Extracting timing and status descriptors for colonoscopy testing from electronic medical records

Abstract

Colorectal cancer (CRC) screening rates are low despite confirmed benefits. The authors investigated the use of natural language processing (NLP) to identify previous colonoscopy screening in electronic records from a random sample of 200 patients at least 50 years old. The authors developed algorithms to recognize temporal expressions and 'status indicators', such as 'patient refused', or 'test scheduled'. The new methods were added to the existing KnowledgeMap concept identifier system, and the resulting system was used to parse electronic medical records (EMR) to detect completed colonoscopies. Using as the 'gold standard' expert physicians' manual review of EMR notes, the system identified timing references with a recall of 0.91 and precision of 0.95, colonoscopy status indicators with a recall of 0.82 and precision of 0.95, and references to actually completed colonoscopies with recall of 0.93 and precision of 0.95. The system was superior to using colonoscopy billing codes alone. Health services researchers and clinicians may find NLP a useful adjunct to traditional methods to detect CRC screening status. Further investigations must validate extension of NLP approaches for other types of CRC screening applications.

Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence

“…We are not aware of any studies investigating a role for NLP in measuring appointment adherence. [19][20][21][32][33][34][35][36][37][38][39][40] Thus, our work highlights a novel method using NLP for this purpose.…”

“…NLP has been used to facilitate numerous assessments of health care quality and safety. [18][19][20][21][22] This project had two major aims: 1) to develop an automated method utilizing NLP that evaluates patients' prior non-adherence with health care services; 2) to use this adherence metric and readily available clinical, demographic, and scheduling data to develop and validate a prediction model that accurately identifies individuals at high risk for non-adherence with scheduled colonoscopy.…”

A hybrid model for automatic identification of risk factors for heart disease

“…Similar to our work, Denny et al [7] used regular expressions to extract temporal expressions. Jung et al [11] proposed a system pipeline that was based on explicit rules for temporal expression extraction and event extraction.…”

Annotating temporal information in clinical narratives

“…Some examples of clinical applications that utilize temporal information include: diagnosis, prognosis and treatment decision support [3, 4], time specific clinical information extraction [5, 6, 7], and time-related question answering [8, 9, 10]. These applications rely on temporal reasoning systems which extract temporal information from natural language, and perform temporal inference over the extracted information.…”