The clinical narrative in the electronic health record (EHR) carries valuable information for predictive analytics, but its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing open-source NLP engines to provide interoperable and standardized CDS at the bedside. This clinical protocol describes a reproducible workflow for a cloud service to ingest, process, and store clinical notes as Health Level 7 messages from a major EHR vendor in an elastic cloud computing environment. We apply the NLP CDS infrastructure to a use-case for hospital-wide opioid misuse screening using an open-source deep learning model that leverages clinical notes mapped to standardized medical vocabularies. The resultant NLP and deep learning pipeline can process clinical notes and provide decision support to the bedside within minutes of a provider entering a note into the EHR for all hospitalized patients. The protocol includes a human-centered design and an implementation framework with a cost-effectiveness and patient outcomes analysis plan.

The authors have declared no competing interest.

The authors acknowledge support from the University of Wisconsin Institute for Clinical and Translational Research supported by the Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS) grant (2UL1TR002373). Research was also supported by the National Institute on Drug Abuse of the National Institutes of Health (NIDA R01DA051464; CJ, DD, MO, MA, RB, BS), the National Library of Medicine THYME project (NLM R01LM010090; DD), and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK R01DK126933). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the other funding sources listed above.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The clinical study met exemption status for human subjects research by the UW Institutional Review Board.

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Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

The raw EHR data are available upon request due to ethical and legal restrictions imposed by the University of Wisconsin-Madison Institutional Review Board. The original data derives from the institutions EHR and contains patients protected health information (PHI). Data are available from University of Wisconsin Health Systems for researchers who meet the criteria for access to confidential data and have a data usage agreement with the health system.

https://github.com/Rush-SubstanceUse-AILab/SMART-AI

https://cwiki.apache.org/confluence/display/CTAKES/