Dashboard visualizations: Supporting real-time throughput decision-making

Early development of local data dashboards to depict the substance use care cascade for youth involved in the legal system: qualitative findings from end users

INTRODUCTION

Rates of substance use are high among youth involved in the legal system (YILS); however, YILS are less likely to initiate and complete substance use treatment compared to their non legally-involved peers. There are multiple steps involved in connecting youth to needed services, from screening and referral within the juvenile legal system to treatment initiation and completion within the behavioral health system. Understanding potential gaps in the care continuum requires data and decision-making from these two systems. The current study reports on the development of data dashboards that integrate these systems' data to help guide decisions to improve substance use screening and treatment for YILS, focusing on end-user feedback regarding dashboard utility.

METHODS

Three focus groups were conducted with n = 21 end-users from juvenile legal systems and community mental health centers in front-line positions and in decision-making roles across 8 counties to gather feedback on an early version of the data dashboards; dashboards were then modified based on feedback.

RESULTS

Qualitative analysis revealed topics related to (1) important aesthetic features of the dashboard, (2) user features such as filtering options and benchmarking to compare local data with other counties, and (3) the centrality of consistent terminology for data dashboard elements. Results also revealed the use of dashboards to facilitate collaboration between legal and behavioral health systems.

CONCLUSIONS

Feedback from end-users highlight important design elements and dashboard utility as well as the challenges of working with cross-system and cross-jurisdiction data.

Diagnosing an overcrowded emergency department from its Electronic Health Records

Emergency department overcrowding is a complex problem that persists globally. Data of visits constitute an opportunity to understand its dynamics. However, the gap between the collected information and the real-life clinical processes, and the lack of a whole-system perspective, still constitute a relevant limitation. An analytical pipeline was developed to analyse one-year of production data following the patients that came from the ED (n = 49,938) at Uppsala University Hospital (Uppsala, Sweden) by involving clinical experts in all the steps of the analysis. The key internal issues to the ED were the high volume of generic or non-specific diagnoses from non-urgent visits, and the delayed decision regarding hospital admission caused by several imaging assessments and lack of hospital beds. Furthermore, the external pressure of high frequent re-visits of geriatric, psychiatric, and patients with unspecified diagnoses dramatically contributed to the overcrowding. Our work demonstrates that through analysis of production data of the ED patient flow and participation of clinical experts in the pipeline, it was possible to identify systemic issues and directions for solutions. A critical factor was to take a whole systems perspective, as it opened the scope to the boundary effects of inflow and outflow in the whole healthcare system.

Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants

Background: Patent ductus arteriosus (PDA) is a prevalent congenital heart defect in premature infants, associated with significant morbidity and mortality. Accurate and timely diagnosis of PDA is crucial, given the vulnerability of this population. Methods: We introduce an artificial intelligence (AI)-based PDA diagnostic support system designed to assist medical professionals in diagnosing PDA in premature infants. This study utilized electronic health record (EHR) data from 409 premature infants spanning a decade at Severance Children's Hospital. Our system integrates a data viewer, data analyzer, and AI-based diagnosis supporter, facilitating comprehensive data presentation, analysis, and early symptom detection. Results: The system's performance was evaluated through diagnostic tests involving medical professionals. This early detection model achieved an accuracy rate of up to 84%, enabling detection up to 3.3 days in advance. In diagnostic tests, medical professionals using the system with the AI-based diagnosis supporter outperformed those using the system without the supporter. Conclusions: Our AI-based PDA diagnostic support system offers a comprehensive solution for medical professionals to accurately diagnose PDA in a timely manner in premature infants. The collaborative integration of medical expertise and technological innovation demonstrated in this study underscores the potential of AI-driven tools in advancing neonatal diagnosis and care.

Quality Dashboards in Hospital Settings: A Systematic Review With Implications for Nurses

BACKGROUND

Dashboards visually display quality and safety data to aid nurses in making informed decisions.

PURPOSE

This systematic review evaluated quality improvement (QI) dashboard characteristics associated with interventions to improve patient outcomes and positive end-user evaluation.

METHODS

Literature was searched from 2012 to 2022 in PubMed, CINAHL, Scopus, MEDLINE, and Google Scholar.

RESULTS

Sixteen articles were included. Varied dashboard characteristics were noted, with mixed patient outcomes and end-user responses. Graphs and tabular presentations were associated with improved patient outcomes, whereas graphs were associated with end-user satisfaction. Benchmarks were noted with improved patient outcomes but not end-user satisfaction. Interactive dashboards were important for end users and improved patient outcomes.

CONCLUSION

Nurses can find dashboards helpful in guiding QI projects. Dashboards may include graphs and/or tables, benchmarks, and interactivity but should be useful, usable, and aligned to unit needs. Future research should focus on the use of quality dashboards in nursing practice.

Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support

BACKGROUND

Critical instability forecast and treatment can be optimized by artificial intelligence (AI)-enabled clinical decision support. It is important that the user-facing display of AI output facilitates clinical thinking and workflow for all disciplines involved in bedside care.

OBJECTIVES

Our objective is to engage multidisciplinary users (physicians, nurse practitioners, physician assistants) in the development of a graphical user interface (GUI) to present an AI-derived risk score.

METHODS

Intensive care unit (ICU) clinicians participated in focus groups seeking input on instability risk forecast presented in a prototype GUI. Two stratified rounds (three focus groups [only nurses, only providers, then combined]) were moderated by a focus group methodologist. After round 1, GUI design changes were made and presented in round 2. Focus groups were recorded, transcribed, and deidentified transcripts independently coded by three researchers. Codes were coalesced into emerging themes.

RESULTS

Twenty-three ICU clinicians participated (11 nurses, 12 medical providers [3 mid-level and 9 physicians]). Six themes emerged: (1) analytics transparency, (2) graphical interpretability, (3) impact on practice, (4) value of trend synthesis of dynamic patient data, (5) decisional weight (weighing AI output during decision-making), and (6) display location (usability, concerns for patient/family GUI view). Nurses emphasized having GUI objective information to support communication and optimal GUI location. While providers emphasized need for recommendation interpretability and concern for impairing trainee critical thinking. All disciplines valued synthesized views of vital signs, interventions, and risk trends but were skeptical of placing decisional weight on AI output until proven trustworthy.

CONCLUSION

Gaining input from all clinical users is important to consider when designing AI-derived GUIs. Results highlight that health care intelligent decisional support systems technologies need to be transparent on how they work, easy to read and interpret, cause little disruption to current workflow, as well as decisional support components need to be used as an adjunct to human decision-making.

Triptychon: Usability evaluation and implementation of a web-based application for patients' lab and vital parameters

Background

A major challenge in healthcare is the interpretation of the constantly increasing amount of clinical data of interest to inpatients for diagnosis and therapy. It is vital to accurately structure and represent data from different sources to help clinicians make informed decisions.

Objective

We evaluated the usability of our tool 'Triptychon' - a three-part visualisation dashboard of essential patients' medical data provided by a direct overview of their hospitalisation information, laboratory, and vital parameters over time.

Methods

The study followed a cohort of 20 participants using the mixed-methods approach, including interviews and the usability questionnaires, Health Information Technology Usability Evaluation Scale (Health-ITUES), and User Experience Questionnaire (UEQ). The participant's interactions with the dashboard were also observed. A thematic analysis approach was applied to analyse qualitative data and the quantitative data's task completion time and success rates.

Results

The usability evaluation of the visualisation dashboard revealed issues relating to the terminology used in the user interface and colour coding in its left and middle panels. The Health-ITUES score was 3.72 (standard deviation (SD) = 1.0), and the UEQ score was 1.6 (SD = 0.74). The study demonstrated improvements in intuitive dashboard use and overall satisfaction with using the dashboard daily.

Conclusion

The Triptychon dashboard is a promising new tool for medical data presentation. We identified design and layout issues of the dashboard for improving its usability in routine clinical practice. According to users' feedback, the three panels on the dashboard provided a holistic view of a patient's hospital stay.

Usability Evaluation of Dashboards: A Systematic Literature Review of Tools

Introduction

In recent years, the use of dashboards in healthcare has been considered an effective approach for the visual presentation of information to support clinical and administrative decisions. Effective and efficient use of dashboards in clinical and managerial processes requires a framework for the design and development of tools based on usability principles.

Objectives

The present study is aimed at investigating the existing questionnaires used for the usability evaluation framework of dashboards and at presenting more specific usability criteria for evaluating dashboards.

Methods

This systematic review was conducted using PubMed, Web of Science, and Scopus, without any time restrictions. The final search of articles was performed on September 2, 2022. Data collection was performed using a data extraction form, and the content of selected studies was analyzed based on the dashboard usability criteria.

Results

After reviewing the full text of relevant articles, a total of 29 studies were selected according to the inclusion criteria. Regarding the questionnaires used in the selected studies, researcher-made questionnaires were used in five studies, while 25 studies applied previously used questionnaires. The most widely used questionnaires were the System Usability Scale (SUS), Technology Acceptance Model (TAM), Situation Awareness Rating Technique (SART), Questionnaire for User Interaction Satisfaction (QUIS), Unified Theory of Acceptance and Use of Technology (UTAUT), and Health Information Technology Usability Evaluation Scale (Health-ITUES), respectively. Finally, dashboard evaluation criteria, including usefulness, operability, learnability, ease of use, suitability for tasks, improvement of situational awareness, satisfaction, user interface, content, and system capabilities, were suggested.

Conclusion

General questionnaires that were not specifically designed for dashboard evaluation were mainly used in reviewed studies. The current study suggested specific criteria for measuring the usability of dashboards. When selecting the usability evaluation criteria for dashboards, it is important to pay attention to the evaluation objectives, dashboard features and capabilities, and context of use.

Requirements and challenges of hospital dashboards: a systematic literature review

Background

Today, the use of data in administrative and clinical processes is quite challenging due to the large volume of data, data collection from various sources, and lack of data structure. As a data management tool, dashboards play an important role in timely visual display of critical information on key performances.

Objectives

This systematic review aimed to identify functional and non-functional requirements, as well as challenges of using dashboards in hospitals.

Methods

In this systematic review, four databases, including the Web of Science, PubMed, EMBASE, and Scopus, were searched to find relevant articles from 2000 until May 30, 2020. The final search was conducted on May 30, 2020. Data collection was performed using a data extraction form and reviewing the content of relevant studies on the potentials and challenges of dashboard implementation.

Results

Fifty-four out of 1254 retrieved articles were selected for this study based on the inclusion and exclusion criteria. The functional requirements for dashboards included reporting, reminders, customization, tracking, alert creation, and assessment of performance indicators. On the other hand, the non-functional requirements included the dashboard speed, security, ease of use, installation on different devices (e.g., PCs and laptops), integration with other systems, web-based design, inclusion of a data warehouse, being up-to-data, and use of data visualization elements based on the user’s needs. Moreover, the identified challenges were categorized into four groups: data sources, dashboard content, dashboard design, implementation, and integration in other systems at the hospital level.

Conclusion

Dashboards, by providing information in an appropriate manner, can lead to the proper use of information by users. In order for a dashboard to be effective in clinical and managerial processes, particular attention must be paid to its capabilities, and the challenges of its implementation need to be addressed.

Anesthetic-, irrigation- and pain-free dentistry? The case for a femtosecond laser enabled intraoral robotic device

By leveraging ultrashort pulse laser and micro-electromechanical systems (MEMS) technologies, we are developing a miniaturized intraoral dental robotic device that clamps onto teeth, is remotely controlled, and equipped with a focusing and scanning system to perform efficient, fast, and ultra-precise laser treatments of teeth and dental restorative materials. The device will be supported by a real-time monitoring system for visualization and diagnostic analysis with appropriate digital controls. It will liberate dentists from repetitive manual operations, physical strain and proximity to the patient's oro-pharyngal area that potentially contains infectious agents. The technology will provide patients with high-accuracy, minimally invasive and pain-free treatment. Unlike conventional lasers, femtosecond lasers can ablate all materials without generating heat, thus negating the need for water irrigation, allowing for a clear field of view, and lowering cross-infection hazards. Additionally, dentists can check, analyze, and perform precise cutting of tooth structure with automatic correction, reducing human error. Performing early-stage diagnosis and intervention remotely will be possible through units installed at schools, rural health centers and aged care facilities. Not only can the combination of femtosecond lasers, robotics and MEMS provide practical solutions to dentistry's enduring issues by allowing more precise, efficient, and predictable treatment, but it will also lead to improving the overall access to oral healthcare for communities at large.

The development and validation of a dashboard prototype for real-time suicide mortality data

Introduction/Aim

Data visualisation is key to informing data-driven decision-making, yet this is an underexplored area of suicide surveillance. By way of enhancing a real-time suicide surveillance system model, an interactive dashboard prototype has been developed to facilitate emerging cluster detection, risk profiling and trend observation, as well as to establish a formal data sharing connection with key stakeholders via an intuitive interface.

Materials and Methods

Individual-level demographic and circumstantial data on cases of confirmed suicide and open verdicts meeting the criteria for suicide in County Cork 2008–2017 were analysed to validate the model. The retrospective and prospective space-time scan statistics based on a discrete Poisson model were employed via the R software environment using the “rsatscan” and “shiny” packages to conduct the space-time cluster analysis and deliver the mapping and graphic components encompassing the dashboard interface.

Results

Using the best-fit parameters, the retrospective scan statistic returned several emerging non-significant clusters detected during the 10-year period, while the prospective approach demonstrated the predictive ability of the model. The outputs of the investigations are visually displayed using a geographical map of the identified clusters and a timeline of cluster occurrence.

Discussion

The challenges of designing and implementing visualizations for suspected suicide data are presented through a discussion of the development of the dashboard prototype and the potential it holds for supporting real-time decision-making.

Conclusions

The results demonstrate that integration of a cluster detection approach involving geo-visualisation techniques, space-time scan statistics and predictive modelling would facilitate prospective early detection of emerging clusters, at-risk populations, and locations of concern. The prototype demonstrates real-world applicability as a proactive monitoring tool for timely action in suicide prevention by facilitating informed planning and preparedness to respond to emerging suicide clusters and other concerning trends.

Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients

The emergency department (ED) is at the forefront of medical care, and the medical team needs to make outright judgments and treatment decisions under time constraints. Thus, knowing how to make personalized and precise predictions is a very challenging task. With the advancement of artificial intelligence (AI) technology, Chi Mei Medical Center (CMMC) adopted AI, the Internet of Things (IoT), and interaction technologies to establish diverse prognosis prediction models for eight diseases based on the ED electronic medical records of three branch hospitals. CMMC integrated these predictive models to form a digital AI dashboard, showing the risk status of all ED patients diagnosed with any of these eight diseases. This study first explored the methodology of CMMC’s AI development and proposed a four-tier AI dashboard architecture for ED implementation. The AI dashboard’s ease of use, usefulness, and acceptance was also strongly affirmed by the ED medical staff. The ED AI dashboard is an effective tool in the implementation of real-time risk monitoring of patients in the ED and could improve the quality of care as a part of best practice. Based on the results of this study, it is suggested that healthcare institutions thoughtfully consider tailoring their ED dashboard designs to adapt to their unique workflows and environments.

Utility of the finance-electronic medical record digital dashboard in pediatric otolaryngology

BACKGROUND

The time and cost of data collection via chart review of the electronic medical record (EMR) is a research barrier. This study describes the development of a digital dashboard conjoining EMR and finance data and its application in a pediatric otolaryngology practice.

METHODS

The dashboard creates a common language crosswalk between surgeries via the EMR, financial data, and national Vizient database. First, all Otolaryngology procedures billed via ICD-10 or CPT codes were categorized into Procedure Groups, which constitute the common language that links all data sources. The joined dataset was inputted into a Tableau workbook supporting dynamic filtering and custom real-time analysis.

RESULTS

The dashboard includes 84 Procedure Groups within Otolaryngology. Examples for pediatrics include Sistrunk procedure and supraglottoplasty. User-friendly dynamic filtering by Procedure Group, surgery date range, age, insurance, hospital, surgeon, and discharge status were developed. Outcomes include length of stay, telephone callbacks, postoperative hemorrhage, reoperations, return to Emergency Department, readmissions, and mortality. National comparisons can be analyzed via embedded Vizient data. The usability of the dashboard was tested by evaluating pediatric tonsillectomy outcomes, which revealed a significantly higher rate of postoperative hemorrhages and reoperations during the COVID-19 pandemic.

CONCLUSION

The hybrid finance/EMR dashboard creates a crosswalk between data sources and shows utility for use in evaluating patient outcomes via real-time data analysis and dynamic filtering. This innovative dashboard expedites data extraction, promoting efficient implementation of quality improvement initiatives and surgical outcomes research.

Development of a low‐dimensional model to predict admissions from triage at a pediatric emergency department

Objectives

This study aims to develop and internally validate a low‐dimensional model to predict outcomes (admission or discharge) using commonly entered data up to the post‐triage process to improve patient flow in the pediatric emergency department (ED). In hospital settings where electronic data are limited, a low‐dimensional model with fewer variables may be easier to implement.

Methods

This prognostic study included ED attendances in 2017 and 2018. The Cross Industry Standard Process for Data Mining methodology was followed. Eligibility criteria was applied to the data set, splitting into 70% train and 30% test. Sampling techniques were compared. Gradient boosting machine (GBM), logistic regression, and naïve Bayes models were created. Variables of importance were obtained from the model with the highest area under the curve (AUC) and used to create a low‐dimensional model.

Results

Eligible attendances totaled 72,229 (15% admission rate). The AUC was 0.853 (95% confidence interval [CI], 0.846–0.859) for GBM, 0.845 (95% CI, 0.838–0.852) for logistic regression and 0.813 (95% CI, 0.806–0.821) for naïve Bayes. Important predictors in the GBM model used to create a low‐dimensional model were presenting complaint, triage category, referral source, registration month, location type (resuscitation/other), distance traveled, admission history, and weekday (AUC 0.835 [95% CI, 0.829‐0.842]).

Conclusions

Admission and discharge probability can be predicted early in a pediatric ED using 8 variables. Future work could analyze the false positives and false negatives to gain an understanding of the implementation of these predictions.

Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses

BACKGROUND

There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts.

METHODS

This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000.

RESULTS

Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit.

CONCLUSION

Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.

Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation of Clinical Analytics in a Digital Hospital

Objective  A learning health care system (LHS) uses routinely collected data to continuously monitor and improve health care outcomes. Little is reported on the challenges and methods used to implement the analytics underpinning an LHS. Our aim was to systematically review the literature for reports of real-time clinical analytics implementation in digital hospitals and to use these findings to synthesize a conceptual framework for LHS implementation.

Methods  Embase, PubMed, and Web of Science databases were searched for clinical analytics derived from electronic health records in adult inpatient and emergency department settings between 2015 and 2021. Evidence was coded from the final study selection that related to (1) dashboard implementation challenges, (2) methods to overcome implementation challenges, and (3) dashboard assessment and impact. The evidences obtained, together with evidence extracted from relevant prior reviews, were mapped to an existing digital health transformation model to derive a conceptual framework for LHS analytics implementation.

Results  A total of 238 candidate articles were reviewed and 14 met inclusion criteria. From the selected studies, we extracted 37 implementation challenges and 64 methods employed to overcome such challenges. We identified common approaches for evaluating the implementation of clinical dashboards. Six studies assessed clinical process outcomes and only four studies evaluated patient health outcomes. A conceptual framework for implementing the analytics of an LHS was developed.

Conclusion  Health care organizations face diverse challenges when trying to implement real-time data analytics. These challenges have shifted over the past decade. While prior reviews identified fundamental information problems, such as data size and complexity, our review uncovered more postpilot challenges, such as supporting diverse users, workflows, and user-interface screens. Our review identified practical methods to overcome these challenges which have been incorporated into a conceptual framework. It is hoped this framework will support health care organizations deploying near-real-time clinical dashboards and progress toward an LHS.

A Patient Outcomes-Driven Feedback Platform for Emergency Medicine Clinicians: Human-Centered Design and Usability Evaluation of Linking Outcomes Of Patients (LOOP)

Background

The availability of patient outcomes–based feedback is limited in episodic care environments such as the emergency department. Emergency medicine (EM) clinicians set care trajectories for a majority of hospitalized patients and provide definitive care to an even larger number of those discharged into the community. EM clinicians are often unaware of the short- and long-term health outcomes of patients and how their actions may have contributed. Despite large volumes of patients and data, outcomes-driven learning that targets individual clinician experiences is meager. Integrated electronic health record (EHR) systems provide opportunity, but they do not have readily available functionality intended for outcomes-based learning.

Objective

This study sought to unlock insights from routinely collected EHR data through the development of an individualizable patient outcomes feedback platform for EM clinicians. Here, we describe the iterative development of this platform, Linking Outcomes Of Patients (LOOP), under a human-centered design framework, including structured feedback obtained from its use.

Methods

This multimodal study consisting of human-centered design studios, surveys (24 physicians), interviews (11 physicians), and a LOOP application usability evaluation (12 EM physicians for ≥30 minutes each) was performed between August 2019 and February 2021. The study spanned 3 phases: (1) conceptual development under a human-centered design framework, (2) LOOP technical platform development, and (3) usability evaluation comparing pre- and post-LOOP feedback gathering practices in the EHR.

Results

An initial human-centered design studio and EM clinician surveys revealed common themes of disconnect between EM clinicians and their patients after the encounter. Fundamental postencounter outcomes of death (15/24, 63% respondents identified as useful), escalation of care (20/24, 83%), and return to ED (16/24, 67%) were determined high yield for demonstrating proof-of-concept in our LOOP application. The studio aided the design and development of LOOP, which integrated physicians throughout the design and content iteration. A final LOOP prototype enabled usability evaluation and iterative refinement prior to launch. Usability evaluation compared to status quo (ie, pre-LOOP) feedback gathering practices demonstrated a shift across all outcomes from “not easy” to “very easy” to obtain and from “not confident” to “very confident” in estimating outcomes after using LOOP. On a scale from 0 (unlikely) to 10 (most likely), the users were very likely (9.5) to recommend LOOP to a colleague.

Conclusions

This study demonstrates the potential for human-centered design of a patient outcomes–driven feedback platform for individual EM providers. We have outlined a framework for working alongside clinicians with a multidisciplined team to develop and test a tool that augments their clinical experience and enables closed-loop learning.

BigMouth: development and maintenance of a successful dental data repository

Few clinical datasets exist in dentistry to conduct secondary research. Hence, a novel dental data repository called BigMouth was developed, which has grown to include 11 academic institutions contributing Electronic Health Record data on over 4.5 million patients. The primary purpose for BigMouth is to serve as a high-quality resource for rapidly conducting oral health-related research. BigMouth allows for assessing the oral health status of a diverse US patient population; provides rationale and evidence for new oral health care delivery modes; and embraces the specific oral health research education mission. A data governance framework that encouraged data sharing while controlling contributed data was initially developed. This transformed over time into a mature framework, including a fee schedule for data requests and allowing access to researchers from noncontributing institutions. Adoption of BigMouth helps to foster new collaborations between clinical, epidemiological, statistical, and informatics experts and provides an additional venue for professional development.

Integration of an ICU Visualization Dashboard (i-Dashboard) as a Platform to Facilitate Multidisciplinary Rounds: A Cluster Randomized Controlled Trial (Preprint)

Background

 Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU).

Objective

i-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touch screens during MDRs. This study aimed to evaluate the performance, including the efficiency of prerounding data gathering, communication accuracy, and information exchange, and clinical satisfaction of integrating i-Dashboard as a platform to facilitate MDRs.

Methods

A cluster-randomized controlled trial was performed in 2 surgical ICUs at a university hospital. Study participants included all multidisciplinary care team members. The performance and clinical satisfaction of i-Dashboard during MDRs were compared with those of the established electronic medical record (EMR) through direct observation and questionnaire surveys.

Results

Between April 26 and July 18, 2021, a total of 78 and 91 MDRs were performed with the established EMR and i-Dashboard, respectively. For prerounding data gathering, the median time was 10.4 (IQR 9.1-11.8) and 4.6 (IQR 3.5-5.8) minutes using the established EMR and i-Dashboard (P<.001), respectively. During MDRs, data misrepresentations were significantly less frequent with i-Dashboard (median 0, IQR 0-0) than with the established EMR (4, IQR 3-5; P<.001). Further, effective recommendations were significantly more frequent with i-Dashboard than with the established EMR (P<.001). The questionnaire results revealed that participants favored using i-Dashboard in association with the enhancement of care plan development and team participation during MDRs.

Conclusions

 i-Dashboard increases efficiency in data gathering. Displaying i-Dashboard on large touch screens in MDRs may enhance communication accuracy, information exchange, and clinical satisfaction. The design concepts of i-Dashboard may help develop visualization dashboards that are more applicable for ICU MDRs.

Trial Registration

ClinicalTrials.gov NCT04845698; https://clinicaltrials.gov/ct2/show/NCT04845698

Caries Risk Documentation And Prevention: eMeasures For Dental Electronic Health Records

BACKGROUND

Longitudinal patient level data available in the electronic health record (EHR) allows for the development, implementation, and validations of dental quality measures (eMeasures).

OBJECTIVE

We report the feasibility and validity of implementing two eMeasures. The eMeasures determined the proportion of patients receiving a caries risk assessment (eCRA) and corresponding appropriate risk-based preventative treatments for patients at elevated risk of caries (appropriateness of care [eAoC]) in two academic institutions and one accountable care organization, in the 2019 reporting year.

METHODS

Both eMeasures define the numerator and denominator beginning at the patient level, populations' specifications, and validated the automated queries. For eCRA, patients who completed a comprehensive or periodic oral evaluation formed the denominator, and patients of any age who received a CRA formed the numerator. The eAoC evaluated the proportion of patients at elevated caries risk who received the corresponding appropriate risk-based preventative treatments.

RESULTS

EHR automated queries identified in three sites 269,536 patients who met the inclusion criteria for receiving a CRA. The overall proportion of patients who received a CRA was 94.4% (eCRA). In eAoC, patients at elevated caries risk levels (moderate, high, or extreme) received fluoride preventive treatment ranging from 56 to 93.8%. For patients at high and extreme risk, antimicrobials were prescribed more frequently site 3 (80.6%) than sites 2 (16.7%) and 1 (2.9%).

CONCLUSION

Patient-level data available in the EHRs can be used to implement process-of-care dental eCRA and AoC, eAoC measures identify gaps in clinical practice. EHR-based measures can be useful in improving delivery of evidence-based preventative treatments to reduce risk, prevent tooth decay, and improve oral health.

Clinical Decision Support for Laboratory Testing

BACKGROUND

As technology enables new and increasingly complex laboratory tests, test utilization presents a growing challenge for healthcare systems. Clinical decision support (CDS) refers to digital tools that present providers with clinically relevant information and recommendations, which have been shown to improve test utilization. Nevertheless, individual CDS applications often fail, and implementation remains challenging.

CONTENT

We review common classes of CDS tools grounded in examples from the literature as well as our own institutional experience. In addition, we present a practical framework and specific recommendations for effective CDS implementation.

SUMMARY

CDS encompasses a rich set of tools that have the potential to drive significant improvements in laboratory testing, especially with respect to test utilization. Deploying CDS effectively requires thoughtful design and careful maintenance, and structured processes focused on quality improvement and change management play an important role in achieving these goals.

Opal: an implementation science tool for machine learning clinical decision support in anesthesia

Opal is the first published example of a full-stack platform infrastructure for an implementation science designed for ML in anesthesia that solves the problem of leveraging ML for clinical decision support. Users interact with a secure online Opal web application to select a desired operating room (OR) case cohort for data extraction, visualize datasets with built-in graphing techniques, and run in-client ML or extract data for external use. Opal was used to obtain data from 29,004 unique OR cases from a single academic institution for pre-operative prediction of post-operative acute kidney injury (AKI) based on creatinine KDIGO criteria using predictors which included pre-operative demographic, past medical history, medications, and flowsheet information. To demonstrate utility with unsupervised learning, Opal was also used to extract intra-operative flowsheet data from 2995 unique OR cases and patients were clustered using PCA analysis and k-means clustering. A gradient boosting machine model was developed using an 80/20 train to test ratio and yielded an area under the receiver operating curve (ROC-AUC) of 0.85 with 95% CI [0.80–0.90]. At the default probability decision threshold of 0.5, the model sensitivity was 0.9 and the specificity was 0.8. K-means clustering was performed to partition the cases into two clusters and for hypothesis generation of potential groups of outcomes related to intraoperative vitals. Opal’s design has created streamlined ML functionality for researchers and clinicians in the perioperative setting and opens the door for many future clinical applications, including data mining, clinical simulation, high-frequency prediction, and quality improvement.

An Architectural Framework for Healthcare Dashboards Design

In today's competitive environment, one of the new tools in the field of information technology is business or organizational dashboards that are a backup in the process of strategic management of organizations. The purpose of the current research is to provide a framework to design the healthcare dashboard through technical architecture with fulfilling the decision-makers' requirements. In this study, a common qualitative research method, metasynthesis, is applied, including a seven-step set of research questions, conducting systematic literature search and selection of suitable papers, data extraction, analysis and findings of the qualitative composition, quality control, and presentation of findings. During this process, 102 articles were found by saturation of information resources and then 12 articles were selected for extracting data using acceptance and rejection criteria. A critical evaluation method was used to evaluate the quality of selected articles. After investigating the selected articles and scoring them, in terms of quality, one article was very good, 10 articles were good, and one article was moderate. Then, with regard to the principles and guidelines of technical architecture, the required information was extracted from the selected articles and was analyzed with the method of open, axial, and selective coding. Following the steps of metasynthesis methods, the principles extracted with major and minor titles principles and guidelines in the form of multilayered system architecture including presentation layer, application layer, data layer, and technical infrastructure layer were classified. In the obtained framework, 15 indicators as the main principles and 66 subcriteria as the subsidiary principles for the design and technical architecture of enterprise dashboards were identified.

Situation awareness and the mitigation of risk associated with patient deterioration: A meta-narrative review of theories and models and their relevance to nursing practice

BACKGROUND

Accurate situation awareness has been identified as a critical component of effective deteriorating patient response systems and an essential patient safety skill for nursing practice. However, situation awareness has been defined and theorised from multiple perspectives to explain how individuals, teams and systems maintain awareness in dynamic task environments.

AIM

Our aim was to critically analyse the different approaches taken to the study of situation awareness in healthcare and explore the implications for nursing practice and research as it relates to clinical deterioration in ward contexts.

METHODS

We undertook a meta-narrative review of the healthcare literature to capture how situation awareness has been defined, theorised and studied in healthcare. Following an initial scoping review, we conducted an extensive search of ten electronic databases and included any theoretical, empirical or critical papers with a primary focus on situation awareness in an inpatient hospital setting. Included papers were collaboratively categorised in accordance with their theoretical framing, research tradition and paradigm with a narrative review presented.

RESULTS

A total of 120 papers were included in this review. Three overarching narratives reflecting philosophical, patient safety and solution focussed framings of situation awareness and seven meta-narratives were identified as follows: individual, team and systems perspectives of situation awareness (meta-narratives 1-3), situation awareness and patient safety (meta-narrative 4), communication tools, technologies and education to support situation awareness (meta-narratives 5-7). We identified a concentration of literature from anaesthesia and operating rooms and a body of research largely located within a cognitive engineering tradition and a positivist research paradigm. Endsley's situation awareness model was applied in over 80% of the papers reviewed. A minority of papers drew on alternative situation awareness theories including constructivist, collaborative and distributed perspectives.

CONCLUSIONS

Nurses have a critical role in identifying and escalating the care of deteriorating patients. There is a need to build on prior studies and reflect on the reality of nurse's work and the constraints imposed on situation awareness by the demands of busy inpatient wards. We suggest that this will require an analysis that complements but goes beyond the dominant cognitive engineering tradition to reflect the complex socio-cultural reality of ward-based teams and to explore how situation awareness emerges in increasingly complex, technologically enabled distributed healthcare systems.

Co-designing a dashboard of predictive analytics and decision support to drive care quality and client outcomes in aged care: a mixed-method study protocol

INTRODUCTION

There is a clear need for improved care quality and quality monitoring in aged care. Aged care providers collect an abundance of data, yet rarely are these data integrated and transformed in real-time into actionable information to support evidence-based care, nor are they shared with older people and informal caregivers. This protocol describes the co-design and testing of a dashboard in residential aged care facilities (nursing or care homes) and community-based aged care settings (formal care provided at home or in the community). The dashboard will comprise integrated data to provide an 'at-a-glance' overview of aged care clients, indicators to identify clients at risk of fall-related hospitalisations and poor quality of life, and evidence-based decision support to minimise these risks. Longer term plans for dashboard implementation and evaluation are also outlined.

METHODS

This mixed-method study will involve (1) co-designing dashboard features with aged care staff, clients, informal caregivers and general practitioners (GPs), (2) integrating aged care data silos and developing risk models, and (3) testing dashboard prototypes with users. The dashboard features will be informed by direct observations of routine work, interviews, focus groups and co-design groups with users, and a community forum. Multivariable discrete time survival models will be used to develop risk indicators, using predictors from linked historical aged care and hospital data. Dashboard prototype testing will comprise interviews, focus groups and walk-through scenarios using a think-aloud approach with staff members, clients and informal caregivers, and a GP workshop.

ETHICS AND DISSEMINATION

This study has received ethical approval from the New South Wales (NSW) Population & Health Services Research Ethics Committee and Macquarie University's Human Research Ethics Committee. The research findings will be presented to the aged care provider who will share results with staff members, clients, residents and informal caregivers. Findings will be disseminated as peer-reviewed journal articles, policy briefs and conference presentations.

Emergency Department Quality Dashboard; a Systematic Review of Performance Indicators, Functionalities, and Challenges

Introduction:

Effective information management in the emergency department (ED) can improve the control and management of ED processes. Dashboards, known as data management tools, efficiently provide information and contribute greatly to control and management of ED. This study aimed to identify performance indicators quality dashboard functionalities, and analyze the challenges associated with dashboard implementation in the ED.

Methods:

This systematic review began with a search in four databases (Web of Science, PubMed, Embase, and Scopus) from 2000 to May 30, 2020, when the final search for papers was conducted. The data were collected using a data extraction form and the contents of the extracted papers were analyzed through ED performance indicators, dashboard functionalities, and implementation challenges.

Results:

Performance indicators reported in the reviewed papers were classified as the quality of care, patient flow, timeliness, costs, and resources. The main dashboard functionalities noted in the papers included reporting, customization, alert creation, resource management, and real-time information display. The dashboard implementation challenges included data sources, data quality, integration with other systems, adaptability of dashboard functionalities to user needs, and selection of appropriate performance indicators.

Conclusions:

Quality dashboards facilitate processes, communication, and situation awareness in the ED; hence, they can improve care provision in this department. To enhance the effectiveness and efficiency of ED dashboards, officials should set performance indicators and consider the conformity of dashboard functionalities with user needs. They should also integrate dashboards with other relevant systems at the departmental and hospital levels.

Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.

Predicting Admissions From a Paediatric Emergency Department - Protocol for Developing and Validating a Low-Dimensional Machine Learning Prediction Model

Introduction: Patients boarding in the Emergency Department can contribute to overcrowding, leading to longer waiting times and patients leaving without being seen or completing their treatment. The early identification of potential admissions could act as an additional decision support tool to alert clinicians that a patient needs to be reviewed for admission and would also be of benefit to bed managers in advance bed planning for the patient. We aim to create a low-dimensional model predicting admissions early from the paediatric Emergency Department.

Methods and Analysis: The methodology Cross Industry Standard Process for Data Mining (CRISP-DM) will be followed. The dataset will comprise of 2 years of data, ~76,000 records. Potential predictors were identified from previous research, comprising of demographics, registration details, triage assessment, hospital usage and past medical history. Fifteen models will be developed comprised of 3 machine learning algorithms (Logistic regression, naïve Bayes and gradient boosting machine) and 5 sampling methods, 4 of which are aimed at addressing class imbalance (undersampling, oversampling, and synthetic oversampling techniques). The variables of importance will then be identified from the optimal model (selected based on the highest Area under the curve) and used to develop an additional low-dimensional model for deployment.

Discussion: A low-dimensional model comprised of routinely collected data, captured up to post triage assessment would benefit many hospitals without data rich platforms for the development of models with a high number of predictors. Novel to the planned study is the use of data from the Republic of Ireland and the application of sampling techniques aimed at improving model performance impacted by an imbalance between admissions and discharges in the outcome variable.

Using cloud-based, open-source technology to evaluate, improve, and rapidly disseminate community-based intervention data

Building Uplifted Families (BUF) is a cross-sector community initiative to improve health and economic disparities in Charlotte, North Carolina. A formative evaluation strategy was used to support iterative process improvement and collaborative engagement of cross-sector partners. To address challenges with electronic data collection through REDCap Cloud, we developed the BUF Rapid Dissemination (BUF-RD) model, a multistage data governance system supplemented by open-source technologies, such as: Stage 1) data collection; Stage 2) data integration and analysis; and Stage 3) dissemination. In Stage 3, results were disseminated through an interactive dashboard developed in RStudio using RShiny and Shiny Server solutions. The BUF-RD model was successfully deployed in a 6-month beta test to reduce the time lapse between data collection and dissemination from 3 months to 2 weeks. Having up-to-date preliminary results led to improved BUF implementation, enhanced stakeholder engagement, and greater responsiveness and alignment of program resources to specific participant needs.

Design and Evaluation of Low-Fidelity Visual Display Prototypes for Multiple Hospital-Acquired Conditions

Hospital-acquired conditions such as catheter-associated urinary tract infection, stage 3 or 4 hospital-acquired pressure injury, and falls with injury are common, costly, and largely preventable. This study used participatory design methods to design and evaluate low-fidelity prototypes of clinical dashboards to inform high-fidelity prototype designs to visualize integrated risks based on patient profiles. Five low-fidelity prototypes were developed through literature review and by engaging nurses, nurse managers, and providers as participants (N = 23) from two hospitals in different healthcare systems using focus groups and interviews. Five themes were identified from participatory design sessions: Need for Integrated Hospital-Acquired Condition Risk Tool, Information Needs, Sources of Information, Trustworthiness of Information, and Performance Tracking Perspectives. Participants preferred visual displays that represented patient comparative risks for hospital-acquired conditions using the familiar design metaphor of a gauge and green, yellow, and red "traffic light" colors scheme. Findings from this study were used to design a high-fidelity prototype to be tested in the next phase of the project. Visual displays of hospital-acquired conditions that are familiar in display and simplify complex information such as the green, yellow, and red dashboard are needed to assist clinicians in fast-paced clinical environments and be designed to prevent alert fatigue.

Development, implementation and preliminary evaluation of clinical dashboards in a department of anesthesia

Clinical dashboards summarize indicators of high-volume patient data in a concise, user-friendly visual format. There are few studies of the use of dashboards to improve professional practice in anesthesiology. The objective of the present study was to describe the user-centered development, implementation and preliminary evaluation of clinical dashboards dealing with anesthesia unit management and quality assessment in a French university medical center. User needs and technical requirements were identified in end user interviews and then synthesized. Several representations were then developed (according to good visualization practice) and submitted to end users for appraisal. Lastly, dashboards were implemented and made accessible for everyday use via the medical center’s network. After a period of use, end user feedback on the dashboard platform was collected as a system usability score (range 0 to 100). Seventeen themes (corresponding to 29 questions and 42 indicators) were identified. After prioritization and feasibility assessment, 10 dashboards were ultimately implemented and deployed. The dashboards variously addressed the unit’s overall activity, compliance with guidelines on intraoperative hemodynamics, ventilation and monitoring, and documentation of the anesthesia procedure. The mean (standard deviation) system usability score was 82.6 (11.5), which corresponded to excellent usability. We developed clinical dashboards for a university medical center’s anesthesia units. The dashboards’ deployment was well received by the center’s anesthesiologists. The dashboards’ impact on activity and practice after several months of use will now have to be assessed.

Improving Health Care Management in Hospitals Through a Productivity Dashboard

Health information systems have been developed to help hospital managers steer daily operations, including key performance indicators (KPIs) for monitoring on a time-aggregated basis. Yet, current literature lacks in proposals of productivity dashboards to assist hospitals stakeholders. This research focuses on two related problems: (1) hospital organizations need access to productivity information to improve access to services; and (2) managers need productivity information to optimize resource allocation. This research consists in the development of dashboards to monitor information obtained from a hospital organization to support decision makers. To develop and evaluate the productivity dashboard, the Design Science Research (DSR) methodology was adopted. The dashboard was evaluated by stakeholders of a large Portuguese hospital who contributed to iteratively improving its design toward a useful decision support tool. Additionally, it was ascertained that monitoring productivity needs more study and that the dashboards on these themes are valuable assets at a monitoring level and subsequent decision-making process.

Web-Based Dashboard for the Interactive Visualization and Analysis of National Risk-Standardized Mortality Rates of Sepsis in the US

Sepsis mortality is heavily influenced by the quality of care in hospitals. Comparing risk-standardized mortality rate (RSMR) of sepsis patients in different states in the United States has potentially important clinical and policy implications. In the current study, we aimed to compare national sepsis RSMR using an interactive web-based dashboard. We analyzed sepsis mortality using the National Inpatient Sample Database of the US. The RSMR was calculated by the hierarchical logistic regression model. We wrote the interactive web-based dashboard using the Shiny framework, an R package that integrates R-based statistics computation and graphics generation. Visual summarizations (e.g., heat map, and time series chart), and interactive tools (e.g., year selection, automatic year play, map zoom, copy or print data, ranking data by name or value, and data search) were implemented to enhance user experience. The web-based dashboard (https://sepsismap.shinyapps.io/index2/) is cross-platform and publicly available to anyone with interest in sepsis outcomes, health inequality, and administration of state/federal healthcare. After extrapolation to the national level, approximately 35 million hospitalizations were analyzed for sepsis mortality each year. Eight years of sepsis mortality data were summarized into four easy to understand dimensions: Sepsis Identification Criteria; Sepsis Mortality Predictors; RSMR Map; RSMR Trend. Substantial variation in RSMR was observed for different states in the US. This web-based dashboard allows anyone to visualize the substantial variation in RSMR across the whole US. Our work has the potential to support healthcare transparency, information diffusion, health decision-making, and the formulation of new public policies.

The potential role of dashboard use and navigation in reducing medical errors of an electronic health record system: a mixed-method simulation handoff study

The dashboards of electronic health record (EHR) systems could potentially support the chart biopsy that occurs before or after physician handoffs. In this study, we conducted a simulation handoff study and recorded the participants' navigation patterns in an EHR system mock-up. We analyzed the navigation patterns of dashboard use in terms of duration, frequency, and sequence, and we examined the relationship between dashboard use in chart biopsy and the errors identified after handoffs. The results show that the participants frequently used the dashboard as an information hub and as an information resource to help them navigate the EHR system and answer the questions in a nursing call. Moreover, using the dashboard as an information hub can help reduce imprecision and factual errors in handoffs. Our findings suggest the need for a "context-aware" dashboard to accommodate dynamic navigation patterns and to support clinical work as well as to reduce medical errors.

Usability Evaluation of Visual Representation Formats for Emergency Department Records

BACKGROUND

Integration of electronic information is a challenge for multitasking emergency providers, with implications for patient safety. Visual representations can assist sense-making of complex data sets; however, benefit and acceptability in emergency care is unproven.

OBJECTIVES

This article evaluates visually focused alternatives to lists or tabular formats, to better understand possible usability in Emergency Department Information System (EDIS).

METHODS

A counterbalanced, repeated-measures experiment, satisfaction surveys, and narrative content analysis was conducted remotely by Web platform. Participants were 37 American emergency physicians; they completed 16 clinical cases comparing 4 visual designs to the control formats from a commercially available EDIS. They then evaluated two additional chart overview representations without controls.

RESULTS

Visual designs provided benefit in several areas compared to controls. Task correctness (90% to 76%; p = 0.003) and completion time (median: 49-74 seconds; p < 0.001) were superior for a medication history timeline with class and schedule highlighting. Completion time (median: 45-60 seconds; p = 0.03) was superior for a past medical history design, using pertinent diagnosis codes in highlighting rules. Less mental effort was reported for visual allergy (p = 0.04), past medical history (p < 0.001), and medication timeline (p < 0.001) designs. Most of the participants agreed with statements of likeability, preference, and benefit for visual designs; nonetheless, contrary opinions were seen, and more complex designs were viewed less favorably.

CONCLUSION

Physician performance with visual representations of clinical data can in some cases exceed standard formats, even in absence of training. Highlighting of priority clinical categories was rated easier-to-use on average than unhighlighted controls. Perceived complexity of timeline representations can limit desirability for a subset of users, despite potential benefit.

A Real-Time Autonomous Dashboard for the Emergency Department: 5-Year Case Study

Background

The task of monitoring and managing the entire emergency department (ED) is becoming more important due to increasing pressure on the ED. Recently, dashboards have received the spotlight as health information technology to support these tasks.

Objective

This study aimed to describe the development of a real-time autonomous dashboard for the ED and to evaluate perspectives of clinical staff on its usability.

Methods

We developed a dashboard based on three principles—“anytime, anywhere, at a glance;” “minimal interruption to workflow;” and “protect patient privacy”—and 3 design features—“geographical layout,” “patient-level alert,” and “real-time summary data.” Items to evaluate the dashboard were selected based on the throughput factor of the conceptual model of ED crowding. Moreover, ED physicians and nurses were surveyed using the system usability scale (SUS) and situation awareness index as well as a questionnaire we created on the basis of the construct of the Situation Awareness Rating Technique.

Results

The first version of the ED dashboard was successfully launched in 2013, and it has undergone 3 major revisions since then because of geographical changes in ED and modifications to improve usability. A total of 52 ED staff members participated in the survey. The average SUS score of the dashboard was 67.6 points, which indicates “OK-to-Good” usability. The participants also reported that the dashboard provided efficient “concentration support” (4.15 points), “complexity representation” (4.02 points), “variability representation” (3.96 points), “information quality” (3.94 points), and “familiarity” (3.94 points). However, the “division of attention” was rated at 2.25 points.

Conclusions

We developed a real-time autonomous ED dashboard and successfully used it for 5 years with good evaluation from users.

Healthcare providers' perceptions of a situational awareness display for emergency department resuscitation: a simulation qualitative study

Importance

Emergency resuscitation of critically ill patients can challenge team communication and situational awareness. Tools facilitating team performance may enhance patient safety.

Objectives

To determine resuscitation team members' perceptions of the Situational Awareness Display's utility.

Design

We conducted focus groups with healthcare providers during Situational Awareness Display development. After simulations assessing the display, we conducted debriefs with participants.

Setting

Dual site tertiary care level 1 trauma centre in Ottawa, Canada.

Participants

We recruited by email physicians, nurses and respiratory therapist.

Intervention

Situational Awareness Display, a visual cognitive aid that provides key clinical information to enhance resuscitation team communication and situational awareness.

Main outcomes and measures

Themes emerging from focus groups and simulation debriefs. Three reviewers independently coded and analysed transcripts using content qualitative analysis.

Results

We recruited a total of 33 participants in two focus groups (n = 20) and six simulation debriefs with three 4-5 member teams (n = 13). Majority of participants (10/13) strongly endorsed the Situational Awareness Display's utility in simulation (very or extremely useful). Focus groups and debrief themes included improved perception of patient data, comprehension of context and ability to project to future decisions. Participants described potentially positive and negative impacts on patient safety and positive impacts on provider performance and team communication. Participants expressed a need for easy data entry incorporated into clinical workflow and training on how to use the display.

Conclusion

Emergency resuscitation team participants felt the Situational Awareness Display has potential to improve provider performance, team communication and situational awareness, ultimately enhancing quality of care.

Variable effectiveness of stepwise implementation of nudge-type interventions to improve provider compliance with intraoperative low tidal volume ventilation

BACKGROUND

Identifying mechanisms to improve provider compliance with quality metrics is a common goal across medical disciplines. Nudge interventions are minimally invasive strategies that can influence behavioural changes and are increasingly used within healthcare settings. We hypothesised that nudge interventions may improve provider compliance with lung-protective ventilation (LPV) strategies during general anaesthesia.

METHODS

We developed an audit and feedback dashboard that included information on both provider-level and department-level compliance with LPV strategies in two academic hospitals, two non-academic hospitals and two academic surgery centres affiliated with a single healthcare system. Dashboards were emailed to providers four times over the course of the 9-month study. Additionally, the default setting on anaesthesia machines for tidal volume was decreased from 700 mL to 400 mL. Data on surgical cases performed between 1 September 2016 and 31 May 2017 were examined for compliance with LPV. The impact of the interventions was assessed via pairwise logistic regression analysis corrected for multiple comparisons.

RESULTS

A total of 14 793 anaesthesia records were analysed. Absolute compliance rates increased from 59.3% to 87.8%preintervention to postintervention. Introduction of attending physician dashboards resulted in a 41% increase in the odds of compliance (OR 1.41, 95% CI 1.17 to 1.69, p=0.002). Subsequently, the addition of advanced practice provider and resident dashboards lead to an additional 93% increase in the odds of compliance (OR 1.93, 95% CI 1.52 to 2.46, p<0.001). Lastly, modifying ventilator defaults led to a 376% increase in the odds of compliance (OR 3.76, 95% CI 3.1 to 4.57, p<0.001).

CONCLUSION

Audit and feedback tools in conjunction with default changes improve provider compliance.

Can teamwork and situational awareness (SA) in ED resuscitations be improved with a technological cognitive aid? Design and a pilot study of a team situation display

Effective teamwork in ED resuscitations, including information sharing and situational awareness, could be degraded. Technological cognitive aids can facilitate effective teamwork.

OBJECTIVE

This study focused on the design of an ED situation display and pilot test its influence on teamwork and situational awareness during simulated resuscitation scenarios.

MATERIAL AND METHODS

The display design consisted of a central area showing the critical dynamic parameters of the interventions with an events time-line below it. Static information was placed at the sides of the display. We pilot tested whether the situation display could lead to higher scores on the Clinical Teamwork Scale (CTS), improved scores on a context-specific Situational Awareness Global Assessment Technique (SAGAT) tool, and team communication patterns that reflect teamwork and situational awareness.

RESULTS

Resuscitation teamwork, as measured by the CTS, was overall better with the presence of the situation display as compared with no situation display. Team members discussed interventions more with the situation display compared with not having the situation display. Situational awareness was better with the situation display only in the trauma scenario.

DISCUSSION

The situation display could be more effective for certain ED team members and in certain cases.

CONCLUSIONS

Overall, this pilot study implies that a situation display could facilitate better teamwork and team communication in the resuscitation event.