Difference between revisions of "Using natural language processing to identify problem usage of prescription opioids"

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(Related Articles)
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==Related Articles==
 
==Related Articles==
[[Evaluating healthcare quality using natural language processing]]  
+
[[Evaluating healthcare quality using natural language processing]]
 +
 
 
[[Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms]]
 
[[Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms]]
 +
 
[[Use of a support vector machine for categorizing free-text notes]]
 
[[Use of a support vector machine for categorizing free-text notes]]
 +
 
[[Visualizing unstructured patient data for assessing diagnostic and therapeutic history]]
 
[[Visualizing unstructured patient data for assessing diagnostic and therapeutic history]]
  

Revision as of 03:10, 1 November 2015

The problem of prescription opiod abuse is well-known and wide-spread, but clinicians often make references to patient opiod abuse in unstructured free-text notes rather than structured, searchable discrete data fields. The article being reviewed here studies the use of Natural language processing (NLP) to help identify the patients whose problem might otherwise go unrecognized. [1]

Abstract[1]

Background

Accurate and scalable surveillance methods are critical to understand widespread problems associated with misuse and abuse of prescription opioids and for implementing effective prevention and control measures. Traditional diagnostic coding incompletely documents problem use. Relevant information for each patient is often obscured in vast amounts of clinical text.

Objective

The authors developed and evaluated a method that combines natural language processing (NLP) and computer-assisted manual review of clinical notes to identify evidence of problem opioid use in electronic health records (EHRs).

Methods

The authors used the EHR data and text of 22,142 patients receiving chronic opioid therapy (≥70 days' supply of opioids per calendar quarter) during 2006-2012 to develop and evaluate an NLP-based surveillance method and compare it to traditional methods based on International Classification of Disease, Ninth Edition (ICD-9) codes. They developed a 1288-term dictionary for clinician mentions of opioid addiction, abuse, misuse or overuse, and an NLP system to identify these mentions in unstructured text. The system distinguished affirmative mentions from those that were negated or otherwise qualified. The authors applied this system to 7336,445 electronic chart notes of the 22,142 patients. Trained abstractors using a custom computer-assisted software interface manually reviewed 7751 chart notes (from 3156 patients) selected by the NLP system and classified each note as to whether or not it contained textual evidence of problem opioid use.

Results

Traditional diagnostic codes for problem opioid use were found for 2240 (10.1%) patients. NLP-assisted manual review identified an additional 728 (3.1%) patients with evidence of clinically diagnosed problem opioid use in clinical notes. Inter-rater reliability among pairs of abstractors reviewing notes was high, with kappa=0.86 and 97% agreement for one pair, and kappa=0.71 and 88% agreement for another pair.

Conclusion

Scalable, semi-automated NLP methods can efficiently and accurately identify evidence of problem opioid use in vast amounts of EHR text. Incorporating such methods into surveillance efforts may increase prevalence estimates by as much as one-third relative to traditional methods.

Comments

IN PROCESS

Related Articles

Evaluating healthcare quality using natural language processing

Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms

Use of a support vector machine for categorizing free-text notes

Visualizing unstructured patient data for assessing diagnostic and therapeutic history

References

  1. 1.0 1.1 Carrell DS, Cronkite D, Palmer RE, et al. Using natural language processing to identify problem usage of prescription opioids. Int J Med Inform. 2015;84(12):1057-64. http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pubmed/26456569