Tempering the Emotionality of Data in Healthcare

The old adage “ignorance is bliss” never resonates more clearly than when watching someone respond emotionally after being presented with “wrong” data — especially in healthcare. Being human, our perceptions of reality can often be way off mark. It happens, and there are many examples. Just consider our once-held geocentric view of the universe. Ouch. Such misconceptions don’t necessarily need to be as dated or abstract, either. When considering one’s own personal perceptions of their strengths and weaknesses, we are all susceptible to self-deception. Here’s an excellent TED Talk describing the limitations of human pattern recognition and false beliefs: Michael Shermer: The pattern behind self-deception.

Suffice it to say, the human mind will go to great lengths to protect its self-image, often twisting reality in order to be more consistent with a personal “understanding.” Healthcare, surgical services, and anesthesia are not immune to this phenomenon.

Practitioners and administrators alike absorb parts of their surroundings as they go about their day and, subconsciously, coalesce those details into a collective perception of reality. Interestingly, the absence of things is just as influential in the formation of our deeply held views of “truth.” For instance, if one never hears the negative aspects of their anesthetic care, it’s naturally very easy to assume all is well. Some may then become defensive and, more concerning, dismissive of any external data inconsistent with their day-to-day perceptions.

How can we reduce the frequency of these painful, level-setting experiences? Simple: measure, manage, and repeat frequently.

  • Measurement: Collection and Analysis

Data is produced by measuring something. If you don’t regularly measure the core operational and quality aspects of your business, you have no point of reference. You have no idea whether or not you’re performing as you should, and you potentially remain blissfully incorrect in your perceptions. Measurement starts with collecting the necessary data points to support an accurate baseline that facilitates comparison over time. Identifying the relevant data points to collect requires a clear understanding of the domain. With iteration, the things that don’t matter will become evident, and you can refine the list of things you are measuring. Without the data, however, one is ill-equipped to develop the understanding necessary to affect positive change, especially when the domain is as complex as today’s healthcare system, and specifically, the practice of anesthesia.

  • Management: Identification, Education, and Accountability

Once you start measuring, you are much better equipped to know what to manage. By removing perception, you have data to show what is wrong. This obviously assumes you have the knowledge and expertise necessary to determine what constitutes “good performance”.

Now that you know the problem areas, they must be managed, and this starts with education. Poor performance doesn’t necessarily implicate negligence or incompetence. Many times it’s simple ignorance, which is very fixable. Other times it may lead to a different, yet improved, understanding of what to measure. In all cases, a data-driven conversation ensues because individuals need to know there’s a problem before they’ll make any effort to change their behavior. It’s management’s job to inform and educate.

After education comes accountability. Those responsible to make the necessary changes are given the opportunity to do so. You then continue to measure and, with new data in hand, hold the responsible parties accountable for their performance. Designing accountability structures that include the appropriate policies and oversight personnel isn’t simple. In the field of anesthesia services, such structures certainly require highly specialized subject matter expertise. However, without the ability to measure and educate, real accountability and real change will hardly materialize.

Conclusion

Repeated measurement and education builds the foundation of solid data-driven decision making. It allows for effective accountability. It also acclimates us to the dynamics of the domain we are measuring, and as we become more acclimated, our tendency to react emotionally to the results is naturally tempered. As our understanding matures, our value increases and we make better decisions. In the field of healthcare, and specifically anesthesia, the ability to make better decisions results in a better surgical patient experience, fewer complications, and a lower overall cost.

What questions do you have?

This post was written in partnership with Matthew Oldham, Director of Data Architecture at Graphium Health.