Table 2.
Primary Studies on the Development of Novel Algorithms to Mine Narrative Electronic Prescribing Instructions
| Author (Year) | Data Source and Setting | Objective | Major Findings: Algorithm Performance |
|---|---|---|---|
| Dos Santos (2019) | Database of CPOE, Brazil | To develop an unsupervised algorithm to detect prescription dosage and frequency outliers using free-text prescribing information | The algorithm featured good recall (0.90) but poor precision (0.61); suitable to generate warnings |
| Karystianis (2016) | Clinical Practice Research Datalink, UK | To develop a model to extract detailed structured medication information from free-text prescribing information and explore variability in free-text | Model accuracy was 91% at the prescription level and 97% across attribute levels; variability was present in ≥1 attribute for 24% of prescriptions |
| Liang (2019) | Hospital discharge data, McGill University Hospital Health Centre, Canada | To develop an automatic parser tool for free-text electronic prescriptions | The tool identified 90% of the doses and 86% of the dose frequencies; the main cause of errors was combination medications |
| Lu (2016) | Pharmacy dispensing data, Veterans Health Affairs Corporate Data Warehouse, US | To develop and evaluate the performance of an NLP tool that computes average weekly doses from elements in free-text prescription instructions | Overall accuracy of the tool was 89% (95% CI: 88% to 90%) |
| MacKinlay (2012) | Subset of prescriptions from a long-term care facility in Australia | To develop an information extracting application that transforms free-text prescription information into a structured representation | ≥92.5% accuracy for individual field and 87.5% accuracy for all fields; able to populate all fields with correct data for 67.5% of prescriptions |
| McTaggart (2018) | Prescription dispensing data from the NHS Scotland Prescribing Information System, Scotland UK | To develop an NLP algorithm that generates structured output from free-text prescribing instructions | The algorithm generated structured output for 92.3% of dose instructions; completeness varied by therapeutic area (from 86.7% to 96.8%) |
| Shah (2006) | Prescription entries in the Full Feature General Practice Research Database, UK | To develop an algorithm to derive the daily dose from free-text prescription instructions | The algorithm calculated dosage fields for 99.35% of prescriptions; accuracy was 98.83% |
| Wong (2019) | Electronic prescription database, Quebec, Canada | To compare a tuned super learner algorithm, an untuned super learner, and a logistic regression model for predicting anti-depressant prescribing for indications other than depression using free-text prescribing information | The tuned super learner algorithm performed slightly better than the untuned super learner and logistic regression model, with Brier scores (reductions in mean squared error relative to random classification) of 32%, 31%, and 31%, respectively. Compared to the tuned super learner, relative efficiency loss was 4%for the untuned super learner and 5% for the logistic regression model |
| Xu (2010) | EHR, Vanderbilt University Medical Center, US | To develop an NLP algorithm to calculate daily doses of medications mentioned in clinical text | The algorithm had high precision (0.90–1.00) and high recall (0.81 to 1.00) across 4 different types of clinical data (clinical documentation, discharge summaries, problem lists, and WizOrders) |
CPOE, Computerized Prescription Order Entry System; NHS, National Health Service; NLP, natural language processing; UK, United Kingdom; US, United States.