Computerized Physician Order Entry Research Articles

Understanding prescribing errors for system optimisation: the technology-related error mechanism classification.

Technology-related prescribing errors curtail the positive impacts of computerised provider order entry (CPOE) on medication safety. Understanding how technology-related errors (TREs) occur can inform CPOE optimisation. Previously, we developed a classification of the underlying mechanisms of TREs using prescribing error data from two adult hospitals. Our objective was to update the classification using paediatric prescribing error data and to assess the reliability with which reviewers could independently apply the classification. Using data on 1696 prescribing errors identified by chart review in 2016 and 2017 at a tertiary paediatric hospital, we identified errors that were technology-related. These errors were investigated to classify their underlying mechanisms using our previously developed classification, and new categories were added based on the data. A two-step process was used to identify and classify TREs involving a review of the error in the CPOE and simulating the error in the CPOE testing environment. The technology-related error mechanism (TREM) classification comprises six mechanism categories, one contributing factor and 19 subcategories. The categories are as follows: (1) incorrect system configuration or system malfunction, (2) opening or using the wrong patient record, (3) selection errors, (4) construction errors, (5) editing errors, (6) errors that occur when using workflows that differ from a paper-based system (7) contributing factor: use of hybrid systems. TREs remain a critical issue for CPOE. The updated TREM classification provides a systematic means of assessing and monitoring TREs to inform and prioritise system improvements and has now been updated for the paediatric setting.

A Descriptive Study: Evaluation of Delayed Laboratory Testing in Emergency Room

Precision, accuracy, and short turnaround time (TAT) are important to improve the quality and effectiveness of laboratory services in the Emergency Room (ER). Several studies reported an average of 60% the ratio of anticoagulant to sample is not appropriate, lysis, and clotted samples occurred in the pre-analytical part. This part consisted of the request/registration of laboratory tests, patient preparation, specimen collection, and transportation to the central laboratory. The aim of this research is to evaluate the cause of the long TAT for laboratory research so as to obtain problem solving based on the target. This observational study was conducted in 2021 at the ER laboratory outlet. Observations were made started from the workload carried out by the laboratory technician, sample registration, and data collection from the Laboratory Information System (LIS). The study obtained 14.709 samples from the LIS at the Clinical Pathology Department, Cipto Mangunkusumo Hospital. The highest sample rejection rate was from the ER laboratory outlet due to specimen lysis (2.79%). The average time required for re-collecting specimens was around 2-2.5 hours. This rejection was below the minimum quality standards based on the International Federation of Clinical Chemistry (IFCC). Other problems include the limited number of medical laboratory experts compared to the workload also inefficient Computerized Physician Order Entry (CPOE) registration methods. The main problem of delayed laboratory testing in the emergency department is the pre-analytical part including sample registration and rejection specimens. That rejection mostly comes from lysis specimens, which caused invalid laboratory result.

Implementation of CPOE, ERBI, and ePPID in Critical Care Operating Rooms at a Tertiary Care Academic Center

Abstract Introduction/Objective Blood transfusion in operating rooms (ORs) is a critical and complex process, often complicated by issues such as paper-based orders, lack of guidelines, and delays in blood delivery. In response, a multidisciplinary task force developed a quality improvement plan. This study assesses the impact of implementing an electronic positive patient identification (ePPID) system, standardized computer provider order entry (CPOE), and electronic remote blood issue (ERBI) process in a critical care OR setting at our institution. Methods/Case Report The team initially implemented ePPID in cardiac surgery ORs before expanding hospital-wide. CPOE was developed in collaboration with the laboratory information systems (LIS) and hospital information technology (IT). ERBI required cross-departmental coordination to establish a bi-directional interface between LIS and ERBI software. Staff received training through a train-the-trainer approach, interactive sessions, videos, and surveys. CPOE focused on order set development and creating the Maximum Surgical Blood Order Schedule (MSBOS). ePPID began with a pilot program and expanded with training sessions. Data collection tracked blood ordering, ERBI workflow, and transfusion data for compliance and utilization rates. Results (if a Case Study enter NA) The implementation of ePPID saw a rise in compliance rates from 71% to 98%, with 60 completed cases and 233 blood products transfused by 24 clinicians. ePPID’s utilization for blood products increased from 60% to 98% within 90 days. ERBI lowered RBC return rates from 30% to 15% and plasma return rates from 45% to 35% over a 2.5 year span. ERBI utilization rates reached 81% at 24 months post-implementation. Conclusion The implementation of ePPID, CPOE, and ERBI in critical care ORs was well-received among clinicians. Significant improvements in compliance and utilization rates for blood products following ePPID implementation were evident. ERBI implementation also led to substantial reductions in return rates of blood products allocated to ORs. The steady increase in ERBI utilization indicates successful integration into OR workflows.

Changes in Acute Care Pharmacist Prescribing and Laboratory Ordering over Time: CAPLET Study.

In Alberta, pharmacists may obtain additional prescribing authorization (APA) and a practice identification number (PRAC-ID) for ordering laboratory tests. Pharmacists working within Alberta Health Services were mandated by the employer to attain APA by 2018, whereas laboratory ordering has been in place since 2009. Five acute care sites within the Calgary Zone had a computerized provider order entry (CPOE) system that allowed tracking of these activities. To describe changes in prescribing and laboratory ordering by acute care pharmacists over time and to compare these activities across hospitals, sites, and specialty teams. A retrospective, descriptive review of acute care pharmacist orders for medications and laboratory tests was completed using data from the CPOE system for the period 2018 to 2021. Over the study period, the rates of prescribing and laboratory ordering by pharmacists increased by 67.5% (from 1423 to 2383 per full-time equivalent [FTE]) and by 5.5% (from 235 to 248 per FTE), respectively. Pharmacists at the 5 hospitals increased their prescribing rates during that time (by proportions ranging from 7% to 176%). Cardiology, intensive care, and mental health teams had the largest increases in prescribing rates, whereas mental health, hospitalist, and intensive care teams had the greatest increases in rates of laboratory ordering. In each year of the study, the most frequently ordered medication for adult patients was vancomycin, and the most frequently ordered laboratory test was measurement of vancomycin before dose administration. The proportion of medication orders conveyed verbally decreased from 60.0% to 47.4% over the study period. The application of expanded scope of practice increased among acute care pharmacists, to a greater extent for prescribing than for laboratory ordering; however, the proportion of verbal medication orders remains high, a situation that should be addressed to improve patient safety. This study showed that prescribing and laboratory ordering are complementary, given that the top medications and laboratory tests were frequently related. The results of this study can be used for practice development and as the basis for further research within an expanded CPOE system.

Preventing Overrides of Severe Drug Allergy Alerts Initiative: an Implemented System Upgrade

Administering medications to patients with documented drug hypersensitivity reactions (DHR) poses a significant risk for adverse events, ranging from mild reactions to life-threatening incidents. Electronic healthcare systems have revolutionized the modern clinical decision-making process, with built in warnings. However, as these alerts become a routine part of healthcare provider’s workflow, alert fatigue becomes a challenge. This study was conducted within the Ministry of National Guard Health Affairs (MNGHA), a government healthcare system in Saudi Arabia. A taskforce of experts was formed to develop an electronic path that would prevent unintentional overrides of severe drug allergy alerts. The system underwent rigorous testing, and monitoring parameters were established. We outline the implementation of a system upgrade designed to trigger an alternative interruption in the computerized physician order entry (CPOE) process, distinct from the regular allergy pop-up alerts. The alternate path is activated upon a CPOE with a drug-to-drug match and a documented severe drug allergy symptom, necessitating co-signature form another prescriber before proceeding. The adopted upgrade is a proactive approach to enhance medication safety in electronic healthcare systems, ensuring that serious allergy-related warnings are not overridden, ultimately enhancing patient safety. Further monitoring will confirm the safety and effectiveness of this measure. This study provides a model for institutions seeking to prevent allergy-related harm within their patient population.

Implementation of Computerized Prescriber Order Entry Systems: A Review of Impacts, Barriers, and Facilitators.

This review evaluated the impact of a digitized computerized prescriber order entry (CPOE) system and described barriers and facilitators for introducing a digitized system. A systematic literature search was conducted in PubMed, Medline, and CINAHL using keywords. Articles in English during the last 10 years were included. Study selection was presented using a PRISMA flow diagram. Forty-eight studies were included. Data from the articles were presented to address each of the three objectives. CPOE systems improved the quality of care provided but also introduced new types of errors. Facilitating factors for implementation included leadership, stakeholder engagement, training, and user-centered design. Inadequate training, software design, changes in workload, and workflow disruptions were identified as barriers. Recommendations for implementation included dedicated training of users, user-centered design, a backup for system downtimes, and stakeholder engagement. Application of knowledge of the facilitators and barriers for the introduction of a CPOE system supports this change-management process within the specific context and augurs for more successful implementation.

Impact of Computerized Physician Order Entry (CPOE) Coupled With Clinical Decision Support (CDS) on Radiologic Services.

Medical imaging is an essential component of healthcare, enabling accurate diagnoses and facilitating effective treatment plans. However, the field is not without its challenges, including medical imaging errors, overutilization of procedures, and adverse reactions to contrast agents. This review explores the impact of computerized physician order entry (CPOE) systems coupled with clinical decision support (CDS) on radiologic services. By analyzing the findings from various studies, this paper highlights how CPOE coupled with CDS can significantly reduce inappropriate imaging, enhance adherence to clinical guidelines, and improve overall patient safety. The implementation of CPOE with CDS optimizes the utilization of radiologic procedures, thereby reducing healthcare costs and minimizing patients' exposure to unnecessary radiation. Despite its benefits, the adoption of CPOE with CDS encounters challenges such as high implementation costs, changes in workflow, and alert fatigue among healthcare providers. Addressing these challenges requires careful system design, including the customization of alerts to reduce override rates and improve the specificity of CDS recommendations. This review underscores the potential of CPOE with CDS to transform radiologic services, enhancing both the quality and safety of patient care. Further research is needed to explore the system's effectiveness in preventing adverse reactions to contrast media and to identify best practices for overcoming the barriers to its broader adoption.

Longitudinal study of the manifestations and mechanisms of technology-related prescribing errors in pediatrics.

To examine changes in technology-related errors (TREs), their manifestations and underlying mechanisms at 3 time points after the implementation of computerized provider order entry (CPOE) in an electronic health record; and evaluate the clinical decision support (CDS) available to mitigate the TREs at 5-years post-CPOE. Prescribing errors (n = 1315) of moderate, major, or serious potential harm identified through review of 35322 orders at 3 time points (immediately, 1-year, and 4-years post-CPOE) were assessed to identify TREs at a tertiary pediatric hospital. TREs were coded using the Technology-Related Error Mechanism classification. TRE rates, percentage of prescribing errors that were TREs, and mechanism rates were compared over time. Each TRE was tested in the CPOE 5-years post-implementation to assess the availability of CDS to mitigate the error. TREs accounted for 32.5% (n = 428) of prescribing errors; an adjusted rate of 1.49 TREs/100 orders (95% confidence interval [CI]: 1.06, 1.92). At 1-year post-CPOE, the rate of TREs was 40% lower than immediately post (incident rate ratio [IRR]: 0.60; 95% CI: 0.41, 0.89). However, at 4-years post, the TRE rate was not significantly different to baseline (IRR: 0.80; 95% CI: 0.59, 1.08). "New workflows required by the CPOE" was the most frequent TRE mechanism at all time points. CDS was available to mitigate 32.7% of TREs. In a pediatric setting, TREs persisted 4-years post-CPOE with no difference in the rate compared to immediately post-CPOE. Greater attention is required to address TREs to enhance the safety benefits of systems.

The electronic health record: does it enhance or distract from patient safety?

The electronic health record (EHR) is an invaluable tool that may be used to improve patient safety. With a variety of different features, such as clinical decision support and computerized physician order entry, it has enabled improvement of patient care throughout medicine. EHR allows for built-in reminders for such items as antibiotic dosing and venous thromboembolism prophylaxis. In anesthesiology, EHR often improves patient safety by eliminating the need for reliance on manual documentation, by facilitating information transfer and incorporating predictive models for such items as postoperative nausea and vomiting. The use of EHR has been shown to improve patient safety in specific metrics such as using checklists or information transfer amongst clinicians; however, limited data supports that it reduces morbidity and mortality. There are numerous potential pitfalls associated with EHR use to improve patient safety, as well as great potential for future improvement.

Nudging Towards Sleep-Friendly Hospitalizations: A Multifaceted Approach on Reducing Unnecessary Overnight Interventions.

Choice architecture refers to the design of decision environments, which can influence healthcare decision-making. Nudges are subtle adjustments in these environments that guide decisions toward desired outcomes. For example, Computerized Provider Order Entry (CPOE) within Electronic Health Records (EHR) recommends frequencies for interventions such as nursing assessments and medication administrations, but these can link to around-the-clock schedules without clinical necessity. This study aimed to evaluate an intervention to promote sleep-friendly practices by optimizing choice architecture and employing targeted nudges on inpatient order frequencies. We employed a quasi-experimental interrupted time series analysis of a multifaceted, multiphase intervention to reduce overnight interventions in a hospital system. Our intervention featured EHR modifications to optimize the scheduling of vital sign checks, neurological checks, and medication administrations. Additionally, we used targeted secure messaging reminders and education on an inpatient neurology unit (INU) to supplement the initiative. Significant increases in sleep-friendly medication orders were observed at the academic medical center (AMC) and community hospital affiliate (CHA), particularly for acetaminophen and heparin at the AMC. This led to a reduction in overnight medication administrations, with the most substantial decrease observed with heparin at all locations (CHA: 18%, AMC: 10%, INU: 10%, p<0.05). Sleep-friendly vital sign orders increased significantly at all sites (AMC: 6.7%, CHA 4.3%, INU: 14%, p<0.05), and sleep-friendly neuro check orders increased significantly at the AMC (8.1%, p<0.05). There was also a significant reduction in overnight neurological checks at the AMC. Tailoring EHR modifications and employing multifaceted nudging strategies emerged as promising approaches for reducing unnecessary overnight interventions. The observed shifts in sleep-friendly ordering translated into decreases in overnight interventions. Multifaceted nudges can effectively influence clinician decision-making and patient care. The varied impacts across nudge types and settings emphasize the importance of thoughtful nudge design and understanding local workflows.

A Case-Study of Metoclopramide Prescription Error : A Grim Reminder

The integration of Computerized Provider Order Entry (CPOE) systems in hospitals has been instrumental in reducing medication errors and enhancing patient safety. This study examines the implications of a software oversight in a CPOE system : Metoclopramide had a concentrated formulation (100 mg) delisted (and then not manufactured) in 2014 due to safety concerns. Despite this, the CPOE system continued to accept prescriptions for this formulation because it was not removed from the medication library by the pharmacist. The objective of our study was to describe this specific prescription error related to an outdated the medication library of the CPOE. We analyzed all metoclopramide prescriptions from 2014, to 2023. Our findings showed that errors involving 100 mg or more dosages were relatively rare, at 2.98 per 1000 prescriptions (34 errors in 11,372 prescriptions). Notably, 47.1% of these errors occurred during on-call shifts, and 68% of these errors led to actual administration. These errors correlated with periods of higher nurse workload. The findings advocate for the integration of dedicated pharmacists into ICU teams to minimize medication errors and enhance patient outcomes, and a proactive medication management in healthcare.

Applying Machine Learning for Prescriptive Support: A Use Case with Unfractionated Heparin in Intensive Care Units.

Continuous unfractionated heparin is widely used in intensive care, yet its complex pharmacokinetic properties complicate the determination of appropriate doses. To address this challenge, we developed machine learning models to predict over- and under-dosing, based on anti-Xa results, using a monocentric retrospective dataset. The random forest model achieved a mean AUROC of 0.80 [0.77-0.83], while the XGB model reached a mean AUROC of 0.80 [0.76-0.83]. Feature importance was employed to enhance the interpretability of the model, a critical factor for clinician acceptance. After prospective validation, machine learning models such as those developed in this study could be implemented within a computerized physician order entry (CPOE) as a clinical decision support system (CDSS).

Prevalence and causes of medication transcription errors among hospitalized patients: An observational study and survey of nurses at a faith-based hospital in Cameroon

Medication errors are a leading cause of death globally; with the burden on resource-limited countries more than double that in high-income countries. Errors can occur at all stages of the medication use process; however, there is limited information on medication transcription errors (MTEs) among hospitalized patients in Africa. This study sought to determine the prevalence and types of MTEs and to assess nurses&amp;rsquo; perceptions of the likely causes of MTEs within the pediatric and general wards at a faith-based hospital in Cameroon. A prospective chart review of medication orders transcribed for patients admitted from April 1 to April 30, 2021, was conducted. Data analyzed included patient demographics, prevalence and types of MTEs, medication classes involved in MTEs, and nurse&amp;rsquo;s perceptions of the causes of MTEs. A total of 578 medication orders were reviewed for 75 patients, of whom 40 (53%) were female, and 30 were admitted to the pediatric ward. The results revealed that 38 MTEs were recorded, with 47% occurring on the medication administration sheet and dispensing cards. The most frequent MTEs were the omission of new and stop medication orders, involved in 28.9% and 26% of all MTEs, respectively. Antibacterials for systemic use were involved in the majority (55.3%) of MTEs. Illegible prescriptions (90.7%), distractions (87.0%), and high workload (77.8%) were ascribed as the most likely causes of MTEs. In conclusion, MTEs are common and pose a potential risk of patient harm in hospitalized patients. The adoption of a computerized provider order entry system could eliminate illegible prescriptions, reduce transcription processes, and alleviate workload, thereby improving patient safety.

Risk evaluation and mitigation strategy compliance for pulmonary hypertension medications after policy implementation with computerized provider order entry support.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Treatment for pulmonary hypertension includes medications with risk evaluation and mitigation strategy (REMS) programs. Health-system inpatient pharmacies dispensing these agents must comply with inpatient REMS dispensing criteria. Implementing a health-system policy with computerized provider order entry (CPOE) decision support may improve REMS compliance. This was a retrospective, quasi-experimental study comparing REMS compliance before and after development of a policy with CPOE decision support that was implemented in August 2019. Patients 18 years of age or older with a diagnosis of pulmonary hypertension were included if they received at least one dose of an endothelin receptor antagonist or riociguat while hospitalized. Patients were included in the preintervention group if they were hospitalized between August 1, 2017, and August 31, 2019, and in the postintervention group if they were hospitalized between September 1, 2019, and August 31, 2021. The primary outcome was the REMS compliance rate. Secondary endpoints included the time to REMS compliance and independent factors associated with failed or delayed REMS compliance. A total of 150 patients were included, with 75 patients in both the pre- and postintervention groups. Compliance increased significantly from the preintervention (50%) to postintervention (92%) group (P < 0.001). Time to compliance was also significantly reduced from 770 minutes in the preintervention group to 140 minutes in the postintervention group (P = 0.031). Factors independently associated with REMS compliance were being in the postintervention group (odds ratio, 16.9; 95% confidence interval, 5.8-49.2) and being admitted to a pulmonary hypertension center for comprehensive care. (odds ratio, 7.8; 95% confidence interval, 2.9-21.2). A health-system policy with CPOE decision support improved both the rate of and time to compliance with inpatient REMS dispensing procedures.

Using the Failure Mode and Effect Analysis Tool to Improve the Automatic Stop Order Process.

Automatic stop orders (ASOs) in computerized prescription order entry (CPOE) systems predefine the length of treatment. This can improve resource use for select therapies (e.g., empirical antibiotics). However, root cause analysis of dose omission errors identified inappropriate ASO-directed termination of medications without prescriber notification. This quality improvement initiative aimed to identify potential failures of the medication ASO processes to develop a new workflow and anticipate issues that may arise after implementation. A failure mode and effect analysis (FMEA) was conducted following Institute of Healthcare Improvement guidance. A multidisciplinary ASO-FMEA team reviewed the existing workflow. Failure modes, risk priority numbers (RPNs), and interventions were identified and assessed. The RPNs calculated for the proposed new workflow (assuming all recommendations were implemented) were compared with those of the existing workflow. Eight failure modes, 17 effects, and 31 causes were identified in the five workflow steps (mean RPN 365.4; median 280). Specific, measurable, achievable, realistic, and time-bound interventions were proposed. Assuming successful implementation of all recommendations, the RPNs of the proposed workflow (mean 117.6; median 112) were significantly lower (p < 0.05). When modifying existing CPOE systems, FMEA may identify possible failures that can be addressed before the implementation of a new process. This may prevent errors, improving medication safety. Regardless, continuous audit and monitoring are required to ensure the effectiveness of implemented changes.

Literature Review: Development of Electronic Medical Records In Hospital Management Information Systems

Introduction: Health technology today is developing very quickly from the initially conventional using paper to being computerized. This literature review aims to map and critically summarize the scientific evidence on the cost-effectiveness and acceptability of Computerized Physician Order Entry (CPOE) and Electronic Health Reports. This Journal have Question that need to be answered how does the development of CPOE in medical records affect cost-effectiveness improvement so that it can be accepted by many parties? Method used in this journal is literature review is conducted on journal articles related to costs and receipts in CPOE. The systematic search was conducted from 5 databases namely PubMed, Science Direct, ProQuest, DOAJ and Ebscohost. Journal articles are selected and selected following PRISMA guidelines. Twenty-five journal articles qualified based on predetermined criteria. At the end as result, Cost-effectiveness with CPOE is more likely to be found that it is easier to reach compared to conventional methods. In addition, the acceptance of patients and health workers is also high. These factors can have a positive or negative influence on the hospital management system because developing countries still need adequate resources so that they can run these methods. Discussion and conclusion CPOE systems can improve patient safety by detecting drug interactions and actions. It is necessary to develop medical records in order to provide effective financing and acceptance.

The use of artificial intelligence to detect voided medication orders in oral and maxillofacial surgery inpatients.

The aim of this study is to determine if supervised machine learning algorithms can accurately predict voided computerized physician order entry in oral and maxillofacial surgery inpatients. Data from Electronic Medical Record included patient demographics, comorbidities, procedures, vital signs, laboratory values, and medication orders were retrospectively collected. Predictor variables included patient demographics, comorbidities, procedures, vital signs, and laboratory values. Outcome of interest is if a medication order was voided or not. Data was cleaned and processed using Microsoft Excel and Python v3.12. Gradient Boosted Decision Trees, Random Forest, K-Nearest Neighbor, and Naïve Bayes were trained, validated, and tested for accuracy of the prediction of voided medication orders. 37,493 medication orders from 1,204 patient admissions over 5 years were used for this study. 3,892 (10.4%) medication orders were voided. Gradient Boosted Decision Trees, Random Forest, K-Nearest Neighbor, and Naïve Bayes had an Area Under the Receiver Operating Curve of 0.802with 95% CI [0.787, 0.825], 0.746 with 95% CI [0.722, 0.765], 0.685 with 95% CI [0.667, 0.699], and 0.505 with 95% CI [0.489, 0.539], respectively. Area Under the Precision Recall Curve was 0.684 with 95% CI [0.679, 0.702], 0.647 with 95% CI [0.638, 0.664], 0.429 with 95% CI [0.417, 0.434], and 0.551 with 95% CI [0.551, 0.552], respectively. Gradient Boosted Decision Trees was the best performing model of the supervised machine learning algorithms with satisfactory outcomes in the test cohort for predicting voided Computerized Physician Order Entry in Oral and Maxillofacial Surgery inpatients.

A STUDY ON PATIENT SAFETY IN HOSPITAL

Patient safety is a fundamental aspect of healthcare quality, aiming to prevent and reduce risks, errors, and harm to patients during the provision of healthcare. This dissertation investigates the multifaceted nature of patient safety, examining its determinants, impacts, and strategies for improvement. The study employs a mixed-methods approach, combining quantitative data analysis from patient safety incident reports and qualitative insights from healthcare professionals through interviews and focus groups. Key findings highlight the prevalence of medication errors, surgical complications, and healthcare-associated infections as primary concerns. Contributing factors include inadequate staffing, insufficient training, and poor communication among healthcare teams. The dissertation emphasizes the critical role of a robust safety culture, where open communication and continuous learning are prioritized. Recommendations for enhancing patient safety include the implementation of standardized protocols, advanced training programs, and the integration of health information technologies such as electronic health records (EHRs) and computerized physician order entry (CPOE) systems. Additionally, fostering a non-punitive environment encourages reporting and addressing errors without fear of retribution. This study underscores the importance of interdisciplinary collaboration and the continuous evaluation of safety practices to adapt to the evolving healthcare landscape. By identifying key areas for intervention and providing actionable strategies, this dissertation contributes to the ongoing efforts to safeguard patient well-being and improve healthcare outcomes. This abstract encapsulates the main elements typically addressed in a comprehensive study on patient safety, focusing on the causes, consequences, and potential solutions to enhance safety in healthcare settings.

Event Analysis for Automated Estimation of Absent and Persistent Medication Alerts: Novel Methodology.

Event analysis is a promising approach to estimate the acceptance of medication alerts issued by computerized physician order entry (CPOE) systems with an integrated clinical decision support system (CDSS), particularly when alerts cannot be interactively confirmed in the CPOE-CDSS due to its system architecture. Medication documentation is then reviewed for documented evidence of alert acceptance, which can be a time-consuming process, especially when performed manually. We present a new automated event analysis approach, which was applied to a large data set generated in a CPOE-CDSS with passive, noninterruptive alerts. Medication and alert data generated over 3.5 months within the CPOE-CDSS at Heidelberg University Hospital were divided into 24-hour time intervals in which the alert display was correlated with associated prescription changes. Alerts were considered "persistent" if they were displayed in every consecutive 24-hour time interval due to a respective active prescription until patient discharge and were considered "absent" if they were no longer displayed during continuous prescriptions in the subsequent interval. Overall, 1670 patient cases with 11,428 alerts were analyzed. Alerts were displayed for a median of 3 (IQR 1-7) consecutive 24-hour time intervals, with the shortest alerts displayed for drug-allergy interactions and the longest alerts displayed for potentially inappropriate medication for the elderly (PIM). Among the total 11,428 alerts, 56.1% (n=6413) became absent, most commonly among alerts for drug-drug interactions (1915/2366, 80.9%) and least commonly among PIM alerts (199/499, 39.9%). This new approach to estimate alert acceptance based on event analysis can be flexibly adapted to the automated evaluation of passive, noninterruptive alerts. This enables large data sets of longitudinal patient cases to be processed, allows for the derivation of the ratios of persistent and absent alerts, and facilitates the comparison and prospective monitoring of these alerts.

Using a rule‐based decision tool for medication dose selection to improve patient safety and the need for pharmacist intervention

AbstractIntroductionAn assortment of alerts has been employed to influence provider order entry, yet many medication orders still require dose adjustment by pharmacists upon order verification.ObjectivesThe primary goals of this study were to evaluate the impact of adding rule‐based decision support to the computerized provider order entry system on the need for medication dose adjustment by a pharmacist and the occurrence of acute kidney injury (AKI) among patients.MethodsThis was a retrospective, pre‐ and post‐implementation observational study on the integration of rule‐based logic into the computerized provider order entry system to automatically select default doses and frequencies for weight‐based or renally‐cleared medications in alignment with health system guidelines. The primary end points were the proportion of medication orders that required pharmacist intervention for dose adjustment and the number of times the AKI pop‐up alert was triggered.ResultsAfter inclusion and exclusion criteria were applied to all available orders, there were 47 393 and 45 767 orders included for final analysis in the pre‐ and post‐implementation periods, respectively. The post‐implementation period showed a significant reduction in pharmacist dosing interventions, with a relative risk of 0.42 (95% confidence interval [CI]: 0.40–0.43; p &lt; 0.0001) and a reduction in AKI (relative risk = 0.58 [95% CI: 0.53–0.64; p &lt; 0.0001]).ConclusionThis study demonstrates the potential of rule‐based decision support to improve initial medication dose selection, reduce the occurrence of AKI, and reduce pharmacist workload, all without increasing alert fatigue.