Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Mar 8, 2022
Open Peer Review Period: Mar 8, 2022 - May 3, 2022
Date Accepted: Nov 9, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Practical Considerations for Developing Natural Language Processing Systems for Population Health Management and Measurement
ABSTRACT
Although NLP techniques support automated information extraction in number of industries, the adoption of NLP methods to extract patient level information from Electronic Health Records has been slow. This could be attributed to a disconnect between state-of-the-art systems developed by researchers and their ability to support healthcare decision making that leads to improved outcomes. We enumerate a set of practical considerations for developing NLP system that are scientifically innovative and have potential to improve health outcomes. The key considerations that we propose include determining (1) the readiness of the data and compute resources for NLP, (2) the organizational incentives to use and maintain the NLP systems and (3) the feasibility of implementation and evaluation. They are intended to help to enable a system that is well-positioned to scale to other health systems in the US, and globally.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.