The effect of a decision support system on the incidence of prescription errors in a PICU

The PrescIT platform: An interoperable Clinical Decision Support System for ePrescription to Prevent Adverse Drug Reactions and Drug-Drug Interactions

INTRODUCTION

Preventable medication errors have been proven to cause significant public health burden, and ePrescription is a key part of the process where medication errors and adverse effects could be prevented. Information systems and "intelligent" computational approaches could provide a valuable tool to prevent such errors with profound impact in clinical practice.

OBJECTIVES

The PrescIT platform is a Clinical Decision Support System (CDSS) that aims to facilitate the prevention of adverse drug reactions (ADRs) and drug-drug interactions (DDIs) in the phase of ePrescription in Greece. The proposed platform could be relatively easily localized for use in other contexts too.

METHODS

The PrescIT platform is based on the use of Knowledge Engineering (ΚΕ) approaches, i.e., the use of Ontologies and Knowledge Graphs (KGs) developed upon openly available data sources. Open standards (i.e., RDF, OWL, SPARQL) are used for the development of the platform enabling the integration with already existing IT systems or for standalone use. The main KG is based on the use of DrugBank, MedDRA, SemMedDB and OpenPVSignal. In addition, the Business Process Management Notation (BPMN) has been used to model long-term therapeutic protocols used during the ePrescription process. Finally, the produced software has been pilot tested in three hospitals by 18 clinical professionals via in-person think-aloud sessions.

RESULTS

The PrescIT platform has been successfully integrated in a transparent fashion in a proprietary Hospital Information System (HIS), and it has also been used as a standalone application. Furthermore, it has been successfully integrated with the Greek National ePrescription system. During the pilot phase, one psychiatric therapeutic protocol was used as a testbed to collect end-users' feedback. Summarizing the feedback from the end-users, they have generally acknowledged the usefulness of such a system while also identifying some challenges in terms of usability and the overall user experience.

CONCLUSIONS

The PrescIT platform has been successfully deployed and piloted in real-world environments to evaluate its ability to support safer medication prescriptions.

Practice-enhancing publications about the medication-use process in 2022

PURPOSE

This article identifies, prioritizes, and summarizes published literature on the medication-use process (MUP) from calendar year 2022 that can impact health-system pharmacy daily practice. The MUP is the foundational system that provides the framework for safe medication utilization within the healthcare environment. The MUP is defined in this article as having the following components: prescribing/transcribing, dispensing, administration, and monitoring. Articles evaluating at least one step of the MUP were assessed for their usefulness toward practice improvement.

SUMMARY

A PubMed search was conducted in January 2023 for articles published in calendar year 2022 using targeted Medical Subject Headings (MeSH) keywords, and searches of the table of contents of selected pharmacy journals were conducted, providing a total of 6,213 articles. A thorough review identified 69 potentially practice-enhancing articles: 13 for prescribing/transcribing, 13 for dispensing, 5 for administration, and 38 for monitoring. Practice trends discussed in the articles are briefly summarized, with a mention of their importance within health-system pharmacy. The articles are listed and summarized in tables for further review and evaluation.

CONCLUSION

It is important to routinely review the published literature and to incorporate significant findings into daily practice. This article assists in identifying and summarizing the most impactful publications. Health-system pharmacists have an active role in improving the MUP in their institution, and awareness of the significant published studies can assist in changing practice at the institutional level.

What characteristics of clinical decision support system implementations lead to adoption for regular use? A scoping review

INTRODUCTION

Digital healthcare innovation has yielded many prototype clinical decision support (CDS) systems, however, few are fully adopted into practice, despite successful research outcomes. We aimed to explore the characteristics of implementations in clinical practice to inform future innovation.

METHODS

Web of Science, Trip Database, PubMed, NHS Digital and the BMA website were searched for examples of CDS systems in May 2022 and updated in June 2023. Papers were included if they reported on a CDS giving pathway advice to a clinician, adopted into regular clinical practice and had sufficient published information for analysis. Examples were excluded if they were only used in a research setting or intended for patients. Articles found in citation searches were assessed alongside a detailed hand search of the grey literature to gather all available information, including commercial information. Examples were excluded if there was insufficient information for analysis. The normalisation process theory (NPT) framework informed analysis.

RESULTS

22 implemented CDS projects were included, with 53 related publications or sources of information (40 peer-reviewed publications and 13 alternative sources). NPT framework analysis indicated organisational support was paramount to successful adoption of CDS. Ensuring that workflows were optimised for patient care alongside iterative, mixed-methods implementation was key to engaging clinicians.

CONCLUSION

Extensive searches revealed few examples of CDS available for analysis, highlighting the implementation gap between research and healthcare innovation. Lessons from included projects include the need for organisational support, an underpinning mixed-methods implementation strategy and an iterative approach to address clinician feedback.

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.

Monitoring for adverse drug events of high-risk medications with a computerized clinical decision support system: a prospective cohort study

Monitoring is recommended to prevent severe adverse drug events, but such examinations are often missed. To increase the number of monitoring that should be ordered for high-risk medications, we introduced a clinical decision support system (CDSS) that alerts and orders the monitoring for high-risk medications in an outpatient setting. We conducted a 2-year prospective cohort study at a tertiary care teaching hospital before (phase 1) and after (phase 2) the activation of a CDSS. The CDSS automatically provided alerts for liver function tests for vildagliptin, thyroid function tests for immune checkpoint inhibitors (ICIs) and multikinase inhibitors (MKIs), and a slit-lamp examination of the eyes for oral amiodarone when outpatients were prescribed the medications but not examined for a fixed period. The order of laboratory tests automatically appeared if alert was accepted. The alerts were hidden and did not appear on the display before activation of the CDSS. The outcomes were the number of prescriptions with alerts and examinations. During the study period, 330 patients in phase 1 and 307 patients in phase 2 were prescribed vildagliptin, 20 patients in phase 1 and 19 patients in phase 2 were prescribed ICIs or MKIs, and 72 patients in phase 1 and 66 patients in phase 2 were prescribed oral amiodarone. The baseline characteristics were similar between the phases. In patients prescribed vildagliptin, the proportion of alerts decreased significantly (38% vs 27%, P < 0.0001), and the proportion of examinations increased significantly (0.9% vs 4.0%, P < 0.0001) after activation of the CDSS. In patients prescribed ICIs or MKIs, the proportion of alerts decreased significantly (43% vs 11%, P < 0.0001), and the proportion of examinations increased numerically, but not significantly (2.6% vs 7.0%, P = 0.13). In patients prescribed oral amiodarone, the proportion of alerts decreased (86% vs 81%, P = 0.055), and the proportion of examinations increased (2.2% and 3.0%, P = 0.47); neither was significant. The CDSS has potential to increase the monitoring for high-risk medications. Our study also highlighted the limited acceptance rate of monitoring by CDSS. Further studies are needed to explore the generalizability to other medications and the cause of the limited acceptance rates among physicians.

Assessing Serious Spinal Pathology Using Bayesian Network Decision Support: Development and Validation Study

BACKGROUND

Identifying and managing serious spinal pathology (SSP) such as cauda equina syndrome or spinal infection in patients presenting with low back pain is challenging. Traditional red flag questioning is increasingly criticized, and previous studies show that many clinicians lack confidence in managing patients presenting with red flags. Improving decision-making and reducing the variability of care for these patients is a key priority for clinicians and researchers.

OBJECTIVE

We aimed to improve SSP identification by constructing and validating a decision support tool using a Bayesian network (BN), which is an artificial intelligence technique that combines current evidence and expert knowledge.

METHODS

A modified RAND appropriateness procedure was undertaken with 16 experts over 3 rounds, designed to elicit the variables, structure, and conditional probabilities necessary to build a causal BN. The BN predicts the likelihood of a patient with a particular presentation having an SSP. The second part of this study used an established framework to direct a 4-part validation that included comparison of the BN with consensus statements, practice guidelines, and recent research. Clinical cases were entered into the model and the results were compared with clinical judgment from spinal experts who were not involved in the elicitation. Receiver operating characteristic curves were plotted and area under the curve were calculated for accuracy statistics.

RESULTS

The RAND appropriateness procedure elicited a model including 38 variables in 3 domains: risk factors (10 variables), signs and symptoms (17 variables), and judgment factors (11 variables). Clear consensus was found in the risk factors and signs and symptoms for SSP conditions. The 4-part BN validation demonstrated good performance overall and identified areas for further development. Comparison with available clinical literature showed good overall agreement but suggested certain improvements required to, for example, 2 of the 11 judgment factors. Case analysis showed that cauda equina syndrome, space-occupying lesion/cancer, and inflammatory condition identification performed well across the validation domains. Fracture identification performed less well, but the reasons for the erroneous results are well understood. A review of the content by independent spinal experts backed up the issues with the fracture node, but the BN was otherwise deemed acceptable.

CONCLUSIONS

The RAND appropriateness procedure and validation framework were successfully implemented to develop the BN for SSP. In comparison with other expert-elicited BN studies, this work goes a step further in validating the output before attempting implementation. Using a framework for model validation, the BN showed encouraging validity and has provided avenues for further developing the outputs that demonstrated poor accuracy. This study provides the vital first step of improving our ability to predict outcomes in low back pain by first considering the problem of SSP.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/21804.

Prescribing errors in children: what is the impact of a computerized physician order entry?

Prescribing errors represent a safety risk for hospitalized patients, especially in pediatrics. Computerized physician order entry (CPOE) might reduce prescribing errors, although its effect has not yet been thoroughly studied on pediatric general wards. This study investigated the impact of a CPOE on prescribing errors in children on general wards at the University Children's Hospital Zurich. We performed medication reviews on a total of 1000 patients before and after the implementation of a CPOE. The CPOE included limited clinical decision support (CDS) such as drug-drug interaction check and checks for duplicates. Prescribing errors, their type according to the PCNE classification, their severity (adapted NCC MERP index), as well as the interrater reliability (Cohen's kappa), were analyzed. Potentially harmful errors were significantly reduced from 18 errors/100 prescriptions (95% CI: 17-20) to 11 errors/100 prescriptions (95% CI: 9-12) after CPOE implementation. A large number of errors with low potential for harm (e.g., "missing information") was reduced after the introduction of the CPOE, and consequently, the overall severity of potential harm increased post-CPOE. Despite general error rate reduction, medication reconciliation problems (PCNE error 8), such as drugs prescribed on paper as well as electronically, significantly increased after the introduction of the CPOE. The most common pediatric prescribing errors, the dosing errors (PCNE errors 3), were not altered on a statistically significant level after the introduction of the CPOE. Interrater reliability showed moderate agreement (Κ = 0.48).  Conclusion: Patient safety increased by reducing the rate of prescribing errors after CPOE implementation. The reason for the observed increase in medication reconciliation problems might be the hybrid system with remaining paper prescriptions for special medication. The lacking effect on dosing errors could be explained by the fact that a web application CDS covering dosing recommendations (PEDeDose) was already in use before the implementation of the CPOE. Further investigations should focus on eliminating hybrid systems, interventions to increase the usability of the CPOE, and full integration of CDS tools such as automated dose checks into the CPOE. What is Known: • Prescribing errors, especially dosing errors, are a common safety threat for pediatric inpatients. •The introduction of a CPOE may reduce prescribing errors, though pediatric general wards are poorly studied. What is New: •To our knowledge, this is the first study on prescribing errors in pediatric general wards in Switzerland investigating the impact of a CPOE. •We found that the overall error rate was significantly reduced after the implementation of the CPOE. The severity of potential harm was higher in the post-CPOE period, which implies that low-severity errors were substantially reduced after CPOE implementation. Dosing errors were not reduced, but missing information errors and drug selection errors were reduced. On the other hand, medication reconciliation problems increased.

Potential Drug-Related Problems in Pediatric Patients-Describing the Use of a Clinical Decision Support System at Pharmacies in Sweden

The clinical support system Electronic Expert Support (EES) is available at all pharmacies in Sweden to examine electronic prescriptions when dispensing to prevent drug-related problems (DRPs). DRPs are common, and result in patient suffering and substantial costs for society. The aim of this research was to study the use of EES for the pediatric population (ages 0-12 years), by describing what types of alerts are generated for potential DRPs, how they are handled, and how the use of EES has changed over time. Data on the number and categories of EES analyses, alerts, and resolved alerts were provided by the Swedish eHealth Agency. The study shows that the use of EES has increased. The most common type of alert for a potential DRP among pediatric patients was regarding high doses in children (30.3% of all alerts generated). The most common type of alert for a potential DRP that was resolved among pediatrics was therapy duplication (4.6% of the alerts were resolved). The most common reason for closing an alert was dialogue with patient for verification of the treatment (66.3% of all closed alerts). Knowledge of which type of alerts are the most common may contribute to increased prescriber awareness of important potential DRPs.

Artificial intelligence-based internet hospital pharmacy services in China: Perspective based on a case study

Background: Recently, internet hospitals have been emerging in China, saving patients time and money during the COVID-19 pandemic. In addition, pharmacy services that link doctors and patients are becoming essential in improving patient satisfaction. However, the existing internet hospital pharmacy service mode relies primarily on manual operations, making it cumbersome, inefficient, and high-risk.

Objective: To establish an internet hospital pharmacy service mode based on artificial intelligence (AI) and provide new insights into pharmacy services in internet hospitals during the COVID-19 pandemic.

Methods: An AI-based internet hospital pharmacy service mode was established. Initially, prescription rules were formulated and embedded into the internet hospital system to review the prescriptions using AI. Then, the “medicine pick-up code,” which is a Quick Response (QR) code that represents a specific offline self-pick-up order, was created. Patients or volunteers could pick up medications at an offline hospital or drugstore by scanning the QR code through the window and wait for the dispensing machine or pharmacist to dispense the drugs. Moreover, the medication consultation function was also operational.

Results: The established internet pharmacy service mode had four major functional segments: online drug catalog search, prescription preview by AI, drug dispensing and distribution, and AI-based medication consultation response. The qualified rate of AI preview was 83.65%. Among the 16.35% inappropriate prescriptions, 49% were accepted and modified by physicians proactively and 51.00% were passed after pharmacists intervened. The “offline self-pick-up” mode was preferred by 86% of the patients for collecting their medication in the internet hospital, which made the QR code to be fully applied. A total of 426 medication consultants were served, and 48.83% of them consulted outside working hours. The most frequently asked questions during consultations were about the internet hospital dispensing process, followed by disease diagnosis, and patient education. Therefore, an AI-based medication consultation was proposed to respond immediately when pharmacists were unavailable.

Conclusion: The established AI-based internet hospital pharmacy service mode could provide references for pharmacy departments during the COVID-19 pandemic. The significance of this study lies in ensuring safe/rational use of medicines and raising pharmacists’ working efficiency.