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By Athena Mandros

Last month I took on a difficult question—why has there been so little adoption of measurement-based care (see Why So Little Measurement-Based Mental Health Care?)? Response rate to treatment is 87% when measurement-based care is used, compared to 63% when the standard of care is used. Even more startling, measurement-based care results in a 74% remission rate, compared to 29% when the standard of care is used. Yet, measurement-based care is an exception rather than the rule in the field.

That piece sparked a lot of reader feedback, including a few interesting perspectives from Scott Zeiter, Executive Vice President, Chief Operating Officer, Grafton Integrated Health Network. Mr. Zeiter spoke about the challenges—and rewards—of making Grafton a “measurement-based” organization when it comes to planning services for children, adolescents, and adults with complex behavioral health challenges. (For more on Grafton, check out Grafton Integrated Health Network: An OPEN MINDS Organizational Profile.)

I sat down with Mr. Zeiter to learn more about why and how Grafton implemented measurement-based care. He explained the “why” this way:

Instead of stories that make us feel good, and feedback from families and referral sources, we wanted to be more data driven. And we wanted to answer the simple question, how do we know this is effective? The oncoming freight train of value-based reimbursement (VBR) means we need to know how to make good judgments as we will now be assuming financial risk for defined outcomes—we need a better idea of what worked and what didn’t work.

His description of the “how”—the journey to measurement-based care—was even more interesting. He had four key elements on how Grafton arrived at data-informed service planning for consumers: creating a culture around outcomes, determining which evidence-based practices (EBP) Grafton would adopt, implementing the right technology to implement this model, and choosing a partner to analyze the data.

Creating a culture around outcomes—After the decision to use data for consumer treatment planning, the next step is getting your clinical team to embrace the concept. Mr. Zeiter was surprised how easily the clinical professional staff agreed to the model and were willing to at least to see if it could work. Mr. Zeiter attributes this to the fact that the organization already has a culture focused around outcomes as a result of their ten-year-old goal mastery initiative, which requires clinical professionals to create actionable, measurable goals for consumers. He explained further:

I was expecting clinicians to say, “don’t tell me what to do.” What we got was the feedback, “let us give this a shot.” The biggest challenge is doing our best to ensure the behavioral data tracking that ends up falling on the people who are very busy and very stressed doesn’t overwhelm them. We needed to make sure the data entry is as simple as it could be.

Determining which EBPs to include—My key question for Mr. Zeiter was how Grafton selected measurements, and evidence-based practices, to include in their decision support model. His answer was that, based on their own data, Grafton developed service models that were “most effective” for consumers; an approach referred to as practice-based evidence. Mr. Zeiter said:

First, you must understand how “evidence” makes its way into EBPs. The first path is to a controlled experimental approach that is conducted in an academic setting to identify the effect of a treatment; this approach is long, costly, and often hard to apply in a treatment setting. The second path, and the one that offers the clearest path forward for organizations that make a commitment to measuring data and then using that to inform their practices, is a practice-based evidence approach that allows organizations to analyze their own “real world” practices (see How Can We Have “Standard Treatments” If There Are No “Standard” Consumers?).

We have a new clinical model that forces the clinician to choose the evidence-based practice they are using. And under that, there are “intervention objectives”—broad statements of method for that evidence based practice. Our hope is to link these approaches with outcomes data, so we can say that the most effective methodology was “X.” Our whole treatment process is methodologically driven. Choose a behavior to attack, clearly express the target of the goal in good behavioral terms, choose the evidence-based practice you will use, and then show how good you did.  We can then correlate all of the data in our EHR with those outcomes to drive practice-based evidence.

Implement the right technology—In order to implement a measurement-based model of care at scale, technology is essential. Grafton is implementing a new electronic health record (EHR) and has customized the EHR to both collect the consumer data they need for clinical decision making and facilitate the selection of preferred clinical pathways for each consumer.

Select a partner to analyze the data—Finally, Mr. Zeiter said they hope to select an academic partner to help them ensure the fidelity of the model and do a deep dive into the data. While the Grafton team analyzes the data, an academic partner will add bandwidth and expertise. The academic affiliation also brings additional interest and credibility to any data that Grafton may choose to publish.

The Grafton model of measurement-based care is still in its infancy and Mr. Zeiter noted that they are certain the decision support model will change over time. This is not a small undertaking but one that Grafton—and other specialty provider organizations—need to optimize outcomes and standardize service delivery for success in a value-based environment.

For more on bringing standardized decision support models to your organization, check out these resources in the OPEN MINDS Industry Library:

  1. The ‘Best Practice’ Challenge
  2. Your Organization Is Ready For VBR When .
  3. Technology As A Workforce Solution
  4. Challenges In Changing To A Culture Of Value (Or Making Any Culture Change)
  5. The Moving Target-Best Practices In ‘Complex’ Care Management
  6. Mapping Performance To Manage Value: The Clinical Data You Need To Manage The Risk Of Value-Based Reimbursement
  7. Building A Workforce For Value-Based Reimbursement = Advice From Four Executives
  8. Clinical & Patient Decision Support Software; Draft Guidance For Industry & FDA Staff
  9. ‘Virtual Psychiatrist’ Telemedicine Decision Support System Effective In Diagnosing Mental Disorders
  10. Preparing For Your ‘Augmented’ Workforce

And for more on leveraging your data, join OPEN MINDS Senior Associate Deb Adler on August 12 for her seminar, How To Build Value-Based Payer Partnerships: An OPEN MINDS Executive Seminar On Best Practices In Marketing, Negotiating, & Contracting With Health Plans.

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