Correction: Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study

[This corrects the article DOI: 10.2196/31862.].

Oscillometry to support clinical assessment in asthmatic preschoolers: Real-life impact

In preschoolers, asthma control is assessed clinically using history and physical examination. In certain centres, oscillometry is used to support clinical assessment; yet its clinical utility for asthma management remains to be quantified. The objectives were to determine if oscillometry, as adjunct to clinical assessment, influences asthma assessment, management and control, compared to clinical assessment alone in preschoolers. We conducted a cross-sectional study in children aged 3-5 years with a confirmed asthma diagnosis. Oscillometry-tested preschoolers were matched by propensity score to untested children. The co-primary outcomes, the likelihood of a persistent asthma phenotype and a maintenance therapy prescription at the index visit, were examined by multivariable logistic regression. Asthma control over the next year was examined by cumulative logistic regression in the nested retrospective cohort with available drug claim data. The cohort comprised 726 (249 oscillometry-tested; 477 untested) children with 57.4% male (median age: 4.6 years). Propensity score matching resulted in comparable groups. Compared to controls, oscillometry-tested children were more frequently labelled with a persistent phenotype (67% vs. 50%; adjusted OR [95% CI]: 2.34 [1.66-3.34]) with no significant difference in maintenance therapy prescription (65% vs. 58%; 1.37 [0.98-1.92]); but experienced a lower likelihood of poor control over the next year (adjusted OR [95% CI]: 0.24 [0.08-0.74]). The association between the addition of oscillometry to clinical assessment with more persistent phenotype labelling and better asthma control supports its clinical utility; no significant impact on maintenance therapy prescription was observed at the index visit.

Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study

BACKGROUND

Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows.

OBJECTIVE

This study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction.

METHODS

Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews.

RESULTS

Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change.

CONCLUSIONS

Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642.