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Deep learning algorithms to identify documentation of serious illness conversations during intensive care unit admissions
Manage episode 222045616 series 1316808
This episode features Dr Alex Chan (Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA). Routine assessment of many established quality indicators is nearly impossible because the information is embedded as unstructured free text within electronic clinical notes. A key example of this is timely documentation of patient care preferences in critically ill older adults. The paper demonstrates that deep learning algorithms can be applied to assess a palliative care quality measure endorsed by the National Quality Forum. The deep learning algorithm analyzed clinical notes >18,000 times faster than clinician coders (0.022 s/note vs 402 s/note). The algorithms can analyze electronic clinical notes in a tiny fraction of the time needed for manual review, offering a practical option for rapid audit and feedback regarding care preference documentation at the system and clinician level. Full paper available from: https://journals.sagepub.com/doi/full/10.1177/0269216318810421
If you would like to record a podcast about your published (or accepted) Palliative Medicine paper, please contact Dr Amara Nwosu: anwosu@liverpool.ac.uk
105 episodes
Manage episode 222045616 series 1316808
This episode features Dr Alex Chan (Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA). Routine assessment of many established quality indicators is nearly impossible because the information is embedded as unstructured free text within electronic clinical notes. A key example of this is timely documentation of patient care preferences in critically ill older adults. The paper demonstrates that deep learning algorithms can be applied to assess a palliative care quality measure endorsed by the National Quality Forum. The deep learning algorithm analyzed clinical notes >18,000 times faster than clinician coders (0.022 s/note vs 402 s/note). The algorithms can analyze electronic clinical notes in a tiny fraction of the time needed for manual review, offering a practical option for rapid audit and feedback regarding care preference documentation at the system and clinician level. Full paper available from: https://journals.sagepub.com/doi/full/10.1177/0269216318810421
If you would like to record a podcast about your published (or accepted) Palliative Medicine paper, please contact Dr Amara Nwosu: anwosu@liverpool.ac.uk
105 episodes
All episodes
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