Abstract
BackgroundThe diagnostic system is fundamental to any health discipline, including mental health, as it defines mental illness and helps inform possible treatment and prognosis. Thus, the procedure to estimate the reliability of such a system is of utmost importance. The current ways of measuring the reliability of the diagnostic system have limitations. In this study, we propose an alternative approach for verifying and measuring the reliability of the existing system.MethodsWe perform Jaccard’s similarity index analysis between first person accounts of patients with the same disorder (in this case Major Depressive Disorder) and between those who received a diagnosis of a different disorder (in this case Bulimia Nervosa) to demonstrate that narratives, when suitably processed, are a rich source of data for this purpose. We then analyse 228 narratives of lived experiences from patients with mental disorders, using Python code script, to demonstrate that patients with the same diagnosis have very different illness experiences.ResultsThe results demonstrate that narratives are a statistically viable data resource which can distinguish between patients who receive different diagnostic labels. However, the similarity coefficients between 99.98% of narrative pairs, including for those with similar diagnoses, are low (< 0.3), indicating diagnostic Heterogeneity.ConclusionsThe current study proposes an alternative approach to measuring diagnostic Heterogeneity of the categorical taxonomic systems (e.g. the Diagnostic and Statistical Manual, DSM). In doing so, we demonstrate the high Heterogeneity and limited reliability of the existing system using patients’ written narratives of their illness experiences as the only data source. Potential applications of these outputs are discussed in the context of healthcare management and mental health research.
Highlights
The diagnostic system is fundamental to any health discipline, including mental health, as it defines mental illness and helps inform possible treatment and prognosis
While we acknowledge that analysing the transcripts enables researchers gain access to rich information, we argue that they are expensive in terms of time and effort which often limits the number of participants that can be analysed
We considered using data that comes directly from patients talking about their lived experiences with mental illness
Summary
The diagnostic system is fundamental to any health discipline, including mental health, as it defines mental illness and helps inform possible treatment and prognosis. The dominant taxonomic system of mental illness - the Diagnostic and Statistical Manual, or DSM - has been argued to be unreliable on the grounds of both Heterogeneity within, and comorbidity across, diagnostic categories [1, 44], suggesting that the current system of classification doesn’t fit well with people’s experiences. How such Heterogeneity is measured, is clearly critical to the assessment of DSM and other taxonomic systems. Later versions of the manual were constructed with more extensive documentation and with more explicit empirical support, concerns persisted over: criteria for revision (i.e. whether the decisions reflected the (potentially biased) perspectives of a small group of persons rather than systematic evidence), participation (i.e. inadequate opportunity for persons with divergent viewpoints to participate in the process), critical review (scepticism with the ability of persons to reach a fair, balanced, or optimal interpretation of inconclusive or inadequate research), and lack of comprehensive pilot testing (see [45])
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