Order Entry System Research Articles

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.

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.

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.

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’ 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’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.

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.

HOSPITAL INFORMATION SYSTEM

Abstract:- hospital information system (his) has evolved as an integration system of order entry systems, an administrative system, and departmental subsystems within a hospital. It has become more and more necessary for every health care staff in a hospital to use a computer terminal at almost everyday’s works. Under this circumstances, his is expected to provide the staffs with various, world-wide information for decision making and better communication environment which can be used just on the terminals for everyday’s works. The implementation of the hospital information system contributes to the hospital informationization construction and is an important part of China’s medical system reform. Informatization construction has been carried out in hospitals across the country, but high investment does not necessarily lead to outstanding performance results, and there is a paradox of IT productivity. This paper analyzes the influence mechanism of the hospital information system on doctors’ work performance from two aspects: personal perception of system and individual self-efficacy. Empirical studies have found that doctors’ perception of usefulness, perception system customization and self-efficacy have a significant positive impact on doctors’ job performance during the use of information systems. This article will deepen and enrich the understanding of hospital information system performance theory, which will help further research on information system performance in the future.

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.

Adoption by clinicians of electronic order communications in NHS secondary care: a descriptive account

BackgroundDue to the rapid advancement in information technology, changes to communication modalities are increasingly implemented in healthcare. One such modality is Computerised Provider Order Entry (CPOE) systems which replace paper,...

Interprofessional Evaluation of a Medication Clinical Decision Support System Prior to Implementation.

Computerized physician order entry (CPOE) and clinical decision support systems (CDSS) are widespread due to increasing digitalization of hospitals. They can be associated with reduced medication errors and improved patient safety, but also with well-known risks (e.g., overalerting, nonadoption). Therefore, we aimed to evaluate a commonly used CDSS containing Medication-Safety-Validators (e.g., drug-drug interactions), which can be locally activated or deactivated, to identify limitations and thereby potentially optimize the use of the CDSS in clinical routine. Within the implementation process of Meona (commercial CPOE/CDSS) at a German University hospital, we conducted an interprofessional evaluation of the CDSS and its included Medication-Safety-Validators following a defined algorithm: (1) general evaluation, (2) systematic technical and content-related validation, (3) decision of activation or deactivation, and possibly (4) choosing the activation mode (interruptive or passive). We completed the in-depth evaluation for exemplarily chosen Medication-Safety-Validators. Moreover, we performed a survey among 12 German University hospitals using Meona to compare their configurations. Based on the evaluation, we deactivated 3 of 10 Medication-Safety-Validators due to technical or content-related limitations. For the seven activated Medication-Safety-Validators, we chose the interruptive option ["PUSH-(&PULL)-modus"] four times (4/7), and a new, on-demand option ["only-PULL-modus"] three times (3/7). The site-specific configuration (activation or deactivation) differed across all participating hospitals in the survey and led to varying medication safety alerts for identical patient cases. An interprofessional evaluation of CPOE and CDSS prior to implementation in clinical routine is crucial to detect limitations. This can contribute to a sustainable utilization and thereby possibly increase medication safety.

Reduced prevalence of drug-related problems in psychiatric inpatients after implementation of a pharmacist-supported computerized physician order entry system - a retrospective cohort study.

In 2021, a computerized physician order entry (CPOE) system with an integrated clinical decision support system (CDSS) was implemented at a tertiary care center for the treatment of mental health conditions in Lübeck, Germany. To date, no study has been reported on the types and prevalence of drug-related problems (DRPs) before and after CPOE implementation in a psychiatric inpatient setting. The aim of this retrospective before-and-after cohort study was to investigate whether the implementation of a CPOE system with CDSS accompanied by the introduction of regular medication plausibility checks by a pharmacist led to a decrease of DRPs during hospitalization and unsolved DRPs at discharge in psychiatric inpatients. Medication charts and electronic patient records of 54 patients before (cohort I) and 65 patients after (cohort II) CPOE implementation were reviewed retrospectively by a clinical pharmacist. All identified DRPs were collected and classified based on 'The PCNE Classification V9.1', the German database DokuPIK, and the 'NCC MERP Taxonomy of Medication Errors'. 325 DRPs were identified in 54 patients with a mean of 6 DRPs per patient and 151.9 DRPs per 1000 patient days in cohort I. In cohort II, 214 DRPs were identified in 65 patients with a mean of 3.3 DRPs per patient and 81.3 DRPs per 1000 patient days. The odds of having a DRP were significantly lower in cohort II (OR=0.545, 95% CI 0.412-0.721, p<0.001). The most frequent DRP in cohort I was an erroneous prescription (n=113, 34.8%), which was significantly reduced in cohort II (n=12, 5.6%, p<0.001). During the retrospective in-depth review, more DRPs were identified than during the daily plausibility analyses. At hospital discharge, patients had significantly less unsolved DRPs in cohort II than in cohort I. The implementation of a CPOE system with an integrated CDSS reduced the overall prevalence of DRPs, especially of prescription errors, and led to a smaller rate of unsolved DRPs in psychiatric inpatients at hospital discharge. Not all DRPs were found by plausibility analyses based on the medication charts. A more interactive and interdisciplinary patient-oriented approach might result in the resolution of more DRPs.

We Ask and Listen: A Group-Wide Retrospective Survey on Satisfaction with Digital Medication Software.

Computerized physician order entry (CPOE) and clinical decision support systems (CDSS) are used internationally since the 1980s. These systems reduce costs, enhance drug therapy safety, and improve quality of care. A few years ago, there was a growing effort to digitize the healthcare sector in Germany. Implementing such systems like CPOE-CDSS requires training for effective adoption and, more important, acceptance by the users. Potential improvements for the software and implementation process can be derived from the users' perspective. The implementation process is globally relevant and applicable across professions due to the constant advancement of digitalization. The study assessed the implementation of medication software and overall satisfaction. In an anonymous voluntary online survey, physicians and nursing staff were asked about their satisfaction with the new CPOE-CDSS. The survey comprised single-choice queries on a Likert scale, categorizing into general information, digital medication administration, drug safety, and software introduction. In addition multiple-choice questions are mentioned. Data analysis was performed using Microsoft Office Excel 2016 and GraphPad PRISM 9.5.0. Nurses and physicians' satisfaction with the new software increased with usage hours. The software's performance and loading times have clearly had a negative impact, which leads to a low satisfaction of only 20% among physicians and 17% among nurses. 53% of nurses find the program's training period unsuitable for their daily use, while 57% of physicians approve the training's scope for their professional group. Both professions agree that drug-related problems are easier to detect using CPOE-CDSS, with 76% of nurses and 75% of physicians agreeing. The study provides unbiased feedback on software implementation. In conclusion, digitizing healthcare requires managing change, effective training, and addressing software functionality concerns to ensure improved medication safety and streamlined processes. Interfaces, performance optimization, and training remain crucial for software acceptance and effectiveness.

Novel data visualization method to measure alert efficiency in computerized physician order entry (CPOE) system

ObjectivesTo introduce a novel visualization technique for evaluating the efficacy of clinical decision support system (CDSS) alerts as perceived by physicians and to differentiate between various alert categories for optimization. MethodsWe developed a visualization method, which segments into four distinct zones: Appropriate (+/+), Over-frequent yet Effective (−/+), Potentially Problematic (−/−), and Less Effective but Acceptably Frequent (+/−). Alerts from a 908-bed academic medical center in Northern Taiwan were collected over two years and classified using this technique, along with three perspectives: Safety, Completeness, and Response. ResultsWe collected the viewpoints of 72 clinical physicians on the system's top 20 most frequent alerts. The proposed visualization technique offers a user-centric, adaptable method for assessing CDSS alerts. Roughly five alerts were categorized as Potentially Problematic, whereas another five were deemed Appropriate. Intriguingly, certain alerts, while not beneficial for patient safety, were found to assist physicians in completing clinical workflows. ConclusionsThis approach, emphasizing visual clarity and adaptability, diverges from traditional methods that lean heavily on expert opinions or statistics. It paves the way for diverse assessment perspectives, furnishing healthcare institutions with a valuable tool to improve CDSS alert systems, ensuring a harmonious balance between user efficiency and patient safety.

Managing Talent Among Healthcare Human Resource: Strategies for a New Normal

The healthcare industry is always susceptible to diverse changes and technology advancements. This mandates that the sector updates its skill set that can help in complying with compliance and regulatory requirements. COVID-19 has added to the complexity by bringing in changes in the working environment, healthcare professionals’ perceptions and patients’ expectations. It has clearly shown that digital technologies are rapidly revolutionising healthcare and also having an impact on human resources. Talent management in the changing scenario is a must for all organisations looking towards resilience, sustainability and gaining a competitive advantage over others. Purpose: The purpose of the current study is to explore the factors that have influenced healthcare talent management during the pandemic, identify the challenges and put forth a strategy for post-pandemic talent management. Design/Methodology/Approach: A literature-based search for identifying articles related to talent management in healthcare during the pandemic, the challenges and lessons learned has been carried out. Findings: Based on the findings, it is suggested that while traditional ways of human resource planning are still applicable, it is imperative that digital tools be utilised for all activities especially talent management. Digital tools such as electronic medical records, telemedicine, computerised physician order entry systems are becoming an inbuilt culture in healthcare. So, while selecting, developing and retaining talent, the existence of digital skill set, capability to work with the tools or learn and adopt should be considered as a mandatory requirement.

Medication Prescribing Errors on a Surgery Service - Addressing the Gap with a Curriculum for Surgery Residents: A Prospective Observational Study.

Educational interventions with proven effectiveness to reduce medication prescribing errors are currently lacking. Our objective was to implement and assess the effectiveness of a curriculum to reduce medication prescribing errors on a surgery service. This was a prospective observational cohort study at a Canadian academic hospital without an electronic order entry system. A pharmacist-led medication prescribing curriculum for surgery residents was developed and implemented over 2 days (2 h/day) in July 2019. Thirteen (76%) out of 17 surgery residents contributed pre-implementation data, while 13 (81%) out of 16 surgery residents contributed post-implementation data. Medication prescribing errors were tracked for 12 months pre-implementation and 6 months post-implementation. Errors were classified as prescription writing (PW) or decision making (DM). There were a total of 1050 medication prescribing errors made in the pre-implementation period with 615 (59%) PW errors and 435 (41%) DM. There were a mean of 87.5 (SD = 14.6) total medication prescribing errors per month in the pre-implementation period with 51.3 (11.9) PW and 36.3 (6.0) DM errors. There were a total of 472 medication prescribing errors made in the post-implementation period with 260 (55%) PW and 212 (45%) DM errors. There were a mean of 78.7 (10.3) total medication prescribing errors per month in the post-implementation period with 43.3 (9.5) PW and 35.3 (4.2) DM errors. In the first quarter of the academic year, there were significantly fewer mean total errors per month post-implementation versus pre-implementation (77.7(12.7) versus 107.3(8.1); P = .035), with significantly fewer PW errors per month (40.7(13.2) versus 68.7(9.3); P = .046) and no difference in DM errors per month (37.0(2.0) versus 38.7(5.7);P = .671). There were no differences noted in the second quarter of the academic year. Medication prescribing errors occurred from PW and DM. Medication prescribing curriculum decreased PW errors; however, a continued education program is warranted as the effect diminished over time.

Diuretic drug utilization in neonates: a French prescription database analysis.

Background: The use of diuretics is extremely common in infants cared for in neonatal wards, despite the lack of proven efficacy for many conditions. The main objective of this study was to assess the rate of diuretics exposure in a multicenter French cohort. The secondary objectives were to describe the evolution of this exposure over time, the indications, the prescription practices, and the exposure rates among centers. Methods: An observational study was conducted in 40 Level 3 French neonatal intensive care units using the same computerized order-entry system. Neonates hospitalized between January 2017 to December 2021 with a corrected age between 24 and 44weeks of gestation at admission were eligible. Results: A total of 86,032 patients were included. The exposure rate was 8.5%, more specifically 29.4% for children born at < 32weeks of gestation and 3.7% for neonates born at term. There was no significant variation over the study period, but the exposure ranged from 2.4% to 26.5% depending on the center. The main drugs prescribed were furosemide, spironolactone and dopamine with a diuretic purpose. The main indications were "fluid retention," and to a lesser extent "bronchopulmonary dysplasia" and "post-transfusion." For furosemide, the first exposure occurred in mean at 16.5 (±17.8) days of life, mean duration of exposure was 6.2 (±9.5) days, and the cumulative dose was in mean 10.7 (23.9) mg/kg. Conclusion: Diuretic prescription practices vary between centers. The administration of these drugs is often non-evidence based, doses and duration of treatment easily exceed toxic thresholds.

Incapacité des logiciels d’aide à la prescription hospitaliers à sécuriser l’utilisation des solutions concentrées de potassium injectable

To determine whether hospital computerised physician order entry (CPOE) systems contribute to securing intravenous potassium chloride (KCl) prescriptions with reference to the recommendations issued by French healthcare agencies. We sent a questionnaire to the members of the Association pour le Digital et l'Information en Pharmacie. More than three quarters of the 84 responses received involving 23 CPOE systems indicate that it is possible to: prescribe an ampoule of concentrated potassium chloride 10% 10mL intravenously without any diluents (80%); prescribe 4g of KCl in a bag of 500mL of NaCl 0,9% (98%); prescribe a solution that contains 6 grams of KCl per liter (94%); prescribe the administration of an injectable ampoule orally by means of a free text comment (83%). Nearly half of the responses indicate that it is possible to prescribe: concentrated KCl ampoules as administration solvent (50%); an injectable vial to be administered by oral route (52%). At least 23 hospital CPOE systems are unable to secure the prescriptions of injectable KCl. This finding lifts the veil on an unthought, namely the role of CPOE systems in securing high-risk medications. In order to solve this problem, it should be mandatory that health information technology vendors pay particular attention to these drugs. With regard to injectable KCl, the utilisation of a dilution vehicle, maximum concentration and maximum infusion flow rate are the first four constraints to be satisfied.

Psychometric Properties of a Machine Learning-Based Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Observational Study.

Medication adherence plays a critical role in controlling the evolution of chronic disease, as low medication adherence may lead to worse health outcomes, higher mortality, and morbidity. Assessment of their patients' medication adherence by clinicians is essential for avoiding inappropriate therapeutic intensification, associated health care expenditures, and the inappropriate inclusion of patients in time- and resource-consuming educational interventions. In both research and clinical practices the most extensively used measures of medication adherence are patient-reported outcome measures (PROMs), because of their ability to capture subjective dimensions of nonadherence. Machine learning (ML), a subfield of artificial intelligence, uses computer algorithms that automatically improve through experience. In this context, ML tools could efficiently model the complexity of and interactions between multiple patient behaviors that lead to medication adherence. This study aimed to create and validate a PROM on medication adherence interpreted using an ML approach. This cross-sectional, single-center, observational study was carried out a French teaching hospital between 2021 and 2022. Eligible patients must have had at least 1 long-term treatment, medication adherence evaluation other than a questionnaire, the ability to read or understand French, an age older than 18 years, and provided their nonopposition. Included adults responded to an initial version of the PROM composed of 11 items, each item being presented using a 4-point Likert scale. The initial set of items was obtained using a Delphi consensus process. Patients were classified as poorly, moderately, or highly adherent based on the results of a medication adherence assessment standard used in the daily practice of each outpatient unit. An ML-derived decision tree was built by combining the medication adherence status and PROM responses. Sensitivity, specificity, positive and negative predictive values (NPVs), and global accuracy of the final 5-item PROM were evaluated. We created an initial 11-item PROM with a 4-point Likert scale using the Delphi process. After item reduction, a decision tree derived from 218 patients including data obtained from the final 5-item PROM allowed patient classification into poorly, moderately, or highly adherent based on item responses. The psychometric properties were 78% (95% CI 40%-96%) sensitivity, 71% (95% CI 53%-85%) specificity, 41% (95% CI 19%-67%) positive predictive values, 93% (95% CI 74%-99%) NPV, and 70% (95% CI 55%-83%) accuracy. We developed a medication adherence tool based on ML with an excellent NPV. This could allow prioritization processes to avoid referring highly adherent patients to time- and resource-consuming interventions. The decision tree can be easily implemented in computerized prescriber order-entry systems and digital tools in smartphones. External validation of this tool in a study including a larger number of patients with diseases associated with low medication adherence is required to confirm its use in analyzing and assessing the complexity of medication adherence.