Analytics and “micro” ORs improve outcomes

New model drives optimization, accountability

Assigning responsibility for outcomes is easier said than done. Our industry is unique in that there are many chefs in the kitchen, at any given time. Is a doctor responsible for the diet choices of a diabetic patient? Is a hospital responsible for an episode of care that largely takes place outside of its facility? Is an insurer responsible for pre-existing conditions? Anywhere you look, at any level, you will find examples of collective responsibility for outcomes that often leads to finger pointing when things go wrong.

This dynamic extends to the perioperative arena where multiple stakeholders – surgeons, nurses, and administrators – have different, often competing, priorities that can result in difficult management decisions and suboptimal outcomes. For example, a perioperative director may make a reasonably sound business decision that has a negative impact for one surgeon only to have that decision reversed when the surgeon talks to the CEO. Nurses may only want to work shifts that start and end at a specific time. If they get the shifts they want, staff satisfaction improves. This, however, may not fit the staff need based on surgical volume. Surgeons may be ready to start a case only to be delayed because the anesthesiologist didn’t induce the patient. All parties are clearly acting in their best interest. The ultimate outcome is often one of frustration, inaction, and inefficiency.

The complexity of managing a full OR suite makes it difficult to optimize its overall efficiency. And what we’ve come to learn is that perioperative services are not always optimized through a centralized management model. When we consider that the OR is the largest single source of revenue for any hospital, it becomes clear that some hospital leaders need a new approach to better manage and optimize one of the most important assets in the hospital.

Recently, the emergence of the “micro OR” concept, also known as service-line management has been gaining traction. The goal of this model is to improve perioperative performance by empowering service line leaders to manage their service lines as a business within a business. Under this model, information is still centralized to ensure a holistic view. Access to relevant data and functionality, however, is role based so that a specialty may be allocated a block of ORs that it can manage directly to accommodate its dedicated needs. For example, a hospital with 40 ORs could allocate a certain number of ORs to orthopedics, allowing the department to manage those ORs as appropriate. This model has great appeal since it assigns clear accountability for both operations and staff to keep those ORs busy, and profitable, for the organization. While there aren’t specific guidelines for when to follow the micro OR model, typically only larger hospitals or high-volume service lines should employ this technique. A large volume of surgical cases, supplies, and staff is required before it is justified to dedicate resources to discrete service lines.

In the micro OR concept, a business manager or surgical chair is held accountable for the outcomes of one or more service lines. While a perioperative director may not know the nuances of each service line, these individuals will know the “gotchas” of their service lines and can effectively address many issues and concerns. Every service line has its own unique challenges and every surgeon has his or her own style and preferences. There may be a surgeon that frequently assists in other surgeries and in order to do so has to give up some of their own block time. Another service line may have a large number of outpatient cases that could be moved to an ambulatory surgery center to make more room for inpatient cases. A surgeon may need to bump surgical cases if more urgent patients are identified, causing scheduling issues. Managing these situations is a challenge most OR suites face. When these difficult decisions come up, it takes someone with localized knowledge to make a call that is not only fair to surgeons, but also maintains business performance.

To ensure that this model works, service line managers have to understand how their performance is measured and have the tools that allow them to understand and improve in these areas. Until recently, hospital software has not kept pace with the requirements of these individuals. A once-a-month spreadsheet of high-level metrics is not frequent or detailed enough to give managers the information they need. Managing the periop space as a system requires tools that go well beyond scheduling and reporting.

Today’s advanced analytics platforms can provide the needed level of actionable insight based on data in existing systems. Electronic medical records and bed management systems have a tremendous amount of operational data that has not been fully exploited. These new solutions automatically process this data and provide the ability for service line managers to see metrics for their respective service lines and to understand underlying performance drivers. This begins with service line based dashboards that allow managers to drill into any metric. For example if first case on time starts (FCOTS) is trending lower for a specific month, users can analyze the most frequent causes of delays. They can even further by looking at detailed breakdowns of scheduled cases in future blocks to identify unused block time that can be used by the service line. Historical and prospective reporting goes a long way toward improving business performance and advanced simulations can improve performance even further. State-of-the-art reporting packages are starting to simulate patient flow through the perioperative and inpatient space to identify bottlenecks and analyze what-if scenarios when making changes to surgical volume and/or the block schedule. Predictive models can be used to forecast case volume and make scheduling and staffing decisions.

Let’s reconsider the surgeon that often assists in other procedures. Without the data-driven insight the service line manager may not even know this negatively affects block utilization unless he has access to detailed metrics. The advanced analytics dashboard will enable the service line manager to see that this same surgeon typically assists on Thursdays when a large volume of a certain type of procedure is performed. Further, he may see that this surgeon never releases her block time on Thursdays, even though most of the dual cases are scheduled a week or more in advance. Using this information, the service line manager could first ask the surgeon to voluntarily release the time when she knows she will assist another case. If she still doesn’t release time, then he could move her block time to another day of week that doesn’t conflict with the majority of the dual-cases.

Under a micro OR model, performance goals can be set by the perioperative director, and then cascaded down from the total performance of the perioperative business to individual surgeon performance. The performance metrics are the same at every level, but different actions may be required based on the scope of responsibility. FCOTS tends to be an important metric that hospitals want to improve. In a Micro OR environment, the perioperative director may set a target of 90% FCOTS. Each service line manager is then responsible for hitting that same target and each surgeon is responsible as well. If a surgeon doesn’t hit his goal, the service line manager may take away that surgeon’s block. If a specific service line doesn’t hit their goal, the perioperative director may take away block time from the service. Another example is case scheduling accuracy. Scheduling accuracy can vary significantly by case type and service line. The perioperative director may set a goal of achieving less than 8% of cases scheduled for 60 minutes or less than the case actually took. Using analytical tools each service line manager can pinpoint surgeons or types of surgeries where they can improve on this metric. In both of these examples, each level of the organization knows their performance and is held accountable.

In addition to improved block utilization, the micro OR model can have other operational benefits. Often, when using a micro OR model the specific ORs assigned to a service line are grouped in close proximity. This helps to streamline operations. Instead of equipment and supplies being stored in multiple locations, they can now be consolidated in storage space that is close to all of the ORs for each service line. Taking it a step further, this model ensures that micro OR is responsible for its inventory management, which is important give that supplies alone are the largest cost center related to perioperative services. Additionally, similar cases can be done in the same room or the same group of rooms, reducing turnover times.

This model also allows service line managers to have a core group of staff who are also dedicated to each service line. Every staffing manager has to balance the skill set of a surgical team with seasoned veterans as well as newer staff. By maintaining the same staff pool for each service line, staff become more qualified in specific types of cases, surgeons are more familiar with their staff, and teams of staff are more cohesive and productive. While the benefits of having a consistent group of staff are clear, this can also lead to staff shortages in other areas and downtime among staff. In order to balance the needs of specialized staff with the need to remain productive, often a hybrid approach is necessary where staff are assigned to cases for one service line, but may float to other service lines if necessary. This type of assignment process can get complicated, especially for large hospitals. It is hard enough to staff a perioperative department without worrying about staff specialties. Again, new software can help solve this problem by predicting surgical volume, identifying the types and number of staff required, and assigning staff based on a hierarchy of priorities, including qualifications for specific service lines and expected floating staff requirements.

Through a combination of management principles and advanced software platforms, large hospitals can now effectively implement the micro OR model. If the old system of centralized management is not working, it may be time to test this exciting new approach.

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