A review of information sources and analysis methods for data driven decision aids in child and adolescent mental health services.

OBJECTIVE: Clinical data analysis relies on effective methods and appropriate data. Recognizing distinctive clinical services and service functions may lead to improved decision-making. Our first objective is to categorize analytical methods, data sources, and algorithms used in current research on information analysis and decision support in child and adolescent mental health services (CAMHS). Our secondary objective is to identify the potential for data analysis in different clinical services and functions in which data-driven decision aids can be useful. MATERIALS AND METHODS: We searched related studies in Science Direct and PubMed from 2018 to 2023(Jun), and also in ACM (Association for Computing Machinery) Digital Library, DBLP (Database systems and Logic Programming), and Google Scholar from 2018 to 2021. We have reviewed 39 studies and extracted types of analytical methods, information content, and information sources for decision-making. RESULTS: In order to compare studies, we developed a framework for characterizing health services, functions, and data features. Most data sets in reviewed studies were small, with a median of 1,550 patients and 46,503 record entries. Structured data was used for all studies except two that used textual clinical notes. Most studies used supervised classification and regression. Service and situation-specific data analysis dominated among the studies, only two studies used temporal, or process features from the patient data. This paper presents and summarizes the utility, but not quality, of the studies according to the care situations and care providers to identify service functions where data-driven decision aids may be relevant. CONCLUSIONS: Frameworks identifying services, functions, and care processes are necessary for characterizing and comparing electronic health record (EHR) data analysis studies. The majority of studies use features related to diagnosis and assessment and correspondingly have utility for intervention planning and follow-up. Profiling the disease severity of referred patients is also an important application area.

Economic evidence of clinical decision support systems in mental health: A systematic literature review.

Mental health conditions are among the highest disease burden on society, affecting approximately 20% of children and adolescents at any point in time, with depression and anxiety being the leading causes of disability globally. To improve treatment outcomes, healthcare organizations turned to clinical decision support systems (CDSSs) that offer patient-specific diagnoses and recommendations. However, the economic impact of CDSS is limited, especially in child and adolescent mental health. This systematic literature review examined the economic impacts of CDSS implemented in mental health services. We planned to follow PRISMA reporting guidelines and found only one paper to describe health and economic outcomes. A randomized, controlled trial of 336 participants found that 60% of the intervention group and 32% of the control group achieved symptom reduction, i.e. a 50% decrease as per the Symptom Checklist-90-Revised (SCL-90-R), a method to evaluate psychological problems and identify symptoms. Analysis of the incremental cost-effectiveness ratio found that for every 1% of patients with a successful treatment result, it added €57 per year. There are not enough studies to draw conclusions about the cost-effectiveness in a mental health context. More studies on economic evaluations of the viability of CDSS within mental healthcare have the potential to contribute to patients and the larger society.

Challenges in Interpreting Norwegian Child and Adolescent Mental Health Records.

The Electronic Health Record system BUPdata served Norwegian Child and Adolescent Mental Health Services (CAMHS) for over 35 years and is still an important source of information for understanding clinical practice. Secondary usage of clinical data enables learning and service quality improvement. We present some insights from explorative data analysis for interpreting the records of patients referred for hyperkinetic disorders. The major challenges were data preparation, pre-analysis, imputation, and validation. We summarize the main characteristics, spot anomalies, and detect errors. The results include observations about the patient referral diversity based on 12 different variables. We modeled the activities in an individual episode of care, described our clinical observations among data, and discussed the challenges of data analysis.

Guideline evaluation and implementation mechanisms in school health services (GuideMe): protocol for a hybrid randomized factorial trial.

BACKGROUND: Norwegian school health services received a national best-practice guideline in 2017. To promote healthy life skills and identify adolescents needing support, the guideline includes strong recommendations for individual consultations with all 8th graders and increased collaboration with schools. To help implement the recommendations, a blended implementation strategy (SchoolHealth) was co-created with school nurses, students, and stakeholders. SchoolHealth consists of three implementation elements: Digital dialog and administration tool (audit and feedback +), Dialog support (external consultation), and Collaboration materials (targeted dissemination). This hybrid study will test the main and combined effects of the elements on guideline fidelity and effectiveness. METHODS: The GuideMe study is a factorial cluster randomized controlled trial examining SchoolHealth's effectiveness on guideline fidelity and guideline effectiveness goals. Forty Norwegian secondary schools will be randomized to eight different combinations of the elements in SchoolHealth. Participants will include school nurses and school personnel from these schools, and 8th grade students (n = 1200). Primary outcomes are school nurses' fidelity to the guidelines and student's ability to cope with their life (i.e., health literacy, positive health behaviors and self-efficacy). Quantitative methods will be used to test effects and mechanisms, while mixed- and qualitative methods will be used to explore mechanisms, experiences, and other phenomena in depth. Participants will complete digital questionnaires at the start and end of the schoolyear, and after the consultation during the schoolyear. The study will run in two waves, each lasting for one school year. The multifactorial design allows testing of interactions and main effects due to equal distribution of all factors within each main effect. Sustainment and scale-up of optimized SchoolHealth elements using national infrastructure are simultaneously prepared. DISCUSSION: The study will investigate possible effects of the implementation elements in isolation and in combination, and hypothesized implementation mechanisms. In-depth study of user experiences will inform improvements to elements in SchoolHealth. The results will yield causal knowledge about implementation strategies and the mechanisms through which they assert effects. Mixed-methods will provide insights into how and when the elements work. Optimizing guideline implementation elements can support adolescents in a crucial life phase. TRAIL REGISTRATION: ISRCTN24173836. Registration date 8 August 2022.

Usability of the IDDEAS prototype in child and adolescent mental health services: A qualitative study for clinical decision support system development.

Introduction: Child and adolescent mental health services (CAMHS) clinical decision support system (CDSS) provides clinicians with real-time support as they assess and treat patients. CDSS can integrate diverse clinical data for identifying child and adolescent mental health needs earlier and more comprehensively. Individualized Digital Decision Assist System (IDDEAS) has the potential to improve quality of care with enhanced efficiency and effectiveness. Methods: We examined IDDEAS usability and functionality in a prototype for attention deficit hyperactivity disorder (ADHD), using a user-centered design process and qualitative methods with child and adolescent psychiatrists and clinical psychologists. Participants were recruited from Norwegian CAMHS and were randomly assigned patient case vignettes for clinical evaluation, with and without IDDEAS. Semi-structured interviews were conducted as one part of testing the usability of the prototype following a five-question interview guide. All interviews were recorded, transcribed, and analyzed following qualitative content analysis. Results: Participants were the first 20 individuals from the larger IDDEAS prototype usability study. Seven participants explicitly stated a need for integration with the patient electronic health record system. Three participants commended the step-by-step guidance as potentially helpful for novice clinicians. One participant did not like the aesthetics of the IDDEAS at this stage. All participants were pleased about the display of the patient information along with guidelines and suggested that wider guideline coverage will make IDDEAS much more useful. Overall, participants emphasized the importance of maintaining the clinician as the decision-maker in the clinical process, and the overall potential utility of IDDEAS within Norwegian CAMHS. Conclusion: Child and adolescent mental health services psychiatrists and psychologists expressed strong support for the IDDEAS clinical decision support system if better integrated in daily workflow. Further usability assessments and identification of additional IDDEAS requirements are necessary. A fully functioning, integrated version of IDDEAS has the potential to be an important support for clinicians in the early identification of risks for youth mental disorders and contribute to improved assessment and treatment of children and adolescents.