BACKGROUND:The prescriber's directions to the patient (Sig) are one of the most quality-sensitive components of a prescription order. Owing to their free-text format, the Sig data that are transmitted in electronic prescriptions (e-prescriptions) have the potential to produce interpretation challenges at receiving pharmacies that may threaten patient safety and also negatively affect medication labeling and patient counseling. Ensuring that all data transmitted in the e-prescription are complete and unambiguous is essential for minimizing disruptions in workflow at prescribers' offices and receiving pharmacies and optimizing the safety and effectiveness of patient care. OBJECTIVES:To (a) assess the quality and variability of free-text Sig strings in ambulatory e-prescriptions and (b) propose best-practice recommendations to improve the use of this quality-sensitive field. METHODS:A retrospective qualitative analysis was performed on a nationally representative sample of 25,000 e-prescriptions issued by 22,152 community-based prescribers across the United States using 501 electronic health records (EHRs) or e-prescribing software applications. The content of Sig text strings in e-prescriptions was classified according to a Sig classification scheme developed with guidance from an expert advisory panel. The Sig text strings were also analyzed for quality-related events (QREs). For purposes of this analysis, QREs were defined as Sig text content that could impair accurate and unambiguous interpretation by staff at receiving pharmacies. RESULTS:A total of 3,797 unique Sig concepts were identified in the 25,000 Sig text strings analyzed; more than 50% of all Sigs could be categorized into 25 unique Sig concepts. Even Sig strings that expressed apparently simple and straightforward concepts displayed substantial variability; for example, the sample contained 832 permutations of words and phrases used to convey the Sig concept of "Take 1 tablet by mouth once daily." Approximately 10% of Sigs contained QREs that could pose patient safety risks or workflow disruptions that could necessitate pharmacist callbacks to prescribers for clarification or other manual interventions. CONCLUSIONS:The quality of free-text patient directions in e-prescriptions can vary dramatically. However, more than half of all patient directions sent in the ambulatory setting can be categorized into only 25 Sig concepts. This suggests an immediate, practical opportunity to improve patient safety and workflow efficiency for both prescribers and pharmacies. Recommendations include implementing enhancements to Sig creation tools in e-prescribing and EHR software applications, adoption of the Structured and Codified Sig format supported by the current national e-prescribing standard, and improved usability testing and end-user training for generating complete and unambiguous patient directions. Such quality improvements are essential for optimizing the safety and effectiveness of patient care as well as for minimizing workflow disruptions to both prescribers and pharmacies. DISCLOSURES:This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Yang, Ward-Charlerie, Dhavle, and Green are employed by Surescripts. Rupp reported receiving consulting fees from Surescripts during the conduct of this study. No other disclosures were reported. The content in this article is solely the responsibility of the authors and does not necessarily represent the official views of Surescripts and Midwestern University or any of the affiliated institutions of the authors. Study concept and design were contributed by all the authors. Yang and Ward-Charlerie collected the data, and data interpretion was performed by Yang, Ward-Charlerie and Dhavle. The manuscript was primarily written by Yang, along with Dhavle and Green, and revised by Yang, Dhavle, Rupp, and Green.

Medication errors are estimated to cost $42 billion in annual global treatment costs. Pharmacy-based Patient Safety Organizations (PSO) are tasked with collecting and analyzing incidents, near misses, and unsafe condition reports as one way of engaging pharmacies in quality improvement efforts. Collectively, these reports are referred to as quality related events (QREs). Large-scale analysis of typed narratives from QRE reports across organizations has been a missing component of quality improvement programs.To identify topics within the components of a proposed medication safety event framework contained in the free-text narrative of QRE reports.A retrospective, observational analysis of data from a PSOs voluntary reporting system, from January 1, 2011 to December 31, 2014. The dataset contained structured and unstructured data elements. A structural topic model extracted themes from the free-text narrative component of the report. These topics were assigned a human label and mapped onto constructs of the medication safety event framework.A total of 531,555 QREs were analyzed from 1660 pharmacies. 90.6% were near miss and unsafe condition reports. There were 40 topics generated. There were 29 topics identified as QRE types, 3 were identified as contributing factors, and 5 were related to signals/alerts that an incident or near miss had occurred. One topic each was identified as a recovery step and a quality improvement strategy. One topic was not assigned a human label. Examples of topics labeled included incorrect tapering directions, needing to double-check work, and attention-related contributing factor.The free-text narrative provided novel information compared to the structured fields of the reports. Topics were mapped onto a proposed medication safety event framework to advance knowledge of medication QREs and identify ways to improve medication safety in community pharmacy. Future work may focus on communicating these topics to the pharmacies to improve medication safety efforts.

Objective: To illustrate the need for wider implementation of the CancelRx message by quantifying and characterizing the inappropriate usage of new electronic prescription (NewRx) messages for communicating discontinuation instructions to pharmacies. Materials and Methods: A retrospective analysis on a nationally representative random sample of 1 400 000 NewRx messages transmitted over 7 days to identify e-prescriptions containing medication discontinuation instructions in NewRx text fields. A vocabulary of search terms signifying cancellation instructions was formulated and then iteratively refined. True-positives were subsequently identified programmatically and through manual reviews. Two independent reviewers identified incidences in which these instructions were associated with high-alert or look-alike-sound-like (LASA) medications. Results: We identified 9735 (0.7% of the total) NewRx messages containing prescription cancellation instructions with 78.5% observed in the Notes field; 35.3% of identified NewRxs were associated with high-alert or LASA medications. The most prevalent cancellation instruction types were medication strength or dosage changes (39.3%) and alternative therapy replacement orders (39.0%). Discussion: While the incidence of prescribers using the NewRx to transmit cancellation instructions was low, their transmission in NewRx fields not intended to accommodate such information can produce significant potential patient safety concerns, such as duplicate or inaccurate therapies. These findings reveal the need for wider industry adoption of the CancelRx message by electronic health record (EHR) and pharmacy systems, along with clearer guidance and improved end-user training, particularly as states increasingly mandate electronic prescribing of controlled substances. Conclusion: Encouraging the use of CancelRx and reducing the misuse of NewRx fields would reduce workflow disruptions and unnecessary risks to patient safety.

In this reply to the commentary, "A Call for a Statewide Medication Reconciliation Program," published in the October 2016 issue of The American Journal of Managed Care®, authors note that although they agree with the authors' assessment of the problem, they believe there is a proven and scalable solution to improve medication reconciliation that is already available to, and used by, clinicians.

IMPORTANCE 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-prescribingmessage 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.