Accuracy of Anxiety Disorder Not Otherwise Specified Diagnosis in Older Veterans

Diagnostic error in mental health: a review

Diagnostic errors are associated with patient harm and suboptimal outcomes. Despite national scientific efforts to advance definition, measurement and interventions for diagnostic error, diagnosis in mental health is not well represented in this ongoing work. We aimed to summarise the current state of research on diagnostic errors in mental health and identify opportunities to align future research with the emerging science of diagnostic safety. We review conceptual considerations for defining and measuring diagnostic error, the application of these concepts to mental health settings, and the methods and subject matter focus of recent studies of diagnostic error in mental health. We found that diagnostic error is well understood to be a problem in mental healthcare. Although few studies used clear definitions or frameworks for understanding diagnostic error in mental health, several studies of missed, wrong, delayed and disparate diagnosis of common mental disorders have identified various avenues for future research and development. Nevertheless, a lack of clear consensus on how to conceptualise, define and measure errors in diagnosis will pose a barrier to advancement. Further research should focus on identifying preventable missed opportunities in the diagnosis of mental disorders, which may uncover generalisable opportunities for improvement.

A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study

BACKGROUND

Anxiety is one of the leading causes of mental health disability around the world. Currently, a majority of the population who experience anxiety go undiagnosed or untreated. New and innovative ways of diagnosing and monitoring anxiety have emerged using smartphone sensor-based monitoring as a metric for the management of anxiety. This is a novel study as it adds to the field of research through the use of nonidentifiable smartphone usage to help detect and monitor anxiety remotely and in a continuous and passive manner.

OBJECTIVE

This study aims to evaluate the accuracy of a novel mental behavioral profiling metric derived from smartphone usage for the identification and tracking of generalized anxiety disorder (GAD).

METHODS

Smartphone data and self-reported 7-item GAD anxiety assessments were collected from 229 participants using an Android operating system smartphone in an observational study over an average of 14 days (SD 29.8). A total of 34 features were mined to be constructed as a potential digital phenotyping marker from continuous smartphone usage data. We further analyzed the correlation of these digital behavioral markers against each item of the 7-item Generalized Anxiety Disorder Scale (GAD-7) and its influence on the predictions of machine learning algorithms.

RESULTS

A total of 229 participants were recruited in this study who had completed the GAD-7 assessment and had at least one set of passive digital data collected within a 24-hour period. The mean GAD-7 score was 11.8 (SD 5.7). Regression modeling was tested against classification modeling and the highest prediction accuracy was achieved from a binary XGBoost classification model (precision of 73%-81%; recall of 68%-87%; F₁-score of 71%-79%; accuracy of 76%; area under the curve of 80%). Nonparametric permutation testing with Pearson correlation results indicated that the proposed metric (Mental Health Similarity Score [MHSS]) had a colinear relationship between GAD-7 Items 1, 3 and 7.

CONCLUSIONS

The proposed MHSS metric demonstrates the feasibility of using passively collected nonintrusive smartphone data and machine learning-based data mining techniques to track an individuals' daily anxiety levels with a 76% accuracy that directly relates to the GAD-7 scale.

Identifying psychiatric diagnostic errors with the Safer Dx Instrument

OBJECTIVE

Diagnostic errors in psychiatry are understudied partly because they are difficult to measure. The current study aimed to adapt and test the Safer Dx Instrument, a structured tool to review electronic health records (EHR) for errors in medical diagnoses, to evaluate errors in anxiety diagnoses to improve measurement of psychiatric diagnostic errors.

DESIGN

The iterative adaptation process included a review of the revised Safer Dx-Mental Health Instrument by mental health providers to ensure content and face validity and review by a psychometrician to ensure methodologic validity and pilot testing of the revised instrument.

SETTINGS

None.

PARTICIPANTS

Pilot testing was conducted on 128 records of patients diagnosed with anxiety in integrated primary care mental health clinics. Cases with anxiety diagnoses documented in progress notes but not included as a diagnosis for the encounter (n = 25) were excluded.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

None.

RESULTS

Of 103 records meeting the inclusion criteria, 62 likely involved a diagnostic error (42 from use of unspecified anxiety diagnosis when a specific anxiety diagnosis was warranted; 20 from use of unspecified anxiety diagnosis when anxiety symptoms were either undocumented or documented but not severe enough to warrant diagnosis). Reviewer agreement on presence/absence of errors was 88% (κ = 0.71).

CONCLUSION

The revised Safer Dx-Mental Health Instrument has a high reliability for detecting anxiety-related diagnostic errors and deserves testing in additional psychiatric populations and clinical settings.