Empathy through Data: How Healthcare Analytics Enables Compassionate Care

Empathy through Data: How Healthcare Analytics Enables Compassionate Care
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We are flooded with information about how big data and advanced analytics are unleashing their power in healthcare. Advanced mathematical modeling techniques and technology have joined forces to tackle tough issues for provider organizations and drive improvements in patient care.

We love data and the insights they glean. With technology’s ability to complete complex calculations in microseconds, it’s changed the way we gather and analyze data – turning the seemingly impossible into possible.

Predictive analytics and advanced analytics have been widely used in other industries for over a decade, but has only recently begun to expand into healthcare. In an industry that is focused on the well-being of others and providing the best possible care, adopting technology fueled by analytics takes careful consideration.

Analytics and big data can certainly reveal insightful information. But without understanding what the data is telling you and then what actions you should take to improve, data is just a collection of numbers – sitting impressively idle.

There is a lot about healthcare that is complicated. But what stays constant is that compassion and empathy are the foundation of providing patient care. As technology and data continue to drive improvements and offer insights, it’s important to remember that it is people who are able to make developments to the delivery of care.

Percentages, bar graphs, row after row of data – this is typically how analysis is presented. While this may be impressive, what does it really mean? What can you do with it and because of it? How can this data be applied in a provider organization to have an impactful change? That is where the role of empathy comes into play in healthcare analytics.

How can healthcare leaders make sense of an endless stream of statistics and formulate an action plan? A good place to start is to define the problem around who it is affecting and what you are trying to achieve. Build a story around the data, and state what needs to happen and why.

For example, in hospitals, the amount of time staff spend on the clock before or after a scheduled shift – referred to as incidental worked time (IWT) – is an important metric to monitor. Because there are clinical justifications that cause IWT, such as staying a little later to ensure a smooth shift transition for a high-acuity patient, most organizations have a reasonable tolerance level. However, according to research conducted by Avantas, these situations only make up about 40 percent of all IWT occurrences, leaving more than half of them deemed unnecessary and preventable.

For a single nursing unit, on average, there is an opportunity of more than twenty-thousand dollars in annual savings tied to IWT. For a hospital with 20 nursing units, this adds up to hundreds of thousands of dollars a year – a significant amount of money that could be invested elsewhere, such as new medical equipment.

A department leader can use analytics to monitor how much IWT is occurring on a particular unit and determine it needs to be reduced. They can go to the unit manager and tell him or her they need to tell their staff to be in and out on time, without considering the underlying causes. This type of approach will likely not do anything to lower incidences of IWT.

A more empathetic approach would be to talk to staff to determine reasons why their shifts are stretching longer. Maybe one staff member is just innately prompt. To them, being early is on time, and being on time is late. This is an opportunity for the manager to explain how clocking in before the time they are scheduled is actually hurting the department. Most people probably see clocking in a few minutes early as harmless because they don’t realize how costly it can be if enough people do it on a routine basis. They most likely think they are being helpful. With the right education, this scenario can be curtailed.

Perhaps another staff member is struggling to get their charting completed in time because of a new electronic records system. The root cause is determined and the manager realizes this staff member just needs additional training and education on the new charting system. By the manager spending time with their staff and seeing the issue from their perspective, it connects staff members to the solution that drives results.

As we become more reliant on data for nearly every aspect of our lives, it’s important not to lose sight that we are human and data is only meaningful if we can make sense of it and use it to make impactful improvements. This is no more important than in healthcare. When considering how to optimize the workforce to improve patient care and organizational outcomes, approaching analytics with empathy and compassion will guide the organization to impactful results.

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