Objective Between 17% and 40% of patients undergoing elective arthroplasty are preoperative opioid users. This US study analyzed patients in this population to illustrate the relationship between preoperative opioid use and adverse surgical outcomes. Design Retrospective study of administrative medical and pharmaceutical claims data. Subjects Adults (aged 18+) who received elective total knee, hip, or shoulder replacement in 2014–2015. Methods A patient was a preoperative opioid user if opioid prescription fills occurred in two periods: 1–30 and 31–90 days presurgery. Zero-truncated Poisson (incidence rate ratio [IRR]), logistic (odds ratio [OR]), Cox (hazard ratio [HR]), and quantile regressions modeled the effects of preoperative opioid use and opioid dose, adjusted for demographics, comorbidities, and utilization. Results Among 34,792 patients (38% hip, 58% knee, 4% shoulder), 6,043 (17.4%) were preoperative opioid users with a median morphine equivalent daily dose of 32 mg. Preoperative opioid users had increased length of stay (IRR = 1.03, 95% CI = 1.02 to 1.05), nonhome discharge (OR = 1.10, 95% CI = 1.00 to 1.21), and 30-day unplanned readmission (OR = 1.43, 95% CI = 1.17 to 1.74); experienced 35% higher surgical site infection (HR = 1.35, 95% CI = 1.14 to 1.59) and 44% higher surgical revision (HR = 1.44, 95% CI = 1.21 to 1.71); had a median $1,084 (95% CI = $833 to $1334) increase in medical spend during the 365 days after discharge; and had a 64% lower rate of opioid cessation (HR = 0.34, 95% CI = 0.33 to 0.35) compared with patients not filling two or more prescriptions across periods. Conclusions Preoperative opioid users had longer length of stay, increased revision rates, higher spend, and persistent opioid use, which worsened with dose. Adverse outcomes after elective joint replacement may be reduced if preoperative opioid risk is managed through increased monitoring or opioid cessation.

Postmarketing population pharmacokinetic (PK) and pharmacodynamic (PD) studies can be useful to capture patient characteristics affecting PK or PD in real‐world settings. These studies require longitudinally measured dose, outcomes, and covariates in large numbers of patients; however, prospective data collection is cost‐prohibitive. Electronic health records (EHRs) can be an excellent source for such data, but there are challenges, including accurate ascertainment of drug dose. We developed a standardized system to prepare datasets from EHRs for population PK/PD studies. Our system handles a variety of tasks involving data extraction from clinical text using a natural language processing algorithm, data processing, and data building. Applying this system, we performed a fentanyl population PK analysis, resulting in comparable parameter estimates to a prior study. This new system makes the EHR data extraction and preparation process more efficient and accurate and provides a powerful tool to facilitate postmarketing population PK/PD studies using information available in EHRs.

Objective We developed medExtractR, a natural language processing system to extract medication information from clinical notes. Using a targeted approach, medExtractR focuses on individual drugs to facilitate creation of medication-specific research datasets from electronic health records. Materials and Methods Written using the R programming language, medExtractR combines lexicon dictionaries and regular expressions to identify relevant medication entities (eg, drug name, strength, frequency). MedExtractR was developed on notes from Vanderbilt University Medical Center, using medications prescribed with varying complexity. We evaluated medExtractR and compared it with 3 existing systems: MedEx, MedXN, and CLAMP (Clinical Language Annotation, Modeling, and Processing). We also demonstrated how medExtractR can be easily tuned for better performance on an outside dataset using the MIMIC-III (Medical Information Mart for Intensive Care III) database. Results On 50 test notes per development drug and 110 test notes for an additional drug, medExtractR achieved high overall performance (F-measures >0.95), exceeding performance of the 3 existing systems across all drugs. MedExtractR achieved the highest F-measure for each individual entity, except drug name and dose amount for allopurinol. With tuning and customization, medExtractR achieved F-measures >0.90 in the MIMIC-III dataset. Discussion The medExtractR system successfully extracted entities for medications of interest. High performance in entity-level extraction provides a strong foundation for developing robust research datasets for pharmacological research. When working with new datasets, medExtractR should be tuned on a small sample of notes before being broadly applied. Conclusions The medExtractR system achieved high performance extracting specific medications from clinical text, leading to higher-quality research datasets for drug-related studies than some existing general-purpose medication extraction tools.

Objective: We developed medExtractR, a natural language processing system to extract medication dose and timing information from clinical notes. Our system facilitates creation of medication-specific research datasets from electronic health records. Materials and Methods: Written using the R programming language, medExtractR combines lexicon dictionaries and regular expression patterns to identify relevant medication information ('drug entities'). The system is designed to extract particular medications of interest, rather than all possible medications mentioned in a clinical note. MedExtractR was developed on notes from Vanderbilt University's Synthetic Derivative, using two medications (tacrolimus and lamotrigine) prescribed with varying complexity, and with a third drug (allopurinol) used for testing generalizability of results. We evaluated medExtractR and compared it to three existing systems: MedEx, MedXN, and CLAMP. Results: On 50 test notes for each development drug and 110 test notes for the additional drug, medExtractR achieved high overall performance (F-measures > 0.95). This exceeded the performance of the three existing systems across all drugs, with the exception of a couple specific entity-level evaluations including dose amount for lamotrigine and allopurinol. Discussion: MedExtractR successfully extracted medication entities for medications of interest. High performance in entity-level extraction tasks provides a strong foundation for developing robust research datasets for pharmacological research. However, its targeted approach provides a narrower scope compared with existing systems. Conclusion: MedExtractR (available as an R package) achieved high performance values in extracting specific medications from clinical text, leading to higher quality research datasets for drug-related studies than some existing general-purpose medication extraction tools.

ObjectiveWe developed a post-processing algorithm to convert raw natural language processing output from electronic health records into a usable format for analysis. This algorithm was specifically developed for creating datasets that can be used for medication-based studies. Materials and MethodsThe algorithm was developed using output from two natural language processing systems, MedXN and medExtractR. We extracted medication information from deidentified clinical notes from Vanderbilt's electronic health record system for two medications, tacrolimus and lamotrigine, which have widely different prescribing patterns. The algorithm consists of two parts. Part I parses the raw output and connects entities together and Part II removes redundancies and calculates dose intake and daily dose. We evaluated both parts of the algorithm by comparing to gold standards that were generated using approximately 300 records from 10 subjects for both medications and both NLP systems. ResultsBoth parts of the algorithm performed well. For MedXN, the F-measures for Part I were at or above 0.94 and for Part II they were at or above 0.98. For medExtractR the Fmeasures for Part I were at or above 0.98 and for Part II they were at or above 0.91. DiscussionOur post-processing algorithm is useful for drug-based studies because it converts NLP output to analyzable data. It performed well, although it cannot handle highly complicated cases, which usually occurred when a NLP incorrectly extracted dose information. Future work will focus on identifying the most likely correct dose when conflicting doses are extracted on the same day. Part I Output:ID6_2015-12-28_note1.txt Lamictal 80 mcg 1 ID6_2015-12-28_note1.txt Lamictal 4.5 mcg

Objective To develop an algorithm for building longitudinal medication dose datasets using information extracted from clinical notes in electronic health records (EHRs). Materials and Methods We developed an algorithm that converts medication information extracted using natural language processing (NLP) into a usable format and builds longitudinal medication dose datasets. We evaluated the algorithm on 2 medications extracted from clinical notes of Vanderbilt’s EHR and externally validated the algorithm using clinical notes from the MIMIC-III clinical care database. Results For the evaluation using Vanderbilt’s EHR data, the performance of our algorithm was excellent; F1-measures were ≥0.98 for both dose intake and daily dose. For the external validation using MIMIC-III, the algorithm achieved F1-measures ≥0.85 for dose intake and ≥0.82 for daily dose. Discussion Our algorithm addresses the challenge of building longitudinal medication dose data using information extracted from clinical notes. Overall performance was excellent, but the algorithm can perform poorly when incorrect information is extracted by NLP systems. Although it performed reasonably well when applied to the external data source, its performance was worse due to differences in the way the drug information was written. The algorithm is implemented in the R package, “EHR,” and the extracted data from Vanderbilt’s EHRs along with the gold standards are provided so that users can reproduce the results and help improve the algorithm. Conclusion Our algorithm for building longitudinal dose data provides a straightforward way to use EHR data for medication-based studies. The external validation results suggest its potential for applicability to other systems.

Aims: Use of electronic health record (EHR) data to estimate population pharmacokinetic (PK) profiles necessitates several assumptions. We sought to investigate sensitivity to some of these assumptions about dose timing and absorption rates.Methods: A population PK study with 363 subjects was performed using real-world data extracted from EHRs to estimate the tacrolimus population PK profile. Data were extracted and built using our automated system, EHR2PKPD, suitable for quickly constructing large PK datasets from the EHR. Population PK studies for oral medications performed using EHR data often assume a regular dosing schedule as prescribed without incorporating exact dosing time. We assessed the sensitivity of the PK parameter estimates to assumptions about dose timing using last-dose times extracted by our own natural language processing system, medExtractR. We also investigated the sensitivity of estimates to absorption rate constants that are often fixed at a published value in tacrolimus population PK analyses. We conducted simulation studies to investigate how drug PK profiles and experimental designs such as concentration measurements design affect sensitivity to incorrect assumptions about dose timing and absorption rates.Results: There was no appreciable difference in parameter estimates with assumed versus extracted last-dose time, and our sensitivity analysis revealed little difference between parameters estimated across a range of assumed absorption rate constants. Conclusion:Our findings suggest that drugs with a slower elimination rate (or a longer half-life) are less sensitive to dose timing errors and that experimental designs which only allow for trough blood concentrations are usually insensitive to deviation in absorption rate.

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