Data is a commodity. We collect and store data. Heck, we even call them server farms. While silos are good for farmers, they are bad for data management — time killing, bottom-line-eroding bad. For a hospital, data silos mean departments are operating on assumptions, and there’s no room for assumptions in healthcare when patient safety and billions of dollars are at stake. Breaking down data is a crucial step in increasing visibility across hospital operations and saving much needed capital.
Over the past 15 years, the number of devices per bed across hospitals has increased by over 60%. That number doesn't seem to be getting smaller when you factor in the continual leaps in technology (more devices) and the roughly three million baby boomers hitting retirement age every year, for the next 20 years. That’s more patients, more assets, and more data. As you can imagine, the consequence of data silos in this type of environment can have exponentially negative effects, costing hospitals dearly in time and money down the road.
Part of the problem is managing data on a departmental level and not sharing insights gleaned from that data across other departments and facilities. Cleveland Clinic shared some of their insights (and growing pains) in a HFMA piece by Gary Baldwin aptly titled “Breaking Down Silos”. According to Pat Sturdy, director of capital at Cleveland Clinic, the 4,500-bed health system had two overlapping, but unreconciled medical equipment inventory databases. Subsequently the data-cleanup has been a time-consuming process that’s still in progress.
“The biggest challenge with our legacy approach was that we were just looking at one piece of a 500-piece puzzle,” Sturdy goes on to say. “If you are not gathering data holistically, you are limited in your view. Conclusions are less than optimal at best and detrimental at worst.”
Breaking Down Silos
The process can be arduous, but if breaking down data silos were easy there’d be kiosks in the mall for it. Remember though, three million boomers every year, for the next twenty years. More beds. More care. More technology. Good data management is the foundation to streamlining hospital operations and creating serious capital cost savings.
According to a 2011 report by the McKinsey Global Institute on big data, “If US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year. Two-thirds of that would be in the form of reducing US healthcare expenditure by about 8 percent.” This 8% seems a lot more substantial when you consider that the average profitability of hospitals in the United States is 2%. This decrease in costs is equivalent to quintupling hospital profits.
Cleveland Clinic is doing their part by chipping away at it. Through their data management efforts they realized the clinic had 14 different ventilator platforms from multiple vendors. Realizing they only needed two platforms, the clinic was able to save $1.5 million over three years.
These kinds of results utilizing good data management aren't unique to Cleveland Clinic either. Oregon Health & Science University (OHSU) has over 560 inpatient beds and conducts research in almost every field of medicine. Getting a handle on their data allowed OHSU to centralize their equipment management. As a result they've been able to stop renting infusion pumps and rarely rent food pumps, saving as much as $80,000 per month in total rental costs.
Asset consolidation is just the beginning. Once a hospital actually has reliable, holistic data on its assets, things like predictive maintenance and capital equipment replacement forecasting (CERF) can start to save healthcare even more money—a lot of money. Improving equipment management can save hospitals thousands of dollars per bed, which drops right to the bottom line. It's time for hospitals to break down these data silos and start harvesting some green.