Behavioral Health Outcomes Measures: Problems, Challenges, Solutions

Psychology’s Ambitions

Human suffering, including mental and behavioral health problems, is elusive to define and measure.  Even with relatively objective markers of diagnosed mental illness, consensus is hard to find. People often have more than one diagnosable condition, and the conditions themselves are increasingly understood as dimensional rather than categorical (APA, 2013).  These   conditions also are often embedded in relational and social contexts.  To complicate things even further, they often occur on a spectrum ranging from absent to severe during the course of a care episode, or a lifetime. All of these factors may have a greater or lesser effect on the person’s functioning at different times and under different circumstances.  It is challenging for both patients and providers to sift through all of the data, all of the layers and all of the noise, in order to identify the most important factors that can guide treatment.

All of this matters because insight into the combinations of history, context, symptoms and current level of severity shape treatment selection and referral options. These insights can then support targeted use of relational and other evidence-based treatment, and potentially can be used to measure the effectiveness of treatment.  Psychology seeks to refine our understandings of these complexities and to provide solutions that focus and guide effective treatment. When are they relevant?

Quality Focus

Patients, providers and payors all want quality and value in their care. Improving our ability to recognize and assess the scope of human problems can improve the quality and value of both psychosocial and medical treatments.   

Patient input clearly is essential. While patient self-report was not believed to be a reliable or valid measure of mental health as recently as the 1980s, we now are more likely to assume that they want to communicate their problems as accurately as possible.  Many, however, require assistance with identifying their concerns – sorting them out from the multiple layers of experience, and communicating them to their providers. Well-designed assessment and outcomes measurement tools can facilitate this process.  

We also need to know whether our treatment is helping.  Studies show that therapist’s subjective evaluation of treatment effectiveness is not an accurate predictor of patient progress (Boswell & Constantino, 2015). The current lack of biomarkers leaves psychotherapy and psychiatric treatment in need of other types of measurement, particularly data reported by the patient.  When outcomes measurement is routinely integrated into clinical care, it enhances treatment effectiveness and provides ecologically valid data on the process of change (Fortney, Sladek, & Unützer, 2015).

In addition, there is steady pressure to provide more healthcare in systems of accountable or value-based reimbursement. This creates another incentive to measure behavioral health status and change as part of the emerging value-based payment model (Burwell, 2015).  In the near future, the value of psychosocial treatments is likely to be further challenged by shrinking budgets that compel both providers and insurance administrators to prioritize pharmaceutical, medical and surgical services. The lack of well-accepted behavioral outcomes measures has made it difficult to demonstrate the value of psychological treatments. In addition, the absence of quality outcomes measures has created barriers for challenging parity problems, as medical conditions may often appear to have better data in support of treatment effectiveness (Glied et al, 2015).

What tools might help in our quest both to improve the quality of our services and to document their value?

Most medical and behavioral health quality measures focus on shaping the treatment process (such as how often a procedure is followed), not on the specifics of measuring treatment outcomes. There is, however, an emerging interest in pragmatic tools with DSM criteria as a reference point. The PHQ-9 (Spitzer, Kroenke, & Williams, 1999) and Health Dynamics Inventory (Saunders & Wojcik, 2003) are examples of this trend. DSM 5 now validates this approach with its own suggested disorder-specific broadband or “cross cutting” tools (Clarke & Kuhl, 2014, APA, 2013). This targeted measurement can enhance the quality of care, helping patients tell providers what needs to be done and when that has been successful.

This does not, however, mean that the standards of psychological testing can be ignored. When tools are standardized and tested across care systems, clinicians can diagnose and assess the needed level of care with greater confidence, accuracy, and efficiency. Clinicians then can use them to improve their understanding of their patients and the effectiveness of their treatment.

Healing and Human Connectedness

Can we improve quality with more assessment without harming the therapeutic connection? The DSM does not describe the totality of a person’s life or capture the nature of their suffering or mental illness. Patients need us to do more than just gather data for diagnosis. They need to feel heard and that they have a connection with their provider.  However, using well-designed assessment and outcomes measures at appropriate times in care can help develop, or enhance, the therapeutic relationship with improved communication and understanding.

Therapeutic listening, one of the main tools of the therapy enterprise, has roots with the shaman (Frank & Frank, 1991). Troubled humans crave heartfelt connectedness.  Psychotherapy consumers experience engagement, trust, and remoralization when an attentive clinician helps them describe concerns and then uses that information in a meaningful way to help them. Meaningful feedback often increases the energy and hope that our clients need for recovery. Patients are more satisfied with comprehensive assessments and a degree of definition of their problems. We need tools that enhance this, as well as demonstrating the value of our services.

Economics

When systems of care both expect and support outcomes data collection, providers see collecting the information on their patients as a worthy effort (Slade, 2002).  However, data collection and handling adds to providers’ work, and needs to be compensated. Even free scales come with handling costs. Outcomes measurement should not be another burden on providers’ already thin profitability. There are now some billing codes (e.g., 96127, 96150, 96103) that may enable billing for use of outcomes tools.  However, much more needs to be done to make these easy to use so systems build them into their practices.

Versatility of PROMs

Patient reported outcomes measures (PROMs) are an evolving type of quality improvement method. PROMs serve to collect information and offer insight into each patient, maximizing effectiveness and minimizing cost. They may enable data gathering prior to an intake or session, increasing the time and effort available for empathic listening during the session. PROM results may support supervision, shared clinical decision-making, and quality improvement. They present objective data to signal when problems are in remission and when treatment can change or stop. PROMs enable the provider to quickly identify the most suitable treatment for an individual patient, meet his or her needs and preferences, and adapt the therapy over time to the changes in medical symptoms or condition (Gallingani et al, 2015).

Diagnosis remains an essential key to defining the nature of a problem, shaping treatment, and communicating with patients and other providers (Cradock & Mynors-Wallace, 2014). It becomes more efficient when clinicians use PROMs to assess health status, validate diagnoses, plan interventions, track results and adjust treatment. They capture more of the range of problems and co-occurring disorders that might complicate or prevent recovery. They demonstrate the clinician's intent to be thorough and comprehensive, benchmark health status for future comparison, and support data-rich referral. Finally, they support effectiveness research on individual, practice, and system levels.  PROMs that cover multiple clinical issues of concern are more efficient and valuable than fragmented ad hoc tools.

Integrating Systems of Care

Integrated behavioral and medical care reflects another contemporary shift in healthcare, using primary care physicians and staff such as behavioral health consultants in an attempt to achieve broader public health benefit. PCPs now furnish half of all mental health treatment in the U.S. (Kessler, 2006), but they also have limits to their time, training, comfort, and resources that shape their responses to mental or behavioral health risks. PROMs can be particularly helpful for PCPs and other medical generalists. Well-designed  PROMs can help providers recognize problems, design interventions, collaborate with each other and the patient, know when to refer for specialty care, reduce errors, and increase sensitivity to problems (Berner, 2009). They can increase the use of evidence-based care (Sim et al., 2001). All providers make better decisions when their judgment and treatments are supported by quality information.

Some disorders are commonly neglected (Patel et al, 2013), and each narrowband, ad hoc scale to screen or measure for the frequent co-occurring disorders adds more fragmentation. Providers have limited time available for diagnosis (Takanyagi, 2015), shaping the reliance on treatment with stimulants for disruptive children, antidepressant medications for mood problems, and anxiolytics for distress and anxiety problems. PROMs that assess for multiple conditions (broadband PROMs) are clinically expedient tools that signal one or more of these problems and then alert the provider, and may increase recognition of treatment options. 

Growing our contributions to public health

Psychosocial treatments have similar efficacy to medication and are often preferred by patients (Friedman, 2015). However, research on psychotherapy and psychosocial treatments is not funded at the level of other treatments, with only 5.4% of NIMH funding for behavioral health research in 2015. As these services evolve in the 21st century, efficient measurement is an essential part of increasing and demonstrating value. PROMs deserve integration into practice, and a higher level of economic support. We can influence this by using PROMs and advocating for fair reimbursement. Psychologists can innovate, demonstrate and guide the practice of relevant, pragmatic assessment. This innovation then can contribute to improving the full spectrum of a culture of health.

Jim Wojcik, Ph.D., LP, is Chief Psychologist and Director of Training for Canvas Health, Oakdale MN. He earned his Ph.D. from the CA School of Professional Psychology in 1984.

Richard Sethre, Psy.D., LP, is in independent practice and is the Behavioral Health Home Team Supervisor for the Natalis Outcomes BHH program. 

References

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th Ed.) Washington D.C.: Author.

Berner, Eta S. (2009). Clinical decision support systems: State of the art. AHRQ Publication No. 09-0069-EF. Rockville, Maryland: Agency for Healthcare Research and Quality. Retrieved from http://healthit.ahrq.gov/sites/default/files/docs/page/09-0069-EF_1.pdf

Boswell, J.F. & Constantino, M.J.  (2015). Measurement-based Care: Enhancing Mental Health Decision Making and Patient Outcomes. The Clinical Psychologist, 68(4), 5-11.

Burwell, Sylvia M.  (2015). Setting Value-Based Payment Goals – HHS Efforts to Improve U.S. Health Care.  The New England Journal of Medicine, 372(10), 893-897.

Clarke, D. & Kuhl, EA. (2014). DSM-5 cross-cutting symptom measures: a step towards the future of psychiatric care? World Psychiatry; 3(3): 314–316.

Cradock, N. & Mynors-Wallace, L. (2014). Psychiatric diagnosis: Impersonal, imperfect, and important. The British Journal of Psychiatry, 204, 93–95. doi: 10.1192/bjp.bp.113.133090.

Fortney J., Sladek , R. Unützer , J. (2015). Fixing behavioral healthcare in America. The Kennedy Forum, Washington D.C.   http://thekennedyforum-dot-org.s3.amazonaws.com/documents/KennedyForum-MeasurementBasedCare_2.pdf

Frank, J.D. & Frank, J.B. (1991). Persuasion and Healing: A Comparative Study of Psychotherapy, 3d edition. Baltimore: Johns Hopkins University Press.

Friedman, R.A. (2015). Psychiatry’s identity crisis. New York Times Sunday Review, July 17, 2015. Retrieved  from http://www.nytimes.com/2015/07/19/opinion/psychiatrys-identity-crisis.html?_r=1

Glied, S.A., Stein, B.D., McGuire, T.G., Beale, R.R., Dufy, F.F., Shugarman, S., and Goldman, H.H. (2015).  Measuring performance in psychiatry: A call to action. Psychiatry Services, 66:8, Aug.

Kessler, R. (2006) Identifying and screening for comorbid psychological and medical disorders in medical settings. Journal of Clinical Psychology, 65(3), 253-267. DOI: 10.1002/jclp 20546

Patel, V, Belkin, G.S., Chockalingam A., Cooper J., Saxena S, Unützer J. (2013). Grand Challenges: Integrating Mental Health Services into Priority Health Care Platforms. PLoS Med, 10(5): e1001448. doi:10.1371/journal.pmed.1001448

Saunders, S. M. & Wojcik, J. V. (2003). The Health Dynamics Inventory. Toronto: Multi-Health Systems.

Sim, I., Gorman, P., Greenes, R..A., Haynes, R.B., Kaplan, B., Lehmann, H., & Tang, P.C. (2001). Clinical decision support systems for the practice of evidence-based medicine. Journal of the American Medical Association, 8(6), 527–534. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC130063/

Slade, F.M. (2002). What outcomes to measure in routine mental health services, and how to assess them: A systematic review. Australian and New Zealand Journal of Psychiatry, 36:743–753. Retrieved from http://www.atsu.edu/research/pdfs/mental_hlth_outcomes.pdf

Spitzer, R., Kroenke K., & Williams, J. (1999). Validation and utility of a self-report version of PRIME-MD: The PHQ Primary Care Study. Journal of the American Medical Association, 282, 1737-1744.

Takayanagi, Y, Spira, A.P., Bienvenu, O.J., Hock, R.S., Carras, M.C., Eaton, W.W. & Mojtabai R. (2015). Antidepressant use and lifetime history of mental disorders in a community sample: Results from the Baltimore Epidemiologic Catchment Area Study. Journal of Clinical Psychiatry, 76(1), 40-4. doi: 10.4088

 

 

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