Analysis of Prescribers’ Notes in Electronic Prescriptions in Ambulatory Practice
Open accessAbstract
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-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 claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The usability of EHR data is generally poor.
“consistent with the notion that the usability of EHR data is generally poor [29,30].”
From The relationship between perceptions of electronic health record usability and clinical importance of social and environmental determinants of health on provider documentation (2024) · cites this paper as [30]
The paper’s discussion (section labeled “Downloaded From,” page not specified) calls for better EHR and e-prescribing interface design to reduce inappropriate use of free-text prescription notes. That supports a specific usability concern, but the study does not establish that EHR data are generally poor in usability. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThis finding is congruent with SEDH data quality issues noted in the literature.
“This finding is congruent with SEDH data quality issues noted in the literature [28–30].”
From The relationship between perceptions of electronic health record usability and clinical importance of social and environmental determinants of health on provider documentation (2024) · cites this paper as [30]
The Abstract reports data-quality problems in electronic prescriptions: inappropriate free-text content and patient directions that conflict with structured fields. That supports the general point that electronic health data can have quality issues, but our copy does not identify these findings as SEDH-specific. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Ash, J. S. (2003) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1471 · Cited by 1,789 · see citing works
Measures of response agreement for qualitative data: Some generalizations and alternatives.
Light, R. J. (1971) · Psychological Bulletin · doi:10.1037/h0031643 · Cited by 747 · see citing works
Singh, H., Mani, S., Espadas, D., Petersen, N., Franklin, V., & Petersen, L. A. (2009) · Archives of Internal Medicine · doi:10.1001/archinternmed.2009.102 · Cited by 104 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 26
- Fischer MA, Vogeli C, Stedman M, Ferris T, Brookhart MA, Weissman JS. Effect of electronic prescribing with formulary decision support on medication use and cost. Arch Intern Med. 2008; 168(22):2433-2439.
- Schiff GD, Rucker TD. Computerized prescribing: building the electronic infrastructure for better medication usage. JAMA. 1998;279(13): 1024-1029.
- Bates DW, Cohen M, Leape LL, Overhage JM, Shabot MM, Sheridan T. Reducing the frequency of errors in medicine using information technology. J Am Med Inform Assoc. 2001;8(4):299-308.
- Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med. 2003;348 (25):2526-2534.
- Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144(10):742-752.
- Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood). 2011;30(3):464-471.
- Kaushal R, Kern LM, Barrón Y, Quaresimo J, Abramson EL. Electronic prescribing improves medication safety in community-based office practices. J Gen Intern Med. 2010;25(6): 530-536.
- Devine EB, Wilson-Norton JL, Lawless NM, et al. Characterization of prescribing errors in an internal medicine clinic. Am J Health Syst Pharm. 2007;64 (10):1062-1070.
- Donyai P, O'Grady K, Jacklin A, Barber N, Franklin BD. The effects of electronic prescribing on the quality of prescribing. Br J Clin Pharmacol. 2008;65 (2):230-237.
- Palchuk MB, Fang EA, Cygielnik JM, et al. An unintended consequence of electronic prescriptions: prevalence and impact of internal discrepancies. J Am Med Inform Assoc. 2010;17(4): 472-476.
- Rupp MT, Warholak TL. Evaluation of e-prescribing in chain community pharmacy: best-practice recommendations. J Am Pharm Assoc (2003). 2008;48(3):364-370.
- Tierney WM, Miller ME, Overhage JM, McDonald CJ. Physician inpatient order writing on microcomputer workstations: effects on resource utilization. JAMA. 1993;269(3):379-383.
- Stein HD, Nadkarni P, Erdos J, Miller PL. Exploring the degree of concordance of coded and textual data in answering clinical queries from a clinical data repository. J Am Med Inform Assoc. 2000;7(1):42-54.
- Hohnloser JH, Fischer MR, König A, Emmerich B. Data quality in computerized patient records: analysis of a haematology biopsy report database. Int J Clin Monit Comput. 1994;11(4):233-240.
- Johnson SB, Bakken S, Dine D, et al. An electronic health record based on structured narrative. J Am Med Inform Assoc. 2008;15(1): 54-64.
- Dhavle AA, Corley ST, Rupp MT, et al. Evaluation of a user guidance reminder to improve the quality of electronic prescription messages. Appl Clin Inform. 2014;5(3):699-707.
- Grossman JM, Cross DA, Boukus ER, Cohen GR. Transmitting and processing electronic prescriptions: experiences of physician practices and pharmacies. J Am Med Inform Assoc. 2012;19 (3):353-359.
- Wolf MS, Shekelle P, Choudhry NK, Agnew-Blais J, Parker RM, Shrank WH. Variability in pharmacy interpretations of physician prescriptions. Med Care. 2009;47(3):370-373.
- Hincapie AL, Warholak T, Altyar A, Snead R, Modisett T. Electronic prescribing problems reported to the Pharmacy and Provider ePrescribing Experience Reporting (PEER) portal. Res Social Adm Pharm. 2014;10(4):647-655.
- Allen AS, Sequist TD. Pharmacy dispensing of electronically discontinued medications. Ann Intern Med. 2012;157(10):700-705.
- McDonald CJ, Overhage JM, Mamlin BW, Dexter PD, Tierney WM. Physicians, information technology, and health care systems: a journey, not a destination. J Am Med Inform Assoc. 2004;11(2): 121-124.
- Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005;293(10): 1197-1203.
- Sittig DF, Singh H. Defining health information technology-related errors: new developments since To Err Is Human. Arch Intern Med. 2011;171(14): 1281-1284.
- Ratwani RM, Fairbanks RJ, Hettinger AZ, Benda NC. Electronic health record usability: analysis of the user-centered design processes of eleven electronic health record vendors. J Am Med Inform Assoc. 2015;22(6):1179-1182.
- Hansen LB, Fernald D, Araya-Guerra R, Westfall JM, West D, Pace W. Pharmacy clarification of prescriptions ordered in primary care: a report from the Applied Strategies for Improving Patient Safety (ASIPS) collaborative. J Am Board Fam Med. 2006;19(1):24-30.
- Nanji KC, Rothschild JM, Salzberg C, et al. Errors associated with outpatient computerized prescribing systems. J Am Med Inform Assoc. 2011; 18(6):767-773.
Cited by
Sorted by citations
Sood, N., Stetter, C., Kunselman, A., & Jasani, S. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000428 · Cited by 6 · see citing works
- Authors
- Dhavle, A. A., Yang, Y., Rupp, M. T., Singh, H., Ward-Charlerie, S., & Ruiz, J.
- Affiliations
- Midwestern University (US)
- Corresponding author
- Dhavle, Ajit A.
- Venue
- JAMA Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2016-04-01
- Volume
- 176
- Issue
- 4
- Pages
- 463
- ISSN
- 2168-6106 (ISSN-L)
- OpenAlex
- W2293366293
- Cited by
- 25
- References
- 3
- Citations by year
- 2024: 3 · 2022: 2 · 2021: 5 · 2020: 1 · 2019: 4 · 2018: 5 · 2017: 4 · 2016: 1
- FWCI
- 7.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Veterans Affairs
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- electronic prescribing, outpatient care, community pharmacy, electronic health records, medication dispensing, patient safety, workflow disruption, medication reconciliation
- MeSH terms
- Ambulatory Care, Humans, Pharmacists, Physicians, Retrospective Studies, Interdisciplinary Communication, Qualitative Research, Electronic Prescribing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review