Measurement-Based Care as a Mental Health Intervention

Whether Measurement-based care is beneficial in addressing psychiatric health concerns has become contentious. According to Lewis et al. (2022), Measurement-based care, or MBC, involves organized collection and utilization of client information throughout treatment. The client data influences the initial screening and evaluation, problem analysis and definition, and completion of treatment goals and intervention approaches (Lewis et al., 2018). More so, client data influences treatment follow-up to ensure sound treatment adjustment. Therefore, MBC is essential to the evidence-based intervention orientation to psychiatric treatment.

Measurement-based care is a 2006 model by Trivedi to prevent health deterioration among patients with potentially failing treatment and, more so, to allow cost-effective patient care. This measure allows physicians to constantly measure patients’ symptoms and how the client responds to each treatment (Trivedi et al., 2013). That facilitates adjusting treatment in compliance with MBC measures. MBC comprises two processes: routine analyses like measuring the symptoms’ severity with rating scales and using assessments to make critical decisions. The development of these rating scales paved the way for the fine-tuning and implementation MBC measures.

Background

Evidence-based practices such as interpersonal and cognitive behavioural therapy consider health progress and outcome monitoring critical. However, supplementing regular care with MBC measures can mitigate clients’ psychological health concerns, social role functioning, and interpersonal problems and improve their lives (Scott & Lewis, 2015). The primary beneficiaries of this measure are patients with potentially failing treatments.

Previous studies have proved MBC’s efficacy in improving patient wellness. According to Trivedi et al. (2013), applying MBC to youth patients who had received treatment progress feedback attracted more health benefits than those who did not. This author linked MBC to increased active involvement in the treatment procedures by patients. More so, patients assigned to physicians who re-examined the symptom identification scale and the self-report in the session were more willing to participate in the treatment decisions than those undergoing normal interventions (Trivedi et al., 2013). In another study, Scott & Lewis (2015) found that patients who finished the depressive symptoms’ self-report reported that MBC enabled them to quantify their syndromes and understand how to manage their ailments.

In the same context, MBC’s ability to alert physicians and caregivers on the lack of health progress helped them choose suitable interventions, leading to improved health outcomes. In concurrence, Coonors et al. (2022) argued that MBC can avail critical information about suitable clinical interventions depending on the clinicians’ measure and approach. Either MBC effectively organized the evaluation procedures and assisted clinicians in differentiating diagnoses. This measure could impact clinical judgment accuracy by objectively evaluating the patient’s treatment progress.

Lewis et al. (2018) argued that MBC could orchestrate collaborative care among caregivers from various organizations. This author found that using standardized depression measures to evaluate symptom severity among patients with comorbid depressive syndromes entails the communication of patients’ scores. Fortunately, sharing patient outcomes with primary care, nurses, and physicians improves depression outcomes significantly. In another study, administering similar depression measure weekly and sing data to recommend treatment among teams improved depression outcomes (Fortney et al., 2016). Constant use of MBC measures can avail assessment data for mental health organizations and detect the organization’s general performance. Such performance analysis can influence funding decisions and provide more quality-of-care management, improving patient care via other initiatives (Trivedi et al., 2013). MBC proved vital in motivating caregivers to comply with standardized medical interventions and optimize evidence-based care for mental health patients.

Development and Standardization

Measurement-based care uses symptom rating scales to determine outcomes that could influence patient-centred clinical decision-making. Despite many rating scales used to measure psychiatric symptoms’ severity, the symptom rating scale offers good outcomes. Fortney et al. (2016) state that the ordinal symptom rating scale measures variables in a natural sequence. This scale penetrates the client’s preferences, attitude and behaviour by explaining the order of responses. In other words, this scale allows clients to self-disclose their perceptions concerning psychiatric syndromes. In this regard, this test allows patients to evaluate their well-being. This test can also identify treatment remitters and responders similar to the clinician-administered rating scale (Fortney et al., 2016). Likewise, the patient-reported symptom rating scale is superior to those that allow clinicians to deliver treatment. When clinician raters contribute to the outcomes, they may attract assessment biases such as provider profiling.

Most symptom rating scales (such as the Patient Health Questionnaire 9PHQ-9) are as practical, reliable, interpretable and sensitive to change as what prevails in medical tests. For instance, PHQ-9 comprises nine items with a single question for each depressive syndrome. This scale has a PHQ-9 severity score that could identify as severe, moderately severe, moderate, mild, and minimal (Lewis et al., 2022). The brief symptom rating scales have received empirical validation to evaluate the severity and changing severity of most psychiatric ailments such as bipolar disorder, depression and anxiety. Also, many of these scales pursue certain health-related quality-of-life domains like insomnia, appetite and concentration abilities.

This study conceptualized the MBC measure as a “best practices” model for implementing, integrating six discrete implementation approaches. These included the needs assessment, electronic health record enhancements, MBC guidelines, training, team formation and tri-weekly group discussion (Coonors et al., 2022). Regarding HER enhancements, researchers included PHQ-9 in the HER and helped caregivers transfer patients’ scores from the paper to customized charts. Here, the HER worked out the sum of PHQ-9 and established overtime symptom trajectories. Caregivers used graphical data to follow up on health outcomes and report symptom changes to caregivers or clients. Physicians integrated many self-reporting questions into the progress note to gather MBC fidelity data that determine if clinicians discussed the outcomes with clients or why the PHQ-9 is incomplete. There was no need for caregivers to seek responses to the above questions. Besides, this study relied on a sample comprising caregivers and clients. Caregivers consisted of Masters level counsellors (mainly women, Caucasian, with half of them licensed). On the other hand, the study used adult clients diagnosed with depressive symptoms (both primary and secondary (Cook et al., 2021). More so, clients’ PHQ-9 score was greater than ten, and participating caregivers had enrolled them on psychotherapy.

Needs Assessment

All caregivers finished self-report measures, with some engaging in a focus group during the baseline needs assessment to identify the factors influencing the six domains in the framework for dissemination. These included clients’ attitudes, norms, structure and process, policies and incentives, resources, network and linkages, media and change agents. Clinic administrators identified focus team members using purposeful sampling to obtain extreme differences. Although the needs assessment outcomes characterized clinics, they failed to inform the implementation procedure (Trivedi et al., 2013). However, some measures encouraged caregivers to consult widely about their areas of weakness.

Initial Training

This period, in which caregivers trained, comprised 4-hour training for a month after the needs assessment. The training introduced caregivers to measurement-based care and the PHQ-9 and established basic knowledge and skills about PHQ-9 administration, reviewing score graphs and sharing the scores with clients. Some critical MBC topics included the introduction to measurement-based care and scientific support, MBC’s core components, clinical tips for mitigating poor health progress and PHQ-9 scientific support (Trivedi et al., 2013). The training included active learning approaches as guided by the adult learning models. More so, the trainer used didactic content that explains PHQ-9 administration. Finally, clinicians engaged in the group dialogue with some earning CEUs and other incentives for participating and recording good productivity.

MBC Guideline

The discussion about MBC implementation guidelines occurred during the first training and consultations. The available literature informed this guideline by clarifying that caregivers should administer and discuss the PHQ-9 with their depressed clients at the onset of each session (Scott & Lewis, 2022). Caregivers used this guideline as a strong suggestion and not a compulsory requirement.

Establishing Consultation teams

Besides caregivers, the consultation meeting organizers prioritized opinion leaders and champions as the primary invitees due to their role in clinical matters. That was in line with the self-report measures like the opinion leadership scale and Sociometric survey.

Consultation team meetings

Behaviour change clinical interventions require more than training. In this regard, consultation helped optimize skill and supplement support implementation. This consultation meeting aims to ensure clinicians embed MBC in its core procedures. Every meeting session consumed 1 hour under the external consultant through virtual tools (Fortney et al., 2016). The meeting spokesperson focused on the barriers to problem-solving in the MBC implementation. Generally, six consultation meetings took place in each participating group for five months of active implementation of this measure. Two caregivers cited clients who experienced hurdles in implementing MBC measures ( Lewis et al., 2018). Caregivers filled out a standardized care consultation form before the convention to provide the basis for the consultants’ argument. Finally, active learning approaches such as modelling, group discussion, practice and feedback helped motivate clinicians.

Description of the Scale Content

The Patient Health Questionnaire (PHQ) comprises three pages of self-administering questions. However, this scale may have nine items of depression hence the name PHQ-9. Concerning this analysis, the PHQ-9 score comprised the following classes based on the condition’s severity: 0-4, 5-9, 10-14, 15-19 and the measure above 20. Cutting points by 5 points pragmatically aimed to make it easy for clinical practitioners to remember and use scores appropriately (Trivedi et al., 2013). The empiric factor entails that diverse cut points could not have changed the associations between an escalating PHQ-9 severity and validity constructing measures.

While evaluating the functional attributes of many PHQ-9 sequences, the independent MHP structured psychiatric interview structured this diagnostic status to focus on the primary depressive ailment, additional depressive conditions or lack of any depressive ailment.

That is because the MHP structured psychiatric interview is the standard criterion and offers the most traditional approximation of the functional attributes of the PHQ-9 score (Fortney et al., 2016). Apart from distributing the specificity and sensitivity of the PHQ-9 over many intervals, researchers analyzed the approximation ratios and used the ROC curve analysis as a quantitative approach for integrating the former and the latter into one metric.

The study assessed the PHQ-9’s construct validity as the depression severity measure by analyzing the functional state symptom-related hurdles, disability days, and clinical visits over the 5 PHQ-9 intervals. We used an analysis of covariance with the PHQ-9 model as an independent variable and adjustment aspect for demographic factors and physical disorders (Cook et al., 2021). Researchers used Bonferroni’s correction to adjust for many comparisons.

This test showed that PHQ-9 had excellent internal reliability alongside the PHQ Primary Care Study of 0.89 and PHQ Ob-Gyn Study of 0.86. More so, this study had an excellent PHQ-9 test–retest reliability. The relationship between the in-patient’s PHQ-9 and that rendered through follow-up by the MHP in two days was 0.86, while the average scores were 5.03 and 5.08. Caregivers needed less than 3 minutes to analyze responses of 85% of the cases on a 3-page questionnaire comprising five modules, between 28-58 items based on the intervals (Coonors et al., 2022). Despite not measuring the PHQ depression items separately, this process consumed the least time because PHQ-9 had less than a third of the items from the complete PHQ.

Appropriate Uses

PHQ scales measure many depression disorders depending on the conditions’ items. Whereas PHQ-2 explains two items of the PHQ-9 to validate the severity degree within 14 days, this scale cannot produce a reliable diagnostic measure of severity. However, it can help screen for depressive symptoms. In contrast, a client with a positive diagnosis on the PHQ-2 scale could undergo the PHQ-9 rating scale diagnostic to find an accurate diagnostic of the patient’s condition (Scott & Lewis, 2015). With this model, clinicians would determine whether the client’s symptoms meet the depressive disorder criteria.

User Qualifications

Clients in the waiting room must complete PHQ-9 before a healthcare professional or clinician scores it. A medical assistant or an administrative staff scores this form before recording the score into the electric health record. Sometimes, the staff member assists clients with reading difficulties in completing the form (Trivedi et al., 2013). More so, all levels of trained clinicians who understand the goal and significance of PHQ tools and engagement approaches and tool completion can assist in completing the tools.

PHQ-9 Administration

The administration of PHQ-9 occurs in two ways. Firstly, a healthcare professional can administer this form by giving the copy to the patient directly to complete on their own. Secondly, this clinician can administer PHQ-9 orally as part of the room process. However, many clients complete the PHQ-9 form independently (Coonors et al., 2022). Nevertheless, oral administration requires that an administrator ask questions as they appear in the form to obtain accurate responses.

PHQ-9 Scoring

We would obtain the PHQ-9 score by adding the score for each question

The total scores ranging from 5 to 20 (at the interval of 5) indicate mild, moderate, moderately severe, and severe depression in that order.

The last question is a single screening question on suicide risk: when the client’s response is “yes,” a professional psychotherapist should further evaluate suicidal risks.

Interpretation of PHQ-9 scores

Psychometric Properties

This study used the diagnostic validity of the 9-item PHQ-9 to assess 28 primary care and 13 obstetrical clinics. In this assessment, PHQ-9 scores ˃10 had 88% sensitivity and a similar measure for specificity for major depressive disorder. The tools’ validity and reliability signified good psychometric properties (Trivedi et al., 2013). Either there is the high internal consistency of the PHQ-9. These results correlate with the previous study in which participants who recorded high PHQ-9 were likely to experience depression.

Conclusion

Measurement-based care, or MBC, is an intervention program comprising organized collection and utilization of client information throughout treatment. This model prioritizes client data throughout the clinical procedures. Therefore, MBC is essential to the evidence-based intervention orientation to psychiatric treatment.

References

Coonors, H. E., Lyon, R. A., Garcia, K., Sichel, E. C., Hoover, S., Weist, D. M. & Tebes, K. J. (2022). Implementation strategies to promote measurement-based care in schools: Evidence from mental health experts across the USA. BMC, June 21. https://doi.org/10.1186/s43058-022-00319-w

Cook, M. K., Palmer, L., Thornton, L., Rush, J. A., Tamminga, A., C., & Ibrahim, M. H. (2021). Setting measurement-based care in motion: Practical lessons in implementing and integrating measurement-based care in clinical psychiatry practice. NCBI, 17, pp.1621-1631. https://doi.org/10.2147%2FNDT.S308615

Fortney, C. J., Unutzer, J., Wrenn, G, M., Pyne, M. J., Smith, R., Schoenbaum, M. & Harbin, T., H. (2016). A tipping point for measurement-based care. Psychiatric Services, September 1. https://doi.org/10.1176/appi.ps.201500439

Lewis, C. C., Puspitasari, A., Boyd, R. M., Scot, K., Mariott, R. B., Hoffman, M., Navarro, E. & Kassab, H. (2018). Implementing measurement-based care in community health: A description of tailored and standardized methods. BMC Research Notes, 11, 76. https://doi.org/10.1186/s13104-018-3193-0

Lewis, C. C., Boyd, R. M., Marti, M.C., & Albright K. (2022). Mediators of measurement-based care implementation in community mental health setting: Results from a mixed methods evaluation. BMC, October 21. https://doi.org/10.1186/s13012-022-01244-1

Scott K. & Lewis, C. C. (2015). Using measurement-based care to enhance any treatment. Cognitive Behavior Practice, 22(1):49-59. https://doi.org/10.1016%2Fj.cbpra.2014.01.010

Trivedi, M., Morris, W. D., Wisniewski, R. S., Lesser, I., Nierenberg, A., A., Daly, E., Kurian, T. B., Grayes, N. B., & Rush, J. A. (2013). Increase in work productivity of depressed individuals with improvement in depressive symptom severity. PubMed, 170(6):633-41. https://doi:10.1176/appi.ajp.2012.12020250.