Detecting Problematic Opioid Use in the Electronic Health Record: Automation of the Addiction Behaviors Checklist in a Chronic Pain Population.
Detecting Problematic Opioid Use in the Electronic Health Record: Automation of the Addiction Behaviors Checklist in a Chronic Pain Population.
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检测电子健康记录中存在问题的阿片类药物使用:慢性疼痛人群成瘾行为检查表的自动化。
DOI:
10.1101/2023.06.08.23290894
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发表时间:
2023
期刊:
影响因子:
--
通讯作者:
Jeffery,AlvinD
中科院分区:
文献类型:
--
作者:
Chatham,AngusH;Bradley,EliD;Schirle,Lori;Sanchez-Roige,Sandra;Samuels,DavidC;Jeffery,AlvinD
ImportanceIndividuals whose chronic pain is managed with opioids are at high risk of developing an opioid use disorder. Large data sets, such as electronic health records, are required for conducting studies that assist with identification and management of problematic opioid use.ObjectiveDetermine whether regular expressions, a highly interpretable natural language processing technique, could automate a validated clinical tool (Addiction Behaviors Checklist1) to expedite the identification of problematic opioid use in the electronic health record.DesignThis cross-sectional study reports on a retrospective cohort with data analyzed from 2021 through 2023. The approach was evaluated against a blinded, manually reviewed holdout test set of 100 patients.SettingThe study used data from Vanderbilt University Medical Center’s Synthetic Derivative, a de-identified version of the electronic health record for research purposes.ParticipantsThis cohort comprised 8,063 individuals with chronic pain. Chronic pain was defined by International Classification of Disease codes occurring on at least two different days.18 We collected demographic, billing code, and free-text notes from patients’ electronic health records.Main Outcomes and MeasuresThe primary outcome was the evaluation of the automated method in identifying patients demonstrating problematic opioid use and its comparison to opioid use disorder diagnostic codes. We evaluated the methods with F1 scores and areas under the curve - indicators of sensitivity, specificity, and positive and negative predictive value.ResultsThe cohort comprised 8,063 individuals with chronic pain (mean [SD] age at earliest chronic pain diagnosis, 56.2 [16.3] years; 5081 [63.0%] females; 2982 [37.0%] male patients; 76 [1.0%] Asian, 1336 [16.6%] Black, 56 [1.0%] other, 30 [0.4%] unknown race patients, and 6499 [80.6%] White; 135 [1.7%] Hispanic/Latino, 7898 [98.0%] Non-Hispanic/Latino, and 30 [0.4%] unknown ethnicity patients). The automated approach identified individuals with problematic opioid use that were missed by diagnostic codes and outperformed diagnostic codes in F1 scores (0.74 vs. 0.08) and areas under the curve (0.82 vs 0.52).Conclusions and RelevanceThis automated data extraction technique can facilitate earlier identification of people at-risk for, and suffering from, problematic opioid use, and create new opportunities for studying long-term sequelae of opioid pain management.
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DOI:
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发表时间:
1989
期刊:
The Journal of biological chemistry
影响因子:
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作者:
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通讯作者:
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1989
期刊:
The Journal of biological chemistry
影响因子:
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1990-03
期刊:
The American journal of pathology
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1991
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The Journal of biological chemistry
影响因子:
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DOI:
10.1073/pnas.86.21.8207
发表时间:
1989-11-01
影响因子:
11.1
作者:
GOLDBERG, GI;MARMER, BL;HE, CS
通讯作者:
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