Natural Language Processing and Enhanced Clinical Decision Making Radiology and VINCI

2012 ◽  
Author(s):  
Eliot Siegel
Keyword(s):  
Decision Making ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals Development and Validation of a Natural Language Processing Tool to Identify Patients Treated for Pneumonia across VA Emergency Departments

2018 ◽  
Vol 09 (01) ◽  
pp. 122-128 ◽  
Author(s):  
B. Jones ◽  
B. South ◽  
Y. Shao ◽  
C.C. Lu ◽  
J. Leng ◽  
...  
Keyword(s):  
Decision Making ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Support Vector ◽  
Search Terms ◽  
Diagnosis Codes ◽  
Diagnostic Coding

Background Identifying pneumonia using diagnosis codes alone may be insufficient for research on clinical decision making. Natural language processing (NLP) may enable the inclusion of cases missed by diagnosis codes. Objectives This article (1) develops a NLP tool that identifies the clinical assertion of pneumonia from physician emergency department (ED) notes, and (2) compares classification methods using diagnosis codes versus NLP against a gold standard of manual chart review to identify patients initially treated for pneumonia. Methods Among a national population of ED visits occurring between 2006 and 2012 across the Veterans Affairs health system, we extracted 811 physician documents containing search terms for pneumonia for training, and 100 random documents for validation. Two reviewers annotated span- and document-level classifications of the clinical assertion of pneumonia. An NLP tool using a support vector machine was trained on the enriched documents. We extracted diagnosis codes assigned in the ED and upon hospital discharge and calculated performance characteristics for diagnosis codes, NLP, and NLP plus diagnosis codes against manual review in training and validation sets. Results Among the training documents, 51% contained clinical assertions of pneumonia; in the validation set, 9% were classified with pneumonia, of which 100% contained pneumonia search terms. After enriching with search terms, the NLP system alone demonstrated a recall/sensitivity of 0.72 (training) and 0.55 (validation), and a precision/positive predictive value (PPV) of 0.89 (training) and 0.71 (validation). ED-assigned diagnostic codes demonstrated lower recall/sensitivity (0.48 and 0.44) but higher precision/PPV (0.95 in training, 1.0 in validation); the NLP system identified more “possible-treated” cases than diagnostic coding. An approach combining NLP and ED-assigned diagnostic coding classification achieved the best performance (sensitivity 0.89 and PPV 0.80). Conclusion System-wide application of NLP to clinical text can increase capture of initial diagnostic hypotheses, an important inclusion when studying diagnosis and clinical decision-making under uncertainty.

scholarly journals CHRONOSIG: Digital Triage for Secondary Mental Healthcare using Natural Language Processing - rationale and protocol

2021 ◽  
Author(s):  
Dan W Joyce ◽  
Andrey Kormilitzin ◽  
Julia Hamer-Hunt ◽  
Anthony James ◽  
Alejo Nevado-Holgado ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Decision Making ◽  
Natural Language Processing ◽  
Natural Language ◽  
Mental Health Care ◽  
Language Processing ◽  
Clinical Decision ◽  
Free Text ◽  
Data Set

ABSTRACTBackgroundAccessing specialist secondary mental health care in the NHS in England requires a referral, usually from primary or acute care. Community mental health teams triage these referrals deciding on the most appropriate team to meet patients’ needs. Referrals require resource-intensive review by clinicians and often, collation and review of the patient’s history with services captured in their electronic health records (EHR). Triage processes are, however, opaque and often result in patients not receiving appropriate and timely access to care that is a particular concern for some minority and under-represented groups. Our project, funded by the National Institute of Health Research (NIHR) will develop a clinical decision support tool (CDST) to deliver accurate, explainable and justified triage recommendations to assist clinicians and expedite access to secondary mental health care.MethodsOur proposed CDST will be trained on narrative free-text data combining referral documentation and historical EHR records for patients in the UK-CRIS database. This high-volume data set will enable training of end-to-end neural network natural language processing (NLP) to extract ‘signatures’ of patients who were (historically) triaged to different treatment teams. The resulting algorithm will be externally validated using data from different NHS trusts (Nottinghamshire Healthcare, Southern Health, West London and Oxford Health). We will use an explicit algorithmic fairness framework to mitigate risk of unintended harm evident in some artificial intelligence (AI) healthcare applications. Consequently, the performance of the CDST will be explicitly evaluated in simulated triage team scenarios where the tool augments clinician’s decision making, in contrast to traditional “human versus AI” performance metrics.DiscussionThe proposed CDST represents an important test-case for AI applied to real-world process improvement in mental health. The project leverages recent advances in NLP while emphasizing the risks and benefits for patients of AI-augmented clinical decision making. The project’s ambition is to deliver a CDST that is scalable and can be deployed to any mental health trust in England to assist with digital triage.

scholarly journals Clinical decision support system, using expert consensus-derived logic and natural language processing, decreased sedation-type order errors for patients undergoing endoscopy

2020 ◽  
Author(s):  
Lin Shen ◽  
Adam Wright ◽  
Linda S Lee ◽  
Kunal Jajoo ◽  
Jennifer Nayor ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Decision Support ◽  
Natural Language ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Expert Consensus ◽  
Order Error

Abstract Objective Determination of appropriate endoscopy sedation strategy is an important preprocedural consideration. To address manual workflow gaps that lead to sedation-type order errors at our institution, we designed and implemented a clinical decision support system (CDSS) to review orders for patients undergoing outpatient endoscopy. Materials and Methods The CDSS was developed and implemented by an expert panel using an agile approach. The CDSS queried patient-specific historical endoscopy records and applied expert consensus-derived logic and natural language processing to identify possible sedation order errors for human review. A retrospective analysis was conducted to evaluate impact, comparing 4-month pre-pilot and 12-month pilot periods. Results 22 755 endoscopy cases were included (pre-pilot 6434 cases, pilot 16 321 cases). The CDSS decreased the sedation-type order error rate on day of endoscopy (pre-pilot 0.39%, pilot 0.037%, Odds Ratio = 0.094, P-value < 1e-8). There was no difference in background prevalence of erroneous orders (pre-pilot 0.39%, pilot 0.34%, P = .54). Discussion At our institution, low prevalence and high volume of cases prevented routine manual review to verify sedation order appropriateness. Using a cohort-enrichment strategy, a CDSS was able to reduce number of chart reviews needed per sedation-order error from 296.7 to 3.5, allowing for integration into the existing workflow to intercept rare but important ordering errors. Conclusion A workflow-integrated CDSS with expert consensus-derived logic rules and natural language processing significantly reduced endoscopy sedation-type order errors on day of endoscopy at our institution.

scholarly journals From WOM to aWOM – the evolution of unpaid influence: a perspective article

2019 ◽  
Vol 75 (1) ◽  
pp. 314-318 ◽  
Author(s):  
Nigel L. Williams ◽  
Nicole Ferdinand ◽  
John Bustard
Keyword(s):  
Decision Making ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Distribution Systems ◽  
Word Of Mouth ◽  
Customer Engagement ◽  
Content Type ◽  
Dominant Source ◽  
Source Of Information

Purpose Advances in artificial intelligence (AI) natural language processing may see the emergence of algorithmic word of mouth (aWOM), content created and shared by automated tools. As AI tools improve, aWOM will increase in volume and sophistication, displacing eWOM as an influence on customer decision-making. The purpose of this paper is to provide an overview of the socio technological trends that have encouraged the evolution of informal infulence strategies from WOM to aWOM. Design/methodology/approach This paper examines the origins and path of development of influential customer communications from word of mouth (WOM) to electronic word of mouth (eWOM) and the emerging trend of aWOM. The growth of aWOM is theorized as a result of new developments in AI natural language processing tools along with autonomous distribution systems in the form of software robots and virtual assistants. Findings aWOM may become a dominant source of information for tourists, as it can support multimodal delivery of useful contextual information. Individuals, organizations and social media platforms will have to ensure that aWOM is developed and deployed responsibly and ethically. Practical implications aWOM may emerge as the dominant source of information for tourist decision-making, displacing WOM or eWOM. aWOM may also impact online opinion leaders, as they may be challenged by algorithmically generated content. aWOM tools may also generate content using sensors on personal devices, creating privacy and information security concerns if users did not give permission for such activities. Originality/value This paper is the first to theorize the emergence of aWOM as autonomous AI communication within the framework of unpaid influence or WOM. As customer engagement will increasingly occur in algorithmic environments that comprise person–machine interactions, aWOM will influence future tourism research and practice.

Sign in / Sign up

Export Citation Format

Share Document