A Preview of My Treatment Outcome Data

As a scientist-practitioner, I am well aware that therapists aren’t always good at judging their patients’ outcomes. That’s why I love statistics. Numbers offer an objective, quantitative view. Numbers paint a clear picture that words cannot.

I believe strongly in evidence-based treatment. I also believe in therapist transparency – that is, that therapists should explain to patients and their families what interventions they are using, why they are using them, and what evidence is behind them.

For these reasons, I have chosen to collect data on my own patients and measure their response to treatment. I want prospective patients and their families to have access to these data to assist them in choosing a clinician. After four years in private practice, I have finally seen enough patients to collect treatment outcome data with a decent-sized sample. I am compiling these data for the purpose of improving the quality of my own practice. The results of my patients’ treatment outcomes are not intended to be used to generalize to other populations.

Since opening my practice in 2009, I have evaluated 138 patients. Eighty-nine percent (n = 123) of these patients were female and (n = 15) 11% were male. They ranged in age from 7-64, with a median age of 18 and a modal age of 15. The majority of patients were between the ages of 10 and 25.

The patients’ primary diagnoses were as follows:
• 54% (n = 75) had eating disorders, including anorexia nervosa, bulimia nervosa, and EDNOS
• 22% (n = 30) had mood disorders, such as major depressive disorder or bipolar disorder
• 10% (n = 14) had anxiety disorders, such as OCD or social anxiety disorder
• 8% (n = 11) did not meet criteria for any psychiatric disorder, but rather came to me for help with a specific problem, such as coping with parents’ divorce or stress management.
• 6% (n = 8) had an assortment of other primary diagnoses, including borderline personality disorder, adjustment disorder, or body dysmorphic disorder.

I work on a sliding scale based on the patient’s ability to pay. Sixty-three percent (n = 87) paid my full rate and 37% (N = 51) paid a reduced rate due to their financial circumstances (e.g., unemployment, low income, single parent supporting children alone, or college student paying for his/her own treatment). I saw 16% of these patients (n = 22) for evaluation and/or consultation only. The remaining 84% (n = 116) attended at least one treatment session with me.

All former patients who attended at least one treatment session with me are included in this sample. In addition, four patients who have completed their treatment but have opted to continue seeing me two or three times per year for “check-ups” were included as well. Patients who are currently in treatment with me were not included.

Over the next few weeks, I will be blogging about the end-of-treatment outcomes of my former patients, categorized by primary diagnosis. I am also in the process of conducting a follow-up study, and I hope to publish those data by the end of the summer. All data will be reported in aggregate form so that no individual patients will be identifiable.

Randomized controlled trials (RCTs) are the gold standard in treatment outcome research. However, other types of studies can be quite useful as well. My study tells a different story from the RCTs – the story of clinical practice in the “real world,” with all of the freedoms and confounds that come with it. While I do use evidence-based treatments such as Family-Based Treatment (FBT) and Cognitive Behavioral Therapy (CBT), I frequently make modifications to the manualized form of treatment based on the needs of the individual patient and family.

In addition, rather than fitting all patients into a 10-session or 20-session protocol, the length of treatment varied based on individual needs. Essentially, patients could stay in treatment until they were completely well. Insurance constraints were not an issue, as I don’t participate on insurance panels, and finances were not a deterrent from completing treatment, as I am very flexible with my sliding scale.

So what do treatment outcomes look like in the real world? You’ll have to keep reading my blog to find out!