Predictive analytics for operating room (OR) management is essential to making this a reality. Calculate your potential ROI for free by completing the form below!
Performance improvement in healthcare is tied to establishing well-defined targets for areas like managing the OR, specifically, improving efficiency and staff productivity while retaining and improving the standard of care. Improving OR efficiency correlates with shorter case lengths, effective scheduling of surgeries, and decreasing non-operative time by restructuring OR tasks. Central to these are motivated staff and collaboration at each point within the patient care process. When OR staff is focused, improving operational effectiveness means working smarter, not faster.
At most high-performing hospitals, the majority of revenue – over 60% of margin – is derived from performing perioperative services. In order to drive the most value for patients, or maximize outcomes versus cost, healthcare organizations must be able to effectively manage service delivery. Unfortunately, many organizations lack the proper tools to make decisions regarding performance that would drive not only dollars, but performance improvement in healthcare delivery for patients.
Arguably, the most important variable in achieving OR utilization is effective staffing. However, without visibility into performance, health systems lack the essential information needed to manage resource utilization. Predictive analytics can assist in staff optimizing OR utilization, while holding quality of patient care constant. Click to learn more about aligning OR resources to meet surgical demand.
The first place analytics can help is related to block design. A block design is a family of subsets whose members are chosen to satisfy some set of properties that are deemed useful for a particular application to maximize access for the most productive procedures any physicians. By utilizing block forecasting, physician practice patterns can be matched to demand for block time. Second, predictive analytic models can allocate appropriate time for urgent, emergent, and elective scheduled cases. Hospitals then use this schedule to forecast ICU admissions and efficiently manage beds—providing a better patient experience and improving staff satisfaction because they know what to expect.
By designing a block schedule based on forecasted demand, several benefits can be achieved:
- OR load balancing which minimizes variance in daily OR schedules
- Improved physician OR access
- Surgeon practice patterns can be integrated into the model to prevent impact to clinic schedules
- Reduction in costly overtime by controlling labor costs
Saving time within the OR can have a significant impact on a health system’s operations. Reducing the time within and between surgical cases results in patients spending less time in, or waiting for, surgery. This is also frees staff so that they can spend more time at the bedside. Typically, in healthcare, attempts to reduce time have primarily involved capital purchases of new equipment, or adding operating rooms to enable parallel processing of patients, but finding ways of minimizing non-operative time (NOT) can yield similar effects. Coupling effective analytics tools with an OR management program drives not only visibility, but accountability amongst OR staff and can generate better performance. With all of the complexity in modern healthcare delivery, analytics are a necessity in maximizing improvements in workflows and can streamline the handoffs from one team to the next for each step of the OR process.
While many OR managers and hospital leaders have dashboards for operational key performance indicators (KPIs), including OR utilization, number of cases, on-time first-case starts, turnovers, length of stay [LOS], etc., most utilize these in a “pull” model―i.e. they have an idea of what a metric should be and then look for the data to disprove, or prove, their assumptions. Problems arise when they don’t know what to look for, or are overwhelmed by the volume of data. Interestingly enough, the problem is not a lack of data, but a lack of insight into the proper data to utilize and create actionable plans to drive OR efficiency. Data science and machine learning, combined with modern data delivery mechanisms such as mobile devices, can pivot an organization into a “push” model, unearthing details dashboards and OR reports can miss―and in the end improve efficiency.
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