The optional free-text Notes field in ambulatory electronic prescriptions (e-prescriptions) allows prescribers to communicate additional prescription-related information to dispensing pharmacists. However, populating this field with irrelevant or inappropriate information can create confusion, workflow disruptions, and potential patient harm. OBJECTIVES To analyze the content of free-text prescriber notes in new ambulatory e-prescriptions and to develop recommendations to improve e-prescribing practices. DESIGN, SETTING, AND PARTICIPANTS We performed a qualitative analysis of e-prescriptions containing free-text prescriber notes for conformance to the intended purpose of the free-text field as established in the national e-prescribing standard. The study sample contained 26 341 new e-prescriptions randomly selected from 3 024 737 e-prescriptions containing notes transmitted to community pharmacies across the United States during a 1-week period (November 10-16, 2013). The study e-prescriptions were issued by 22 549 community-based prescribers using 492 different electronic health record (EHR) or e-prescribing software application systems. Data analysis was conducted from February 23, 2014, to November 4, 2015. MAIN OUTCOMES AND MEASURES Reviewers classified free-text prescriber notes as appropriate, inappropriate (content for which a standard, structured data-entry field is available in the widely implemented national e-prescribing standard), or unnecessary (irrelevant to dispensing pharmacists). We developed and applied a classification scheme to further characterize and quantify types of appropriate and inappropriate content. RESULTS Of the 26 341 free-text notes, 17 421 (66.1%) contained inappropriate content, 7522 (28.6%) contained appropriate content, and 1398 (5.3%) contained information considered to be unnecessary. Further characterization of inappropriate content resulted in 20 192 classification codes, of which 3841 codes (19.0%) were assigned because of patient directions that conflicted with directions included in the designated standard field intended for this purpose. Characterization of appropriate content resulted in 7785 classification codes, of which 3685 (47.3%) contained information that could be communicated using structured fields already approved in a yet-to-be implemented version of the e-prescribing standard. An additional 745 (9.6%) were prescription cancellation requests for which a separate e-prescribing message currently exists but is not widely supported by software vendors or used by prescribers. CONCLUSIONS AND RELEVANCE The free-text Notes field in e-prescriptions is frequently used inappropriately, suggesting the need for better prerelease usability testing, consistent end user training and feedback, and rigorous postmarketing evaluation and surveillance of EHR or e-prescribing software applications. Accelerated implementation of new e-prescribing standards and rapid adoption of existing ones could also reduce prescribers' reliance on free-text use in ambulatory e-prescriptions.

The National Library of Medicine continues to enhance the RxNorm terminology and expand its scope. This study illustrates the need for technology vendors to improve their implementation of RxNorm; doing so will accelerate the adoption of RxNorm as the preferred alternative to using the NDC terminology in e-prescribing.

We found the use of NDC identifiers in our sample of e-prescriptions to be relatively high. However, approximately one-third consisted of unrepresentative NDC numbers (obsolete, repackaged, unit dose, or private label) that have the potential to create workflow disruptions at the dispensing pharmacy. Most disturbing was our finding that more than 2 out of every 1,000 e-prescriptions in our sample contained a free-text drug description that pointed to a completely different drug concept than that associated with its NDC value. Our study suggests the need for e-prescribing technology vendors to maintain accurate and up-to-date drug database files within their systems and to conduct regular validation checks to ensure that the drug descriptions associated with the NDC identifier and the free-text drug description that is sent in the e-prescription message point to the same drug concept. The FDA may need to consider a more active role in ensuring the accuracy of NDC assignment by drug manufacturers.

Insertion of a targeted watermark reminder statement in the Notes field of an e-prescribing application significantly reduced the incidence of inappropriate Sig-related information in Notes and decreased prescribers' use of this field.

Background Medicines reconciliation and medicines reviews by hospital pharmacists can reduce drug-related problems in older people during care transitions. Purpose To evaluate the incidence of reconciliation and medicines errors, as well as acceptance rate of recommendations made by pharmacists at the admission process of elderly polymedicated patients at the Emergency Department of an acute care tertiary hospital. Materials and methods For one month (September 2013) the authors reviewed the electronic prescriptions of patients over 75 years of age coming from nursing homes with more than five prescribed drugs upon admission, and compared it with the medicines record provided by their nursing homes. Undocumented discrepancies and medicines errors were recorded and, when necessary, correct usual treatment and pharmacists’ recommendations were placed on the electronic clinical record system. Follow-up of the interventions was made during patients’ hospitalisation. Results 64 patients were reconciled (mean age 85.7, 43.8% women), with an average of 10.9 chronic drugs per patient. 68.8% belonged to Internal Medicine (IM), 15.6% Traumatology, 6.3% Pneumology, 4.7% Gastroenterology, 1.6% Neurosurgery, 1.6% General Surgery and 1.6% Cardiology. 47 undocumented discrepancies (51.1% different dose/frequency, 27.7% omission, 10.6% presentation, 8.5% addition of a drug that the patient was not previously taking and 2.1% duplication) and 6 medicines errors were identified (66.7% untreated medical conditions, 16.7% contraindications and 16.7% STOPP/START criteria). 52 recommendations were made (0.8 per patient) and 36 of these (69.2%) were accepted by the physician. Conclusions The pharmacist-driven medicines reconciliation and medicines review programme led to the detection of numerous undocumented discrepancies and medicines errors. The most frequent type of discrepancy was difference in dose or frequency and the main medicines error was the lack of treatment of a medical condition. The majority of the recommendations related to these discrepancies and medicines errors were accepted, reinforcing the role of the pharmacist in this task. No conflict of interest.