Using natural language processing to identify problem usage of prescription opioids.

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.

The prevalence of problem opioid use in patients receiving chronic opioid therapy: computer-assisted review of electronic health record clinical notes.

To estimate the prevalence of problem opioid use, we used natural language processing (NLP) techniques to identify clinical notes containing text indicating problem opioid use from over 8 million electronic health records (EHRs) of 22,142 adult patients receiving chronic opioid therapy (COT) within Group Health clinics from 2006 to 2012. Computer-assisted manual review of NLP-identified clinical notes was then used to identify patients with problem opioid use (overuse, misuse, or abuse) according to the study criteria. These methods identified 9.

Automated prediction of risk for problem opioid use in a primary care setting.

Unlabelled: Identification of patients at increased risk for problem opioid use is recommended by chronic opioid therapy (COT) guidelines, but clinical assessment of risks often does not occur on a timely basis. This research assessed whether structured electronic health record (EHR) data could accurately predict subsequent problem opioid use. This research was conducted among 2,752 chronic noncancer pain patients initiating COT (≥70 days' supply of an opioid in a calendar quarter) during 2008 to 2010.

Controlling for systematic selection in retrospective analyses: an application to fluoxetine and sertraline prescribing in the United Kingdom.

Background: Criticism has been made of observational studies in clinical practice because of their failure to control for unobserved factors that correlate with both initial treatment selection and observed outcomes.

Method: A two-stage statistical model was applied to data obtained from a large general practitioner medical records database (DIN-LINK) to estimate the effect of initial antidepressant selection on the duration of antidepressant therapy and on the likelihood of being prescribed an average daily dose above the minimum recommended dose. The statistical model controlled for unobserved factors correlated with initial treatment selection and the observed outcomes as well as for observed confounders.

Antidepressant use and resource utilization in the general practitioner setting in The Netherlands.

Objective: To assess antidepressant use and resource utilization in the general practitioner (GP) setting in the Netherlands following initiation of antidepressant therapy.

Design: Longitudinal study in a retrospective database.

Participants: Sample of 869 patients from a new database in the Netherlands who initiated therapy on a selective serotonin re-uptake inhibitor (SSRI) or a tricyclic antidepressant (TCA).