Clinically useful and evidence-based mental health assessment requires the identification of strategies that maximize diagnostic accuracy, inform treatment planning, and make efficient use of clinician and patient time and resources. This study uses classification tree analyses to determine whether parent- and child-report instruments, alone or in combination, can accurately predict diagnoses as measured by the Anxiety Disorders Interview Schedule (ADIS). The ADIS, which is the gold-standard semistructured interview for anxiety disorders in children and adolescents, requires formal training and lengthy administration. Data were collected as part of the standard diagnostic assessment process for 201 patients (ages 5 to 17 years) in an urban outpatient psychiatry specialty clinic. Analyses examined 2 models to determine which predictors reached an acceptable level of diagnostic accuracy for generalized anxiety, social anxiety, and separation anxiety disorders. The first model used scores on a parent- and child-report anxiety measure combined with demographic factors, and the second model incorporated a broad-band measure of child psychopathology and a depression measure into the analysis. Although demographic factors did not emerge as accurate predictors in either model, particular measures, either alone or in combination, were able to predict specific ADIS diagnoses in some cases, allowing for the potential streamlining of ADIS administration. These results suggest that a classification-tree analysis lends itself to the construction of simple algorithms that have high clinical utility and may advance the feasibility and utility of evidence-based assessment strategies in real-world practice settings by balancing cost effectiveness, administration demands, and accuracy.
Objective: Collaborative approaches to pediatric primary care are increasingly recognized as a way to improve access to mental health care, but certain collaborative care models are not well suited for smaller, independent pediatric practices. We describe the development of the Mood, Anxiety, ADHD Collaborative Care (MAACC) program, based on the Chronic Care Model (CCM) and a hub-and-spoke organization for collaborating with such practices. Method: MAACC's clinical team (coordinator, psychologist, psychiatrist) trained and collaborated with 46 pediatricians in 13 independent practices. Key services included a diagnostic evaluation by the psychologist, treatment planning for both psychotherapy and pharmacotherapy, tailored referrals to evidence-based therapy, pediatrician access to a psychiatrist for medication consultation, and centralized measurement-based progress monitoring. Results: During the 15-month start-up period, 234 patients were referred; 149 patients received an evaluation, 83 received a new referral for therapy, and 88 received medication recommendations for combined psychotherapy and medication. Patients experienced significant improvement in attention deficit/hyperactivity disorder and anxiety disorder symptoms. Pediatrician attitudes and access to care substantially improved. Conclusion: MAACC demonstrates the feasibility of implementing a CCM-derived model for collaborative care with independent pediatric practices.
Implications for Impact StatementCollaboration among mental health and pediatric primary care providers is essential to addressing the substantial pediatric mental health burden. Financially sustainable extensions of collaborative care models to small, independent pediatric practices are needed. The Mood, Anxiety, ADHD Collaborative Care program is a high-quality collaborative model designed for pediatric primary care.
Increasing care accessibility, integrating MH services into primary care settings, and targeting socioeconomically disadvantaged subgroups could improve rates of PCMH care among adolescents with MH needs.
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