Table 1.

Primary Studies Evaluating the Quality of Narrative Electronic Prescribing Instructions

Author (Year)	Data Source and Setting	Objective	Major Findings: Result Summary
Ai (2018)	EHR, Brigham and Women’s Hospital, Boston Massachusetts, US	To examine the frequency and potential impact of entering information intended for pharmacists into electronic prescribing fields	11.7% of prescriptions had comments intended for the pharmacist; 37.5% of which had the potential for significant harm and 2.8% had the potential for severe harm
Dhavle (2014)	Electronic prescriptions, Surescripts Electronic Prescription Network, US	To evaluate the effect of a reminder statement on the incidence of inappropriate patient directions in electronic prescriptions	The incidence of inappropriate Sig-related information in the notes field decreased from 2.8% at baseline to 1.8% at 3 months and 15 months after implementation
Dhavle (2016)	Electronic prescriptions, community pharmacies across the US	To analyze content of free-text notes in electronic prescriptions and develop recommendations for improvement	The free-text notes field was frequently (66.1%) used inappropriately, of which 19% conflicted with directions in designated fields; of the appropriate content, 47.3% of could have been communicated using structured fields
Hagstedt (2011)	Interviews and assessment of CPOEs at primary care centers, Sweden	To develop and implement a model to evaluate the usability of CPOEs for medication ordering	The evaluation model included five categories comprising 73 single criteria; the most common deficiencies in CPOEs were a non-intuitive interface and incorrect dosage function, which was most often presented in free-text
Hogan (1996)	EHR, University of Pittsburgh Medical Center Pennsylvania, US	To study the frequency with which supplemental free-text alters or contradicts structured data in EHR	The prevalence of free-text entries that altered the meaning of coded data in EHR was high (81%); upon review, clinicians confirmed that the free-text contained the correct representation of what the patient was taking in 75% of cases
Maat (2013)	Electronic prescriptions, University Medical Center Utrecht, The Netherlands	To examine the frequency and characteristics of prescriptions requiring interventions	Interventions were made for 1.1% of prescriptions, of which 81% might have had adverse clinical consequences if not corrected; the strongest determinant of interventions was free-text entry (OR 4.71, 95% CI 3.61 to 6.13)
Magrabi (2010)	Task-based study, teaching hospital attached to the University of New South Wales, UK	To examine the effect of interruptions and task complexity on error rates while using a CPOE system for various experimental scenarios	Errors were detected, ranging from 0.5%−16%, including omission of free-text qualifiers (12% of cases in one scenario). Interruptions did not influence error rates but complex tasks, once interruptions occurred, took significantly longer to complete.
Odukoya (2012)	Group interviews, community pharmacies in Wisconsin, US	To assess use of electronic prescribing technology and associated workflow challenges	Confusing or inaccurate e-prescriptions was problematic for pharmacy personnel, specifically free-text directions, which are often incomplete or duplicated.
Palchuk (2010)	EHR, Partners HealthCare System, Boston, Massachusetts, US	To evaluate the frequency and potential impact of discrepancies between structured and free text fields in electronic prescriptions	16.1% of prescriptions had ≥1 discrepancy; the majority (83.8%) of prescriptions with discrepancies could have led to adverse events, and 16.8% had the potential to lead to hospitalization or death
Patel (2016)	Electronic prescriptions, University of Mississippi Medical Center, US	To assess whether optimization of CPOE can reduce errors in electronic prescriptions	The optimization resulted in a statistically significant decline in the error rate from 20.27% to after the changes 12.96%; cost savings were estimated at $76 per 100 prescriptions
Salazar (2019)	Electronic prescriptions, Northwestern Medical Faculty Foundation, Chicago, Illinois, US	To examine the frequency with which indications are documented in electronic prescription instructions	Although it is well-recognized that adding the purpose of the medication to prescription orders can improve safety, indications were included in only 7.41% of prescriptions, of which 77.18% were for PRN orders
Schiff (2015)	United States Pharmacopeia MEDMARX reporting system, US	To analyze medication errors caused by CPOE to determine what went wrong and why, and identify potential prevention strategies	6.1% of medication errors reported to MEDMARX were CPOE related; most common CPOE-related errors included missing or erroneous SIG or patient instructions
Singh (2009)	Electronic prescriptions, Michael E. DeBakey Veterans Affairs Medical Center (MEDVAMC), Houston, Texas, US	To describe the impact, frequency, and predictors of inconsistent information in electronic prescriptions	The estimated overall rate of inconsistent information was 1%; inconsistencies were most commonly drug dosage (44.9%), duration for inpatients (24.4%), and administration schedule (20.5%); about 20% of errors could have resulted in moderate to severe harm
Turchin (2011)	EHR, Partners HealthCare System, Boston, Massachusetts, US	To determine whether internal discrepancies (when information in the structured fields conflicts with instructions in the free-text field) in warfarin prescriptions are associated with an increased risk of hemorrhage	11.1% of the warfarin prescriptions had at least one internal discrepancy; the most common discrepancies involved a complex regimen (75.7%) or dose discrepancy (15.8%); the odds of having an internal discrepancy in the most recent warfarin prescription was almost 40% lower among cases compared to controls (OR=0.61, p=0.045)
Turchin (2014)	EHR, Partners HealthCare System, Boston, Massachusetts, US	To determine whether changes in EHR user interface are associated with a change in incidence of discrepancies between the structured and narrative components of electronic prescriptions	18.4% of prescriptions had discrepancies over the study period; two user interface changes significantly reduced the frequency of discrepancies: addition of an “as directed” option to the <Frequency> dropdown (p=0.0004) and a pop-up warning about the potential of internal prescription discrepancies that appeared when the used placed the cursor into the <Special Instructions> field (p=0.0319)
Villamañán (2013)	Electronic prescriptions, La Paz University Hospital, Madrid, Spain	To assess the frequency of medication errors caused by CPOE	The medication error rate was 0.8 % (95 % CI 0.6–0.7), of which 77.7% were associated with CPOE; 15.4% of errors were related to inappropriate use of the free-text field (e.g. duplication or discrepancies between the medications selected via the structured template and the free-text comments)
Weingart (2012)	EHR, Dana-Farber/Partners Cancer Care, Massachusetts and New Hampshire, US	To assess the performance of an enhanced prescription-writing module for EHR intended to prevent oral chemotherapy errors	Clinicians used the module extensively and without resistance; optional fields for diagnosis (46%) and intent of therapy (13%) were inconsistently used; customized instructions using a free-text field were entered for 64% of prescriptions
Yang (2018)	Electronic prescriptions, outlets of a national retail drugstore chain, US	To assess the quality and variability of free-text in electronic prescriptions	There was substantial variability even for simple and straightforward concepts (e.g. “Take 1 tablet by mouth once daily”); approximately 10% of Sigs contained ≥1 error that was likely to lead to patient harm or cause workflow disruptions
Zhou (2012)	EHR, Partners HealthCare System, Boston, Massachusetts, US	To explore the quality of and incidence of free-text medication order entries involving hypoglycemic agents	9.3% of prescriptions for hypoglycemic agents were entered as free-text, of which 17.4% contained misspellings; more than 40% of dose, frequency, and dispense quantity details, and approximately 80% of duration information were missing

CI, confidence interval; CPOE; Computerized Prescription Order Entry; EHR, Electronic Health Record; OR, odds ratio; Sig, signatura; UK, United Kingdom; US, United States.