Toward vocabulary control for chief complaint

Calibrating a chief complaint list for low resource settings: a methodologic case study

BACKGROUND

The chief or presenting complaint is the reason for seeking health care, often in the patient's own words. In limited resource settings, a diagnosis-based approach to quantifying burden of disease is not possible, partly due to limited availability of an established lexicon or coding system. Our group worked with colleagues from the African Federation of Emergency Medicine building on the existing literature to create a pilot symptom list representing an attempt to standardize undifferentiated chief complaints in emergency and acute care settings. An ideal list for any setting is one that strikes a balance between ease of use and length, while covering the vast majority of diseases with enough detail to permit epidemiologic surveillance and make informed decisions about resource needs.

METHODS

This study was incorporated as a part of a larger prospective observational study on human immunodeficiency virus testing in Emergency Departments in South Africa. The pilot symptom list was used for chief complaint coding in three Emergency Departments. Data was collected on 3357 patients using paper case report forms. Chief complaint terms were reviewed by two study team members to determine the frequency of concordance between the coded chief complaint term and the selected symptom(s) from the pilot symptom list.

RESULTS

Overall, 3537 patients' chief complaints were reviewed, of which 640 were identified as 'potential mismatches.' When considering the 191 confirmed mismatches (29.8%), the Delphi process identified 6 (3.1%) false mismatches and 185 (96.9%) true mismatches. Significant chief-complaint clustering was identified with 9 sets of complaints frequently selected together for the same patient. "Pain" was used 2076 times for 58.7% of all patients. A combination of user feedback and expert-panel modified Delphi analysis of mismatched complaints and clustered complaints resulted in several substantial changes to the pilot symptom list.

CONCLUSIONS

This study presented a systematic methodology for calibrating a chief complaint list for the local context. Our revised list removed/reworded symptoms that frequently clustered together or were misinterpreted by health professionals. Recommendations for additions, modifications, and/or deletions from the pilot chief complaint list we believe will improve the functionality of the list in low resource environments.

Disagreements between emergency patients and physicians regarding chief complaint - Patient factors and prognostic implications

INTRODUCTION

The predictive power of chief complaints reported at presentation to the emergency department (ED) is well known. However, there is a lack of research on the coherence of patient versus physician reported chief complaints. The aim of this study was to determine the rate of disagreement between patients and physicians regarding chief complaint and its significance for the prediction of the outcomes number of resources used during ED work-up, hospitalisation, ICU admission, in-hospital mortality and hospital length of stay.

METHODS

In this secondary analysis of a study conducted over a time course of 9 weeks, consecutive emergency patients and their physicians were independently asked to report the chief complaint upon presentation. The two reports were assessed for pair-wise agreement.

RESULTS

Of 6722 emergency patients (mean age 53.3, 46.8% female), the median number of symptoms reported by patients was two and one reported by physicians. The rate of disagreement on chief complaints was 32.6%. Disagreement was associated with a higher number of resources (β = 0.24; CI, 0.18, 0.31, P <.001) and hospitalisation (OR = 1.31; CI, 1.16, 1.48, P <.001), using multivariable analyses. Patient factors associated with disagreement were age (OR = 1.01; CI, 1.01, 1.01, P <.001), number of patient reported symptoms (OR = 1.27; CI, 1.23, 1.32, P <.001) and male gender (OR = 1.12; 1.01, 1.25, P =.0285).

CONCLUSION

Disagreement on chief complaint between patient and physician may be an early marker for a complex work-up, requiring more resources and hospitalisations. The relevance of this finding is the newly identified signal of chief complaint replacement. It is easy to identify and should generate attention, as it affects a certain phenotype (older male patients with higher numbers of complaints).

Generating contextual embeddings for emergency department chief complaints

Objective

We learn contextual embeddings for emergency department (ED) chief complaints using Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language model, to derive a compact and computationally useful representation for free-text chief complaints.

Materials and methods

Retrospective data on 2.1 million adult and pediatric ED visits was obtained from a large healthcare system covering the period of March 2013 to July 2019. A total of 355 497 (16.4%) visits from 65 737 (8.9%) patients were removed for absence of either a structured or unstructured chief complaint. To ensure adequate training set size, chief complaint labels that comprised less than 0.01%, or 1 in 10 000, of all visits were excluded. The cutoff threshold was incremented on a log scale to create seven datasets of decreasing sparsity. The classification task was to predict the provider-assigned label from the free-text chief complaint using BERT, with Long Short-Term Memory (LSTM) and Embeddings from Language Models (ELMo) as baselines. Performance was measured as the Top-k accuracy from k = 1:5 on a hold-out test set comprising 5% of the samples. The embedding for each free-text chief complaint was extracted as the final 768-dimensional layer of the BERT model and visualized using t-distributed stochastic neighbor embedding (t-SNE).

Results

The models achieved increasing performance with datasets of decreasing sparsity, with BERT outperforming both LSTM and ELMo. The BERT model yielded Top-1 accuracies of 0.65 and 0.69, Top-3 accuracies of 0.87 and 0.90, and Top-5 accuracies of 0.92 and 0.94 on datasets comprised of 434 and 188 labels, respectively. Visualization using t-SNE mapped the learned embeddings in a clinically meaningful way, with related concepts embedded close to each other and broader types of chief complaints clustered together.

Discussion

Despite the inherent noise in the chief complaint label space, the model was able to learn a rich representation of chief complaints and generate reasonable predictions of their labels. The learned embeddings accurately predict provider-assigned chief complaint labels and map semantically similar chief complaints to nearby points in vector space.

Conclusion

Such a model may be used to automatically map free-text chief complaints to structured fields and to assist the development of a standardized, data-driven ontology of chief complaints for healthcare institutions.

Consensus Development of a Modern Ontology of Emergency Department Presenting Problems-The Hierarchical Presenting Problem Ontology (HaPPy)

OBJECTIVE

Numerous attempts have been made to create a standardized "presenting problem" or "chief complaint" list to characterize the nature of an emergency department visit. Previous attempts have failed to gain widespread adoption as they were not freely shareable or did not contain the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency medicine community. Using real-world data, we constructed a presenting problem list that addresses these challenges.

MATERIALS AND METHODS

We prospectively captured the presenting problems for 180,424 consecutive emergency department patient visits at an urban, academic, Level I trauma center in the Boston metro area. No patients were excluded. We used a consensus process to iteratively derive our system using real-world data. We used the first 70% of consecutive visits to derive our ontology, followed by a 6-month washout period, and the remaining 30% for validation. All concepts were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT).

RESULTS

Our system consists of a polyhierarchical ontology containing 692 unique concepts, 2,118 synonyms, and 30,613 nonvisible descriptions to correct misspellings and nonstandard terminology. Our ontology successfully captured structured data for 95.9% of visits in our validation data set.

DISCUSSION AND CONCLUSION

We present the HierArchical Presenting Problem ontologY (HaPPy). This ontology was empirically derived and then iteratively validated by an expert consensus panel. HaPPy contains 692 presenting problem concepts, each concept being mapped to SNOMED CT. This freely sharable ontology can help to facilitate presenting problem-based quality metrics, research, and patient care.

Chief Complaints of Elderly Individuals on Presentation to Emergency Department: A Retrospective Analysis of South Korean National Data 2014

PURPOSE

We aimed to assess the chief complaints (CCs) of elderly individuals on presentation to the emergency department (ED) according to gender, age, and disease-related and injury-related visits.

METHODS

The 2014 registry database of the National Emergency Department Information System in South Korea, which included data on 908,761 ED visits by individuals aged 65 years and over, was reviewed.

RESULTS

We found that 80.7% ED visits were related to disease, whereas the remaining visits were related to injury. The most common CCs presented by elderly male and female individuals with disease-related visits were dyspnea and dizziness, respectively. The 10 most common CCs accounted for 45.5% and 49.2% of the total disease-related visits for male and female individuals, respectively. The most common CC in male and female individuals with injury-related visits was headache and hip pain, respectively. The CC rank showed minimal variance among the different age groups, but a difference was observed between male and female individuals. The most common mechanism of injury in elderly male and female individuals was slipping, wherein females showed a higher occurrence rate than their male counterparts.

CONCLUSIONS

These findings can be used to establish an ED training curriculum for nursing students and ED nurses, particularly for ED triage in the elderly.

Chief complaint-based performance measures: a new focus for acute care quality measurement

Performance measures are increasingly important to guide meaningful quality improvement efforts and value-based reimbursement. Populations included in most current hospital performance measures are defined by recorded diagnoses using International Classification of Diseases, Ninth Revision codes in administrative claims data. Although the diagnosis-centric approach allows the assessment of disease-specific quality, it fails to measure one of the primary functions of emergency department (ED) care, which involves diagnosing, risk stratifying, and treating patients' potentially life-threatening conditions according to symptoms (ie, chief complaints). In this article, we propose chief complaint-based quality measures as a means to enhance the evaluation of quality and value in emergency care. We discuss the potential benefits of chief complaint-based measures, describe opportunities to mitigate challenges, propose an example measure set, and present several recommendations to advance this paradigm in ED-based performance measurement.

Making recording and analysis of chief complaint a priority for global emergency care research in low-income countries

The chief complaint is a patient's self-reported primary reason for presenting for medical care. The clinical utility and analytical importance of recording chief complaints have been widely accepted in highly developed emergency care systems, but this practice is far from universal in global emergency care, especially in limited-resource areas. It is precisely in these settings, however, that the use of chief complaints may have particular benefit. Chief complaints may be used to quantify, analyze, and plan for emergency care and provide valuable information on acute care needs where there are crucial data gaps. Globally, much work has been done to establish local practices around chief complaint collection and use, but no standards have been established and little work has been done to identify minimum effective sets of chief complaints that may be used in limited-resource settings. As part of the Academic Emergency Medicine consensus conference, "Global Health and Emergency Care: A Research Agenda," the breakout group on data management identified the lack of research on emergency chief complaints globally-especially in low-income countries where the highest proportion of the world's population resides-as a major gap in global emergency care research. This article reviews global research on emergency chief complaints in high-income countries with developed emergency care systems and sets forth an agenda for future research on chief complaints in limited-resource settings.

North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) and the National Hospital Ambulatory Medical Care Survey (NHAMCS): comparison of emergency department data

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) is a near-real-time database of emergency department (ED) visits automatically extracted from hospital information system(s) in the state of North Carolina. The National Hospital Ambulatory Medical Care Survey (NHAMCS) is a retrospective probability sample survey of visits to U.S. hospital EDs. This report compares data from NC DETECT (2006) with NHAMCS (2005) ED visit data to determine if the two data sets are consistent. Proportions, rates, and confidence intervals (CIs) were calculated for ED visits by age and gender; arrival method and age; expected source of payment; disposition; hospital admissions; NHAMCS top 20 diagnosis groups and top five primary diagnoses by age group; International Classifications of Disease, 9th revision, Clinical Modification (ICD-9-CM) primary diagnosis codes; and cause of injury. North Carolina DETECT captured 79% of statewide ED visits. Twenty-eight persons for every 100 North Carolina residents visited a North Carolina ED that reports to NC DETECT at least once in 2006, compared to 20% nationally. Twenty-seven percent of ED visits in North Carolina had private insurance as the expected payment source, compared with 40% nationwide. The proportion of injury-related ED visits in North Carolina is 25%, compared to 36.4% nationally. Rates and proportions of disease groups are similar. Similarity of NC DETECT rates and proportions to NHAMCS provides support for the face and content validity of NC DETECT. The development of statewide near-real-time ED databases is an important step toward the collection, aggregation, and analysis of timely, population-based data by state, to better define the burden of illness and injury for vulnerable populations.