Case Not Closed: Prescription Errors 12 Years after Computerized Physician Order Entry Implementation

Use of Computerized Physician Order Entry with Clinical Decision Support to Prevent Dose Errors in Pediatric Medication Orders: A Systematic Review

BACKGROUND

Prescribing is a high-risk task within the pediatric medication-use process and requires defenses to prevent errors. Such system-centric defenses include electronic health record systems with computerized physician order entry (CPOE) and clinical decision support (CDS) tools that assist safe prescribing. The objective of this study was to examine the effects of CPOE systems with CDS functions in preventing dose errors in pediatric medication orders.

MATERIAL AND METHODS

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 criteria and Synthesis Without Meta-Analysis (SWiM) items. The study protocol was registered in PROSPERO (CRD42021277413). The final literature search on MEDLINE (Ovid), Scopus, Web of Science, and EMB Reviews was conducted on 10 September 2023. Only peer-reviewed studies considering both CPOE and CDS systems in pediatric inpatient or outpatient settings were included. Study selection, data extraction, and evidence quality assessment (JBI critical appraisal tool assessment and GRADE approach) were carried out by two individual reviewers. Vote counting method was used to evaluate the effects of CPOE-CDS systems on dose errors rates.

RESULTS

A total of 17 studies published in 2007-2021 met the inclusion criteria. The most used CDS tools were dose range check (n = 14), dose calculator (n = 8), and dosing frequency check (n = 8). Alerts were recorded in 15 studies. A statistically significant reduction in dose errors was found in eight studies, whereas an increase of dose errors was not reported.

CONCLUSIONS

The CPOE-CDS systems have the potential to reduce pediatric dose errors. Most beneficial interventions seem to be system customization, implementing CDS alerts, and the use of dose range check. While human factors are still present within the medication use process, further studies and development activities are needed to optimize the usability of CPOE-CDS systems.

Medication Prescription Errors in the Intensive Care Unit: Prospective Observational Study

Introduction

The WHO launched a 5-year global initiative to address the problem of medication errors on March 29, 2017, targeting a decrease in severe and avoidable medication-related harm by 50% in all the countries. Since prescription errors are preventable, this study was conducted to determine incidence and severity of medication prescription errors (MPEs).

Settings and design

Intensive care unit of a tertiary care academic hospital, prospective observational study.

Methods and materials

For all patients admitted in a medical ICU, baseline data (demographic, APACHE II, length of ICU stay, and days of mechanical ventilation) were noted. Treatment charts were reviewed daily, and each prescription was compared against a master chart prepared using standardized references to study the incidence of prescription errors. Severity classification was done using National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP) classification. Mean and median, along with standard deviation and interquartile range, were calculated for all quantitative variables. Multivariate linear regression analysis model was used.

Results

Out of the total 24,572 medication orders, 2,624 had prescription errors, an error rate of 10.7% (95% CI, 10.3–11.1). When analyzed for severity, 1,757 (7.15%) (95% CI, 6.8–7.5) MPEs did not result in patient harm and 867 (3.52%) (95% CI, 3.3–3.8) MPEs required interventions and/or resulted in patient harm. Patients with deranged creatinine (p <0.001) and INR (p = 0.024) had higher number of severe MPEs.

Conclusion

The incidence of MPEs in the medical ICU at the tertiary care hospital was 10.7%, 3.52% being severe errors.

How to cite this article

Kumar M, Sahni N, Shafiq N, Yaddanapudi LN. Medication Prescription Errors in the Intensive Care Unit: Prospective Observational Study. Indian J Crit Care Med 2022;26(5):555–559.

Description of a clinical decision support tool with integrated dose calculator for paediatrics

Medication errors, especially dosing errors are a leading cause of preventable harm in paediatric patients. The paediatric patient population is particularly vulnerable to dosing errors due to immaturity of metabolising organs and developmental changes. Moreover, the lack of clinical trial data or suitable drug forms, and the need for weight-based dosing, does not simplify drug dosing in paediatric or neonatal patients. Consequently, paediatric pharmacotherapy often requires unlicensed and off-label use including manipulation of adult dosage forms. In practice, this results in the need to calculate individual dosages which in turn increases the likelihood of dosing errors. In the age of digitalisation, clinical decision support (CDS) tools can support healthcare professionals in their daily work. CDS tools are currently amongst the gold standards in reducing preventable errors. In this publication, we describe the development and core functionalities of the CDS tool PEDeDose, a Class IIa medical device software certified according to the European Medical Device Regulation. The CDS tool provides a drug dosing formulary with an integrated calculator to determine individual dosages for paediatric, neonatal, and preterm patients. Even a technical interface is part of the CDS tool to facilitate integration into primary systems. This enables the support of the paediatrician directly during the prescribing process without changing the user interface.Conclusion: PEDeDose is a state-of-the-art CDS tool for individualised paediatric drug dosing that includes a certified calculator. What is Known: • Dosing errors are the most common type of medication errors in paediatric patients. • Clinical decision support tools can reduce medication errors effectively. Integration into the practitioner's workflow improves usability and user acceptance. What is New: • A clinical decision support tool with a certified integrated dosing calculator for paediatric drug dosing. • The tool was designed to facilitate integration into clinical information systems to directly support the prescribing process. Any clinical information system available can interoperate with the PEDeDose web service.

Clinical decision support system, using expert consensus-derived logic and natural language processing, decreased sedation-type order errors for patients undergoing endoscopy

OBJECTIVE

Determination of appropriate endoscopy sedation strategy is an important preprocedural consideration. To address manual workflow gaps that lead to sedation-type order errors at our institution, we designed and implemented a clinical decision support system (CDSS) to review orders for patients undergoing outpatient endoscopy.

MATERIALS AND METHODS

The CDSS was developed and implemented by an expert panel using an agile approach. The CDSS queried patient-specific historical endoscopy records and applied expert consensus-derived logic and natural language processing to identify possible sedation order errors for human review. A retrospective analysis was conducted to evaluate impact, comparing 4-month pre-pilot and 12-month pilot periods.

RESULTS

22 755 endoscopy cases were included (pre-pilot 6434 cases, pilot 16 321 cases). The CDSS decreased the sedation-type order error rate on day of endoscopy (pre-pilot 0.39%, pilot 0.037%, Odds Ratio = 0.094, P-value < 1e-8). There was no difference in background prevalence of erroneous orders (pre-pilot 0.39%, pilot 0.34%, P = .54).

DISCUSSION

At our institution, low prevalence and high volume of cases prevented routine manual review to verify sedation order appropriateness. Using a cohort-enrichment strategy, a CDSS was able to reduce number of chart reviews needed per sedation-order error from 296.7 to 3.5, allowing for integration into the existing workflow to intercept rare but important ordering errors.

CONCLUSION

A workflow-integrated CDSS with expert consensus-derived logic rules and natural language processing significantly reduced endoscopy sedation-type order errors on day of endoscopy at our institution.

Medication errors related to computerized provider order entry systems in hospitals and how they change over time: A narrative review

BACKGROUND

Evaluations of computerized provider order entry (CPOE) systems have revealed that reductions in certain types of medication errors occur simultaneously with the emergence of system-related errors - errors that are unlikely or not possible to occur with the use of paper-based medication charts. System-related errors appear to persist many years post-implementation of CPOE, although little is known about whether the types and rates of system-related errors that occur immediately following CPOE implementation are similar to those that endure or emerge after years of system use.

OBJECTIVE

To analyze and synthesize the literature on system-related errors, specifically in relation to the length of time that CPOE systems have been in use, to determine what is currently known about how system-related errors change over time.

METHODS

A literature search was undertaken using the PubMed database to identify English language articles published between January 2005 and March 2020 that provided original data on system-related errors resulting from CPOE system use. Studies were included if they provided results on system-related errors and information relating to the length of time that CPOE had been in use.

RESULTS

Thirty-one studies met the inclusion criteria for this narrative review. System-related errors were identified and described during short, medium and long-term use of CPOE systems, but no single study examined how errors changed over time. In comparing findings across studies, results suggest that system-related errors persist with long-term use of CPOE systems, although likely to occur at a reduced rate.

CONCLUSIONS

This review has highlighted a significant gap in knowledge on how system-related errors change over time. Determining what and when system-related errors occur and the system factors that contribute to their occurrence at different time points after CPOE implementation is necessary for the future prevention and mitigation of these errors.

Computerized provider order entry-related medication errors among hospitalized patients: An integrative review

The Institute of Medicine estimates that 7,000 lives are lost yearly as a result of medication errors. Computerized physician and/or provider order entry was one of the proposed solutions to overcome this tragic issue. Despite some promising data about its effectiveness, it has been found that computerized provider order entry may facilitate medication errors.The purpose of this review is to summarize current evidence of computerized provider order entry -related medication errors and address the sociotechnical factors impacting the safe use of computerized provider order entry. By using PubMed and Google Scholar databases, a systematic search was conducted for articles published in English between 2007 and 2019 regarding the unintended consequences of computerized provider order entry and its related medication errors. A total of 288 articles were screened and categorized based on their use within the review. One hundred six articles met our pre-defined inclusion criteria and were read in full, in addition to another 27 articles obtained from references. All included articles were classified into the following categories: rates and statistics on computerized provider order entry -related medication errors, types of computerized provider order entry -related unintended consequences, factors contributing to computerized provider order entry failure, and recommendations based on addressing sociotechnical factors. Identifying major types of computerized provider order entry -related unintended consequences and addressing their causes can help in developing appropriate strategies for safe and effective computerized provider order entry. The interplay between social and technical factors can largely affect its safe implementation and use. This review discusses several factors associated with the unintended consequences of this technology in healthcare settings and presents recommendations for enhancing its effectiveness and safety within the context of sociotechnical factors.

Prevalence of Medication Errors Among Paediatric Inpatients: Systematic Review and Meta-Analysis

INTRODUCTION

The risk of medication errors is high in paediatric inpatient settings. However, estimates of the prevalence of medication errors have not accounted for heterogeneity across studies in error identification methods and definitions, nor contextual differences across wards and the use of electronic or paper medication charts.

OBJECTIVE

Our aim was to conduct a systematic review and meta-analysis to provide separate estimates of the prevalence of medication errors among paediatric inpatients, depending on hospital ward and the use of electronic or paper medication charts, that address differences in error identification methods and definitions.

METHODS

We systematically searched five databases to identify studies published between January 2000 and December 2018 that assessed medication error rates by medication chart audit, direct observation or a combination of methods.

RESULTS

We identified 71 studies, 19 involved paediatric wards using electronic charts. Most studies assessed prescribing errors with few studies assessing administration errors. Estimates varied by ward type. Studies of paediatric wards using electronic charts generally reported a reduced error prevalence compared to those using paper, although there were some inconsistencies. Error detection methods impacted the rate of administration errors in studies of multiple wards, however, no other difference was found. Definition of medication error did not have a consistent impact on reported error rates.

CONCLUSIONS

Medication errors are a frequent occurrence in paediatric inpatient settings, particularly in intensive care wards and emergency departments. Hospitals using electronic charts tended to have a lower rate of medication errors compared to those using paper charts. Future research employing controlled designs is needed to determine the true impact of electronic charts and other interventions on medication errors and associated harm among hospitalized children.

Risk Factors for Electronic Prescription Errors in Pediatric Intensive Care Patients

OBJECTIVES

To assess risk factors for electronic prescription errors in a PICU.

DESIGN

A database of electronic prescriptions issued by a computerized physician order entry with clinical decision support system was analyzed to identify risk factors for prescription errors.

MEASUREMENTS AND MAIN RESULTS

Of 6,250 prescriptions, 101 were associated with errors (1.6%). The error rate was twice as high in patients older than 12 years than in patients children 6-12 and 0-6 years old (2.4% vs 1.3% and 1.2%, respectively, p < 0.05). Compared with patients without errors, patients with errors had a significantly higher score on the Pediatric Index of Mortality 2 (-3.7 vs -4.5; p = 0.05), longer PICU stay (6 vs 3.1 d; p < 0.0001), and higher number of prescriptions per patient (40.8 vs. 15.7; p < 0.0001). In addition, patients with errors were more likely to have a neurologic main admission diagnosis (p = 0.008) and less likely to have a cardiologic diagnosis (p = 0.03) than patients without errors.

CONCLUSIONS

Our findings suggest that older patient age and greater disease severity are risk factors for electronic prescription errors.

[Legitimate resistance without technophobia: Analysis of electronic medical records impacts on the medical profession]

The objective of this short narrative literature review is to highlight the different difficulties encountered by medical doctor in the daily use of EMR. We show that these are not simple transitional phenomena related to a "resistance to change", but rather the fact of a deeper and unfinished transformation. Beyond the "perception of misfit with work processes" or the threat of a loss of autonomy, we propose to analyze this so-called "resistance" in relation to the formalization of medical work induced by EMR. Our question concerns the compatibility of the multiple objectives of EMR, the potential influence of computerization on the steps of entering and consulting medical information, the impact on the clinical reasoning, the reality of assistance to medical "performance". The question is not so much what EMRs do less well than the paper record, but to provide insights into how tomorrow's EMRs will do better than today's.

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.

Analysis of dosing-button compliance

PURPOSE

A project was undertaken at an academic medical center to assess use of available dosing buttons within the computerized provider-order-entry (CPOE) system in order to identify opportunities for optimization of medication builds.

METHODS

A retrospective observational study was conducted to identify medication records within a CPOE system meeting prespecified inclusion and exclusion criteria. A report capturing all inpatient adult medication orders associated with the identified medication records over a 6-month period was generated. The primary endpoint was percent dosing-button compliance, calculated as the number of orders with doses consistent with existing dosing-button options divided by the total number of orders during the study period. Secondary study objectives included a comparison of high- and low-performing medication record samples and identification of potential reasons for lack of dosing-button use.

RESULTS

A total of 2,506 CPOE medication records associated with a total of 694,877 medication orders entered during the study period were analyzed. Median percent dosing-button compliance was 99.92% (interquartile range, 83.33-100%). High-performing records (n = 1243) were more likely to be associated with anti-infective medications (p = 0.041) and medications not on formulary at the study institution (p < 0.001). Medications in the sample of poor-performing CPOE records (n = 614) were more likely to be agents delivered via the i.v. route (p < 0.001). There were 45 records for which poor dosing-button compliance was attributed to lack of a clinically reasonable dosing option.

CONCLUSION

A high level of dosing-button compliance was demonstrated despite the lack of routine revalidation of dosing buttons after initial medication builds. Some opportunity for optimization was identified during the project, which established a quality assurance method to facilitate future auditing of medication builds.

Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data

Artificial intelligence (AI) is currently regaining enormous interest due to the success of machine learning (ML), and in particular deep learning (DL). Image analysis, and thus radiomics, strongly benefits from this research. However, effectively and efficiently integrating diverse clinical, imaging, and molecular profile data is necessary to understand complex diseases, and to achieve accurate diagnosis in order to provide the best possible treatment. In addition to the need for sufficient computing resources, suitable algorithms, models, and data infrastructure, three important aspects are often neglected: (1) the need for multiple independent, sufficiently large and, above all, high-quality data sets; (2) the need for domain knowledge and ontologies; and (3) the requirement for multiple networks that provide relevant relationships among biological entities. While one will always get results out of high-dimensional data, all three aspects are essential to provide robust training and validation of ML models, to provide explainable hypotheses and results, and to achieve the necessary trust in AI and confidence for clinical applications.