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Eric Meier, President and CEO of Owl, talks about what are some of the latest methods for measuring outcomes in our field, why it’s important, and how an organization implement measurement-based care in behavioral health successfully.

"Measurement-based care" is a term that has grown in popularity in the past year, but in behavioral health, it has not been without its challenges. I spoke with Eric Meier, President and CEO of Owl, about the latest methods for measuring outcomes in our field, why it’s important, and how an organization can implement measurement-based care in behavioral health successfully.

Given the high spend and significant investments into behavioral health, how will we know if we’re making progress?  

We are at the point of undisputed realization that behavioral health issues are very real and at times, serious.  With $225 billion spent on behavioral  health treatment and services in 2019, according to OpenMinds, and $1.5B in VC funding towards behavioral health technology and services in 2020, we must ask ourselves, are we making progress?

It’s promising to see the increase in resources being deployed into the field, but I still believe there is a critical missing component to the conversation - how are we measuring quality and outcomes?

After all, how can one improve something that they cannot measure?

Though widely understood that measurement-based care, the systematic evaluation of patient symptoms before or during an encounter to inform behavioral health treatment, is clinically validated to increase treatment effectiveness and improve patient outcomes, only 18% of psychiatrists and 11% of psychologists use it in routine practice (Jensen-Doss et al., 2018).

One of the biggest reasons why the adoption rate of measurement-based care has been so slow is because until very recently, such tools and approaches have not been easy to use. They were complex and would not integrate easily with existing clinical workflows and health care IT infrastructure, giving clinicians great pause. The good news is that today we have some incredibly user-friendly measurement-based care solutions that seamlessly integrate with clinical workflows so that clinicians can easily incorporate measurement-based care into their care model without any wasted time or disruption of their regular routines. Furthermore, these solutions are very simple for patients to use, delivering highly engaging experiences. 

With measurement-based care, clinicians receive patient-reported information on their moods, behaviors and feelings prior to their appointment throughout the entire course of treatment. This gives clinicians powerful context to focus on with the patient during the encounter, leading to a more personalized, targeted and efficient session. By answering evidence-based, clinically validated questionnaires delivered automatically to their phone, patients provide valuable data that can be tracked longitudinally to monitor progress and measure outcomes. Measurement-based care is proven to improve clinical outcomes and efficiency,  and improve the therapeutic alliance between the clinician and patient. 

Why is measurement-based care in behavioral health so important?

The more we measure care, the more we can show improvement and ultimately accelerate patient recovery times and improve patient access. The more we speed up patient recovery times and open up access to care, the better off for everyone.

Another way to answer this question is to flip it around a bit:  Imagine if there was no widely adopted form of measuring treatment effectiveness for physical medical issues including hypertension, diabetes, and cancer. The concept of measuring patient progress is so tightly embedded within the practice that the term “measurement based care” barely exists. It’s just part of the everyday routine. After all, can you think of a situation where an oncologist or cardiologist would not rely on measurements to prove patient progress and guide care? Of course not, so why should it be any different with behavioral health?

And as we are seeing the move from fee for service towards value-based reimbursement contracts in many segments of behavioral health, measuring and proving outcomes will be a necessity.

How can an organization implement measurement-based care successfully?

Based on experience helping many organizations successfully implement measurement-based care, here are my four critical success factors:

High patient engagement: measurement-based care only works if patients are engaged, so you have to provide them with an easy approach to access and complete the clinical assessments with the user-friendly experience they have come to expect from every other online or app experience that they use daily. I’ve seen well over 80% patient engagement rates when this is done right.

Ability to track treatment throughout the course of care: measurement-based care isn’t a one or even two-time occurrence at the beginning and/or end of treatment. It is a continual, regular part of the ongoing treatment approach to track and monitor progress and achieve the desired treatment target.  If you need to adjust care, you can do so with confidence. With an automated solution, you can ‘set it and forget it,’ so that the right assessments are sent to the right patients at the right time based on the nature of the condition, symptom severity, and treatment approach.

Data that not only looks at the individual patient level, but the population in aggregate for the organization: Today’s behavioral health executives need analytical tools to understand their patient population and any trends and insights that can help them further improve clinical care and financial results. Understanding treatment and clinician effectiveness by individual and group empowers leaders to make decisions that improve clinical outcomes.

Data to communicate effectively with payers: As payment models move towards value-based care, behavioral health executives need critical data on patients, their conditions, and effectiveness of care to share with their payers to inform accurate reimbursement models based on a patient’s condition, treatment progress, and not simply based on time.