Why Psychiatry Needs Data Science and Data Science Needs Psychiatry

Torous, John; Baker, Justin T.

doi:10.1001/jamapsychiatry.2015.2622

Cited by 70 publications

(52 citation statements)

References 7 publications

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“…Complexities around defining diagnoses present particular challenges for mental health research. Richly annotated, longitudinal datasets matched to data science analytics offer an unprecedented opportunity for more robust diagnostics, and also the prediction of outcome, treatment response, and patient preferences to inform interventions 7 . It may also provide more effective targeting of recruitment to observational and interventional studies.…”

Section: Why Mental Health and Why Now?mentioning

confidence: 99%

Data science for mental health: a UK perspective on a global challenge

McIntosh

Stewart

John

et al. 2016

The Lancet Psychiatry

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Section: Why Mental Health and Why Now?mentioning

confidence: 99%

Data science for mental health: a UK perspective on a global challenge

McIntosh

Stewart

John

et al. 2016

The Lancet Psychiatry

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“…While patients may wish to share this type of data with their clinical providers, there are few tools that help providers access, integrate, and review the large volumes of data that can be collected through mobile devices (Chung, Cook, Bales, Zia, & Munson, 2015; Torous & Baker, 2016). Additional research is needed to identify methods for efficiently extracting actionable information from mobile data with minimal effort on behalf of providers, and to understand the extent to which this data impacts clinical decision-making and depression outcomes.…”

Section: Measurement-based Carementioning

confidence: 99%

Digital technology and clinical decision making in depression treatment: Current findings and future opportunities

2017

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Clinical decision-making encompasses a broad set of processes that contribute to the effectiveness of depression treatments. There is emerging interest in using digital technologies to support effective and efficient clinical decision-making. In this paper, we provide ‘snapshots’ of research and current directions on ways that digital technologies can support clinical decision-making in depression treatment. Practical facets of clinical decision-making are reviewed, then research, design, and implementation opportunities where technology can potentially enhance clinical decision-making are outlined. Discussions of these opportunities are organized around three established movements designed to enhance clinical decision-making for depression treatment, including measurement-based care, integrated care, and personalized medicine. Research, design, and implementation efforts may support clinical decision-making for depression by (a) improving tools to incorporate depression symptom data into existing electronic health record systems, (b) enhancing measurement of treatment fidelity and treatment processes, (c) harnessing smartphone and biosensor data to inform clinical decision-making, (d) enhancing tools that support communication and care coordination between patients and providers and within provider teams, and (e) leveraging treatment and outcome data from electronic health record systems to support personalized depression treatment. The current climate of rapid changes in both healthcare and digital technologies facilitates an urgent need for research, design, and implementation of digital technologies that explicitly support clinical decision-making. Ensuring that such tools are efficient, effective, and usable in frontline treatment settings will be essential for their success and will require engagement of stakeholders from multiple domains.

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“…Technology can support and enhance clinical practice (Torous and Baker 2016), for example, by incorporating data from the app and physiological data into electronic medical records. This method can allow a prompt detection of mood episodes (Grof et al 1993), and possibly allow intermittent treatment, with lithium salts, for example, in those patients with a classical type of bipolar illness, which will decrease side effects and improve compliance.…”

Section: Electronic Mood Monitoring and Utilization Of Appsmentioning

confidence: 99%

Electronic monitoring of self-reported mood: the return of the subjective?

Ortiz

Grof

2016

Int J Bipolar Disord

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This narrative review describes recent developments in the use of technology for utilizing the self-monitoring of mood, provides some relevant background, and suggests some promising directions. Subjective experience of mood is one of the valuable sources of information about the state of an integrated mind/brain system. During the past century, psychiatry and psychology moved away from subjectivity, emphasizing external observation, precise measurement, and laboratory techniques. This shift, however, provided only a limited improvement in the understanding of mood disorders, and it appears that self-monitoring of mood has the potential to enrich our knowledge, particularly when combined with the advances in technology. Modern technology, with its ability to transfer information from the individual directly to the researcher via electronic applications, enables us now to study mood regulation more thoroughly. Frequent subjective ratings can be helpful in identifying individualized treatment with effective mood stabilizers and recognizing subtypes of mood disorders. The variability of subjective ratings may also help us estimate the increased risk of recurrence and guide a tailored treatment.

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Why Psychiatry Needs Data Science and Data Science Needs Psychiatry

Cited by 70 publications

References 7 publications

Data science for mental health: a UK perspective on a global challenge

Data science for mental health: a UK perspective on a global challenge

Digital technology and clinical decision making in depression treatment: Current findings and future opportunities

Electronic monitoring of self-reported mood: the return of the subjective?

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