Big Data, Big Medicine
From Intuition to Science Big Data Helps Run Your Practice Efficiently Mudit Garg and Brent Newhouse How do you plan? Every day, care providers and managers make critical decisions regarding how they manage their departments. They must answer questions such as, “If I don’t know how many patients are coming tomorrow, how do I staff appropriately?” and “How do I keep wait times down?” or, for surgery centers, “How should I optimally allocate OR blocks?” These are critical operational decisions. They impact not only the efficiency of a clinical service but, importantly, the quality of care that is delivered. Getting these decisions wrong means potentially turning patients away, violating nursing ratios, creating bottlenecks in patient flow, or worse. Getting them right means improving resource use, effectively managing cost, and creating a more satisfying patient experience. But today, many clinicians and managers are forced to “fly blind.” They are left to make operational decisions based on some combination of intuition, past habits, and perhaps a spreadsheet or two. Imagine an Emergency Department manager, Peter, who has to plan staffing for the upcoming week in his department. Intuitively, he believes that Mondays and Fridays are busier days, and that the volume usually starts to pick up around 2:00 p.m. So he assigns an extra physician to the afternoon swing shift, and he starts the evening nursing shift a few hours earlier than usual. Though the staff is a little resistant to making the last-minute change, they go ahead. The change feels like the right one, Peter decides. In reality, it might turn out that Mondays and Fridays are busy for a variety of environmental factors, some of which are about to change in the upcoming week. And as for the 2:00 p.m. spike, the data shows that the spike starts at different times of the day depending on seasonal and other external factors, which Peter does not consider. But it’s hard to blame him; it’s almost impossible to discern these nuanced statistical relationships without some sort of computational help. This is where the idea for our company, analyticsMD, was born. It is possible to supports clinicians and hospital managers with these operational decisions by converting data into usable and actionable insights. Forecasting Demand and Staff Planning
As the anecdote above highlights, patient demand can be difficult to predict in many departments in hospitals or clinics. Sometimes patient demand can appear almost completely random. Some days are busy, others aren’t. Some days very sick patients come in, other days they don’t. This apparent randomness can make it difficult to choose the appropriate level of staffing. In response, many department managers and 20 San 21 SanFrancisco FranciscoMedicine Medicine October October2013 2013
clinicians choose a flat level of staffing—again, largely based on historical experience and intuition—and then make adjustments right before or during shifts. This method of demand planning has costs, both visible and invisible. The most obvious cost to last-minute adjustments are the overtime, on-call, or agency hours that must be incurred to meet an unexpectedly large demand. But more subtle costs exist as well. Sudden changes in schedules can be disruptive to staff and cause dissatisfaction and, over the long term, higher turnover rates. Additionally, a last-minute scramble to find nurses can put statutory patient ratios at risk of violation. In some cases, a consistent tendency to over- or under staff can result in gaming by staff. For instance, if it is commonly known within a team that “we’re always overstaffed at the end of the week,” some team members might be inclined to call in sick. This makes it even harder to properly plan ahead. It is interesting to note that in many other industries— consumer packaged goods, retail, and manufacturing, among others—rigorous mathematical models are used to forecast demand so that sufficient supply can be allocated. Large teams of analysts use complicated tools to predict and plan for every potential demand scenario, so just the right number of “widgets” can be ready for consumers. But these practices are much less common in clinical environments, where their use could save lives. Managers and clinicians are left to their intuition. The absence of rigorous, data-driven forecasting practices in clinical settings is especially perplexing when one also considers the vast quantity of data that most physician offices and hospital departments collect in their health IT systems. But the data is rarely used for these operational purposes.
Optimizing Patient Flow
In addition to staff planning, a second important area of operational decision making is patient flow optimization. Today, it is common for hospitals and clinics to carry out multimonth “transformations” of a given department, wherein members of that department work with a lean guru to redesign aspects of the care delivery process. The Emergency Department presents a classic example. In such a project, nurses, physicians, and staff will brainstorm improvements (“Let’s set up a low-acuity FastTrack”), implement those ideas, then measure and track the outcomes (in this example, perhaps focusing on patient length of stay). With strong support from leadership and an engaged front line, these performance improvement projects can drive meaningful gains in key metrics. But what happens when the
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San Francisco Medicine, Vol. 86, No. 8, October 2013