Outpatient Emergency Department Utilization: Measurement and Prediction: A Dissertation

Approximately half of all emergency department (ED) visits are primary-care sensitive (PCS) – meaning that they could potentially be avoided with timely, effective primary care. Reducing undesirable types of healthcare utilization (including PCS ED use) requires the ability to define, measure, and predict such use in a population. In this retrospective, observational study, we quantified ED use in 2 privately insured populations and developed ED risk prediction models. One dataset, obtained from a Massachusetts managed-care network (MCN), included data from 2009-11. The second was the MarketScan database, with data from 2007-08. The MCN study included 64,623 individuals enrolled for at least 1 base-year month and 1 prediction-year month in Massachusetts whose primary care provider (PCP) participated in the MCN. The MarketScan study included 15,136,261 individuals enrolled for at least 1 base-year month and 1 prediction-year month in the 50 US states plus DC, Puerto Rico, and the US Virgin Islands. We used medical claims to identify principal diagnosis codes for ED visits, and scored each according to the New York University Emergency Department algorithm. We defined primary-care sensitive (PCS) ED visits as those in 3 subcategories: nonemergent, emergent but primary-care treatable, and emergent but preventable/avoidable. We then: 1) defined and described the distributions of 3 ED outcomes: any ED use; number of ED visits; and a new outcome, based on the NYU algorithm, that we call PCS ED use; 2) built and validated predictive models for these outcomes using administrative claims data; 3) compared the performance of models predicting any ED use, number of ED visits, and PCS ED use; 4) enhanced these models by adding enrollee characteristics from electronic medical records, neighborhood characteristics, and payor/provider characteristics, and explored differences in performance between the original and enhanced models. In the MarketScan sample, 10.6% of enrollees had at least 1 ED visit, with about half of utilization scored as PCS. For the top risk group (those in the 99.5th percentile), the model’s sensitivity was 3.1%, specificity was 99.7%, and positive predictive value (PPV) was 49.7%. The model predicting PCS visits yielded sensitivity of 3.8%, specificity of 99.7%, and PPV of 40.5% for the top risk group. In the MCN sample, 14.6% (±0.1%) had at least 1 ED visit during the prediction period, with an overall rate of 18.8 (±0.2) visits per 100 persons and 7.6 (±0.1) PCS ED visits per 100 persons. Measuring PCS ED use with a threshold-based approach resulted in many fewer visits counted as PCS, discarding information unnecessarily. Out of 45 practices, 5 to 11 (11-24%) had observed values that were statistically significantly different from their expected values. Models predicting ED utilization using age, sex, race, morbidity, and prior use only (claims-based models) had lower R2 (ranging from 2.9% to 3.7%) and poorer predictive ability than the enhanced models that also included payor, PCP type and quality, problem list conditions, and covariates from the EMR, Census tract, and MCN provider data (enhanced model R2 ranged from 4.17% to 5.14%). In adjusted analyses, age, claims-based morbidity score, any ED visit in the base year, asthma, congestive heart failure, depression, tobacco use, and neighborhood poverty were strongly associated with increased risk for all 3 measures (all P\u3c.001)

Improving the Quality of Clinical Coding through Mapping of National Classification of Diseases (NCoD) and International Classification of Disease (ICD-10).

AbstractIntroduction: Medical coding is the transformation of healthcare diagnosis, procedures, medical services, and equipment into universal medical alphanumeric codes. Utilization of international disease classification provides higher-quality information for measuring healthcare service quality, safety, and efficacy. The Ethiopian National classification of disease (NCoD) was developed as part of Health Management information System (HMIS) reform with consideration of accommodating code in International Classification of disease (ICD-10). There is limited resource about the utilization status and related determinants of NCoD by health care professionals at tertiary level hospitals. This study is designed to assess the utilization status of NCoD and improve the quality of clinical coding through mapping of NCoD and ICD-10. Methods: Quasi-experimental study considering “Mapping” as an intervention was employed in this study. Retrospective medical record reviews were carried out to assess the utilization of NCoD and its challenges at Tikur Anebsa Specialized Hospital (TASH) for a period of one year (2018/2019). Qualitative approach used to get expert insight on NCoD implementation challenges and design of mapping exercises as an intervention. Seven thousand five hundred forty-seven (20%) of the medical records from the total of 37,734 medical records were selected randomly for review. A data abstraction checklist was developed to collect relevant information on individual patient charts, patient electronic records specific on a confirmed diagnosis. The reference mapping approach was employed for the mapping output between ICD-10 and NCoD. Both ICD-10 and NCoD were mapped side by side using percentage comparison and absolute difference. Result: Data for document review was taken from the electronic medical record database. Out of the total, 3021 (40%) of records were miss-classified based on the national classification of disease. From the miss-coded record, 1749 (58%) of them used ICD code to classify the diagnosis. Reasons provided for poor utilization of NCoD among physicians include, perception of having a limited list of diagnosis in the NCoD, not being familiarized, inadequate capacity building about NCoD use, and absence of enforcing mechanism on the use of standard diagnostic coding among professionals. Utilization of disease classification coding provides higher-quality information for measuring healthcare service quality, safety, and efficacy. This will in turn provide better data for quality measurement and medical error reduction (patient safety), outcomes measurement, operational planning, and healthcare delivery systems design and reporting. Conclusion: Extended NCoD categories were mapped from ICD-10. Standard ways of coding disease diagnosis and coding of new cases into the existing category was established. This study recommends that due emphasis should be given in monitoring and evaluation of medical coding knowledge and adherence of health professionals, and it should be supported with appropriate technologies to improve the accessibility and quality of health information. [Ethiop. J. Health Dev. 2021; 35(SI-1):59-65] Keywords: Mapping, NCoD, ICD, Clinical Coding, Diagnosis, Health Information Syste

Distributed knowledge based clinical auto-coding system

Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) methods and techniques to resolve the problem of manual coding of clinical narratives. Most of the studies are focused on classification systems relevant to the U.S and there is a scarcity of studies relevant to Australian classification systems such as ICD- 10-AM and ACHI. Therefore, we aim to develop a knowledge-based clinical auto-coding system, that utilise appropriate NLP and ML techniques to assign ICD-10-AM and ACHI codes to clinical records, while adhering to both local coding standards (Australian Coding Standard) and international guidelines that get updated and validated continuously

Patient-Reported Morbidity Instruments: A Systematic Review

Objectives: Although comorbidities play an essential role in risk adjustment and outcomes measurement, there is little consensus regarding the best source of this data. The aim of this study was to identify general patient-reported morbidity instruments and their measurement properties. Methods: A systematic review was conducted using multiple electronic databases (Embase, Medline, Cochrane Central, and Web of Science) from inception to March 2018. Articles focusing primarily on the development or subsequent validation of a patient-reported morbidity instrument were included. After including relevant articles, the measurement properties of each morbidity instrument were extracted by 2 investigators for narrative synthesis. Results: A total of 1005 articles were screened, of which 34 eligible articles were ultimately included. The most widely assessed instruments were the Self-Reported Charlson Comorbidity Index (n = 7), the Self-Administered Comorbidity Questionnaire (n = 3), and the Disease Burden Morbidity Assessment (n = 3). The most commonly included conditions were diabetes, hypertension, and myocardial infarction. Studies demonstrated substantial variability in item-level reliability versus the gold standard medical record review (κ range 0.66-0.86), meaning that the accuracy of the self-reported comorbidity data is dependent on the selected morbidity. Conclusions: The Self-Reported Charlson Comorbidity Index and the Self-Administered Comorbidity Questionnaire were the most frequently cited instruments. Significant variability was observed in reliability per comorbid condition of patient-reported morbidity questionnaires. Further research is needed to determine whether patient-reported morbidity data should be used to bolster medical records data or serve as a stand-alone entity when risk adjusting observational outcomes data

Using structured pathology data to predict hospital-wide mortality at admission

Early prediction of in-hospital mortality can improve patient outcome. Current prediction models for in-hospital mortality focus mainly on specific pathologies. Structured pathology data is hospital-wide readily available and is primarily used for e.g. financing purposes. We aim to build a predictive model at admission using the International Classification of Diseases (ICD) codes as predictors and investigate the effect of the self-evident DNR ("Do Not Resuscitate") diagnosis codes and palliative care codes. We compare the models using ICD-10-CM codes with Risk of Mortality (RoM) and Charlson Comorbidity Index (CCI) as predictors using the Random Forests modeling approach. We use the Present on Admission flag to distinguish which diagnoses are present on admission. The study is performed in a single center (Ghent University Hospital) with the inclusion of 36 368 patients, all discharged in 2017. Our model at admission using ICD-10-CM codes (AUCROC = 0.9477) outperforms the model using RoM (AUCROC = 0.8797 and CCI (AUCROC = 0.7435). We confirmed that DNR and palliative care codes have a strong impact on the model resulting in a decrease of 7% for the ICD model (AUCROC = 0.8791) at admission. We therefore conclude that a model with a sufficient predictive performance can be derived from structured pathology data, and if real-time available, can serve as a prerequisite to develop a practical clinical decision support system for physicians

Explainable Prediction of Medical Codes from Clinical Text

Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated, obscuring the reasons and details behind specific diagnoses and treatments. We present an attentional convolutional network that predicts medical codes from clinical text. Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of 0.54, which are both better than the prior state of the art. Furthermore, through an interpretability evaluation by a physician, we show that the attention mechanism identifies meaningful explanations for each code assignmentComment: NAACL 201

Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data

Background Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods. Materials and Methods We developed an EHR dataset of emergency department visits involving OOD cases or patients considered at risk for an OOD and ascertained true OOD status through manual chart reviews. We developed and validated prediction models using Random Forest, Extreme Gradient Boost, and Elastic Net models that incorporated 717 features involving primary and second diagnoses, chief complaints, medications prescribed, vital signs, laboratory results, and procedural codes. We also developed models limited to single data types. Results A total of 1718 records involving 1485 patients were manually reviewed; 541 (36.4%) patients had one or more OOD. Prediction performance was similar for all models; sensitivity varied from 94% to 97%; and area under the receiver operating characteristic curve (AUC) was 98% for all methods. The primary diagnosis and chief complaint were the most important contributors to AUC performance; primary diagnoses and medication class contributed most to sensitivity; chief complaint, primary diagnosis, and vital signs were most important for specificity. Models limited to decision support data types available in real time demonstrated robust prediction performance. Conclusions Substantial prediction performance improvements were demonstrated for identifying OODs in EHR data. Our e-phenotypes could be applied in surveillance, retrospective empirical applications, or clinical decision support systems