SmartTriage: A system for personalized patient data capture, documentation generation, and decision support

Ilya Valmianski, Nave Frost, Navdeep Sood, Yang Wang, Baodong Liu, James J. Zhu, Sunil Karumuri, Ian M. Finn, Daniel S. Zisook
Proceedings of Machine Learning for Health, PMLR 158:75-96, 2021.

Abstract

Symptom checkers have emerged as an important tool for collecting symptoms and diagnosing patients, minimizing the involvement of clinical personnel. We developed a machine-learning-backed system, SmartTriage, which goes beyond conventional symptom checking through a tight bi-directional integration with the electronic medical record (EMR). Conditioned on EMR-derived patient history, our system identifies the patient’s chief complaint from a free-text entry and then asks a series of discrete questions to obtain relevant symptomatology. The patient-specific data are used to predict detailed ICD-10-CM codes as well as medication, laboratory, and imaging orders. Patient responses and clinical decision support (CDS) predictions are then inserted back into the EMR. To train the machine learning components of SmartTriage, we employed novel data sets of over 25 million primary care encounters and 1 million patient free-text reason-for-visit entries. These data sets were used to construct: (1) a long short-term memory (LSTM) based patient history representation, (2) a fine-tuned transformer model for chief complaint extraction, (3) a random forest model for question sequencing, and (4) a feed-forward network for CDS predictions. In total, our system supports 337 patient chief complaints, which together make up >90% of all primary care encounters at aiser Permanente.

Cite this Paper

BibTeX
@InProceedings{pmlr-v158-valmianski21a, title = {SmartTriage: A system for personalized patient data capture, documentation generation, and decision support}, author = {Valmianski, Ilya and Frost, Nave and Sood, Navdeep and Wang, Yang and Liu, Baodong and Zhu, James J. and Karumuri, Sunil and Finn, Ian M. and Zisook, Daniel S.}, booktitle = {Proceedings of Machine Learning for Health}, pages = {75--96}, year = {2021}, editor = {Roy, Subhrajit and Pfohl, Stephen and Rocheteau, Emma and Tadesse, Girmaw Abebe and Oala, Luis and Falck, Fabian and Zhou, Yuyin and Shen, Liyue and Zamzmi, Ghada and Mugambi, Purity and Zirikly, Ayah and McDermott, Matthew B. A. and Alsentzer, Emily}, volume = {158}, series = {Proceedings of Machine Learning Research}, month = {04 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v158/valmianski21a/valmianski21a.pdf}, url = {https://proceedings.mlr.press/v158/valmianski21a.html}, abstract = {Symptom checkers have emerged as an important tool for collecting symptoms and diagnosing patients, minimizing the involvement of clinical personnel. We developed a machine-learning-backed system, SmartTriage, which goes beyond conventional symptom checking through a tight bi-directional integration with the electronic medical record (EMR). Conditioned on EMR-derived patient history, our system identifies the patient’s chief complaint from a free-text entry and then asks a series of discrete questions to obtain relevant symptomatology. The patient-specific data are used to predict detailed ICD-10-CM codes as well as medication, laboratory, and imaging orders. Patient responses and clinical decision support (CDS) predictions are then inserted back into the EMR. To train the machine learning components of SmartTriage, we employed novel data sets of over 25 million primary care encounters and 1 million patient free-text reason-for-visit entries. These data sets were used to construct: (1) a long short-term memory (LSTM) based patient history representation, (2) a fine-tuned transformer model for chief complaint extraction, (3) a random forest model for question sequencing, and (4) a feed-forward network for CDS predictions. In total, our system supports 337 patient chief complaints, which together make up >90% of all primary care encounters at aiser Permanente.} }
Endnote
%0 Conference Paper %T SmartTriage: A system for personalized patient data capture, documentation generation, and decision support %A Ilya Valmianski %A Nave Frost %A Navdeep Sood %A Yang Wang %A Baodong Liu %A James J. Zhu %A Sunil Karumuri %A Ian M. Finn %A Daniel S. Zisook %B Proceedings of Machine Learning for Health %C Proceedings of Machine Learning Research %D 2021 %E Subhrajit Roy %E Stephen Pfohl %E Emma Rocheteau %E Girmaw Abebe Tadesse %E Luis Oala %E Fabian Falck %E Yuyin Zhou %E Liyue Shen %E Ghada Zamzmi %E Purity Mugambi %E Ayah Zirikly %E Matthew B. A. McDermott %E Emily Alsentzer %F pmlr-v158-valmianski21a %I PMLR %P 75--96 %U https://proceedings.mlr.press/v158/valmianski21a.html %V 158 %X Symptom checkers have emerged as an important tool for collecting symptoms and diagnosing patients, minimizing the involvement of clinical personnel. We developed a machine-learning-backed system, SmartTriage, which goes beyond conventional symptom checking through a tight bi-directional integration with the electronic medical record (EMR). Conditioned on EMR-derived patient history, our system identifies the patient’s chief complaint from a free-text entry and then asks a series of discrete questions to obtain relevant symptomatology. The patient-specific data are used to predict detailed ICD-10-CM codes as well as medication, laboratory, and imaging orders. Patient responses and clinical decision support (CDS) predictions are then inserted back into the EMR. To train the machine learning components of SmartTriage, we employed novel data sets of over 25 million primary care encounters and 1 million patient free-text reason-for-visit entries. These data sets were used to construct: (1) a long short-term memory (LSTM) based patient history representation, (2) a fine-tuned transformer model for chief complaint extraction, (3) a random forest model for question sequencing, and (4) a feed-forward network for CDS predictions. In total, our system supports 337 patient chief complaints, which together make up >90% of all primary care encounters at aiser Permanente.
APA
Valmianski, I., Frost, N., Sood, N., Wang, Y., Liu, B., Zhu, J.J., Karumuri, S., Finn, I.M. & Zisook, D.S.. (2021). SmartTriage: A system for personalized patient data capture, documentation generation, and decision support. Proceedings of Machine Learning for Health, in Proceedings of Machine Learning Research 158:75-96 Available from https://proceedings.mlr.press/v158/valmianski21a.html.

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