Driving Change Through Data Analytics: The Crucial Component to Operating Room Success

By Autum Shingler-Nace, VP of Perioperative and Procedural Operations, Copper Health University

Today’s healthcare landscape is complex. Health systems are highly focused on efficiency, productivity, and optimal patient outcomes; however, being efficient while maintaining high level of quality and impeccable patient satisfaction is not simple. Having success in the healthcare industry involves commitment, effort, and intricate coordination from highly skilled teams and leaders to drive efficiency and reach goals. Meeting goals and being efficient will also support the financial stability and growth endeavors of health systems. Even in the non-profit healthcare sector, there needs to be a financial margin to continue to support the needs of the community and provide exceptional care to those who need it. One way to meet these demands is through data analytics. Data analytics can assist in driving the direction of work within a healthcare system by telling a story. Data can assist leaders in making informed decisions, identifying trends, optimizing processes, benchmarking success, and ultimately driving change when needed.

Identifying areas of inefficiency and implementing performance improvement initiatives is a standard process within healthcare to improve outcomes. To be efficient, organizations need to use available resources effectively, while minimizing waste. Resources can be people, supplies, and even time. Every year, there is an estimated health spending loss due to inefficiency. Reducing health system inefficiencies can improve the availability of quality healthcare to the communities that need it, which in turn will yield better health outcomes overall. Operational efficiency can be monitored through many different data analytics platforms, in many different areas of a health system. It is imperative that data is available to teams to improve workflows and minimize waste. There should be a standard approach to measuring, analyzing, and utilizing data to engage teams to close gaps when opportunities exist. Some areas within health care that might use data analytics to improve workflows could be the: operating room (OR), emergency department (ED), or even hospital medicine to improve quality metrics or length of stay (LOS) standards.

Prioritizing the opportunities from data analysis should be a strategic step to understand how to drive success.

The OR is an area of intricate skill and complex coordination. OR efficiency is crucial for patient safety, employee satisfaction, and overall healthcare effectiveness. Often, a significant amount of hospital revenue comes from the OR. Hospitals facing economic challenges from shrinking reimbursement should consider maximizing resources within the OR to allow for continued financial success and data analytics can be a means to guide and focus workstreams. Some of the data-driven outcomes that assist with OR efficiency include turn over time (TOT), first case on time starts (FCOTS), room utilization, cancellation rates, and scheduling accuracy.

Additionally, data from supporting departments such as sterile processing can assist with care coordination and efficiency. Benchmarking this data is also important to show success rates based on like organizations/departments in the region/country. Many benchmarks will show that OR utilization between 75%-80% is acceptable and appropriate; however, what happens if utilization is under or over the benchmark? In an organization where the utilization rate is over 85%, a cascade of challenges can occur as the volume outpaces the space available. Data is imperative in these situations to assist with short-term and long-term strategies for success.

OR utilization refers to the percentage of time an operating room is being occupied over a certain period. It’s a key metric used to assess the efficiency of OR operations. Through deep dives with data analytics, care delivery can be transformed to provide better care and profitability for a health system. In ORs, leadership typically focus on room and block utilization. Room utilization focuses on how efficiently a physical single operating room is used. Block utilization looks at how surgeons use their allocated room throughout a period of time. Focusing on these outcome metrics can assist with developing an action plan and process metrics to yield success. Many action plans or process metrics will involve workflow redesign.

When working on OR workflow redesign, data analytics can support several areas. Optimizing patient preparation, minimizing delays, and enhancing team collaboration can create safer and more efficient surgical environments. Standardizing physical and workflow designs will also assist in workflow efficiency. Assessing the previously discussed data can allow teams to focus on areas of opportunity to improve workflow. Additionally, data analytics can assist teams in developing simulation platforms to work through barriers or obstacles in workflow and design a collaborative approach to parallel processing or other methods to assist with efficiency. Data analytics in the OR can ultimately enhance decision-making, assist with resource management, allow for surveillance of processes, and reduce healthcare costs. Data must be collected and shared routinely, but more importantly, data must be acted upon.

There are a multitude of platforms to receive data in the OR. The primary data source is the electronic health record (EHR); however, there are other platforms, such as patient surveys, employee surveys, wearable devices, and financial data. All this data can be analyzed through multiple tools to explore opportunities as well as existing areas of operational excellence. Prioritizing the opportunities from data analysis should be a strategic step to understand how to drive success. Focusing on standard pillars such as quality, growth and finance, patient experience, and research or scientific advancement may all yield opportunities of focus to be key drivers of optimal outcomes. It is up to each healthcare organization to strategically analyze their data and identify priorities. Prioritizing initiatives will enable organizations to develop strategy, play books, or other action plans to optimize efficiency, improve patient outcomes, positively impact financial performance, and ultimately allow for continued success to support community needs and healthcare sustainability. Data is the future of medicine and trends are moving toward machine learning (ML) and artificial intelligence (AI). Systems that are poised with data will be well positioned for success in the rapidly changing healthcare landscape.