Boosting the Bottom Line

Photo by National Cancer Institute on Unsplash

By Michael Larson

April 13, 2020

Improving hospital operations by managing the unexpected

Most of us have experienced emergency department triage. You have an urgent health problem, yet you are forced to wait uncomfortably for treatment as your position in the queue gets bumped by those with more serious medical conditions. Or perhaps you’re going in for a planned surgical procedure, but when you arrive, you find that they aren’t ready for you. Another common scenario: Your surgery is complete, but there are no beds available in the appropriate ward.

The challenge in managing dynamic systems like hospitals is unexpected variability. There is no way to anticipate with 100 percent certainty the number of people who will walk into the emergency department, or the severity of their conditions. There is also no way to know how long each patient will need to remain in the hospital. While historical patterns can provide clues,they aren’t perfect predictors.

As a result, hospital decision making is based on a series of trade-offs. Should the emergency department be staffed based on expected patient volume, knowing that if walk-ins exceed historical averages then wait times will grow unacceptably long? Or should staffing decisions assume above-average volume, which will help manage wait times but risk breaking the bank?

Better predictive power leads to increased efficiency

The structure of the underlying provider network can also be a source of uncertainty. Data silos between departments lead to imperfect information transferrals. And constantly evolving medical technologies can also contribute to difficulties in predictive power as historical performance becomes less representative. The cumulative effect of seemingly minor technology advancements can impact equilibrium over time. Solutions require the ability for continuous refinement.

Compounding the challenge is the time-critical nature of the work. Caregivers often must make split-second, high-stakes decisions based on imperfect information. Care must continue uninterrupted. Closing up shop in order to design, test, and implement new systems isn’t an option.

Solutions must be robust enough to accommodate multiple sources of volatility. Quiviq understands how to create flexible systems and actionable solutions that self-correct over time.

transforming healthcare systems for better efficiency, lower costs, and better patient outcomes

Better predictive power leads to increased efficiency, which in turn tends to improve a hospital’s bottom line. An agile response to unanticipated events, such as surgical complications or last-minute cancellations, is critical to maintaining smooth operations.

Thoughtful application of predictive analytics and machine learning will help proactively identify potential network bottlenecks in time to react appropriately. And robust, data-driven scheduling strategies will contribute to caregiver stability, while also driving patient satisfaction.

Tackling these issues is challenging—both technically and operationally. The issues facing healthcare are deep, systemic, and impactful. The stakes are high and the costs are steep. The Quiviq team has a history of chasing big problems and coming up with big wins. We’ve been instrumental in revolutionizing several other industries. And we’re excited to be on the frontline of transforming healthcare systems for better efficiency, lower costs, and better patient outcomes.


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