Towards Automatic Diabetes Case Detection and ABCS Protocol Compliance Assessment

Abstract

Objective: According to the American Diabetes Association, the implementation of the standards of care for diabetes has been suboptimal in most clinical settings. Diabetes is a disease that had a total estimated cost of $174 billion in 2007 for an estimated diabetes-affected population of 17.5 million in the United States. With the advent of electronic medical records (EMR), tools to analyze data residing in the EMR for healthcare surveillance can help reduce the burdens experienced today. This study was primarily designed to evaluate the efficacy of employing clinical natural language processing to analyze discharge summaries for evidence indicating a presence of diabetes, as well as to assess diabetes protocol compliance and high risk factors.Methods: Three sets of algorithms were developed to analyze discharge summaries for:(1) identification of diabetes, (2) protocol compliance, and (3) identification of high risk factors. The algorithms utilize a common natural language processing framework that extracts relevant discourse evidence from the medical text. Evidence utilized in one or more of the algorithms include assertion of the disease and associated findings in medical text, as well as numerical clinical measurements and prescribed medications. Results:The diabetes classifier was successful at classifying reports for the presence and absence of diabetes. Evaluated against 444 discharge summaries, the classifier's performance included macro and micro F-scores of 0.9698 and 0.9865, respectively. Furthermore, the protocol compliance and high risk factor classifiers showed promising results, with most F-measures exceeding 0.9. Conclusions:The presented approach accurately identified diabetes in medical discharge summaries and showed promise with regards to assessment of protocol compliance and high risk factors. Utilizing free-text analytic techniques on medical text can complement clinical-public health decision support by identifying cases and high risk factors.

Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review

“…This is particularly surprising because patients with diabetes can be identified with a reasonable degree of accuracy using structured data, such as diagnoses, medications and laboratory test results. 47,56 Furthermore, most studies in this area showed a similarly high level of accuracy, with almost all F 1 scores above 0.9. It is therefore likely that return on further investment in this particular area will be low and researchers' efforts would be more productively redirected elsewhere.…”

“Big Data” and the Electronic Health Record

“…The field continues to develop and some healthcare systems are partnering with industry to build CDSSs [78]. Recent work includes an NLP approach to assess adherence to treatment protocols and guidelines [79,80], automated medication dosing reminders in the operating room [81], screening for disease [82,83], prediction of hospital readmission [84,85], creation of a life-expectancy index for hospitalized elderly patients [86], determination of early indicators of patient deterioration [87], and guided urinary tract infection treatment [88]. Clinical decision support is not fully automated and issues such as human error in algorithm design can lead to underperformance [89].…”

Detecting the association of health problems in consumer-level medical text

“…In recent decades, text-mining methods have been used to analyse medical literature and professional documents (e.g. electronic medical records (EMR)) in order to detect risk factors of certain diseases from EMR [14] and the co-occurrence patterns of psychiatric and somatic diseases from medical literature [15]. Whereas, the associations were usually built on certain diseases and biological objects, such as genes, proteins and drugs [16,17].…”

“…Common approaches in this line of studies are usually based on terms co-occurrence [16,17], ontology-based concept association [15] and machine learning methods, such as classification [14]. Because the terms in professional medical documents are more standard than those in consumer-level medical text, medical thesaurus or ontologies are popularly used.…”