Exploring the Frontier of Electronic Health Record Surveillance

Abstract

Background The aim of this study was to build electronic algorithms using a combination of structured data and natural language processing (NLP) of text notes for potential safety surveillance of nine post-operative complications. Methods Post-operative complications from six medical centers in the Southeastern United States were obtained from the Veterans Affairs Surgical Quality Improvement Program (VASQIP) registry. Development and test datasets were constructed using stratification by facility and date of procedure for patients with and without complication. Algorithms were developed from VASQIP outcome definitions using NLP coded concepts, regular expressions, and structured data. The VASQIP nurse reviewer served as the reference standard for evaluating sensitivity and specificity. The algorithms were designed in the development and evaluated in the test dataset. Results Sensitivity and specificity in the test set were 85% and 92% for acute renal failure, 80% and 93% for sepsis, 56% and 94% for deep vein thrombosis, 80% and 97% for pulmonary embolism, 88% and 89% for acute myocardial infarction, 88% and 92% for cardiac arrest, 80% and 90% for pneumonia, 95% and 80% for urinary tract infection, and 80% and 93% for wound infection, respectively. A third of the complications occurred outside of the hospital setting. Conclusions Computer algorithms on data extracted from the electronic health record produced respectable sensitivity and specificity across a large sample of patients seen in six different medical centers. This study demonstrates the utility of combining natural language processing with structured data for mining the information contained within the electronic health record.

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

“…The use of the EMR has considerable potential to improve institutional performance, including decreasing the readmission rate for prevalent conditions such as AMI. [11][12][13] Because EMRs are widely adopted, predictive models that exploit these resources could be seamlessly integrated into clinical pathways, offering an inexpensive tool to assist clinicians in assessing risk. However, the dataset in a comprehensive EMR is complex and unless methods are developed to interpret and present data, the full benefit of the EMR may not be realised.…”

“…[11][12][13] In the present study we examined all the available administrative hospital factors associated with readmission in a cohort of patients admitted with AMI to a large regional hospital in Geelong (Vic., Australia). We used data from the EMR to derive and internally validate a model to predict unplanned ischaemic heart disease (IHD) readmissions over a 30-day period and all-cause readmission over 12 months after an admission with AMI.…”

Natural Language Processing and the Promise of Big Data

“…5,6 More recent NLP innovations have used keyword searching and other techniques to facilitate quality and safety monitoring in a variety of healthcare settings. 7,8 Despite these early applications, although, NLP remains a nascent technology in healthcare. Most of its current successes are restricted to research settings and have served more as a supportive technology to supplement the analysis of largely structured data rather than a standalone tool.…”

Lesão renal aguda no pós-operatório de cirurgia cardíaca

“…(12) In this scenario, studies have shown that active surveillance of changes in serum creatinine and urine output can automate alerts, guide the dosing of drugs, reduce the incidence of acute kidney injury, improve patient safety and help with identifying the occurrence of complications. (13,14) The objective of this study was to identify the occurrence of acute kidney injury in the postoperative period of cardiac surgery.…”