staff and surgeons and be able to tell the story behind that data in a way that they will understand.

An example of the above being put into practice was presented at the OR Business Management Conference in February 2023, during the session “The Benefits of Leveraging Technology to Automate Scheduling and Improve OR Efficiency.” In this session, UCHealth, a health system in Colorado, showcased how an intentional effort to mine data using information collected by the electronic health record Epic helped them achieve more scheduling efficiency. Their strategy focused on Poudre Valley Hospital, where a third of its surgical cases, according to Epic, were under scheduled, which leadership realized was leading to higher costs, more callback, and dissatisfied patients and staff.

First, they realized they had a definition problem. How long does a surgical procedure take? Case length can be a complicated question because different stakeholders will have different answers. Surgeons, for instance, will give a timeframe, but they might be counting from the moment they stepped into the OR to the moment they step out. An OR nurse might count from wheels in to wheels out. Others might include turnover, the time it takes to setup and/or teardown the room. UCHealth, for the purpose of their efficiency project, chose to measure the length of a scheduled case from wheels in to wheels out and not include turnover time.

They then turned to Epic to find the average length of their cases. Another issue they identified was Epic’s tendency to drop off the highest and lowest scheduled length times from its average case length calculation. That proved to be a problem because it was exactly the less frequent instances of cases being unexpectedly heavy or complex that were proving to be challenging to accurately predict. They utilized a personalized case length finder tool powered with machine learning (ML) to better account for the types of cases they were getting. ML prevented outlier cases from skewing the data without fully dropping them off the calculation, but by regularly updating case lengths to address changing patterns.

The tool, therefore, calculated a median instead of an average, which proved to be more advantageous. They found that, according to Epic, the average length of all their procedures was roughly 78 minutes. However, according to the personalized tool, the median for the length of all their procedures was only 59 minutes. Pitting the average against the median, they discovered they were well overscheduling most of their cases. In the process of accurately mining data, UCHealth first sought to identify the real problem before employing the right solution. The staff are now better informed and can access that information anytime. Surgeons, for instance, can trust that the data is being regularly updated and go look at their times each month to see if they are getting faster or longer.

Reducing risk in block schedule management

Managing an OR block schedule is a high-profile, high-risk task. There is no shortage of elements or individuals at play—from surgeons and OR schedulers, to considering patient care, hospital revenue, and more. Rigid scheduling processes can create frustrations and tension among staff, who are affected in different ways by OR scheduling conflicts.

When ORs are not staffed to demand and rather to a schedule, a cycle of underutilization is created—the Tetris of matching surgeries to available OR time, which often results in empty ORs and unsatisfied staff. By moving to a demand model, surgeons are less likely to delay cases, nursing leaders can close staffing gaps, and perioperative leaders have insight into the data that will help effectively reallocate blocks. In the case that a block goes unused, this model provides an easier approach to reclaiming unused time, promoting available time, redistributing available time and staff, and optimizing staff schedules.

When all of the stakeholders are well-informed on the data to which they have access and align on OR schedules and priorities, a number of elements are upleveled. Whether it be improved quality of patient care, increased hospital revenue, or seamless workforce communication, staffing ORs to demand creates more positive outcomes, staff satisfaction, and asset utilization. Staffing ORs to demand is a tested way to improve operational dexterity within your organization.

Having greater insight into, and control over, the surgical schedule is a win-win for patients and OR staff. When OR leaders have the ability to know what to expect and balance the daily workload, this knowledge ultimately provides the work-life balance that they and their staff deserve and require for optimal performance. For an industry with increasingly high turnover rates and coupled with an increased risk of distress and mental health issues, taking the necessary steps to improve day-to-day operations by becoming more efficient and predictable is certainly worth the investment.

—Ashley Walsh, MHA, is vice president of Client Services at LeanTaaS. She leads a nationwide team responsible for product sales and customer implementations for iQueue for Operating Rooms and iQueue for Inpatient Flow, and was instrumental in the development and advancement of the iQueue for Operating Rooms product.

References

Nursing in crisis: Hospital IQ survey highlights significant patient care challenges due to hospital staffing shortages. Business Wire. November 17, 2021.

Press Office. News release: The employment situation—February 2023. US Department of Labor, Bureau of Labor Statistics. March 10, 2023.

Stobinski J X, Hendrickson S, Homme C L. The demographics of perioperative nursing: What we know now. OR Manager. 2022;28(3):20–21,23–25.