Using Predictive Analytics to Avoid Hospital Layoffs

Using Predictive Analytics to Avoid Hospital Layoffs

It seems as if news about another Hospital laying off workers - or worse, closing - hits the news every day; at last count, 21 hospitals stopped treating patients in 2016. Hospitals and healthcare systems across the country, including some of the largest, laid off thousands of employees. In November 2016 alone, 13 providers eliminated more than 1,000 jobs.

In September, HCCA's Vice President of Marketing Kathleen Johnson said, "The actions taken recently are in accordance with our planned and ongoing strategic realignment of the Hospital's operations. We continue to streamline operations, improve efficiencies and healthcare delivery to our community."

In October, UC Irvine Health spokesman John Murray said, "The layoffs are part of a multipronged approach to improving the health of the system. Before considering the layoffs, we looked for opportunities to reduce expenses as well as maximize potential revenue."

What's Going On?

Steep operating losses and severe budget shortfalls, forced by a rapidly changing landscape toward value-based reimbursements, are causing hospitals to consolidate operations as much as they can: merging operations, closing labs, reducing workers and finding every possible way to lower costs without affecting quality of care - all while continuing to meet the spiraling demand on the clinical side.

As the industry shifts to value-based care, hospitals and healthcare systems face increasing pressure to reduce their cost structures - a difficult challenge  - without affecting quality of care. For many, consolidation is the next step in the progression. But after the merger is completed and redundancies eliminated, the big question is "How do we make the most out of all these resources?"

Simply put, hospitals are under constant pressure to do more with less - in many cases, much more with much less. Every day, they face an operational paradox; scarce resources are both overbooked and underutilized within the same day. This leads to several undesirable outcomes: millions of dollars of unnecessary operational costs, long patient wait times, overworked staff and an insatiable appetite for expanding existing facilities or constructing entirely new ones.

Data to the Rescue

Electronic health records (EHRs), fueled by government regulations and incentives, are opening opportunities for operational efficiencies. In the past decade, hospitals have invested hundreds of millions of dollars in EHRs, business intelligence and Lean/Six Sigma initiatives. Now, CIOs are asking "How can we leverage all this data to reduce costs?"

In a  CIO survey conducted earlier this year, IT optimization ranked as one of the top 10 areas of focus. "Call it performance, call it tech-based enabled improvement, call it optimization. Call it whatever the buzzword is, but [we're] trying to get more capability out of the investment we've made in the past few years in EHRs," said Todd Hollowell, COO of Impact Advisors.

EHRs capture every bit of information related to a patient: historical conditions, medications, treatments, everything. The goal is to give that information to whoever needs it, whenever they need it. An obvious use case is to give everyone, from physicians to pharmacists, a valuable "situational awareness" that avoids obvious waste such as duplicate lab tests, unnecessary lab orders, etc. But that's just the beginning. If you look at the enormous amount of data stored per patient (more than two terabytes), it is possible to build sophisticated interconnected systems that can provide much better situational awareness to everyone involved in the core patient pathways.

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