When it comes to priorities for health care information technology, meaningful use has officially taken a back seat. A recent College of Healthcare Information Management Executives survey ranks analytics as the C-suite's top HIT priority, followed by population health at second and ICD-10 at third.
Analytics' ability to fuel widespread improvements in quality, cost-containment and efficiency undoubtedly has made it appealing to executives, but its recent popularity is also due to health care's transition to value-based reimbursement. The transition from fee for service to value-based reimbursement will require providers to gather, analyze and report on new data — an unprecedented amount of it. In addition, to optimize value-based reimbursement, providers will need to make processes leaner and contain costs while achieving better quality.
Until recently, analytics was not a common HIT priority. As a result, many executives are now in rapid deployment mode and want to ramp up quickly, so they can make improvements and successfully transition to the value-based model. Perioperative services is the ideal department in which to launch an analytics initiative.
Because the perioperative services department makes a substantial contribution to the hospital's bottom line but also has a high cost of operations, any efficiency gains or cost-containment measures can yield a significant financial impact. The operating room is an innately complex and fast-paced environment with numerous stakeholders, offering an abundance of analytics that can be collected and used to make improvements.
Laying the Foundation for High-impact Change
Susquehanna Health, a central Pennsylvania system with four hospitals, 332 licensed acute and 259 long-term care beds, two skilled nursing units, 22 bassinets, a home care division, physician services and ambulance service, recently implemented a perioperative analytics project to enable high-impact clinical and operational process improvement.
In most process improvement and technology implementation projects, team leaders develop goals and objectives up front — ultimately establishing the endgame at the beginning. That approach, however, may not be the best.
While we, on the Susquehanna team, had a general idea of the improvements we'd like to make, we used a slightly different tactic: We let the data take us where we needed to go. We got a head start with a specialized perioperative analytics solution, the expertise and support to develop a comprehensive understanding of overall operations, and more deep-dive views into specifics. For the deep dives, we reviewed data by OR room, department or team (e.g., pre-op and post-anesthesia), as well as by specific entities, patient diagnosis and physician.
Our plan was to pinpoint high-impact financial, clinical or operational improvements, and then create a road map for each (for instance, outline how staff would need to change their processes and workflow). To identify our improvements, we looked for large variations or metrics that surprised the leadership team. The analytic evidence allowed us to engage in data-driven decision-making from the very start of the project.
Identifying High-impact Improvements
The team immediately identified several high-impact improvements that would yield significant results:
Pre-op wait times. Initially, patients waited in the pre-op holding area for up to three hours, separated from their families who were sitting in the waiting room thinking the patient was in surgery. Knowing how this delay affected efficiency, operating costs, and patient and family satisfaction, we made a significant, large-scale change.
We re-engineered patient throughput, deploying a new patient-centric model that allows patients to remain in one place (with family) during pre-op; the care team goes to them. As a result, we eliminated the old "holding" area and reduced time waiting prior to surgery by a mean of 60 minutes. Patient satisfaction moved from 93 to 96 percent in the first quarter.
Cost of care. Using analytics in our contract negotiations with disposable supply chain and implant vendors, we were able to lower the total cost of care. For instance, we saved $302,000 in 2014 in orthopedic implant costs, and we project savings of $400,000 for 2015.
Utilization. We reviewed caseloads at all campuses to determine underutilization. We eliminated underused services on nights, holidays and weekends at one campus, which yielded a $100,000 annual savings in call coverage.
Post-anesthesia care unit wait times. Our data showed that the average PACU holding time was at least 90 minutes for each patient waiting for a post-op bed. Lack of PACU space meant patients recovered in the OR, creating gridlock, paralyzing case delays and resulting in astronomical costs. Our team developed a business case to build adjacent temporary and shelled space for same-day surgery and 12 additional PACU bays.
Our statistical data showed that we could decrease wait times for patients, surgeons and anesthesia providers by increasing the number of pre- and post-care locations. The board of directors approved the investment, and the new temporary PACU bays have decreased gridlock, increased efficiency and created zero need for recovery in the OR. Turnover time for cases moved from a high of 90 minutes to an average of 27 minutes.
Smaller Improvements for Big Results
While the large-scale improvements were our highest priority, we also saw the opportunity to correct smaller processes where we perceived high variation. For example, we identified a large discrepancy between surgeon arrival times compared with actual case start time. Via our analytic tools, we examined each surgeon's arrival in relationship to case scheduled time.
We made adjustments to the assigned block so that surgeons arriving early or on time got earlier block assignments, and those with consistently late arrivals were moved to later blocks. This reduced late starts and cases running past scheduled end time.
While we went where the data took us rather than developing preconceived ideas, we created a strong foundation of information to guide our process improvement. Our project relied heavily on process mapping, which enabled us to understand current workflows and determine where we needed to make changes.
We have learned that before implementing analytics, all decision-makers must understand their starting point. By mapping patient flow along with all supporting processes, including all handoffs between providers and staff, organizations will be able to interpret analytics better and decide on the ideal action plan.
Getting the Most Out of Your Analytics
In addition to process mapping, creating the big picture means you and your leadership team must include every part of the perioperative journey in the analytics system. If pieces reside in other systems, your view will be incomplete and your decision-making might be flawed. Ensure that all necessary data are available for your analytics solution.
Of course, documenting the complete perioperative journey requires commitment by all clinicians and staff involved. The Susquehanna team had to carefully scrutinize data in the surgeon block time, for example, and we often go back to ensure that the information was input accurately. Getting team buy-in for the analytics project is important, and it often requires ongoing communication and encouragement.
Furthermore, you must develop standards and quality checks of data to ensure the long-term accuracy of the analytics. Along the way, conduct audits and spot-check phone calls and other measures to make sure the team is entering each type of data — charge, block time, nursing — conscientiously. At Susquehanna, we audit 100 percent of our charge data and have regular audits of nursing data to help maintain quality.
Continuous Improvement for the Future
A large part of analytics' value is its ability to fuel rapid-cycle improvement changes. After implementing our initial cycle of changes, we continue to evaluate data and look for new improvements.
Health care leaders face new and uncharted territory each day as we address health reform, changing regulatory requirements and new reimbursement models. In the midst of this change, perioperative analytics can help leaders to make the right decisions and prepare for the road ahead.
Lori Beucler, R.N., is vice president and chief nursing officer for Susquehanna Health in Williamsport, Pa.