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Yakob N, Laliberté S, Doyon-Poulin P, Jouvet P, Noumeir R. Data Representation Structure to Support Clinical Decision-Making in the Pediatric Intensive Care Unit: Interview Study and Preliminary Decision Support Interface Design. JMIR Form Res 2024; 8:e49497. [PMID: 38300695 PMCID: PMC10870206 DOI: 10.2196/49497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/11/2023] [Accepted: 11/22/2023] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Clinical decision-making is a complex cognitive process that relies on the interpretation of a large variety of data from different sources and involves the use of knowledge bases and scientific recommendations. The representation of clinical data plays a key role in the speed and efficiency of its interpretation. In addition, the increasing use of clinical decision support systems (CDSSs) provides assistance to clinicians in their practice, allowing them to improve patient outcomes. In the pediatric intensive care unit (PICU), clinicians must process high volumes of data and deal with ever-growing workloads. As they use multiple systems daily to assess patients' status and to adjust the health care plan, including electronic health records (EHR), clinical systems (eg, laboratory, imaging and pharmacy), and connected devices (eg, bedside monitors, mechanical ventilators, intravenous pumps, and syringes), clinicians rely mostly on their judgment and ability to trace relevant data for decision-making. In these circumstances, the lack of optimal data structure and adapted visual representation hinder clinician's cognitive processes and clinical decision-making skills. OBJECTIVE In this study, we designed a prototype to optimize the representation of clinical data collected from existing sources (eg, EHR, clinical systems, and devices) via a structure that supports the integration of a home-developed CDSS in the PICU. This study was based on analyzing end user needs and their clinical workflow. METHODS First, we observed clinical activities in a PICU to secure a better understanding of the workflow in terms of staff tasks and their use of EHR on a typical work shift. Second, we conducted interviews with 11 clinicians from different staff categories (eg, intensivists, fellows, nurses, and nurse practitioners) to compile their needs for decision support. Third, we structured the data to design a prototype that illustrates the proposed representation. We used a brain injury care scenario to validate the relevance of integrated data and the utility of main functionalities in a clinical context. Fourth, we held design meetings with 5 clinicians to present, revise, and adapt the prototype to meet their needs. RESULTS We created a structure with 3 levels of abstraction-unit level, patient level, and system level-to optimize clinical data representation and display for efficient patient assessment and to provide a flexible platform to host the internally developed CDSS. Subsequently, we designed a preliminary prototype based on this structure. CONCLUSIONS The data representation structure allows prioritizing patients via criticality indicators, assessing their conditions using a personalized dashboard, and monitoring their courses based on the evolution of clinical values. Further research is required to define and model the concepts of criticality, problem recognition, and evolution. Furthermore, feasibility tests will be conducted to ensure user satisfaction.
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Affiliation(s)
- Najia Yakob
- École de technologie supérieure, Montreal, QC, Canada
| | | | | | - Philippe Jouvet
- Pediatric Intensive Care Unit, CHU Sainte-Justine, Montreal, QC, Canada
| | - Rita Noumeir
- École de technologie supérieure, Montreal, QC, Canada
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Helman S, Terry MA, Pellathy T, Hravnak M, George E, Al-Zaiti S, Clermont G. Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support. Appl Clin Inform 2023; 14:789-802. [PMID: 37793618 PMCID: PMC10550364 DOI: 10.1055/s-0043-1775565] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/26/2023] [Indexed: 10/06/2023] Open
Abstract
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.
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Affiliation(s)
- Stephanie Helman
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Martha Ann Terry
- Department of Behavioral and Community Health Sciences, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Tiffany Pellathy
- Veterans Administration Center for Health Equity Research and Promotion, Pittsburgh, Pennsylvania, United States
| | - Marilyn Hravnak
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Elisabeth George
- Department of Nursing, University of Pittsburgh Medical Center, Presbyterian Hospital, Pittsburgh, Pennsylvania, United States
| | - Salah Al-Zaiti
- Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
- Division of Cardiology at University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
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Busse TS, Jux C, Laser J, Rasche P, Vollmar HC, Ehlers JP, Kernebeck S. Involving Health Care Professionals in the Development of Electronic Health Records: Scoping Review. JMIR Hum Factors 2023; 10:e45598. [PMID: 37428524 PMCID: PMC10366971 DOI: 10.2196/45598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 04/15/2023] [Accepted: 05/17/2023] [Indexed: 07/11/2023] Open
Abstract
BACKGROUND Electronic health records (EHRs) are a promising approach to document and map (complex) health information gathered in health care worldwide. However, possible unintended consequences during use, which can occur owing to low usability or the lack of adaption to existing workflows (eg, high cognitive load), may pose a challenge. To prevent this, the involvement of users in the development of EHRs is crucial and growing. Overall, involvement is designed to be very multifaceted, for example, in terms of the timing, frequency, or even methods used to capture user preferences. OBJECTIVE Setting, users and their needs, and the context and practice of health care must be considered in the design and subsequent implementation of EHRs. Many different approaches to user involvement exist, each requiring a variety of methodological choices. The aim of the study was to provide an overview of the existing forms of user involvement and the circumstances they need and to provide support for the planning of new involvement processes. METHODS We conducted a scoping review to provide a database for future projects on which design of inclusion is worthwhile and to show the diversity of reporting. Using a very broad search string, we searched the PubMed, CINAHL, and Scopus databases. In addition, we searched Google Scholar. Hits were screened according to scoping review methodology and then examined, focusing on methods and materials, participants, frequency and design of the development, and competencies of the researchers involved. RESULTS In total, 70 articles were included in the final analysis. There was a wide range of methods of involvement. Physicians and nurses were the most frequently included groups and, in most cases, were involved only once in the process. The approach of involvement (eg, co-design) was not specified in most of the studies (44/70, 63%). Further qualitative deficiencies in the reporting were evident in the presentation of the competences of members of the research and development teams. Think-aloud sessions, interviews, and prototypes were frequently used. CONCLUSIONS This review provides insights into the diversity of health care professionals' involvement in the development of EHRs. It provides an overview of the different approaches in various fields of health care. However, it also shows the necessity of considering quality standards in the development of EHRs together with future users and the need for reporting this in future studies.
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Affiliation(s)
- Theresa Sophie Busse
- Institute of General Practice and Family Medicine (AM RUB), Medical Faculty, Ruhr University Bochum, Bochum, Germany
| | - Chantal Jux
- School of Nursing, Saint Elisabeth Group GmbH, Catholic Hospitals Rhine-Ruhr, Herne, Germany
| | - Johannes Laser
- Chair of Didactics and Educational Research in Healthcare, Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Peter Rasche
- Institute of General Practice and Family Medicine (AM RUB), Medical Faculty, Ruhr University Bochum, Bochum, Germany
- Faculty of Health Care, Niederrhein University of Applied Sciences, Krefeld, Germany
| | - Horst Christian Vollmar
- Institute of General Practice and Family Medicine (AM RUB), Medical Faculty, Ruhr University Bochum, Bochum, Germany
| | - Jan P Ehlers
- Chair of Didactics and Educational Research in Healthcare, Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Sven Kernebeck
- Chair of Didactics and Educational Research in Healthcare, Faculty of Health, Witten/Herdecke University, Witten, Germany
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Dash D, Gokhale A, Patel BS, Callahan A, Posada J, Krishnan G, Collins W, Li R, Schulman K, Ren L, Shah NH. Building a Learning Health System: Creating an Analytical Workflow for Evidence Generation to Inform Institutional Clinical Care Guidelines. Appl Clin Inform 2022; 13:315-321. [PMID: 35235994 PMCID: PMC8890914 DOI: 10.1055/s-0042-1743241] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background
One key aspect of a learning health system (LHS) is utilizing data generated during care delivery to inform clinical care. However, institutional guidelines that utilize observational data are rare and require months to create, making current processes impractical for more urgent scenarios such as those posed by the COVID-19 pandemic. There exists a need to rapidly analyze institutional data to drive guideline creation where evidence from randomized control trials are unavailable.
Objectives
This article provides a background on the current state of observational data generation in institutional guideline creation and details our institution's experience in creating a novel workflow to (1) demonstrate the value of such a workflow, (2) demonstrate a real-world example, and (3) discuss difficulties encountered and future directions.
Methods
Utilizing a multidisciplinary team of database specialists, clinicians, and informaticists, we created a workflow for identifying and translating a clinical need into a queryable format in our clinical data warehouse, creating data summaries and feeding this information back into clinical guideline creation.
Results
Clinical questions posed by the hospital medicine division were answered in a rapid time frame and informed creation of institutional guidelines for the care of patients with COVID-19. The cost of setting up a workflow, answering the questions, and producing data summaries required around 300 hours of effort and $300,000 USD.
Conclusion
A key component of an LHS is the ability to learn from data generated during care delivery. There are rare examples in the literature and we demonstrate one such example along with proposed thoughts of ideal multidisciplinary team formation and deployment.
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Affiliation(s)
- Dev Dash
- Department of Medicine, Stanford University School of Medicine Stanford, California, United States
| | - Arjun Gokhale
- Department of Medicine, Stanford University School of Medicine Stanford, California, United States
| | - Birju S Patel
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, United States
| | - Alison Callahan
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, United States
| | - Jose Posada
- Department of Medicine, Stanford University School of Medicine Stanford, California, United States
| | - Gomathi Krishnan
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, United States
| | - William Collins
- Department of Medicine, Stanford University School of Medicine Stanford, California, United States
| | - Ron Li
- Department of Medicine, Stanford University School of Medicine Stanford, California, United States
| | - Kevin Schulman
- Department of Medicine, Stanford University School of Medicine Stanford, California, United States
| | - Lily Ren
- Department of Medicine, Stanford University School of Medicine Stanford, California, United States
| | - Nigam H Shah
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, United States
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Pinsky MR, Dubrawski A, Clermont G. Intelligent Clinical Decision Support. SENSORS (BASEL, SWITZERLAND) 2022; 22:1408. [PMID: 35214310 PMCID: PMC8963066 DOI: 10.3390/s22041408] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/04/2022] [Accepted: 02/06/2022] [Indexed: 06/14/2023]
Abstract
Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to adequately respond to cardiorespiratory stress. The enormous volume of patient data available in ICU environments, both of high-frequency numeric and waveform data accessible from bedside monitors, plus Electronic Health Record (EHR) data, presents a platform ripe for Artificial Intelligence (AI) approaches for the detection and forecasting of instability, and data-driven intelligent clinical decision support (CDS). Building unbiased, reliable, and usable AI-based systems across health care sites is rapidly becoming a high priority, specifically as these systems relate to diagnostics, forecasting, and bedside clinical decision support. The ICU environment is particularly well-positioned to demonstrate the value of AI in saving lives. The goal is to create AI models embedded in a real-time CDS for forecasting and mitigation of critical instability in ICU patients of sufficient readiness to be deployed at the bedside. Such a system must leverage multi-source patient data, machine learning, systems engineering, and human action expertise, the latter being key to successful CDS implementation in the clinical workflow and evaluation of bias. We present one approach to create an operationally relevant AI-based forecasting CDS system.
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Affiliation(s)
- Michael R. Pinsky
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA;
| | - Artur Dubrawski
- Auton Laboratory, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA;
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El-Kareh R, Sittig DF. Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond. Crit Care Clin 2022; 38:129-139. [PMID: 34794627 PMCID: PMC8608279 DOI: 10.1016/j.ccc.2021.08.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.
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Affiliation(s)
- Robert El-Kareh
- University of California, San Diego, 9500 Gilman Drive, #0881 La Jolla, CA 92093-0881, USA.
| | - Dean F Sittig
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, UT-Memorial Hermann Center for Healthcare Quality & Safety, Houston, TX 77030, USA. https://twitter.com/DeanSittig
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Abstract
OBJECTIVE Human factors and ergonomics (HF/E) frameworks and methods are becoming embedded in the health informatics community. There is now broad recognition that health informatics tools must account for the diverse needs, characteristics, and abilities of end users, as well as their context of use. The objective of this review is to synthesize the current nature and scope of HF/E integration into the health informatics community. METHODS Because the focus of this synthesis is on understanding the current integration of the HF/E and health informatics research communities, we manually reviewed all manuscripts published in primary HF/E and health informatics journals during 2020. RESULTS HF/E-focused health informatics studies included in this synthesis focused heavily on EHR customizations, specifically clinical decision support customizations and customized data displays, and on mobile health innovations. While HF/E methods aimed to jointly improve end user safety, performance, and satisfaction, most HF/E-focused health informatics studies measured only end user satisfaction. CONCLUSION HF/E-focused health informatics researchers need to identify and communicate methodological standards specific to health informatics, to better synthesize findings across resource intensive HF/E-focused health informatics studies. Important gaps in the HF/E design and evaluation process should be addressed in future work, including support for technology development platforms and training programs so that health informatics designers are as diverse as end users.
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