Automation to optimise physician treatment of individual patients: examples in psychiatry
Abstract
There is widespread agreement by health-care providers, medical associations, industry, and governments that automation using digital technology could improve the delivery and quality of care in psychiatry, and reduce costs. Many benefits from technology have already been realised, along with the identification of many challenges. In this Review, we discuss some of the challenges to developing effective automation for psychiatry to optimise physician treatment of individual patients. Using the perspective of automation experts in other industries, three examples of automation in the delivery of routine care are reviewed: (1) effects of electronic medical records on the patient interview; (2) effects of complex systems integration on e-prescribing; and (3) use of clinical decision support to assist with clinical decision making. An increased understanding of the experience of automation from other sectors might allow for more effective deployment of technology in psychiatry.
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The simulated environment is unable to capture the full complexity of high-risk decision-making in a live-care delivery setting like the hospital environment, where there are many competing time and attention demands.
“However, the simulated environment is unable to capture the full complexity of high-risk decision-making in a live-care delivery setting like the hospital environment, where there are many competing time and attention demands 4,15,16,29 .”
From Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing (2022) · cites this paper as [4]
Provider impressions can differ from their actual use.
“In order to better understand factors impacting the adoption of CDS, previous studies of deployed systems have used post-hoc surveys or interviews to gauge provider impressions 28,44,53 ; however, provider impressions can differ from their actual use 4,18,27 .”
From Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing (2022) · cites this paper as [4]
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- Authors
- Bauer, M., Monteith, S., Geddes, J., Gitlin, M. J., Grof, P., Whybrow, P. C., & Glenn, T.
- Affiliations
- University Hospital Carl Gustav Carus (DE)
- Michigan State University (US)
- Warneford Hospital (GB)
- University of California, Los Angeles (US)
- University of Toronto (CA)
- Corresponding author
- Bauer, Michael
- Venue
- The Lancet Psychiatry
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 6
- Issue
- 4
- Pages
- 338-349
- ISSN
- 2215-0366 (ISSN-L)
- OpenAlex
- W2924046673
- Cited by
- 44
- References
- 31
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 6 · 2023: 7 · 2022: 10 · 2021: 6 · 2020: 7 · 2019: 1 · 2012: 1
- FWCI
- 1.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft
- Medical Research Council
- Bundesministerium für Bildung und Forschung
- Wellcome Trust
- National Institute for Health Research
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- digital health technologies, electronic health records, patient interviews, electronic prescribing, clinical decision support, system integration, quality of care, health care costs
- MeSH terms
- Automation, Mental Disorders, Humans, Interview, Psychological, Physicians, Psychiatry, Decision Support Systems, Clinical, Electronic Prescribing, Electronic Health Records, Quality Improvement
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