This fifth in a series of posts about health data thinks about the value we get from that data. In my last post I said, The best data has value because it reflects or motivates action. Action to improve health and wellbeing of and reduce cost to individuals and populations. Action to improve life flow of persons at the center of care and work flow for people who work in health care agencies.
Let’s think about times when data helped motivate action. At few years ago I weighed almost 200 pounds. My primary care physician turned the computer screen toward me and showed me a graph of increase in my weight since I started seeing her 4 years before. A very dramatic line graph of weight increasing from 160 to 198 pounds. OMG. No wonder my new pants were getting bigger and I went from a belt to suspenders (I still have no behind to hold up my pants-the new weight did not give me more of a butt). I was motivated to lose weight. I kept a running spreadsheet of my weight, tracked my calories, changed my eating habits considerably, and lost 35 pounds in 2 years. Hurray. Data motivated me to start losing and keep losing. I bought several hand-made vests from Etsy to celebrate. Now they’re almost too tight again. Although I’ve tracked my weight since, tracking the data didn’t keep motivating me to keep the weight off.
When I worked for St. Peter’s Addiction Recovery Center in Albany, NY, managing the care of CDPHP members with behavioral health benefits, we saw that our rate of outpatient 30 day follow-up after inpatient addictions treatment was less than 30%. Outpatient follow-up is a HEDIS measure of NCQA (The National Committee for Quality Assurance accredits health plans). This Outpatient Follow-up measure made sense to us because we knew that a person’s success with sobriety was strongly associated with going directly from inpatient to outpatient treatment. Our Follow-up rate of less than 30% embarrassed us. It motivated us to make significant changes in our practice. We made sure that people in inpatient care had an outpatient appointment before they were discharged. We stayed in touch after discharge to make sure they had transportation and child care to get to their appointment and if they didn’t make it, we recontacted and helped further. The Follow-up rate increased to 75%. The data was meaningful, we looked bad, wanted to be better, and were willing to make significant changes to workflow. Increasing the score made us feel good about the changes we made. I don’t know if the Follow-up Rates are still so high. I’ve lost touch.
The moral of these stories is that meaningful data can motivate change. Going from bad to good is pretty easy (Good to great is much harder). In either case changing personal or population results requires doing something really different about work flow, life flow and habits. Adding education, training, teaching is a weak intervention and usually doesn’t move the dial much. Sustaining improvement is a whole other challenge.
