Initial Results of the American Burn Association (ABA) Observational Multi-Center Evaluation on the Effectiveness of the Burn Navigator

The objective of this multi-center observational study was to evaluate resuscitation volumes and outcomes of patients who underwent fluid resuscitation utilizing the Burn Navigator (BN), a resuscitation clinical decision support tool. Two analyses were performed: examination of the first 24 hours of resuscitation, and the first 24 hours post-burn regardless of when the resuscitation began, to account for patients who presented in a delayed fashion. Patients were classified as having followed the BN (FBN) if all hourly fluid rates were within ±20 mL of BN recommendations for that hour at least 83% of the time, otherwise they were classified as not having followed BN (NFBN). Analysis of resuscitation volumes for FBN patients in the first 24 hours resulted in average volumes for primary crystalloid) and total fluids administered of 4.07 ± 1.76 mL/kg/TBSA (151.48 ± 77.46 mL/kg), and 4.68 ± 2.06 mL/kg/TBSA (175.01 ± 92.22 mL/kg), respectively. Patients who presented in a delayed fashion revealed average volumes for primary and total fluids of 5.28 ± 2.54 mL/kg/TBSA (201.11 ± 106.53 mL/kg), 6.35 ± 2.95 mL/kg/TBSA (244.08 ± 133.5 mL/kg), respectively. There was a significant decrease in the incidence of burn shock in the FBN group (p< 0.05). This study shows that the BN provides comparable resuscitation volumes of primary crystalloid fluid to the Parkland Formula, recommends total fluid infusion less than the Ivy Index, and was associated with a decreased incidence of burn shock. Early initiation of the BN device resulted in lower overall fluid volumes.

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

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

Reducing Delays in Diagnosing Primary Immunodeficiency Through the Development and Implementation of a Clinical Decision Support Tool: A Study Protocol (Preprint)

BACKGROUND Primary immunodeficiencies are a set of heterogeneous chronic disorders characterized by immune dysfunction. They are diagnostically challenging because of their clinical heterogeneity, knowledge gaps among primary care physicians, and continuing shortages of clinically trained immunologists. As a result, patients with undiagnosed primary immunodeficiencies are at increased risk for recurrent infections, cancers, and autoimmune diseases. OBJECTIVE This article outlines a quality improvement protocol to develop and implement a clinical decision support tool that helps reduce delays in diagnosing primary immunodeficiencies. METHODS We will develop and implement a clinical decision support tool for the identification of underlying primary immunodeficiencies among patients who receive primary care through a health care provider at the University of Iowa Hospitals and Clinics. The clinical decision support tool will function through an algorithm that is based on the Immune Deficiency Foundation’s 10 Warning Signs for Primary Immunodeficiency. Through the course of a year, we will use Lean Six Sigma principles and the DMAIC (Define-Measure-Analyze-Improve-Control) Framework to guide the project. The primary measure is the number of newly diagnosed primary immunodeficiency patients per month. Secondary measures include: (1) the number of new patients identified by the CDS at high risk for PI, (2) the number of new PI cases in which IVIG or rotating antibiotics are started, (3) the cost of evaluation of each patient identified by CDS tool at high risk for PIs, (4) the number of new consults not diagnosed with a PI, and (5) patient satisfaction with the process of referral to Immunology Clinic. RESULTS This study was determined to be non-human subjects research by the Institutional Review Board at the University of Iowa. Data collection will begin in August 2021. CONCLUSIONS The development and implementation of a clinical decision support tool is a promising approach to identifying patients with underlying primary immunodeficiency. This protocol assesses whether such an approach will be able to achieve its objective of reducing diagnostic delays. The disciplined approach, using Lean Six Sigma and the Define-Measure-Analyze-Improve-Control framework, will guide the implementation to maximize opportunities for success.

Utilization and uptake of the UpToDate clinical decision support tool at the Makerere University College of Health Sciences (MakCHS), Uganda

Background: The use of point-of-care, evidence-based tools is becoming increasingly popular. They can provide easy-to- use, high-quality information which is regularly updated and has been shown to improve clinical outcomes. Integrating such tools into clinical practice is an important component of improving the quality of health care. However, because such tools are rarely used in resource-limited settings, there is limited research on uptake especially among medical students. Objective: This paper explores the uptake of one such tool, Up-To-Date, when provided free of cost at a medical school in Africa. Methods: In partnership with the Better Evidence at Ariadne Labs free access to UpToDate was granted through the MakCHS IP address. On-site librarians facilitated training sessions and spread awareness of the tool. Usage data was aggre- gated, based on log ins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows evidence of meaningful usage, with 43,043 log ins and 15,591 registrations between August 2019 and August 2020. The most common topics viewed were in obstetrics and gynecology, pediatrics, drug information, and infectious diseases. Access occurred mainly through the mobile phone app. Conclusion: Findings show usage by various user categories, but with inconsistent uptake and low usage. Librarians can draw upon these results to encourage institutions to support uptake of point-of-care tools in clinical practice. Keywords: UpToDate clinical decision support tool; Makerere University College of Health Sciences; Uganda.