Using EHR Data to Detect Prescribing Errors in Rapidly Discontinued Medication Orders

Identification of Prescribing Errors in an Electronic Health Record Using a Retract-and-Reorder Tool: A Pilot Study

OBJECTIVES

The aims of this study were to develop and to validate an adapted Retract-and-Reorder (RAR) tool to identify and quantify near-miss/intercepted prescribing errors in an electronic health record.

METHODS

This is a cross-sectional study between February and March 2021 in an Irish maternity hospital. We used the RAR tool to detect near-miss prescribing errors in audit log data. Potential errors flagged by the tool were validated using prescriber interviews. Chart reviews were performed if the prescriber was unavailable for interview. Errors were judged to be clinical decisions in chart reviews through review of narrative notes, order components, and patient's clinical history. Interviews were analyzed with reference to the London Protocol, a process of incident analysis that categorizes causes of errors into various contributory factors including patient factors, task and technology factors, and work environment. Logistic regression with robust clustered standard errors was used to determine predictors for near-miss prescribing errors. We calculated the positive predictive value of the RAR tool by dividing the number of confirmed near-miss prescribing errors by the total number of RAR events identified.

RESULTS

Eighty-four RAR events were identified in 27,407 medication orders. Seventy-one events were confirmed near-miss prescribing errors, resulting in a positive predictive value of 85.0% (95% confidence interval, 75%-91%) and an estimated near-miss prescribing error rate of 259/100,000 medication orders. Duplicate prescribing errors were most common (54/71, 76.1%). No errors were reported by prescribers. Consultants were less likely to make an error than nonconsultant hospital doctors (adjusted odds ratio, 0.10; 95% confidence interval, 0.01-0.84). Factors associated with errors included workload, staffing levels, and task structure.

CONCLUSIONS

Our adapted RAR tool identified a variety of near-miss prescribing errors not otherwise reported. The tool has been implemented in the study hospital as a patient safety resource. Further implementations are planned across Irish hospitals.

Locked the Car, Why Not the Computer: A Qualitative and Quantitative Study on Data Safety Compliance

Information technology has become an integral part of health care in the United Kingdom National Health Service (NHS). All health care professionals are required to have a certain level of cyber ethics and knowledge of computers. This is assured by regular mandatory training. The government of the United Kingdom has charted out a course to strengthen cyber security and prevent any crises like Wannacry. Simple things like leaving a computer unlocked can pose a potential threat to the cyber security of the whole NHS. These cannot be addressed with money alone, as they involve complex interactions of human factors. Such seemingly simple non-compliance results often in harm to the patient or breach of confidentiality. We tried to find out the compliance among junior doctors to the Trust Information Technology (IT) Safe Usage Policy. We made interventions and interviewed junior doctors to find out the reasons for non-compliance. We re-audited in order to see if our interventions helped. We also audited compliance in another Trust independently, which showed that this problem is not specific to a particular trust. Here we suggest the changes that all Trusts can make and follow our model to audit their compliance.

The Impact of Technology on Prescribing Errors in Pediatric Intensive Care: A Before and After Study

BACKGROUND

Increased use of health information technology (HIT) has been advocated as a medication error reduction strategy. Evidence of its benefits in the pediatric setting remains limited. In 2012, electronic prescribing (ICCA, Philips, United Kingdom) and standard concentration infusions (SCIs)-facilitated by smart-pump technology-were introduced into the pediatric intensive care unit (PICU) of an Irish tertiary-care pediatric hospital.

OBJECTIVE

The aim of this study is to assess the impact of the new technology on the rate and severity of PICU prescribing errors and identify technology-generated errors.

METHODS

A retrospective, before and after study design, was employed. Medication orders were reviewed over 24 weeks distributed across four time periods: preimplementation (Epoch 1); postimplementation of SCIs (Epoch 2); immediate postimplementation of electronic prescribing (Epoch 3); and 1 year postimplementation (Epoch 4). Only orders reviewed by a clinical pharmacist were included. Prespecified definitions, multidisciplinary consensus and validated grading methods were utilized.

RESULTS

A total of 3,356 medication orders for 288 patients were included. Overall error rates were similar in Epoch 1 and 4 (10.2 vs. 9.8%; p = 0.8), but error types differed (p < 0.001). Incomplete and wrong unit errors were eradicated; duplicate orders increased. Dosing errors remained most common. A total of 27% of postimplementation errors were technology-generated. Implementation of SCIs alone was associated with significant reductions in infusion-related prescribing errors (29.0% [Epoch 1] to 14.6% [Epoch 2]; p < 0.001). Further reductions (8.4% [Epoch 4]) were identified after implementation of electronically generated infusion orders. Non-infusion error severity was unchanged (p = 0.13); fewer infusion errors reached the patient (p < 0.01). No errors causing harm were identified.

CONCLUSION

The limitations of electronic prescribing in reducing overall prescribing errors in PICU have been demonstrated. The replacement of weight-based infusions with SCIs was associated with significant reductions in infusion prescribing errors. Technology-generated errors were common, highlighting the need for on-going research on HIT implementation in pediatric settings.

Detection and Remediation of Misidentification Errors in Radiology Examination Ordering

BACKGROUND

Despite progress in patient safety, misidentification errors in radiology such as ordering imaging on the wrong anatomic side persist. If undetected, these errors can cause patient harm for multiple reasons, in addition to producing erroneous electronic health records (EHR) data.

OBJECTIVES

We describe the pilot testing of a quality improvement methodology using electronic trigger tools and preimaging checklists to detect "wrong-side" misidentification errors in radiology examination ordering, and to measure staff adherence to departmental policy in error remediation.

METHODS

We retrospectively applied and compared two methods for the detection of "wrong-side" misidentification errors among a cohort of all imaging studies ordered during a 1-year period (June 1, 2015-May 31, 2016) at our tertiary care hospital. Our methods included: (1) manual review of internal quality improvement spreadsheet records arising from the prospective performance of preimaging safety checklists, and (2) automated error detection via the development and validation of an electronic trigger tool which identified discrepant side indications within EHR imaging orders.

RESULTS

Our combined methods detected misidentification errors in 6.5/1,000 of study cohort imaging orders. Our trigger tool retrospectively identified substantially more misidentification errors than were detected prospectively during preimaging checklist performance, with a high positive predictive value (PPV: 88.4%, 95% confidence interval: 85.4-91.4). However, two third of errors detected during checklist performance were not detected by the trigger tool, and checklist-detected errors were more often appropriately resolved (p < 0.00001, 95% confidence interval: 2.0-6.9; odds ratio: 3.6).

CONCLUSION

Our trigger tool enabled the detection of substantially more imaging ordering misidentification errors than preimaging safety checklists alone, with a high PPV. Many errors were only detected by the preimaging checklist; however, suggesting that additional trigger tools may need to be developed and used in conjunction with checklist-based methods to ensure patient safety.