If you are at the Doctor’s for a check-up, what is often the first thing you do after being called into the nurse’s office from the waiting room?

Usually, the nurse asks you to “step on the scale please”. Followed by collecting your height, blood pressure, and temperature to name a few. These are all examples of measurement. The process of providing these measurements has become so common and routine that we often don’t think of them at all.

We especially don’t think of measurement as fundamental to the development of modern healthcare. Interestingly, the pragmatic application of measurement in healthcare is a relatively new phenomenon. For example, in the U.S., the standardized use of measurement techniques in the training of medical doctors was not established until 1893 (Clark & Kruse, 1990). For perspective, in the behavioral sciences, criteria for evidence based practice in psychology was not established until the 1990s (Task Force on Promotion and Dissemination of Psychological Procedures, 1995).

    Today, we cannot imagine a world without measurement. Our smart phones measure our locations and predict our destinations. Magnetic Resonance Imaging (MRI) uses magnets to examine the spaces inside our bodies without any invasive tools. Smart watches and biometric wearables (i.e. Geek Chic) measure our heart rates, sleep schedules, and daily patterns. We easily monitor these measurements throughout our day-to-day lives. Such a reality was unimaginable 25 years ago.

    What about the behavioral sciences? Do we have standards for measurement?

    The only universally applied measurement process in behavioral health are manualized diagnoses criteria. Both the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the International Statistical Classification of Diseases and Related Health Problems (ICD-10) are used in standard practice. Both manuals are not without issue.

    Problems associated with manualized diagnostic categories lie within the philosophy of measurement in psychology. For example, think about the differences between strep throat and a Substance Use Disorder (SUD). One illness state, bacterial infection, is measured using techniques that are considered manifest (can be directly measured or observed). While the other illness state, SUD, is widely measured using techniques that attempt to quantify a latent variable (cannot be directly measured or observed). Although a Substance Use Disorder is observable per say, the operational construct of the disease is not located in distinct cells or neurological spaces. These realities compound our efforts to identify who struggles and to what extend an individual has been effected by a SUD—contributing to our poor diagnosis standards.

    There are no other standards for measurement in behavioral health treatment contexts.

    Think about that for a moment!

    Is measurement really one of our concerns in context of today’s addiction pandemic?

    Thought experiment! How do we know treatment works?

    Throughout the field we see claims that describe 90% ‘success rates’, ‘rich alumni bases’, and very rarely some obscure statistics. Unfortunately, very few treatment systems actively publish their data in a form to which others can examine. To be clear, I believe in current treatment modalities. However, the lack of mathematical literacy and low training standards in the field of SUD treatment has severely stunted our growth (Borsboom, 2006).

    The relationship between measurement and treatment is clear. By increasing our ability to measure patients, we can increase our ability to model treatment response. Thus, expanding our understanding of what works for each individual person based on their unique symptom profile. By implementing small measurement processes we can then shift into creating feedback loops that prescribe treatments based on a patient’s symptom presentation.

    These are not new ideas. This is not science fiction. The future of addiction treatment is fundamentally tied to our ability to measure and monitor patient change.

    Where do we start? Can technology help?

    Perhaps, part of the answer, lies in the palm of your hand.

    That’s right, you’ve heard this before! Emerging health technologies can improve treatment access, support long term patient outcomes, and optimize treatment profiles that match the unique symptomatology for each individual patient (as opposed to the average patient). In other words, these processes could identify the right treatments, at the right time, for the right person—every time. These concepts have already begun to transform other domains of healthcare.

    Verily, a company owned by Alphabet (whom also owns a little company called Google), is developing tools aimed at collecting large healthcare data sets through the creation of miniaturized sensors (e.g. smart contact lenses). They hope these sophisticated biosensors can better inform clinical practice and transform healthcare.

    Mindstrong, a company founded in part by former NIMH director Thomas Insel, is focused on creating applications that measure your individual digital phenotype (i.e. digital footprint codified from your digital exhaust) (Insel, 2017). Mindstrong hopes to develop robust indicators of health and disease that can be leveraged in real-time. All of this, without any sophisticated biological sample or invasive scan (e.g. fMRI).

    The ingenuity of these large data science applications revolves around the reality that each of us already participates in the data collection process. Today, over three billion smart phones are in use worldwide. Over 75% of Americans own smart phones. The data already exist. Creating programs that collect, analyze, and disseminate these data are now in high demand. We need to participate as stakeholders.

    Is there movement in our field? Who is doing what?

    Change has already begun to take root. In 2018, the Joint Commission adopted new standards requiring all providers to begin using standardized assessments. Commercial insurance companies are developing collaborative scorecarding that combines traditional claims data with that of patient data collected throughout treatment course. The American Society for Addiction Medicine (ASAM) has created the Level of Care certification. The National Association of Addiction Treatment Providers (NAATP) launched a national advisory committee aimed at developing a robust research and outcomes agenda. Simultaneously, an explosion of turnkey outcomes management platforms and patient applications has saturated the SUD field—almost overnight. The presence of these tools is welcome change!

    A few providers are also progressively investing in efforts to support patient change through research. Hazelden Betty Ford has the Butler Center for Research and employs several full-time research scientists. Cumberland Heights Foundation has founded a Research Institute and is collaborating with academic institutions to focus on creating data feedback loops that inform treatment practice and generating predictive treatment dosage algorithms

    So, where are we going?

    The future of addiction treatment will be prescriptive and tailor-made. Measurement will not be limited to doctor’s offices, assessment interviews, or self-identification of illness states. Diagnoses and treatment dosage will be optimized based on your digital phenotype, neuroimaging profile, and/or psychometric assessment index. The use of these markers will allow professionals to monitor for treatment completion based on valid tools—not opinion.

    Emerging health technologies and data science will fundamentally transform all domains of behavioral health engagement (i.e. prevention, treatment, and long-term monitoring). Ultimately, these efforts will support our ability to increase access to recovery and help to walk with patients after discharge from traditional acute programs.

    Very simply, the future of addiction treatment lies within our data.