Why data and analytics aren’t enough to change healthcare

Healthcare operations are undergoing change as dramatic as the discovery of penicillin to treat bacterial infection or hand washing to reduce patient mortality. The opportunity to use data to improve efficiency and drive innovation is within reach for most hospitals.

Every other major industry from shipping (UPS, Fedex) to retail (Amazon, Walmart) and airlines (United, American) has been revolutionized by their data operations. Data helps these industries know how many trucks to deploy, how much inventory to order and how to plan for unpredictable weather or traffic.

Now that hospitals have updated to their first and even second generation electronic health records, the data exists for a similar revolution in healthcare administration. All that remains is the will to change.

If availability of data was all that was necessary, hospitals would already be lowering costs while providing higher-quality patient care. Clearly, data alone is not enough. What’s missing is a cultural revolution where both clinical and non-clinical hospital leadership value data operations as the key to effective and efficient care.

Think of it this way—data provides the raw materials to build a boat and predictive analytics provide the design. Together, these build a seaworthy vessel capable of taking hospital leadership through transformation never before imagined. To continue this metaphor, most hospital innovation navigates visually and never strays far from shore. This new generation of data analytics enables leadership to embark on hospital rationalization projects that were simply unimaginable with the previous generation of tools and data.

Like all new tools, there will be a learning curve, and results will not come immediately. Investing in these tools and taking action on the results requires a cultural revolution from the top down that trusts and values data. The good news is this doesn’t have to happen all at once. Hospitals can start out with smaller projects that build confidence and validate the process. Additional projects can build on early successes to make continual incremental improvements that add up over time.

For example, it is generally very difficult to make changes to the surgical block schedule. However, hospitals can still improve operational efficiency by adjusting the staff schedule to match the historical pattern of surgical demand. Hospitals can start with a subset of staff, perhaps just on weekends, or just for a single service. Alternatively, block schedules are only changed when a surgeon retires and a new one is hired. Reliable data operations means these situations can be modeled in real-time with fresh data that incrementally nudges hospitals toward a larger shift.

Change based on data is hard to launch and easy to sink. Detractors do not need to develop an alternative plan—they generally only need to introduce just enough doubt and uncertainty to keep the plan from getting off the ground. A solid well-documented data operation can address criticism from skeptics to keep projects moving forward.

This transition is not like a new and improved drug, where the same pill produces better results. It can be imagined as a conglomerate in which contributions from all spokes in a hospital system are symmetrical.

Like all winds of change, this will be a gradual process to reach some level of maturation. It will, of course, require a meticulous collaborative effort. A cultural revolution will come only once hospital executive staff and management realize that not all innovation needs to entirely overhaul a system, but instead can make incremental improvements, resulting in a better-run hospital for both patients and staff.