Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration

Shems Saleh, William Boag, Lauren Erdman, Tristan Naumann
Proceedings of the 5th Machine Learning for Healthcare Conference, PMLR 126:783-812, 2020.

Abstract

Clinical Machine Learning (ML) is a rapidly-growing field due to the digitization of hospital records, recent advances in ML techniques, and the ability to leverage increasing computational power for large and complex models. The high stakes and often unintuitive nature of clinical data make effective collaboration between clinicians and ML researchers one of the most important aspects of working in this interdisciplinary space. However, there are few resources codifying best practices for collaboration on Clinical ML projects. In this paper, we interviewed 18 experts in the Clinical ML field and distilled their advice and experiences into a list of questions (a Clinical Collabsheet) ML scientists and clinicians can use to promote effective discussion when working on a new project. We intend this for a broad audience as checklist of discussion points to hit at a kickoff meeting. This resource will enable more successful partnerships in Clinical ML with improved interdisciplinary communication and organization.

Cite this Paper

BibTeX
@InProceedings{pmlr-v126-saleh20a, title = {Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration}, author = {Saleh, Shems and Boag, William and Erdman, Lauren and Naumann, Tristan}, booktitle = {Proceedings of the 5th Machine Learning for Healthcare Conference}, pages = {783--812}, year = {2020}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {126}, series = {Proceedings of Machine Learning Research}, month = {07--08 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v126/saleh20a/saleh20a.pdf}, url = {https://proceedings.mlr.press/v126/saleh20a.html}, abstract = {Clinical Machine Learning (ML) is a rapidly-growing field due to the digitization of hospital records, recent advances in ML techniques, and the ability to leverage increasing computational power for large and complex models. The high stakes and often unintuitive nature of clinical data make effective collaboration between clinicians and ML researchers one of the most important aspects of working in this interdisciplinary space. However, there are few resources codifying best practices for collaboration on Clinical ML projects. In this paper, we interviewed 18 experts in the Clinical ML field and distilled their advice and experiences into a list of questions (a Clinical Collabsheet) ML scientists and clinicians can use to promote effective discussion when working on a new project. We intend this for a broad audience as checklist of discussion points to hit at a kickoff meeting. This resource will enable more successful partnerships in Clinical ML with improved interdisciplinary communication and organization.} }
Endnote
%0 Conference Paper %T Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration %A Shems Saleh %A William Boag %A Lauren Erdman %A Tristan Naumann %B Proceedings of the 5th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2020 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v126-saleh20a %I PMLR %P 783--812 %U https://proceedings.mlr.press/v126/saleh20a.html %V 126 %X Clinical Machine Learning (ML) is a rapidly-growing field due to the digitization of hospital records, recent advances in ML techniques, and the ability to leverage increasing computational power for large and complex models. The high stakes and often unintuitive nature of clinical data make effective collaboration between clinicians and ML researchers one of the most important aspects of working in this interdisciplinary space. However, there are few resources codifying best practices for collaboration on Clinical ML projects. In this paper, we interviewed 18 experts in the Clinical ML field and distilled their advice and experiences into a list of questions (a Clinical Collabsheet) ML scientists and clinicians can use to promote effective discussion when working on a new project. We intend this for a broad audience as checklist of discussion points to hit at a kickoff meeting. This resource will enable more successful partnerships in Clinical ML with improved interdisciplinary communication and organization.
APA
Saleh, S., Boag, W., Erdman, L. & Naumann, T.. (2020). Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration. Proceedings of the 5th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 126:783-812 Available from https://proceedings.mlr.press/v126/saleh20a.html.

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