ReviewDialogues in clinical neuroscience2021
Improving clinical decision-making in psychiatry: implementation of digital phenotyping could mitigate the influence of patient's and practitioner's individual cognitive biases.
Review in Dialogues in clinical neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Neuroimmune pharmacotherapy across the skin-brain axis: mechanisms, therapeutic targets, and AI-enabled precision approaches.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- A multimodal Bayesian network for symptom-level depression and anxiety prediction from voice and speech data.Scientific reports · 2026Article
- Developing a Suicide Risk Prediction Algorithm Using Electronic Health Record Data in Mental Health Care: Real-World Case Study.JMIR medical informatics · 2026Article
- Innovative Digital Cognitive Behavioral Treatment for Insomnia Disorder in Adults (dCBT-i): Framework Development.JMIR human factors · 2025Article
- Differential diagnosis of autism, attachment disorders, complex post-traumatic stress disorder and emotionally unstable personality disorder: A Delphi study.British journal of psychology (London, England : 1953) · 2025Article
- Forecasting mental states in schizophrenia using digital phenotyping data.PLOS digital health · 2025Article
- Risks to the clinician of risk management: recalled and anticipated consequences of decision-making.Frontiers in psychiatry · 2025Article
- Neural mechanisms of maladaptive risk decision-making across psychiatric disorders.Frontiers in behavioral neuroscience · 2025Review
- Diagnostic overshadowing in psychiatry: A case report.Indian journal of psychiatry · 2024Article
- Disparities Associated With Electronic Behavioral Alerts for Safety and Violence Concerns in the Emergency Department.Annals of emergency medicine · 2024Article
- What digital health technology types are used in mental health prevention and intervention? Review of systematic reviews for systematization of technologies.Journal of occupational health · 2024Review
- Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health.Journal of medical Internet research · 2023Article
- Digital mental health: challenges and next steps.BMJ mental health · 2023Review
- Translational chasm and dialogues in clinical neuroscience.Dialogues in clinical neuroscience · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High stake clinical choices in psychiatry can be impacted by external irrelevant factors. A strong understanding of the cognitive and behavioural mechanisms involved in clinical reasoning and decision-making is fundamental in improving healthcare quality. Indeed, the decision in clinical practice can be influenced by errors or approximations which can affect the diagnosis and, by extension, the prognosis: human factors are responsible for a significant proportion of medical errors, often of cognitive origin. Both patient's and clinician's cognitive biases can affect decision-making procedures at different time points. From the patient's point of view, the quality of explicit symptoms and data reported to the psychiatrist might be affected by cognitive biases affecting attention, perception or memory. From the clinician's point of view, a variety of reasoning and decision-making pitfalls might affect the interpretation of information provided by the patient. As personal technology becomes increasingly embedded in human lives, a new concept called digital phenotyping is based on the idea of collecting real-time markers of human behaviour in order to determine the 'digital signature of a pathology'. Indeed, this strategy relies on the assumption that behaviours are 'quantifiable' from data extracted and analysed through connected tools (smartphone, digital sensors and wearable devices) to deduce an 'e-semiology'. In this article, we postulate that implementing digital phenotyping could improve clinical reasoning and decision-making outcomes by mitigating the influence of patient's and practitioner's individual cognitive biases.
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