Dataset Information

Computer-generated vs. physician-documented history of present illness (HPI): results of a blinded comparison.

ABSTRACT: Healthcare delivery now mandates shorter visits with higher documentation requirements, undermining the patient-provider interaction. To improve clinic visit efficiency, we developed a patient-provider portal that systematically collects patient symptoms using a computer algorithm called Automated Evaluation of Gastrointestinal Symptoms (AEGIS). AEGIS also automatically "translates" the patient report into a full narrative history of present illness (HPI). We aimed to compare the quality of computer-generated vs. physician-documented HPIs.We performed a cross-sectional study with a paired sample design among individuals visiting outpatient adult gastrointestinal (GI) clinics for evaluation of active GI symptoms. Participants first underwent usual care and then subsequently completed AEGIS. Each individual thereby had both a physician-documented and a computer-generated HPI. Forty-eight blinded physicians assessed HPI quality across six domains using 5-point scales: (i) overall impression, (ii) thoroughness, (iii) usefulness, (iv) organization, (v) succinctness, and (vi) comprehensibility. We compared HPI scores within patient using a repeated measures model.Seventy-five patients had both computer-generated and physician-documented HPIs. The mean overall impression score for computer-generated HPIs was higher than physician HPIs (3.68 vs. 2.80; P<0.001), even after adjusting for physician and visit type, location, mode of transcription, and demographics. Computer-generated HPIs were also judged more complete (3.70 vs. 2.73; P<0.001), more useful (3.82 vs. 3.04; P<0.001), better organized (3.66 vs. 2.80; P<0.001), more succinct (3.55 vs. 3.17; P<0.001), and more comprehensible (3.66 vs. 2.97; P<0.001).Computer-generated HPIs were of higher overall quality, better organized, and more succinct, comprehensible, complete, and useful compared with HPIs written by physicians during usual care in GI clinics.

SUBMITTER: Almario CV

PROVIDER: S-EPMC4289091 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Computer-generated vs. physician-documented history of present illness (HPI): results of a blinded comparison.

Almario Christopher V CV Chey William W Kaung Aung A Whitman Cynthia C Fuller Garth G Reid Mark M Nguyen Ken K Bolus Roger R Dennis Buddy B Encarnacion Rey R Martinez Bibiana B Talley Jennifer J Modi Rushaba R Agarwal Nikhil N Lee Aaron A Kubomoto Scott S Sharma Gobind G Bolus Sally S Chang Lin L Spiegel Brennan M R BM

The American journal of gastroenterology 20141202 1

<h4>Objectives</h4>Healthcare delivery now mandates shorter visits with higher documentation requirements, undermining the patient-provider interaction. To improve clinic visit efficiency, we developed a patient-provider portal that systematically collects patient symptoms using a computer algorithm called Automated Evaluation of Gastrointestinal Symptoms (AEGIS). AEGIS also automatically "translates" the patient report into a full narrative history of present illness (HPI). We aimed to compare ...[more]

PMID: 25461620

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Project description:BACKGROUND:Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task. OBJECTIVE:We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient's history of present illness typically occurring in the beginning of a psychiatric initial evaluation note. MATERIALS AND METHODS:We clean and process the 1000 records made available through the N-GRID clinical NLP task into a key-value dictionary and build a dataset of 986 examples for which there is a narrative for history of present illness as well as Yes/No responses with regards to presence of specific mental conditions. We propose two independent deep neural network models: one based on convolutional neural networks (CNN) and another based on recurrent neural networks with hierarchical attention (ReHAN), the latter of which allows for interpretation of model decisions. We conduct experiments to compare these methods to each other and to baselines based on linear models and named entity recognition (NER). RESULTS:Our CNN model with optimized thresholding of output probability estimates achieves best overall mean micro-F score of 63.144% for 11 common mental conditions with statistically significant gains (p<0.05) over all other models. The ReHAN model with interpretable attention mechanism scored 61.904% mean micro-F1 score. Both models' improvements over baseline models (support vector machines and NER) are statistically significant. The ReHAN model additionally aids in interpretation of the results by surfacing important words and sentences that lead to a particular prediction for each instance. CONCLUSIONS:Although the history of present illness is a short text segment averaging 300 words, it is a good predictor for a few conditions such as anxiety, depression, panic disorder, and attention deficit hyperactivity disorder. Proposed CNN and RNN models outperform baseline approaches and complement each other when evaluating on a per-label basis.

Dataset Information

Computer-generated vs. physician-documented history of present illness (HPI): results of a blinded comparison.

Publications

Computer-generated vs. physician-documented history of present illness (HPI): results of a blinded comparison.

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