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Tony Rousmaniere, Psy.D., has described dropouts as the “invisible plague” of psychotherapy but as with many clinical issues, measurement is a problem. The dropout literature tries to identify patients who prematurely terminate but the operational definition of premature termination is quite varied. This literature does not help the practicing clinician determine if dropouts are a problem in his or her practice.
At Carepaths we are interested in how data and technology can help clinicians practice more effectively, so we decided to see if we could identify some dropout benchmarks in our data. We started with looking at patients who do not return after the initial session. We looked at patients whose first clinical service was a diagnostic evaluation (90791) in November 2016. Our sample included 1335 patients from 20 group practices. Drop out was defined as failure to attend a follow up appointment within 30 days of the index visit. 81.1% or about 4 in 5 patients return for a follow-up appointment.
This chart shows variance by clinician.
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If we assume 80% as a benchmark for dropout, we see that most clinicians, 75 of 105, or 71% meet or exceed the benchmark. About 29% (30 of 105) fall below the benchmark. These data directionally accord with a recent study that estimated that 12.6% of drop outs is due to therapist effects.
A takeaway from this analysis is that private practitioners as a group do a good job at engaging patients in therapy. These good results may in turn be due to the competitive pressures of clinical practice whereby therapists better at engaging patients survive.
In upcoming blogs we will be looking further at the patients who do not return. We plan to look at case mix to guauge the effect of patient age, gender and diagnosis. A lower benchmark may be appropriate for some patient populations. We are interested in tools to enhance engagement such as automated appointment reminders (e.g. are text reminders better than email reminders?) and the use of our mobile assessment and daily monitoring. We would also like to tease out the effect of cost on patients following up.
And finally, we are also interested in a benchmark for patients who dropout later in treatment. For this analysis, we can use the presence of a scheduled appointment and indication of the clinician’s judgement that more treatment is indicated.
