Prescription Errors and Outcomes Related to Inconsistent Information Transmitted Through Computerized Order Entry: A Prospective Study
Open accessAbstract
BACKGROUND: Although several types of computerized provider order entry (CPOE)-related errors may occur, errors related to inconsistent information within the same prescription (ie, mismatch between the structured template and the associated free-text field) have not been described, to our knowledge. We determined the nature and frequency of such errors and identified their potential predictive variables. METHODS: In this prospective study, we enrolled pharmacists to report prescriptions containing inconsistent communication over a 4-month period at a tertiary care facility. We also electronically retrieved all prescriptions written during the study period containing any comments in the free-text field and then randomly selected 500 for manual review to determine inconsistencies between free-text and structured fields. Of these, prescriptions without inconsistencies were categorized as controls. Data on potentially predictive variables from reported and unreported errors and controls were collected. For all inconsistencies, we determined their nature (eg, drug dosage or administration schedule) and potential harm and used multivariate logistic regression models to identify factors associated with errors and harm. RESULTS: Of 55 992 new prescriptions, 532 (0.95%) were reported to contain inconsistent communication, a rate comparable to that obtained from the unreported group. Drug dosage was the most common inconsistent element among both groups. Certain medications were more likely associated with errors, as was the inpatient setting (odds ratio, 3.30; 95% confidence interval, 2.18-5.00) and surgical subspecialty (odds ratio, 2.45; 95% confidence interval, 1.57-3.82). About 20% of errors could have resulted in moderate to severe harm, for which significant independent predictors were found. CONCLUSIONS: Despite standardization of data entry, inconsistent communication in CPOE poses a significant risk to safety. Improving the usability of the CPOE interface and integrating it with workflow may reduce this risk.
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- Findingown data
Medications needing a complex order were highly correlated with error.
“Medications needing a complex order (eg, a tapering dose of prednisone) were highly correlated with error.”
Quote in the paperNot a substantial claim - Findingnone given
Comments were transferred to the new prescription when modifying an existing prescription.
“Second, comments were transferred to the new prescription when modifying an existing prescription.”
Quote in the paperSubstantial claim - Findingown data
Certain medication classes were more likely to have errors, with the highest odds for steroids (OR, 7.62; 95% CI, 3.11-18.63) compared with analgesics.
“Certain medication classes were also more likely to have errors, with the highest odds of error occurring for steroids (OR, 7.62; 95% CI, 3.11–18.63) compared with analgesics.”
Quote in the paperNot a substantial claim - Findingown data
Most errors (68.3%) occurred in prescriptions for inpatients.
“Most errors (68.3%) occurred in prescriptions for inpatients,”
Quote in the paperNot a substantial claim - Findingown data
112 errors (20.1%) could have resulted in moderate to severe harm.
“112 errors could have resulted in moderate to severe harm (considerable, very serious, serious permanent damage, or death) (20.1%).”
Quote in the paperSubstantial claim
- Findingown data
Orders with inconsistent communication were most commonly entered by trainees (59.0%), followed by attending physicians (24.9%), physician assistants (10.2%), and nurse practitioners (5.4%).
“Orders with inconsistent communication were most commonly entered by trainees (59.0%), followed by attending physicians (24.9%), physician assistants (10.2%), and nurse practitioners (5.4%).”
Quote in the paperNot a substantial claim - Findingown data
In the 26 unreported errors, dosage inconsistency was also the most common (n=10).
“In the 26 unreported errors, dosage inconsistency was also the most common (n=10; data not shown).”
Quote in the paperNot a substantial claim - Findingown data
Of 55,992 new prescriptions processed and reviewed by pharmacists during the study period, 532 (0.95%) were reported to contain inconsistent communication.
“Of 55 992 new prescriptions processed and reviewed by the pharmacists in the study period, 532 were reported by pharmacists to contain inconsistent communication (0.95%).”
Quote in the paperSubstantial claim - Findingown data
19.0% (n=103) of errors had 2 or more inconsistencies.
“and 19.0% (n=103) had 2 or more inconsistencies.”
Quote in the paperNot a substantial claim - Findingown data
Drug dosage was the most common inconsistent element across reported prescriptions (44.9%).
“The most common inconsistent element across reported prescriptions was drug dosage (44.9%),”
Quote in the paperNot a substantial claim - Findingown data
Medications ordered by providers in specialties other than primary care and surgery had lower risk (OR, 0.30; 95% CI, 0.14-0.63) than those ordered by providers in primary care.
“Medications ordered by providers in specialties other than primary care and surgery had lower risk (OR, 0.30; 95% CI, 0.14–0.63) than those ordered by providers in primary care.”
Quote in the paperSubstantial claim - Findingown data
Anticoagulants and cardiovascular drugs were the most frequent categories involving moderate to severe harm.
“Anticoagulants and cardiovascular drugs were the most frequent categories involving moderate to severe harm.”
Quote in the paperNot a substantial claim - Findingown data
Medications ordered by providers in the outpatient setting had higher risk (OR, 2.23; 95% CI, 1.15-4.35) for causing moderate to severe harm than those ordered for inpatients.
“Medications ordered by providers in the outpatient setting had higher risk (OR, 2.23; 95% CI, 1.15–4.35) for causing moderate to severe harm than those ordered for inpatients.”
Quote in the paperSubstantial claim - Findingown data
Among 491 randomly selected prescriptions containing free-text comments, 26 (5.3%) contained inconsistent communication.
“From the remaining 491 prescriptions containing comments, 26 were found to contain inconsistent communication (5.3%); none reported by pharmacists.”
Quote in the paperSubstantial claim - Findingown data
The medication classes most commonly affected by errors were cardiovascular and antihypertensives (15.8%), analgesics (14.7%), antibiotics (10.8%), psychiatric medications (10.4%), and laxatives (7.5%).
“The following medication classes were most commonly affected by errors: cardiovascular and antihypertensives (15.8%), analgesics (14.7%), antibiotics (10.8%), psychiatric medications (10.4%), and laxatives (7.5%).”
Quote in the paperNot a substantial claim - Findingown data
Providers often used the free-text window to write complex prescriptions that were frequently associated with error (e.g., prednisone taper).
“providers would often use the free-text window to write complex prescriptions that were frequently associated with error (eg, prednisone taper).”
Quote in the paperSubstantial claim - Findingown data
Pharmacists called providers in 149 cases (28.0%).
“Pharmacists called providers in 149 cases (28.0%).”
Quote in the paperNot a substantial claim - Findingown data
Anticoagulants were associated with the highest risk of causing potentially moderate to severe harm (OR, 61.89; 95% CI, 10.73-356.93).
“Of all medication classes, anticoagulants were associated with the highest risk of causing potentially moderate to severe harm (OR, 61.89; 95% CI, 10.73–356.93).”
Quote in the paperSubstantial claim - Findingown data
Medications considered high alert (OR, 3.43; 95% CI, 1.62-7.27) had a high potential for causing moderate to severe harm.
“As expected, medications considered high alert (OR, 3.43; 95% CI, 1.62–7.27) had a high potential for causing moderate to severe harm.”
Quote in the paperNot a substantial claim - Premisecitation
The actual frequency of errors facilitated by computerized provider order entry is not known.
“Errors facilitated by CPOE have received increasing attention in the literature, yet the true frequency of the problem is unknown.9,11,12”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [9] Koppel R Metlay JP Cohen A Role of computerized physician order entry systems in facilitating medication errors JAMA 2005 March 9 293 10 1197 203 15755942
Not checked in the free run.
- cites [11] Kaushal R Shojania KG Bates DW Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review Arch Intern Med 2003 June 23 163 12 1409 16 12824090
Not checked in the free run.
- cites [12] Nebeker JR Hoffman JM Weir CR Bennett CL Hurdle JF High rates of adverse drug events in a highly computerized hospital Arch Intern Med 2005 May 23 165 10 1111 6 15911723
Not checked in the free run.
- Premisecitation
Computerization of prescribing can create new pathways for error.
“However, computerization may introduce new error pathways.5–10”
Quote in the paperSubstantial claim0 of 6 sources checked- cites [5] Ash JS Berg M Coiera E Some unintended consequences of information technology in health care: the nature of patient care information system-related errors J Am Med Inform Assoc 2004 March 11 2 104 12 14633936 PMC353015
Not checked in the free run.
- cites [6] Berger RG Kichak JP Computerized physician order entry: helpful or harmful? J Am Med Inform Assoc 2004 March 11 2 100 3 14633934 PMC353014
Not checked in the free run.
- cites [7] Harrison MI Koppel R Bar-Lev S Unintended consequences of information technologies in health care--an interactive sociotechnical analysis J Am Med Inform Assoc 2007 September 14 5 542 9 17600093 PMC1975796
Not checked in the free run.
- cites [8] Horsky J Kuperman GJ Patel VL Comprehensive analysis of a medication dosing error related to CPOE Journal Of The American Medical Informatics Association: JAMIA 12 4 377 82 15802485 PMC1174881
Not checked in the free run.
- cites [9] Koppel R Metlay JP Cohen A Role of computerized physician order entry systems in facilitating medication errors JAMA 2005 March 9 293 10 1197 203 15755942
Not checked in the free run.
- cites [10] Weiner JP Kfuri T Chan K Fowles JB “e-Iatrogenesis”: the most critical unintended consequence of CPOE and other HIT J Am Med Inform Assoc 2007 May 14 3 387 8 17329719 PMC2244888
Not checked in the free run.
- Interpretationown data
Insufficient knowledge of the ordering mechanism appears to be a common problem.
“Third, insufficient knowledge of the ordering mechanism appears to be a common problem,”
Quote in the paperSubstantial claim - Interpretationown data
Despite attempted standardization of data entry, inconsistent communication in CPOE poses a significant risk to safety achieved by computerized medication order entry.
“Our results suggest that despite attempted standardization of data entry, inconsistent communication poses significant risks to safety achieved by computerized medication order entry.2”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [2] Bates DW Leape LL Cullen DJ Effect of computerized physician order entry and a team intervention on prevention of serious medication errors JAMA 1998 October 21 280 15 1311 6 9794308
Not checked in the free run.
- Interpretationcitation
The study adds to the growing body of knowledge that computerized information systems may introduce new types of errors, including those involving communication processes.
“Our study adds to the growing body of knowledge that computerized information systems may introduce new types of errors, including those involving communication processes.5,25”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [5] Ash JS Berg M Coiera E Some unintended consequences of information technology in health care: the nature of patient care information system-related errors J Am Med Inform Assoc 2004 March 11 2 104 12 14633936 PMC353015
Not checked in the free run.
- cites [25] Campbell EM Sittig DF Ash JS Guappone KP Dykstra RH Types of Unintended Consequences Related to Computerized Provider Order Entry J Am Med Inform Assoc 2006 September 1 13 5 547 56 16799128 PMC1561794
Not checked in the free run.
- Study design and conductown data
A review of 80 random records validated data entry quality; 5,591 of 5,600 pieces of information (99.8%) were correct.
“A review of 80 random records validated quality of data entry. Of 5600 pieces of information collected in these 80 records, 5591 (99.8%) were found to be correct.”
Quote in the paperNot a substantial claim - Interpretationnone given
Providers often selected an automated dosage default rather than typing the desired dosage.
“First, providers often selected an automated dosage default rather than typing the desired dosage.”
Quote in the paperSubstantial claim - Interpretationreasoning
The flexibility of free-text comments can produce discrepancies between the structured template selections and the provider's free-text comments.
“The flexibility, however, can lead to discrepancies between the elements selected through the structured template and the provider’s free-text comments (Figure 1).”
Quote in the paperSubstantial claim - Interpretationown data
Based on the 5.3% error rate in prescriptions with comments, an estimated 8,324 of 157,055 prescriptions with comments contained inconsistent communication.
“Based on a 5.3% error rate in prescriptions with comments, we estimated the 157 055 prescriptions with comments might contain 8324 prescriptions with inconsistent communication (5.3%).”
Quote in the paperSubstantial claim - Interpretationown data
Providers did not realize many default settings could be changed.
“it appeared that providers did not realize many could be changed.”
Quote in the paperSubstantial claim - Premisecitation
Narrative instructions in a free-text field are advantageous in highly structured order entry forms because they let providers clarify timing, dosage, or route.
“Narrative instructions in the free-text field have advantages in highly structured order entry forms14 because they allow flexibility for providers to enter clarifications regarding timing, dosage, or route of administration.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14] 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 March 1 11 2 121 4 15027445 PMC353017
Not checked in the free run.
- Interpretationcitation
Many of the errors and issues suggest that standardization through CPOE was not adequately integrated with the workflow, needs, and preferences of the providers.
“Finally, many of the errors and issues suggest that standardization through CPOE was not adequately integrated with the workflow, needs, and preferences of the providers.23,24”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [23] Campbell EM Guappone KP Sittig DF Dykstra RH Ash JS Computerized Provider Order Entry Adoption: Implications for Clinical Workflow Journal of General Internal Medicine In press 2008 PMC2607519 19020942
Not checked in the free run.
- cites [24] Aarts J Ash J Berg M Extending the understanding of computerized physician order entry: implications for professional collaboration, workflow and quality of care Int J Med Inform 2007 June 76 Suppl 1 S4 13 16798068
Not checked in the free run.
- Study design and conductreasoning
Underreporting may have caused the findings to underestimate the problems caused by inconsistencies in the orders.
“Because underreporting is a well recognized phenomenon in similar studies, our findings may underestimate the problems caused by inconsistencies in the orders;”
Quote in the paperNot a substantial claim- Rests on an assumption: Underreporting is a well-recognized phenomenon in similar studies.
- Interpretationreasoning
The overall rate of errors of inconsistent information was estimated to be 1% (8,324 of 830,823 prescriptions).
“we estimate the overall rate of errors of inconsistent information to be 1% (8324 of 830 823).”
Quote in the paperSubstantial claim
- May combine two claims. 17
- Classified as probably not a substantial claim. 15
May combine two claims.
Findingown dataPharmacists used the free-text comment in 88.9% of cases to fill the final prescription, the structured template in 9.2%, and a combination in 1.9%.
“Pharmacists used the free-text comment in 88.9% of cases to fill the final prescription, the structured template in 9.2% of cases, and in combination in the remainder (1.9%).”
- Also stated in: § COMMENT · hedged
“in over 90% of cases the pharmacists used the free-text information either by itself or combined it with information from the structured template”
Quote in the paperSubstantial claim- Also stated in: § COMMENT · hedged
May combine two claims.
Findingown dataOnly 2 reported errors were discovered after reaching the patient and did not result in harm.
“Only 2 reported errors were discovered after reaching the patient and did not result in harm.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataOrdering provider type, specialty, inpatient versus outpatient setting, and medication class differed significantly between prescriptions with inconsistent communication and controls.
“The following variables differed significantly between the 2 groups: ordering provider type (P=.04) and specialty (P<.001), inpatient vs outpatient setting (P<.001), and medication class (P<.001).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data23.1% of unreported errors could have resulted in moderate to severe harm, statistically no different from reported errors (P=.62).
“Of note, 23.1% of unreported errors could have resulted in moderate to severe harm, statistically no different from reported errors (P=.62).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataInpatient setting (OR, 3.30; 95% CI, 2.18-5.00) and non-primary care specialties such as surgery (OR, 2.45; 95% CI, 1.57-3.82) were more likely to have errors.
“Inpatient setting (OR, 3.30; 95% CI, 2.18–5.00) and non–primary care specialties such as surgery (OR, 2.45; 95% CI, 1.57–3.82) were more likely to have errors.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationMany kinds of CPOE-related errors have been reported in recent years.
“many types of CPOE-related errors have been described in recent years.5–10”
Quote in the paperNot a substantial claim0 of 6 sources checked- cites [5] Ash JS Berg M Coiera E Some unintended consequences of information technology in health care: the nature of patient care information system-related errors J Am Med Inform Assoc 2004 March 11 2 104 12 14633936 PMC353015
Not checked in the free run.
- cites [6] Berger RG Kichak JP Computerized physician order entry: helpful or harmful? J Am Med Inform Assoc 2004 March 11 2 100 3 14633934 PMC353014
Not checked in the free run.
- cites [7] Harrison MI Koppel R Bar-Lev S Unintended consequences of information technologies in health care--an interactive sociotechnical analysis J Am Med Inform Assoc 2007 September 14 5 542 9 17600093 PMC1975796
Not checked in the free run.
- cites [8] Horsky J Kuperman GJ Patel VL Comprehensive analysis of a medication dosing error related to CPOE Journal Of The American Medical Informatics Association: JAMIA 12 4 377 82 15802485 PMC1174881
Not checked in the free run.
- cites [9] Koppel R Metlay JP Cohen A Role of computerized physician order entry systems in facilitating medication errors JAMA 2005 March 9 293 10 1197 203 15755942
Not checked in the free run.
- cites [10] Weiner JP Kfuri T Chan K Fowles JB “e-Iatrogenesis”: the most critical unintended consequence of CPOE and other HIT J Am Med Inform Assoc 2007 May 14 3 387 8 17329719 PMC2244888
Not checked in the free run.
May combine two claims.
Study design and conductown dataThe study obtained a comparable estimate of error rate (1%) using both reporting and electronic search, adding strength to the findings.
“We obtained a comparable estimate of error rate (ie, 1%) using both reporting and electronic search, adding strength to our findings.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe study was approved by the local institutional review board and the MEDVAMC research and development committee.
“approved by the local institutional review board and the MEDVAMC research and development committee.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataMost errors studied did not reach the patient because of pharmacist intervention, but they pose a significant potential risk to patients.
“Although most errors we studied did not reach the patient because of pharmacist intervention, they pose a significant potential risk to patients.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductnone givenThe study included a large variety of medications and a large number of providers (>500).
“Additional study strengths include a large variety of medications, a large number of providers (>500), and an advanced CPOE system fairly representative of other systems.”
Quote in the paperNot a substantial claim- Rests on an assumption: The advanced CPOE system used in the study is fairly representative of other CPOE systems.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationChused et al. reported communication failures from free-text comments entered in response to alert overrides, suggesting that commercial CPOE systems may not protect against the problem.
“Chused et al21 recently reported communication failures resulting from free-text comments entered in response to alert overrides, suggesting that commercial CPOE systems may not also protect against the problem.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Chused et al21
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
Interpretationnone givenDefaults may not be comprehensive and may be based on inventory rather than clinical guidelines.
“Although defaults can be helpful, they may not be comprehensive and may be based on inventory rather than clinical guidelines.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningThe potential for moderate to severe harm is likely more significant given the expected increase in the volume of prescriptions written through future CPOE systems.
“The potential for moderate to severe harm is likely more significant given the expected increase in the volume of prescriptions written through future CPOE systems.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPoor communication is frequently cited as a preventable factor in adverse events.
“Because poor communication is an often-cited preventable factor in adverse events,15”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [15] Joint Commission on Accreditation of Healthcare Organizations Sentinel event statistics, March 31, 2006 Joint Commission on Accreditation of Healthcare Organizations 2005 Available at: URL: http://www.jointcommission.org/NR/rdonlyres/FA465646-5F5F-4543-AC8F-E8AF6571E372/0/root_cause_se.jpg 17139928
Not checked in the free run.
May combine two claims.
Interpretationown dataErrors could be prevented by minor modifications in the CPOE template regarding administration time or duration and/or stop time for a medication.
“we found errors that could be prevented by minor modifications in the CPOE template regarding administration time or duration and/or stop time for a medication.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningVariables with small numbers of patients with a given characteristic were removed or combined to ensure stable and precise estimates of odds ratios.
“To ensure that the model had stable and precise estimates of ORs, variables with small numbers of patients with a given characteristic (eg, ordering providers listed as clinical support, clinical specialist, or unknown) were removed or combined (eg, geriatrics, other medicine, spinal cord injury, and psychiatry specialties were all combined as “other specialties” in the model to predict harm), and”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductreasoningOnly slightly more than two-thirds of eligible pharmacists participated, and hence there is almost certainly selection bias.
“Only slightly more than two-thirds of eligible pharmacists participated in the study, and hence there is almost certainly selection bias.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataEven though most errors of inconsistent communication would have caused inconvenience or minor harm, the potential for more severe harm is significant given the large volume of prescriptions written.
“Even though most errors of inconsistent communication would have caused inconvenience or minor harm, the potential for more severe harm is significant, given the large volume of prescriptions written.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductown dataA generalized estimating equation approach was used to account for potential correlation among errors nested within providers.
“A generalized estimating equation approach in which errors were nested within providers allowed us to account for the potential correlation among errors.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
InterpretationreasoningSimilar communication breakdowns likely occur in other Veterans Affairs facilities using the same CPOE system, but the true prevalence across Veterans Affairs institutions is unknown.
“while it is likely that similar communication breakdowns occur in other Veterans Affairs facilities using the same CPOE system, we ac-knowledge that the true prevalence across Veterans Affairs institutions is unknown.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningFree-text comments are printed on the dispensed medication label, which may confuse or harm the patient.
“the free-text comments are printed on the label of the dispensed medication, which may confuse or harm the patient.”
Quote in the paperSubstantial claim- Rests on an assumption: Free-text comments are printed on the label of the dispensed medication.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningSimple training on the use of complex orders for certain prescriptions could reduce errors.
“simple training on the use of complex orders for certain prescriptions could reduce errors.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA prior study identified 22 different medication error types, including fragmented CPOE displays and inflexible ordering formats.
“a study using both qualitative and quantitative methods to study house staff interaction with a CPOE system at a tertiary care facility identified 22 different types of medication errors, including fragmented CPOE displays and inflexible ordering formats.9”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [9] Koppel R Metlay JP Cohen A Role of computerized physician order entry systems in facilitating medication errors JAMA 2005 March 9 293 10 1197 203 15755942
Not checked in the free run.
May combine two claims.
Study design and conductown dataThe study was conducted at a single site where all providers used the same CPOE system, and generalizability may be limited.
“It was conducted at a single site where all providers used the same CPOE system, and although it involved a large number of patients and providers, including trainees, generalizability may be limited.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataTwo investigators assigned harm categories by consensus using the Human Error Consequence Scale.
“Two investigators (a physician and pharmacist) assigned harm categories by consensus using the Human Error Consequence Scale20 after evaluating the results from the initial data collection.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [20] Gilpatrick E Health Services Mobility Study Final Report (October 1967-March 1972) New York, NY City Univ. of New York Research Foundation 1972 Jan 1 Report No.: ERIC Reports ED066613
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe free-text window offers a valuable option to communicate clarifications or nonstandard information.
“the free text window offers a valuable option to communicate clarifications or nonstandard information.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataOf 45 pharmacists, 33 agreed to report errors.
“Of 45 pharmacists, 33 agreed to report errors.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Order entry errors related to inconsistent information within the same prescription had not been described before this study.
“To our knowledge, order entry errors related to inconsistent information within the same prescription have not been described.”
The paper relies on this without support.
This is the first study reporting errors of communication due to free-text comments in CPOE systems.
“To our knowledge, this is also the first study reporting errors of communication due to free-text comments in CPOE systems.”
The paper relies on this without support.
All CPOE systems have some method of entering free text.
“All CPOE systems have some method of entering free text.”
The paper relies on this without support.
Underreporting is a well-recognized phenomenon in similar studies.
“Because underreporting is a well recognized phenomenon in similar studies”
The paper relies on this without support.
Free-text comments are printed on the label of the dispensed medication.
“the free-text comments are printed on the label of the dispensed medication”
The paper relies on this without support.
The advanced CPOE system used in the study is fairly representative of other CPOE systems.
“an advanced CPOE system fairly representative of other systems”
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?
Rapid adoption of electronic health records has revealed potential safety concerns related to EHR design, implementation, and use.
“In particular, rapid adoption of electronic health records (EHRs) has revealed potential safety concerns related to EHR design, implementation, and use.12 17–21”
From An analysis of electronic health record-related patient safety concerns (2014) · cites this paper as [17]
Increasing adoption of electronic health records has revealed potential safety implications related to EHR design, implementation, and use.
“the increasing adoption of electronic health records (EHR) has revealed potential safety implications related to EHR design, implementation, and use.”
From Exploring the sociotechnical intersection of patient safety and electronic health record implementation (2014) · cites this paper as [11]
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“Our study adds to the growing body of knowledge that computerized information systems may introduce new types of errors, including those involving communication processes.(5,25) Future studies should address the usability of the CPOE interface to identify breakdown points and maximize the effectiveness, efficiency, an…”
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Types of Unintended Consequences Related to Computerized Provider Order Entry
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“17 To overcome some of these disadvantages, we electronically searched for similar types of errors.”
MethodSelf-citationHarnessing the Power of Default Options to Improve Health Care
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“While default options may improve care in some settings, 22 our results suggest a need for more careful consideration before their use.”
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“erature, yet the true frequency of the problem is unknown.(9,11,12) Most medication errorsarerelatedtotheorderingprocess,(13) and”
BackgroundThe Epidemiology of Prescribing Errors: The Potential Impact of Computerized Prescriber Order Entry
Bobb, A., Gleason, K., Husch, M., Feinglass, J., Yarnold, P. R., & Noskin, G. A. (2004) · Archives of Internal Medicine · doi:10.1001/archinte.164.7.785 · Cited by 359 · cited 1× · see citing works
“31 We note several limitations of our study.”
BackgroundPhysician Reporting Compared with Medical-Record Review to Identify Adverse Medical Events
O'Neil, A. C., Petersen, L. A., Cook, E. F., Bates, D. W., Lee, T. H., & Brennan, T. A. (1993) · Annals of Internal Medicine · doi:10.7326/0003-4819-119-5-199309010-00004 · Cited by 315 · cited 1× · see citing works
“Use of a single method (eg, stimulated self-reporting) has potential limitations in studies of medical errors.(16) For instance, it cannot estimate error rate accurately.”
MethodSelf-citationComputer-based Physician Order Entry: The State of the Art
Sittig, D. F., & Stead, W. W. (1994) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.1994.95236142 · Cited by 273 · see citing works
Computerized Physician Order Entry: Helpful or Harmful?
Berger, R. G. (2003) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1411 · Cited by 223 · see citing works
Comprehensive Analysis of a Medication Dosing Error Related to CPOE: Table 1
Horsky, J., Kuperman, G. J., & Patel, V. L. (2005) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1740 · Cited by 197 · see citing works
Aarts, J., Ash, J. S., & Berg, M. (2006) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2006.05.009 · Cited by 192 · cited 1× · see citing works
“23,24 For instance, we found errors that could be prevented by minor modifications in the CPOE template regarding administration time or duration and/or stop time for a medication.”
Background"e-Iatrogenesis": The Most Critical Unintended Consequence of CPOE and other HIT
Weiner, J. P., Kfuri, T., Chan, K., & Fowles, J. B. (2007) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2338 · Cited by 189 · see citing works
Computerized Provider Order Entry Adoption: Implications for Clinical Workflow
Campbell, E. M., Guappone, K. P., Sittig, D. F., Dykstra, R. H., & Ash, J. S. (2009) · Journal of General Internal Medicine · doi:10.1007/s11606-008-0857-9 · Cited by 139 · cited 1× · see citing works
“Finally, many of the errors and issues suggest that standardization through CPOE was not adequately integrated with the workflow, needs, and preferences of the providers.(23,24) For instance, we found errors that could be prevented by minor modifications in the CPOE template regarding administration time or duration a…”
To err is not entirely human: Complex technology and user cognition
Horsky, J., Zhang, J., & Patel, V. L. (2005) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2005.05.002 · Cited by 138 · cited 1× · see citing works
“28,29 Interventions aimed at reducing communication errors at the human-CPOE interface 30 will need grounding in principles of human-computer interaction.”
BackgroundComputerized physician order entry and quality of care
Bates, D. W., Kuperman, G. J., & Teich, J. M. (1994) · Quality Management in Health Care · doi:10.1097/00019514-199402040-00005 · Cited by 112 · see citing works
Qualitative evaluation of an electronic prescribing and administration system
Barber, N., Cornford, T., & Klecuń, E. (2007) · BMJ Quality & Safety · doi:10.1136/qshc.2006.019505 · Cited by 69 · cited 1× · see citing works
“26,27 Using a multifaceted interdisciplinary approach to the problem would generate the best solutions.”
BackgroundPhysicians, Information Technology, and Health Care Systems: A Journey, Not a Destination
McDonald, C. J. (2003) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1488 · Cited by 54 · cited 1× · see citing works
“Given other advantages of using unconstrained, narrative information in computerized information systems, 5,14 we believe the free-text window should be maintained in CPOE systems but with improvements to avoid inconsistencies.”
BackgroundAlert override reasons: a failure to communicate.
Chused, A. E., Kuperman, G. J., & Stetson, P. D. (2008) · PubMed Central · Cited by 33 · see citing works
Kaushal, R., Shojania, K. G., & Bates, D. W. (2003) · Archives of Internal Medicine · doi:10.1001/archinte.163.12.1409 · Cited by 30 · cited 1× · see citing works
“erature, yet the true frequency of the problem is unknown.(9,11,12) Most medication errorsarerelatedtotheorderingprocess,(13) and”
BackgroundKoppel, R., Localio, A. R., Cohen, A., & Strom, B. L. (2005) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2005.05.006 · Cited by 30 · cited 1× · see citing works
“Using a multifaceted interdisciplinary approach to the problem would generate the best solutions.(28,29) Interventions aimed at reducing communication errors at the human-CPOE interface(30) will need grounding in principles of humancomputer interaction.”
BackgroundMedication Error Reporting by Community Pharmacists in Vermont
Kennedy, A. G., & Littenberg, B. (2004) · Journal of the American Pharmacists Association · doi:10.1331/1544345041475724 · Cited by 21 · cited 1× · see citing works
“32 Only slightly more than two-thirds of eligible pharmacists participated in the study, and hence there is almost certainly selection bias.”
BackgroundUpdate: sentinel event statistics.
(2006) · PubMed · Cited by 8 · see citing works
Flagged first, then by citations
1 of the 29 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [15]Joint Commission on Accreditation of Healthcare Organizations Sentinel event statistics, March 31, 2006 Joint Commission on Accreditation of Healthcare Organizations 2005 Available at: URL: http://www.jointcommission.org/NR/rdonlyres/FA465646-5F5F-4543-AC8F-E8AF6571E372/0/root_cause_se.jpg 17139928
- [26]Schoeffel R The concept of product usability ISO Bulletin 34[3], 6–7 2003 Ref Type: Generic
Cited by
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Types and Origins of Diagnostic Errors in Primary Care Settings
Singh, H., Giardina, T. D., Meyer, A. N. D., Forjuoh, S. N., Reis, M. D., & Thomas, E. J. (2013) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2013.2777 · Cited by 559 · see citing works
Self-citationHealth IT and Patient Safety: Building Safer Systems for Better Care
Chou, D. (2012) · JAMA · doi:10.1001/jama.308.21.2282-a · Cited by 496 · see citing works
Kim, M. O., Coiera, E., & Magrabi, F. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw154 · Cited by 263 · see citing works
“…(2005)42 548 7 Mortality increased from 2.8% to 6.7%, no further details provided Nanji et al. (2011)40 466 35 N/A Palchuk et al. (2010)39 470 84 N/A Singh et al. (2009)29 558a 71 N/A Walsh et al. (2006)38 20 50 N/A 50 Westbrook et al. (2012)51 and (2013)41 493 2 N/A aErrors analyzed for…”
BackgroundInfluentialAn analysis of electronic health record-related patient safety concerns
Meeks, D. W., Smith, M. W., Taylor, L., Sittig, D. F., Scott, J. M., & Singh, H. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002578 · Cited by 207 · see citing works
Self-citationElectronic health records and national patient-safety goals
Sittig, D. F., & Singh, H. (2012) · New England Journal of Medicine · doi:10.1056/nejmsb1205420 · Cited by 204 · see citing works
Self-citationDefining Health Information Technology–Related Errors
Sittig, D. F. (2011) · Archives of Internal Medicine · doi:10.1001/archinternmed.2011.327 · Cited by 167 · see citing works
“5 mg Mon-Fri only”) that contradict information contained in the structured section of the medication order (eg, “Warfarin tabs 10 mg Q day”).(13) Errors may also arise when 2 or more parts of the HIT system (eg, CPOE application and the pharmacy’s medication-dispensing system) interact in an unpredicted manner, resul…”
Magrabi, F., Ong, M.-S., Runciman, W., & Coiera, E. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.002444 · Cited by 162 · see citing works
“Singh and colleagues found that 20% of 532 errors resulting from inconsistent entry of dosage information within a CPOE could have resulted in moderate to severe harm.(16) Reports of patient safety incidents paint a broader picture.”
BackgroundWestbrook, J. I., Baysari, M. T., Li, L., Burke, R. A., Richardson, K. L., & Day, R. O. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001745 · Cited by 113 · see citing works
Barlev, A. (2010) · Journal of Managed Care Pharmacy · doi:10.18553/jmcp.2010.16.9.693 · Cited by 101 · see citing works
Meeks, D. W., Takian, A., Sittig, D. F., Singh, H., & Barber, N. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001762 · Cited by 91 · see citing works
Self-citationHarrington, L., Kennerly, D., & Johnson, C. (2011) · Journal of Healthcare Management · doi:10.1097/00115514-201101000-00006 · Cited by 93 · see citing works
Grossman, J. M., Cross, D. A., Boukus, E. R., & Cohen, G. R. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000515 · Cited by 86 · see citing works
Cognitive ergonomics, socio-technical systems, and the impact of healthcare information technologies
Lawler, E., Hedge, A., & Pavlovic-Veselinovic, S. (2011) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2011.02.006 · Cited by 74 · see citing works
Palchuk, M. B., Fang, E., Cygielnik, J. M., Labreche, M. J., Shubina, M., Ramelson, H. Z., Hamann, C., Broverman, C. A., Einbinder, J. S., & Turchin, A. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2010.003335 · Cited by 71 · see citing works
“3%) rate of errors among 500 prescriptions with a narrative component.(24) The inter-reviewer agreement on the presence of discrepancies, although good, was less than expected; probably due to the complexity of the underlying data.”
BackgroundMagrabi, F., Li, S. Y. W., Day, R. O., & Coiera, E. W. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001719 · Cited by 67 · see citing works
“The range of prescribing errors include selection of the wrong patient; missed drug allergies”
BackgroundSalahuddin, L. B., & Ismail, Z. (2015) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.07.004 · Cited by 60 · see citing works
Electronic Health Records and Use of Clinical Decision Support
Mills, S. (2019) · Critical Care Nursing Clinics of North America · doi:10.1016/j.cnc.2019.02.006 · Cited by 61 · see citing works
Tortorella, G. L., Fogliatto, F. S., Vijaya Sunder, M., Vergara, A., & Vassolo, R. S. (2021) · International Journal of Production Research · doi:10.1080/00207543.2021.1912429 · Cited by 57 · see citing works
“…demand and supply Patient information/diagnostic References Poor emergency triage system Crellin and Johnston (2003); Pantanowitz, Henricks, and Beckwith (2007); Fraser et al. (2007); Singh et al. (2009); Straus et al. (2011); Wallace (2015); Wager, Lee, and Glaser (2017); Borges et al. (2019).”
BackgroundPrescription writing practices in a rural tertiary care hospital in Western Maharashtra, India
Phalke, V. D. (2011) · Australasian Medical Journal · doi:10.4066/amj.2011.515 · Cited by 56 · see citing works
Nelson, S. D., Poikonen, J., Reese, T., ElHalta, D., & Weir, C. R. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw044 · Cited by 52 · see citing works
Potential medication errors associated with computer prescriber order entry
Villamañán, E., Larrubia, Y., Ruano, M., Vélez, M., Armada, E., Herrero, A., & Álvarez-Sala, R. (2013) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-013-9771-2 · Cited by 50 · see citing works
“Often, this inflexibility, the lack of program management or distrust of the electronic prescription system, leads to an overuse of the free-text field increasing the probability of error [6, 23].”
BackgroundInfluentialEHR‐related medication errors in two ICUs
Carayon, P., Du, S., Brown, R., Cartmill, R. S., Johnson, M., & Wetterneck, T. B. (2017) · Journal of Healthcare Risk Management · doi:10.1002/jhrm.21259 · Cited by 43 · see citing works
Barnett, M. L., Mehrotra, A., & Jena, A. B. (2016) · BMJ · doi:10.1136/bmj.i3835 · Cited by 44 · see citing works
Miller, A. M., Boro, M. S., Korman, N. E., & Davoren, J. B. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000262 · Cited by 41 · see citing works
“However, other studies, including that of Singh et al, have suggested that anticoagulants are associated with the highest risk of potential ADE in the VA environment and are probably deserving of cDDI alerts with improved usability, to improve patient safety.(23) Some data have recently been published regarding the op…”
ResultClinical pharmacy interventions in paediatric electronic prescriptions
Maat, B., Au, Y. S., Bollen, C. W., van Vught, A. J., Egberts, T. C. G., & Rademaker, C. M. A. (2012) · Archives of Disease in Childhood · doi:10.1136/archdischild-2012-302817 · Cited by 36 · see citing works
Improving outpatient safety through effective electronic communication: a study protocol
Hysong, S. J., Sawhney, M. K., Wilson, L., Sittig, D. F., Esquivel, A., Watford, M., Giardina, T. D., Espadas, D., & Singh, H. (2009) · Implementation Science · doi:10.1186/1748-5908-4-62 · Cited by 34 · see citing works
“[25], five scenarios will be created to observe providers' strategies for entering these commonly errorprone prescriptions.”
Self-citationPontefract, S. K., Coleman, J. J., Vallance, H. K., Hirsch, C., Shah, S., Marriott, J. F., & Redwood, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207450 · Cited by 35 · see citing works
Enabling Medication Management through Health Information Technology
(2011) · SciVee · doi:10.4016/37397.01 · Cited by 35 · see citing works
Arques-Armoiry, E., Cabelguenne, D., Stamm, C., Janoly-Duménil, A., Grosset-Grange, I., Vantard, N., Maire, P. H., & Charpiat, B. (2010) · La Revue de Médecine Interne · doi:10.1016/j.revmed.2010.08.001 · Cited by 34 · see citing works
Effect of E‐Prescribing Systems on Patient Safety
Kannry, J. L. (2011) · Mount Sinai Journal of Medicine A Journal of Translational and Personalized Medicine · doi:10.1002/msj.20298 · Cited by 32 · see citing works
Concomitant prescribing and dispensing errors at a Brazilian hospital: a descriptive study
Silva, M. D. D. G., Rosa, M. B., Franklin, B. D., et al. (2011) · Clinics · doi:10.1590/s1807-59322011001000005 · Cited by 34 · see citing works
“Thus, after implementing an electronic system, hospitals should attempt to identify the critical error points and create appropriate preventive measures.(34)”
How many medication orders are entered through free-text in EHRs?--a study on hypoglycemic agents.
Zhou, L., Mahoney, L. M., Shakurova, A., et al. (2012) · PubMed · Cited by 32 · see citing works
The Impact of Technology on Prescribing Errors in Pediatric Intensive Care: A Before and After Study
Howlett, M. M., Butler, E., Lavelle, K. M., et al. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1709508 · Cited by 32 · see citing works
Elshayib, M., & Pawola, L. M. (2020) · Health Informatics Journal · doi:10.1177/1460458220941750 · Cited by 31 · see citing works
Towards creating the perfect electronic prescription
Dhavle, A. A., & Rupp, M. T. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-002738 · Cited by 32 · see citing works
Carayon, P., Wetterneck, T. B., Cartmill, R. S., Blosky, M. A., Brown, R., Hoonakker, P., Kim, R., Kukreja, S., Johnson, M., Paris, B. L., et al. (2017) · Journal of Patient Safety · doi:10.1097/pts.0000000000000358 · Cited by 31 · see citing works
“The provision of default values may reduce omitted information, but may be accepted without sufficient thought.(35,36) ↑ Wrong drug orders Medication selection errors due to pick lists of medications Wrong patient Wrong drug for patient Relevant contextual information should be provided during ordering (e.”
Millery, M. P., & Kukafka, R. (2010) · Medical Care Research and Review · doi:10.1177/1077558710373769 · Cited by 30 · see citing works
“Eslami, Abu-Hanna, de Keizer, & de Jonge, 2006; Han et al., 2005; Hsieh et al., 2004; Koppel et al., 2005; Nebeker, Hoffman, Weir, Bennett, & Hurdle, 2005; Singh et al., 2009; van den Bemt, Idzinga, Robertz, Kormelink, & Perls, 2009; Walsh et al., 2006), and other studies that found mixed results.”
Charpiat, B., Bedouch, P., Conort, O., Rose, F.-X., Juste, M., Roubille, R., & Allenet, B. (2012) · Annales Pharmaceutiques Françaises · doi:10.1016/j.pharma.2012.02.002 · Cited by 31 · see citing works
Effects of hospital leadership, organizational systems, and ESWOS on medical error reduction
Lee, D., Hong, K. S., & Kim, N. Y. (2014) · Service Business · doi:10.1007/s11628-014-0262-x · Cited by 28 · see citing works
Ruano, M., Villamañán, E., Pérez, E., Herrero, A., & Álvarez-Sala, R. (2015) · World Journal of Pediatrics · doi:10.1007/s12519-015-0067-6 · Cited by 25 · see citing works
Why Test Results Are Still Getting “Lost” to Follow-up: a Qualitative Study of Implementation Gaps
Zimolzak, A. J., Shahid, U., Giardina, T. D., Memon, S. A., Mushtaq, U., Zubkoff, L., Murphy, D. R., Bradford, A. M., & Singh, H. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-021-06772-y · Cited by 27 · see citing works
Self-citationAnalysis of Prescribers’ Notes in Electronic Prescriptions in Ambulatory Practice
Dhavle, A. A., Yang, Y., Rupp, M. T., Singh, H., Ward-Charlerie, S., & Ruiz, J. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2015.7786 · Cited by 25 · see citing works
Self-citationDose Omissions in Hospitalized Patients in a UK Hospital
Coleman, J. J., McDowell, S. E., & Ferner, R. E. (2012) · Drug Safety · doi:10.1007/bf03261964 · Cited by 24 · see citing works
“We and others have demonstrated some differences between coded and free text, which may highlight potential limitations of coding structures from electronic prescribing systems.([17,18])”
BackgroundMeaningful Use and Certification of Health Information Technology: What about Safety?
Hoffman, S., & Podgurski, A. (2011) · The Journal of Law Medicine & Ethics · doi:10.1111/j.1748-720x.2011.00572.x · Cited by 21 · see citing works
Evaluation of the NCPDP Structured and Codified Sig Format for e-prescriptions
Liu, H., Burkhart, Q., & Bell, D. S. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2010-000034 · Cited by 21 · see citing works
“A recent study by Singh et al highlighted the potential for new kinds of error, showing that nearly 1% of electronic prescriptions contained inconsistent instructions, with the most common error being conflicting drug dosages.(11) Palchuk et al reported that 16% of electronic prescriptions contained internal discrepan…”
BackgroundYang, Y., Ward-Charlerie, S., Dhavle, A. A., Rupp, M. T., & Green, J. (2018) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2018.17404 · Cited by 21 · see citing works
“However, as with any technology, the benefits from e-prescribing are directly dependent on the manner in which it is implemented and used, and prior research has demonstrated that e-prescribing can elevate the risks of some errors or even introduce new quality challenges.(5-8) Few components of a prescription order ar…”
Background(2011) · American Journal of Health-System Pharmacy · doi:10.2146/sp100011e · Cited by 20 · see citing works
Kinlay, M., Ho, L. M., Zheng, W. Y., Burke, R. A., Juraskova, I., Moles, R. J., & Baysari, M. T. (2021) · Applied Clinical Informatics · doi:10.1055/s-0041-1739196 · Cited by 19 · see citing works
Vélez‐Díaz‐Pallarés, M., Álvarez‐Díaz, A. M., Gramage, T., Oliveros, N. V., Delgado‐Silveira, E., García, M. M., Cruz-Jentoft, A. J., & Bermejo-Vicedo, T. (2017) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-017-0474-y · Cited by 22 · see citing works
Paediatric in-patient prescribing errors in Malaysia: a cross-sectional multicentre study
Khoo, T. B., Tan, J. W., Ng, H. P., Choo, C. M., Shukor, I. N. C. B. A., & Teh, S. H. (2017) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-017-0463-1 · Cited by 18 · see citing works
A red‐flag‐based approach to risk management of EHR‐related safety concerns
Sittig, D. F., & Singh, H. (2013) · Journal of Healthcare Risk Management · doi:10.1002/jhrm.21123 · Cited by 17 · see citing works
Self-citationCoded entry versus free-text and alert overrides: What you get depends on how you ask
Seidling, H. M., Paterno, M. D., Haefeli, W. E., et al. (2010) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2010.08.003 · Cited by 17 · see citing works
Salahuddin, L. B., Ismail, Z., Hashim, U. R., Ikram, R. R. R., Ismail, N. H., & Naim, M. H. (2018) · Health Informatics Journal · doi:10.1177/1460458218759698 · Cited by 17 · see citing works
Khoong, E. C., Nouri, S. S., Tuot, D. S., Nundy, S., Fontil, V., & Sarkar, U. (2021) · Medical Decision Making · doi:10.1177/0272989x211031209 · Cited by 13 · see citing works
“We assessed the potential for meaningful harm based on a scale previously used to classify the harm of errors.(7,29) For each recommendation, we used a binary outcome that focused on the potential for at least moderate meaningful harm, which included initiation or cessation of medications without indication or with co…”
MethodKang, H., Wang, J., Yao, B., Zhou, S., & Gong, Y. (2018) · JAMIA Open · doi:10.1093/jamiaopen/ooy042 · Cited by 16 · see citing works
Cochran, G. L., Klepser, D. G., Morien, M., Lomelin, D. E., Schainost, R., & Lander, L. I. (2013) · BMJ Quality & Safety · doi:10.1136/bmjqs-2013-002089 · Cited by 15 · see citing works
Braund, R., Furlan, H. M., George, K., et al. (2009) · Pharmacy World & Science · doi:10.1007/s11096-009-9343-7 · Cited by 15 · see citing works
“• Whilst computer generated prescriptions may reduce some types of errors (such as legal requirements), they may introduce new errors [ 10 ] such as incorrect dose and dosage form selection and prescribers still need to exercise due care.”
BackgroundSafe use of hospital information systems: an evaluation model based on a sociotechnical perspective
Salahuddin, L. B., Ismail, Z., Ikram, R. R. R., Hashim, U. R., Idris, A., Ismail, N. H., Hassan, N. H., & Rahim, F. A. (2019) · Behaviour and Information Technology · doi:10.1080/0144929x.2019.1597164 · Cited by 14 · see citing works
“For example, inadequate knowledge of the prescription ordering mechanism could cause the healthcare practitioners to use the system in a way in which it was not intended (Blijleven et al. 2017; Singh et al. 2009).”
BackgroundHealth Care Information Technology
Liebovitz, D. (2009) · Archives of Internal Medicine · doi:10.1001/archinternmed.2009.79 · Cited by 14 · see citing works
Yang, Y., Ward-Charlerie, S., Kashyap, N., deMayo, R., Agresta, T. P., & Green, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy100 · Cited by 13 · see citing works
Qian, S., Munyisia, E. N., Reid, D., et al. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104292 · Cited by 13 · see citing works
Hellot-Guersing, M., Jarre, C., Molina, C., Leromain, A., Derharoutunian, C., Gadot, A., & Roubille, R. (2015) · Annales Pharmaceutiques Françaises · doi:10.1016/j.pharma.2015.06.001 · Cited by 12 · see citing works
Modeling the mind: How do we design effective decision-support?
Weir, C. R., Rubin, M. A., Nebeker, J. R., & Samore, M. H. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.06.008 · Cited by 11 · see citing works
Turchin, A., Shubina, M., & Goldberg, S. I. (2011) · PubMed Central · Cited by 11 · see citing works
SAFER Electronic Health Records
Sittig, D. F., & Singh, H. (2015) · Apple Academic Press eBooks · doi:10.1201/b18371 · Cited by 11 · see citing works
Self-citationEffect of EHR User Interface Changes on Internal Prescription Discrepancies
Sawarkar, A., Dementieva, Y. A., Breydo, E. M., Ramelson, H. Z., & Turchin, A. (2014) · Applied Clinical Informatics · doi:10.4338/aci-2014-03-ra-0023 · Cited by 10 · see citing works
Evaluation of a User Guidance Reminder to Improve the Quality of Electronic Prescription Messages
Corley, S. T., Rupp, M. T., Ruiz, J., et al. (2014) · Applied Clinical Informatics · doi:10.4338/aci-2014-03-cr-0022 · Cited by 10 · see citing works
Klappe, E. S., van Putten, F. J. P., de Keizer, N. F., & Ronald Cornet, A. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01477-y · Cited by 7 · see citing works
POP-PL: a patient-oriented prescription programming language
Florence, S. P., Fetscher, B., Flatt, M., Temps, W., Kiguradze, T., West, D., Niznik, C. M., Yarnold, P., Findler, R., & Belknap, S. (2015) · International Conference on Generative Programming: Concepts and Experiences · doi:10.1145/2814204.2814221 · Cited by 8 · see citing works
“Singh et al. (2009) have shown how physicians struggle with the limited data-field entry programs that current Computerized Physician Order Entry (CPOE) and ePrescribing systems provide.”
BackgroundInteroperable Electronic Patient Records for Health Care Improvement
Balas, E. A., & Sanousi, A. A. (2009) · Studies in health technology and informatics · doi:10.3233/978-1-60750-044-5-19 · Cited by 7 · see citing works
“In the evolution of electronic health records, a synergistic trend is the appearance of personal electronic health record systems include those promoted by major commercial vendors like Google Health, Healthvault and others.”
BackgroundPotential value of electronic prescribing in health economic and outcomes research
Cooke, C. E. (2010) · Patient Related Outcome Measures · doi:10.2147/prom.s13033 · Cited by 7 · see citing works
Minding the Gaps: Assessing Communication Outcomes of Electronic Preconsultation Exchange
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Development and Field Testing of a Self-Assessment Guide for Computer-Based Provider Order Entry
Vartian, C. V., Singh, H., Russo, E. M., & Sittig, D. F. (2014) · Journal of Healthcare Management · doi:10.1097/00115514-201409000-00007 · Cited by 6 · see citing works
“…Han et al., 2005; Horsky et al., 2005; Koppel et al., 2005, 2008; Kop-pel, Wetterneck, Telles, & Karsh, 2008; Metzger, Welebob, Bates, Lipsitz, & Classen, 2010; Nanji et al., 2011; Nebeker, Hoffman, Weir, Bennett, & Hurdle, 2005; Singh et al., 2009; Zhan, Hicks, Blanchette, Keyes, & Cousins, 2006).”
Self-citationDefining Health Information Technology-Related Errors
Singh, H. (2013) · Apple Academic Press eBooks · doi:10.1201/b16306-6 · Cited by 6 · see citing works
Self-citationÉvaluation des bases de données médicamenteuses disponibles sur le marché français
Hacin, L., Mainar, A., & Edouard, B. (2013) · Annales Pharmaceutiques Françaises · doi:10.1016/j.pharma.2012.12.001 · Cited by 5 · see citing works
Clinician Perceptions on the Use of Free-Text Communication Orders
Kandaswamy, S., Pruitt, Z. M., Kazi, S., Marquard, J. L., Owens, S., Hoffman, D., Ratwani, R. M., & Hettinger, A. Z. (2021) · Applied Clinical Informatics · doi:10.1055/s-0041-1731002 · Cited by 5 · see citing works
POP-PL: a patient-oriented prescription programming language
Florence, S. P., Fetscher, B., Flatt, M., et al. (2015) · ACM SIGPLAN Notices · doi:10.1145/2936314.2814221 · Cited by 4 · see citing works
Sittig, D. F., Longhurst, C., Russo, E. M., & Singh, H. (2015) · Health informatics · doi:10.1007/978-3-319-20765-0_2 · Cited by 3 · see citing works
Self-citationLow, S. G. (2011) · Proceedings of Singapore Healthcare · doi:10.1177/201010581102000203 · Cited by 3 · see citing works
Effects of hospital leadership, organizational systems, and ESWOS on medical error reduction
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Weight and size descriptors for drug dosing: Too many options and too many errors
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Free Text as Part of Electronic Health Record Orders: Context or Concern?
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A Continuous Quality Improvement Initiative for Electronic Prescribing in Ambulatory Care
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Quality improvement and patient safety in the age of reform: preconditions for success.
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“A large study of CPOE systems found that 1% of prescriptions contained free-text instructions that were inconsistent with the contents of the structured data fields, and 20% of these inconsistencies risked moderate or severe patient injury (Singh et al. 2009).”
BackgroundDose Omissions in Hospitalized Patients in a UK Hospital
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Pharmacy Data Integrity for Optimal Analytics
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CONCEPTUAL FOUNDATION OF SAFER GUIDES
Sittig, D. F., & Singh, H. (2015) · Apple Academic Press eBooks · doi:10.1201/b18371-5 · Cited by 1 · see citing works
Self-citationClinical decision support systems in the care of hospitalised patients with diabetes
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Safety in health care systems : a synthesis of the literature
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Self-citationTHE CONTEXT OF EHR SAFETY AND THE NEED FOR RISK ASSESSMENT
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Enforcing Data Integrity in Pharmacy
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- Authors
- Singh, H., Mani, S., Espadas, D., Petersen, N., Franklin, V., & Petersen, L. A.
- Affiliations
- Michael E. DeBakey VA Medical Center (US)
- Venue
- Archives of Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2009-05-25
- Volume
- 169
- Issue
- 10
- Pages
- 982
- ISSN
- 0003-9926 (ISSN-L)
- PMC
- PMC2919338
- OpenAlex
- W2077178507
- Semantic Scholar
- ec2c80787b615a3670292bc6644dbb7d00e3d068
- Cited by
- 104
- References
- 29
- Citations by year
- 2026: 1 · 2024: 1 · 2022: 1 · 2021: 8 · 2020: 3 · 2019: 2 · 2018: 7 · 2017: 5 · 2016: 7 · 2015: 13 · 2014: 9 · 2013: 11 · 2012: 7
- FWCI
- 25.33
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Archives of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (K23CA125585)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
- Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
- Keywords
- computerized physician order entry, prescribing errors, data inconsistency, structured templates, medication errors, drug dosage errors, inpatient prescriptions, surgical subspecialties, drug safety
- MeSH terms
- Group Practice, Humans, Medication Errors, Drug Prescriptions, Prospective Studies, Quality Assurance, Health Care, Clinical Pharmacy Information Systems, Medical Order Entry Systems
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.
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