Share this post:
Mike Chamberlain, President, Simpler Consulting – part of Watson Health
Sally Akers, RN, MSN, CNS, Managing Partner, Provider Consulting, Watson Health
We’ve all felt that slight pang of guilt when we buy a 12 oz. bottle of spring water at a convenience store for $3 when we know we could have purchased a case of them for $6 at a big box store. But when we’re busy, thirsty and need a quick fix, we pay more. That same phenomenon occurs on a much larger scale in the supply chains and administrative processes of hospitals and health systems throughout the country, where millions are wasted each year on inefficiencies that could be eliminated with better planning.
Until recently, however, the kind of data-driven analysis required to identify the areas where these inefficiencies are causing waste simply did not exist. Just as you would be hard-pressed to calculate how much extra money you spend each year at the convenience store, hospital administrators were in the dark when it came to quantifying the cost of inefficient supply control, labor management, and clinical resource utilization.
Now, thanks to innovations in data analytics and the ability to unlock disparate data sets that were once hidden in different silos of an organization, IBM Watson Health is helping hospitals and health systems transform their businesses.
Consider the example of Caldwell Memorial Hospital, a 110-bed not-for-profit community hospital that is part of the UNC Health Care System. As part of an organizational transformation initiative, the hospital wanted to fine tune its supply chain to make sure clinicians consistently had easy access to the right items, in the right place and at the lowest possible cost.
Too often, that was not the case. By collecting data on inventory visibility, demand flow optimization and management of physician preference items, the hospital found that it had excess inventory on a number of items, was paying too much in distribution costs, and had widespread variation in items and resources requested by different physicians.
The analysis found two primary causes for these inefficiencies: 1) the hospital was ordering items based on forecasted need, which was often inaccurate, and 2) there was no continuity or standardization from one physician to the next in terms of what preference items were stocked in the hospital. By implementing a visual replenishment system that stocks supplies based on actual need, the hospital realized $2.62 million in annualized savings over the course of the 13 month initiative. The hospital then uncovered another $4.1 million in cost savings, by working with a consultancy and applying a proprietary tool to break down resource utilization by diagnosis, physician, revenue center and intensity of service, and using the results to streamline the number and variety of materials used.
CHRISTUS Trinity Mother Frances Health System (CHRISTUS) in Northeast Texas had a similar experience. In this example, however, the stakes were even greater. A number of its payer contracts changed, leaving the six hospital health system staring down a $25 million budget shortfall. The system needed to find cost savings quickly, but rather than implement a blanket, across-the-board expense reduction, CHRISTUS Trinity Mother Frances turned to data analytics to spotlight the areas where it could improve the most.
In this case, the key data set for identifying inefficiencies came from other hospitals. Using an industry leading comparative database, the health system was able to benchmark its performance in cost, productivity, and resource utilization against best-in-class facilities of similar size and demographics. This black-and-white comparison allowed leadership to quickly identify areas where the system was underperforming its peers, notably in supply and labor expenses, labor productivity, skill mix, patient length of stay and costs associated with purchased services.
Once the database helped identify the hot spots, CHRISTUS Trinity Mother Frances was able to assemble a team to start tackling these issues head-on. The health system ultimately saved $6 million by renegotiating contracts that were not in line with industry norms, $10 million by setting new standards around overtime and outside agency utilization, $7 million by reducing waste in its supply chain and $3 million by reducing average patient length of stay. In just one year, the health system carved $26 million in waste out of its administrative processes, outperforming its goal by a million dollars.
This idea of finding opportunities for performance improvement through deep analysis of a health system’s administrative data, and the data of its peers, is not limited to controlling expenses. Indiana University Health System (IU Health) was able to mine this data for insights on how to make its service lines more efficient.
Leveraging the peer group benchmark data from that same industry leading database that CHRISTUS Trinity Mother Frances used, IU Health quickly found that it was underutilizing its outpatient oncology services and emergency department capacity, while over utilizing telemetry. In fact, telemetry usage at one of its hospitals was 70 percent higher than average among peer group hospitals.
Armed with this data, IU Health administrators were able assemble a diverse team of operational, clinical, and managerial staff to redirect patient volumes, staffing and resource utilization to be more in line with industry best practices, and more consistent from hospital-to-hospital within the IU Health System.
In each of these examples, information hidden in hospital administrative data sets became the cornerstone to performance improvement initiatives by helping administrators and clinicians make better decisions. Increasingly, the ability to unlock, analyze and operationalize the insights from that data will be critical to achieving value-based payment thresholds, reducing waste, and improving operational efficiency.