During the past year, a great deal of attention has been paid to the physical expansion of the healthcare industry. Kettering Health Network announced it will soon begin construction of a $70 million, five-story tower at Soin Medical Center in Beavercreek, Ohio. Phoenix-based Banner Health currently has 386 active construction projects across its markets, totaling $1.7 billion. And Jersey Shore University Medical Center is close to completing its $265 million outpatient tower.

The expectation is that these new facilities and expansions will drive revenue for hospitals through the delivery of new, high-quality services, as well as increase volume for existing services.

However, building new physical capacity or starting a new service line isn’t always the right approach to strategic revenue growth. First, this approach might not be financially viable for many hospitals and health systems. Second, and potentially more important, it might not be necessary. The opportunity for growth may be hidden, lying dormant across existing resources and services.

Most hospitals have grown by offering layers of new services, incrementally, over time. The risk is that hospital leaders will pull critical day-to-day operational data from disparate sources and systems that were implemented with each service expansion and may not be well integrated. This results in poor visibility into current operational performance and little to no ability to proactively anticipate future demand, and align physical and staffing resource availability.

When data is hard to come by or siloed, staying current with day-to-day activities easily consumes a leader’s ability to look ahead or across the enterprise. Instead, hospital leaders focus on discrete bottlenecks and resource shortages, and this can lead to more capacity is needed. In the absence of automated decision-support systems, administrators are left to treat the symptoms of more fundamental operational problems.

At any hospital, there may be at least half a dozen operational challenges that could improve efficiency. The trick comes in understanding which of these challenges presents the greatest opportunity for improvement.

Advanced analytics platforms can help to synthesize data from disparate IT systems and give hospital leaders a holistic view of hospital-wide operations. These platforms can help them determine which operational challenges should be tackled first. Hospital leaders can use data, rather than guesswork, to make process improvements that will increase efficiency and improve capacity utilization and staff productivity.

The insight gained from trusted historical reports and “what-if” simulation enables hospital leaders to optimize service line activity and increase case volume. This drives revenue growth while helping leaders identify services that have the potential for more growth.

For example, one Midwestern hospital used analytics to the improve processes and outcomes for total joint replacements at its main and suburban campuses. Hospital leadership wanted to consolidate orthopedic services to its suburban campus and then smooth the utilization of the OR so surgeries were not front-loaded every Monday and Tuesday. Surgeons and nursing staff were concerned that Friday’s surgical patients would still be in the hospital on Monday, creating bottlenecks for the upcoming week.

Using historical data on lengths of stay, leadership evaluated potential outcomes and chose a plan that would add surgeries later in the week without increasing the demand for inpatient beds or concurrent ORs. Analytics enabled this hospital to better optimize its existing resources to realize a 44 percent increase in orthopedic surgery volume with no increase in peak bed demand or concurrent OR demand.

As hospital leaders turn to the market for predictive analytic solutions, they should be aware that some platforms have limitations that lead to poor results—such as ineffective reports, micro-optimizations that make the overall system worse and “alert fatigue” that wears down staff. To avoid these pitfalls, ensure your operational analytics platform of choice can offer the following.

Flexible policy engine. The platform should enable the institution to incorporate its agreed-upon operational and clinical policies to ensure that all measurements, analysis, optimizations and predictions are within the context of what’s been agreed upon by the key constituents.

A holistic, up-to-date view. The platform should bridge together disparate data silos—EHRs, bed, case, ED, workforce, scheduling and OR management systems—and keep data in each system timely. Hospitals are complex operations, and without a comprehensive, real-time view, the analysis will fall short.

Historical accuracy. An effective platform should collect and cross-reference data across IT systems and continually check for discrepancies across data sources. Confidence in data integrity is a must, because there is too much data to rely on manual checks alone.

Predictive accuracy and notifications. The platform should provide forecasts, such as predicted occupancy rates, surgical volume and block fill rates and appointment demand. The platform must provide clear prediction ranges (not just absolute numbers), measuring the accuracy of past forecasts and using machine learning to yield actions that achieve continuous improvement.

Actionable recommendations. The platform’s analytics should answer the “so what?” question by providing specific recommendations, like an early warning system for initiating a surge plan to mitigate forecasted bottlenecks, a prioritized list of patients to check on for likely discharge, the surgeons or service lines that likely need more or less block time or an optimized appointment schedule to improve throughput. The reports should be designed to identify outliers from normal activity.

What-if capabilities. The platform should provide a simple and accessible way of running simulations to prioritize improvement projects based on anticipated clinical, operational, and financial measures.

Advanced analytics brings a new level of sophistication to operational management in healthcare. Building a new facility or adding new services is no longer the only answer to combat decreasing margins and ensure the delivery of high-quality care. That said, there are times when building new capacity or replacing existing capacity is the right decision. A building might be too old and expensive to maintain, or service lines have changed in a manner that require a facility to be outfitted in an entirely new way.

In these cases, advanced analytics can provide administrators with a clear picture of how much additional capacity they actually need. Because the average cost of a new hospital or wing is approximately $1 million per bed, knowing whether you need to 350 or 400 beds is significant.

Whether making the decision to expand capacity or finding better ways to optimize existing capacity, advanced analytics platforms give hospital leaders the ability to meet key performance and patient care objectives. This will lead directly to improvements in quality, patient access and patient satisfaction and with the added benefit of improved financial performance.

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