Improving Guideline-Congruent Care for Chemotherapy-Induced Nausea and Vomiting Prophylaxis in Pediatric Oncology Patients

PURPOSE Chemotherapy-induced nausea and vomiting (CINV) is a very common side effect of pediatric cancer therapy. High-quality, evidence-based, pediatric-specific guidelines for prophylaxis and treatment of CINV are available. At many centers, guideline-concordant care is uncommon. We formed a multidisciplinary quality improvement team to implement guideline-concordant care for CINV prophylaxis at our center. We present the results following the first year of our interventions. METHODS We planned and implemented a multipronged approach in three key phases: (1) developing and publishing an acute CINV prophylaxis pathway, (2) education of providers, and (3) updating the computerized provider order entry system. We used iterative, sequential Plan-Do-Study-Act cycles and behavioral economic strategies to improve adherence to guideline-concordant CINV prophylaxis. We focused on aprepitant usage as a key area for improvement. RESULTS At the beginning of the study period, < 50% of patients were receiving guideline-concordant CINV prophylaxis and < 15% of eligible patients were receiving aprepitant. After 1 year, more than 60% of patients were receiving guideline-concordant care and 50% of eligible patients were receiving aprepitant. CONCLUSION We describe the development and implementation of a standardized pathway for prevention of acute CINV in pediatric oncology patients. With a multidisciplinary, multifaceted approach, we demonstrate significant improvements to guideline-congruent CINV prophylaxis.

A Study on Design, Development, Implementation, and Evaluation of Severe Drug-Drug Interaction Alert System in ICU

Background and objective: The overridden rate of Drug-Drug Interaction Alerts (DDIAs) in the Intensive Care Unit (ICU) is very high. Therefore, this study aimed to design, develop, implement, and evaluate a severe Drug-Drug Alert System (DDIAS) in ICU and measure the override rate of DDIAs. Methods This is a cross-sectional study for the design, development, implementation, and evaluation of severe DDIAs into a Computerized Provider Order Entry(CPOE) system in the ICUs of Nemazee general teaching hospitals in 2021. The patients exposed to the volume of DDIAs, acceptance and overridden of DDIAs, and usability of DDIAS have been collected. Results The knowledge base of DDIAS contains 9,809 severe DDIs. A total of 2672 medications were prescribed in the population study. The volume and acceptance rate for severe DDIAs were 81 and 97.5%, respectively. However, the override rate was 2.5%. The mean System Usability Scale (SUS) score of the DDIAS was 75. Conclusion This study demonstrated that the implementation of high-risk DDIAs at point of prescribing in ICU improved adherence to alerts. In addition, the usability of DDIAS was reasonable. Further studies are need to investigate the establishment of severe DDIAS and measure the physician's response to DDIAS on a larger scale.

Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry

Evaluation of sustainability after quality improvement (QI) projects in healthcare settings is an essential part of monitoring and future QI planning. With limitations in adopting quasi-experimental study design in real-world practice, healthcare professionals find it challenging to present the sustained effect of QI changes effectively. To provide quantitative methodological approaches for demonstrating the sustainability of QI projects for healthcare professionals, we conducted data analyses based on a QI project to improve the computerized provider order entry system to reduce patients’ dosing frequencies in Korea. Data were collected for 5 years: 24-month pre-intervention, 12-month intervention, and 24-month post-intervention. Then, analytic approaches including control chart, Analysis of Variance (ANOVA), and segmented regression were performed. The control chart intuitively displayed how the outcomes changed over the entire period, and ANOVA was used to test whether the outcomes differed between groups. Last, segmented regression analysis was conducted to evaluate longitudinal effects of interventions over time. We found that the impact of QI projects in healthcare settings should be initiated following the Plan–Do–Study–Act cycle and evaluated long-term effects while widening the scope of QI evaluation with sustainability. This study can serve as a guide for healthcare professionals to use a number of statistical methodologies in their QI evaluations.

Clinical Decision Support Systems and Computerized Provider Order Entry: Contributions from 2020

Summary Objectives: To summarize research contributions published in 2020 in the field of clinical decision support systems (CDSS) and computerized provider order entry (CPOE), and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook 2021. Methods: Two bibliographic databases were searched for papers referring to clinical decision support systems. From search results, section editors established a list of candidate best papers, which were then peer-reviewed by seven external reviewers. The IMIA Yearbook editorial committee finally selected the best papers on the basis of all reviews including the section editors’ evaluation. Results: A total of 1,919 articles were retrieved. 15 best paper candidates were selected, the reviews of which resulted in the selection of two best papers. One paper reports on the use of electronic health records to support a public health response to the COVID-19 pandemic in the United States. The second paper proposes a combination of CDSS and telemedicine as a technology-based intervention to improve the outcomes of depression as part of a cluster trial. Conclusions: As shown by the number and the variety of works related to clinical decision support, research in the field is very active. This year's selection highlighted the application of CDSS to fight COVID-19 and a combined technology-based strategy to improve the treatment of depression.

Harnessing the Electronic Health Record and Computerized Provider Order Entry Data for Resource Management During the COVID-19 Pandemic: Development of a Decision Tree (Preprint)

BACKGROUND The COVID-19 pandemic has resulted in shortages of diagnostic tests, personal protective equipment, hospital beds, and other critical resources. OBJECTIVE We sought to improve the management of scarce resources by leveraging electronic health record (EHR) functionality, computerized provider order entry, clinical decision support (CDS), and data analytics. METHODS Due to the complex eligibility criteria for COVID-19 tests and the EHR implementation–related challenges of ordering these tests, care providers have faced obstacles in selecting the appropriate test modality. As test choice is dependent upon specific patient criteria, we built a decision tree within the EHR to automate the test selection process by using a branching series of questions that linked clinical criteria to the appropriate SARS-CoV-2 test and triggered an EHR flag for patients who met our institutional persons under investigation criteria. RESULTS The percentage of tests that had to be canceled and reordered due to errors in selecting the correct testing modality was 3.8% (23/608) before CDS implementation and 1% (262/26,643) after CDS implementation (<i>P</i>&lt;.001). Patients for whom multiple tests were ordered during a 24-hour period accounted for 0.8% (5/608) and 0.3% (76/26,643) of pre- and post-CDS implementation orders, respectively (<i>P</i>=.03). Nasopharyngeal molecular assay results were positive in 3.4% (826/24,170) of patients who were classified as asymptomatic and 10.9% (1421/13,074) of symptomatic patients (<i>P</i>&lt;.001). Positive tests were more frequent among asymptomatic patients with a history of exposure to COVID-19 (36/283, 12.7%) than among asymptomatic patients without such a history (790/23,887, 3.3%; <i>P</i>&lt;.001). CONCLUSIONS The leveraging of EHRs and our CDS algorithm resulted in a decreased incidence of order entry errors and the appropriate flagging of persons under investigation. These interventions optimized reagent and personal protective equipment usage. Data regarding symptoms and COVID-19 exposure status that were collected by using the decision tree correlated with the likelihood of positive test results, suggesting that clinicians appropriately used the questions in the decision tree algorithm.

Implementation of a Computerized Provider Order Entry System in a Pediatric Hospital in Canada

The Centre Hospitalier Universitaire Sainte-Justine (Montreal, Canada) is a pediatric academic tertiary hospital that has begun the implementation of a commercial computerized provider order entry system (CPOE) in October 2019. The objectives of this paper are 1) to estimate the impact of the CPOE system on medication errors, and 2) to identify vulnerability issues related to the configuration of the CPOE system’s design. Using a pre-post implementation methodology measuring medication errors captured by clinical pharmacists revealed that the implementation of a CPOE has eliminated all prescription conformity (e.g., missing fields) and legibility errors. Pharmacists have continued to detect medication errors, especially inappropriate dosing instructions, and to intervene in similar clinical situations (medication reconciliation, deprescribing, adjusting orders). Additionally, the vulnerability analysis, based on typical clinical order test cases in an inpatient pediatric setting, highlighted the need to configure a clinical decision support system that can identify inappropriate dosing instructions for pediatric patients.