Five ways to break through the perioperative performance ceiling

Hospitals and health systems today are facing payment model shifts from volume to value, bringing significant margin pressures.

Those that cannot achieve strong financial performance may face difficult choices. They may become part of the industry rollup through mergers and acquisitions, and some may face closing their doors. Certain crucial resources, including staffed beds and ORs drive both revenues and costs and must be managed precisely to achieve financial success. There is little room for error, and little room for guesswork. The flip side of this challenge is that those who can use new approaches to manage these resources adeptly can seize a competitive opportunity.

Hospitals need to ensure their clinical and operational plans align with and support their financial plans by using the data that already exists from billions of dollars invested in EHRs, bed management, patient flow and OR systems. They need to find ways to maximize clinical activity with existing resources (increase admissions, transfers and overall surgical volume), reduce ED and PACU boarding (decrease ED walkouts, official or unofficial ED ambulance diversions and lost transfers), staff with greater accuracy and with more lead time to reduce both total full time equivalent (FTE) and overtime costs, as well as optimize capacity utilization across the entire hospital or health system.

The OR is a core financial driver for most hospitals and systems. Every hospital is looking to improve their operating room efficiency. Almost all hospitals and systems have a familiar set of perioperative key performance indicators (KPIs) that help them understand surgical volume, room utilization, financial and efficiency performance. Many have made great strides through measurement, analysis, feedback and accountability. How then can we set our sights higher and take performance to the next level? The answer lies with moving beyond the technologies of the 19th century to embrace powerful new predictive and prescriptive analytics that will improve block scheduling and staffing structure.

Perioperative KPIs are, ultimately, all about one thing: delivering more care to more people with the available resources. On-Time Starts, Turnaround Time, Block Utilization, Prime Time Utilization and Units of Service are all measured in order to maximize surgical volume delivered for each minute of OR time and each minute of the surgical team’s time.

At many institutions, the measurement and feedback regarding these performance measures come from our eyes and ears, our conversations with colleagues, and simple arithmetical calculations such as percentages. Block schedules are often devised without considering “downstream” resources such as availability of ICU and floor beds. Instead, block schedules may be devised around historical precedent, surgeons’ office days, seniority or trades between departments. Creating block schedules without factoring in bed availability means paying to keep excessive numbers of empty beds open for surgical patients, or having patients stuck in the PACU waiting for beds, or even canceling cases.

Investments in EHRs, bed-tracking software and financial systems have created a huge amount of data. Yet without sophisticated analytics that data cannot by synthesized into actionable information to drive decisions. New solutions now exist to do just that.

Consider how analytics and simulation-based modeling platforms can help hospitals break through the performance ceiling:

1. Take a more holistic approach. Look beyond the OR and the PACU and factor in the availability of surgical ICU and floor beds postoperatively. Even hospitals that run at extremely high census levels have some days in the week with open beds. It makes sense to schedule surgeries so that patients can use these beds. This avoids having elective cases compete with emergency admissions for beds. “Elective smoothing” analytics driven by discrete event simulation can highlight opportunities for increasing surgical volume without building another tower.

2. Consider multiple angles. Look at utilization by block, by room, by day of week, by week of month, by specialty, by individual surgeon. Combine these views to hone in on opportunities.

3. Factor in hospital policy. Make sure your analysis represents policy around things such as block release and maximum turnover time so that surgeons who follow the rules are recognized. This builds transparency, trust and alignment among providers. In addition, simulation allows ‘scenario testing’ of potential policy changes to determine whether there will be the intended impact, and whether there might be unintended consequences.

4. Align skills with schedule. In building a staffing structure, calculate the mix of staff and skills that will be ramped up and ramped down each day across ORs. Factor this into the block schedule for efficient staffing structures.

5. Identify the ideal surgical case mix. Factor marginal costs and revenues from cases directly into your utilization calculations, so that schedules can be optimized to contribution margin rather than simply volume.

What these approaches have in common is that they cannot be achieved through direct observation, arithmetic, or an advanced spreadsheet. For instance, elective smoothing is best achieved with discrete event simulation, which creates a simulated virtual hospital or health system. In this virtual system, real clinical encounters take patients on journeys between the emergency department, the OR, the PACU, the ICUs and the floor. The full variety of presentations and procedures are resampled thousands of times to faithfully model the patterns and variability of hospital operations. This complex, holistic model allows scenario testing on almost any question of interest.

To put elective smoothing into effect, cases can be added to various days or blocks, or moved around between blocks. There is immediate feedback from the model on whether this allows additional patients to be operated on and provided with a bed without opening new beds and without worsening boarding in the emergency department.

Other industries have benefitted from such approaches for decades now. Airlines, hotels, theme parks and restaurant chains all use these approaches to grow their businesses and match capacity precisely to demand. When to schedule flights? What type of plane? How many staff and which positions to ramp up and down around different days of the week, and where in the park to best deploy them? These decisions are not left to guesswork or back-of-the-envelope calculations, but are driven by proven and repeatable data analysis.

A sophisticated analytics and simulation-based modeling platform can generate block schedules that optimize every hour of OR time, staff and surgical beds. The ability to drill down on individual input at the department, staff or patient level can eliminate data skepticism by seeing exactly what contributed to a particular outcome or prescribed direction. It’s critical to instill a framework of accountability that allocates OR time and surgical beds according to activity and need.

In deploying these types of data-driven solutions, internal culture and politics will play a major role. These will influence the priority-payoff matrix for choosing where to make change and when. It may be that junior surgeons or new hires are allocated blocks for purposes of smoothing while more senior surgeons’ schedules remain more driven by other priorities. However, small incremental gains add up quickly, and you don’t have to add many cases to see a dramatic return.

Whatever political uncertainties lie ahead it is a safe bet that hospitals and health systems will continue to face both financial and operational challenges as they work to exactingly control costs while seeking marginal revenue. For those willing to embrace powerful computing and actionable data analytics and insights, these challenges represent a chance not only to deliver more care to more people, but also an inviting competitive opportunity.

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