How many medication orders are entered through free-text in EHRs?--a study on hypoglycemic agents.

Computerized Provider Order Entry (CPOE) can reduce medication errors; however, its benefits are only achieved when data are entered in a structured format and entries are properly coded. This paper aims to explore the incidence of free-text medication order entries involving hypoglycemic agents in an ambulatory electronic health record (EHR) system with CPOE. Our results showed that free-text order entry continues to be frequent. During 2010, 9.3% of hypoglycemic agents were entered as free-text for 2,091 patients. 17.4% of the entries contained misspellings. The highest proportion of free-text entries were found in urgent care clinics (49.4%) and among registered nurses (31.5%). Additionally, 92 drug-drug interaction alerts were not triggered due to free-text entries. Only 25.9% of the patients had diabetes recorded in their problem list. Solutions will require policy to enforce structured entry, ongoing improvement in user-interface design, improved training for users, and strategies for maintaining a complete medication list.

Supratherapeutic dosing of acetaminophen among hospitalized patients.

BACKGROUND: We investigated acetaminophen use and identify factors contributing to supratherapeutic dosing of acetaminophen in hospitalized patients. METHODS: We retrospectively reviewed the electronic health records of adult patients who were admitted to 2 academic tertiary care hospitals (hospital A amd hospital B) from June 1, 2010, to August 31, 2010, and who received acetaminophen during their hospitalization. Patients' acetaminophen administration records (including drug name, dose, administration time, hospital units, etc), demographic data, diagnoses, and results from liver function tests were obtained. The main outcome measures included acetaminophen exposure rate and supratherapeutic dosing rate among hospitalized patients, hazard ratios (HRs) and 95% confidence intervals (CIs) for risk factors for supratherapeutic dosing, and changes in liver function test before and after supratherapeutic dosing. RESULTS: A total of 14 411 patients (60.7%) were exposed to acetaminophen, of whom 955 (6.6%) exceeded the 4 g per day maximum recommended dose. In addition, 22.3% of patients who were 65 years or older and 17.6% of patients with chronic liver diseases exceeded the recommended limit of 3 g per day. Patients receiving excessive doses of acetaminophen tended to have significant alkaline phosphatase elevations, although causal relationship cannot be concluded. A significantly higher risk of supratherapeutic dosing was observed in hospital A (HR, 1.6 [95% CI, 1.4-1.8]), white patients (HR, 1.5 [95% CI, 1.3-1.7]), patients diagnosed as having osteoarthritis (HR, 1.4 [95% CI, 1.3-1.6]), and those who received scheduled administrations (HR, 16.6 [95% CI, 13.5-20.6]), multiple product formulations (HR, 2.4 [95% CI 2.0-2.9]), or the 500-mg strength formulation (HR, 1.9 [95% CI, 1.5-2.3]). A lower risk was found for pro re nata (as needed) administrations (HR, 0.7 [95% CI, 0.6-0.9]) and in nonsurgical and non­intensive care units (HR, 0.6 [95% CI, 0.5-0.7]). CONCLUSIONS: Supratherapeutic dosing of acetaminophen was significantly associated with multiple factors. Interventions to reduce the incidence of some risk factors may prevent supratherapeutic acetaminophen dosing in hospitalized patients.

Mapping Partners Master Drug Dictionary to RxNorm using an NLP-based approach.

OBJECTIVE: To develop an automated method based on natural language processing (NLP) to facilitate the creation and maintenance of a mapping between RxNorm and a local medication terminology for interoperability and meaningful use purposes. METHODS: We mapped 5961 terms from Partners Master Drug Dictionary (MDD) and 99 of the top prescribed medications to RxNorm. The mapping was conducted at both term and concept levels using an NLP tool, called MTERMS, followed by a manual review conducted by domain experts who created a gold standard mapping. The gold standard was used to assess the overall mapping between MDD and RxNorm and evaluate the performance of MTERMS. RESULTS: Overall, 74.7% of MDD terms and 82.8% of the top 99 terms had an exact semantic match to RxNorm. Compared to the gold standard, MTERMS achieved a precision of 99.8% and a recall of 73.9% when mapping all MDD terms, and a precision of 100% and a recall of 72.6% when mapping the top prescribed medications. CONCLUSION: The challenges and gaps in mapping MDD to RxNorm are mainly due to unique user or application requirements for representing drug concepts and the different modeling approaches inherent in the two terminologies. An automated approach based on NLP followed by human expert review is an efficient and feasible way for conducting dynamic mapping.

Using Medical Text Extraction, Reasoning and Mapping System (MTERMS) to process medication information in outpatient clinical notes.

Clinical information is often coded using different terminologies, and therefore is not interoperable. Our goal is to develop a general natural language processing (NLP) system, called Medical Text Extraction, Reasoning and Mapping System (MTERMS), which encodes clinical text using different terminologies and simultaneously establishes dynamic mappings between them. MTERMS applies a modular, pipeline approach flowing from a preprocessor, semantic tagger, terminology mapper, context analyzer, and parser to structure inputted clinical notes. Evaluators manually reviewed 30 free-text and 10 structured outpatient clinical notes compared to MTERMS output. MTERMS achieved an overall F-measure of 90.6 and 94.0 for free-text and structured notes respectively for medication and temporal information. The local medication terminology had 83.0% coverage compared to RxNorm's 98.0% coverage for free-text notes. 61.6% of mappings between the terminologies are exact match. Capture of duration was significantly improved (91.7% vs. 52.5%) from systems in the third i2b2 challenge.