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Born C, Schwarz R, Böttcher TP, Hein A, Krcmar H. The role of information systems in emergency department decision-making-a literature review. J Am Med Inform Assoc 2024:ocae096. [PMID: 38781289 DOI: 10.1093/jamia/ocae096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/11/2024] [Accepted: 04/15/2024] [Indexed: 05/25/2024] Open
Abstract
OBJECTIVES Healthcare providers employ heuristic and analytical decision-making to navigate the high-stakes environment of the emergency department (ED). Despite the increasing integration of information systems (ISs), research on their efficacy is conflicting. Drawing on related fields, we investigate how timing and mode of delivery influence IS effectiveness. Our objective is to reconcile previous contradictory findings, shedding light on optimal IS design in the ED. MATERIALS AND METHODS We conducted a systematic review following PRISMA across PubMed, Scopus, and Web of Science. We coded the ISs' timing as heuristic or analytical, their mode of delivery as active for automatic alerts and passive when requiring user-initiated information retrieval, and their effect on process, economic, and clinical outcomes. RESULTS Our analysis included 83 studies. During early heuristic decision-making, most active interventions were ineffective, while passive interventions generally improved outcomes. In the analytical phase, the effects were reversed. Passive interventions that facilitate information extraction consistently improved outcomes. DISCUSSION Our findings suggest that the effectiveness of active interventions negatively correlates with the amount of information received during delivery. During early heuristic decision-making, when information overload is high, physicians are unresponsive to alerts and proactively consult passive resources. In the later analytical phases, physicians show increased receptivity to alerts due to decreased diagnostic uncertainty and information quantity. Interventions that limit information lead to positive outcomes, supporting our interpretation. CONCLUSION We synthesize our findings into an integrated model that reveals the underlying reasons for conflicting findings from previous reviews and can guide practitioners in designing ISs in the ED.
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Affiliation(s)
- Cornelius Born
- School of Computation, Information and Technology, Technical University of Munich, 85748 Garching bei München, Germany
| | - Romy Schwarz
- School of Computation, Information and Technology, Technical University of Munich, 85748 Garching bei München, Germany
| | - Timo Phillip Böttcher
- School of Computation, Information and Technology, Technical University of Munich, 85748 Garching bei München, Germany
| | - Andreas Hein
- Institute of Information Systems and Digital Business, University of St. Gallen, 9000 St. Gallen, Switzerland
| | - Helmut Krcmar
- School of Computation, Information and Technology, Technical University of Munich, 85748 Garching bei München, Germany
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2
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Qaurooni D, Herr BW, Zappone SR, Wojciechowska K, Börner K, Schleyer T. Visual Analytics for Data-Driven Understanding of the Substance Use Disorder Epidemic. INQUIRY : A JOURNAL OF MEDICAL CARE ORGANIZATION, PROVISION AND FINANCING 2024; 61:469580241227020. [PMID: 38281107 PMCID: PMC10823843 DOI: 10.1177/00469580241227020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 12/15/2023] [Accepted: 01/02/2024] [Indexed: 01/29/2024]
Abstract
The substance use disorder epidemic has emerged as a serious public health crisis, presenting complex challenges. Visual analytics offers a unique approach to address this complexity and facilitate effective interventions. This paper details the development of an innovative visual analytics dashboard, aimed at enhancing our understanding of the substance use disorder epidemic. By employing record linkage techniques, we integrate diverse data sources to provide a comprehensive view of the epidemic. Adherence to responsive, open, and user-centered design principles ensures the dashboard's usefulness and usability. Our approach to data and design encourages collaboration among various stakeholders, including researchers, politicians, and healthcare practitioners. Through illustrative outputs, we demonstrate how the dashboard can deepen our understanding of the epidemic, support intervention strategies, and evaluate the effectiveness of implemented measures. The paper concludes with a discussion of the dashboard's use cases and limitations.
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Affiliation(s)
| | - Bruce W. Herr
- Indiana University Bloomington, Bloomington, IN, USA
| | | | | | - Katy Börner
- Indiana University Bloomington, Bloomington, IN, USA
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Balch JA, Ruppert MM, Loftus TJ, Guan Z, Ren Y, Upchurch GR, Ozrazgat-Baslanti T, Rashidi P, Bihorac A. Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review. JMIR Med Inform 2023; 11:e48297. [PMID: 37646309 PMCID: PMC10468818 DOI: 10.2196/48297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/15/2023] [Accepted: 06/17/2023] [Indexed: 09/01/2023] Open
Abstract
Background Machine learning-enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable. Objective This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs. Methods Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system's functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems. Results A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy. Conclusions Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.
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Affiliation(s)
- Jeremy A Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
| | - Matthew M Ruppert
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
| | - Ziyuan Guan
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Yuanfang Ren
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Gilbert R Upchurch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Tezcan Ozrazgat-Baslanti
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Parisa Rashidi
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Azra Bihorac
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
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Nan J, Xu LQ. Designing Interoperable Health Care Services Based on Fast Healthcare Interoperability Resources: Literature Review. JMIR Med Inform 2023; 11:e44842. [PMID: 37603388 PMCID: PMC10477925 DOI: 10.2196/44842] [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: 12/05/2022] [Revised: 04/07/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND With the advent of the digital economy and the aging population, the demand for diversified health care services and innovative care delivery models has been overwhelming. This trend has accelerated the urgency to implement effective and efficient data exchange and service interoperability, which underpins coordinated care services among tiered health care institutions, improves the quality of oversight of regulators, and provides vast and comprehensive data collection to support clinical medicine and health economics research, thus improving the overall service quality and patient satisfaction. To meet this demand and facilitate the interoperability of IT systems of stakeholders, after years of preparation, Health Level 7 formally introduced, in 2014, the Fast Healthcare Interoperability Resources (FHIR) standard. It has since continued to evolve. FHIR depends on the Implementation Guide (IG) to ensure feasibility and consistency while developing an interoperable health care service. The IG defines rules with associated documentation on how FHIR resources are used to tackle a particular problem. However, a gap remains between IGs and the process of building actual services because IGs are rules without specifying concrete methods, procedures, or tools. Thus, stakeholders may feel it nontrivial to participate in the ecosystem, giving rise to the need for a more actionable practice guideline (PG) for promoting FHIR's fast adoption. OBJECTIVE This study aimed to propose a general FHIR PG to facilitate stakeholders in the health care ecosystem to understand FHIR and quickly develop interoperable health care services. METHODS We selected a collection of FHIR-related papers about the latest studies or use cases on designing and building FHIR-based interoperable health care services and tagged each use case as belonging to 1 of the 3 dominant innovation feature groups that are also associated with practice stages, that is, data standardization, data management, and data integration. Next, we reviewed each group's detailed process and key techniques to build respective care services and collate a complete FHIR PG. Finally, as an example, we arbitrarily selected a use case outside the scope of the reviewed papers and mapped it back to the FHIR PG to demonstrate the effectiveness and generalizability of the PG. RESULTS The FHIR PG includes 2 core elements: one is a practice design that defines the responsibilities of stakeholders and outlines the complete procedure from data to services, and the other is a development architecture for practice design, which lists the available tools for each practice step and provides direct and actionable recommendations. CONCLUSIONS The FHIR PG can bridge the gap between IGs and the process of building actual services by proposing actionable methods, procedures, and tools. It assists stakeholders in identifying participants' roles, managing the scope of responsibilities, and developing relevant modules, thus helping promote FHIR-based interoperable health care services.
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Affiliation(s)
- Jingwen Nan
- Health IT Research, China Mobile (Chengdu) Industrial Research Institute, Chengdu, China
| | - Li-Qun Xu
- Health IT Research, China Mobile (Chengdu) Industrial Research Institute, Chengdu, China
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Jones EK, Hultman G, Schmoke K, Ninkovic I, Dodge S, Bahr M, Melton GB, Marquard J, Tignanelli CJ. Combined Expert and User-Driven Usability Assessment of Trauma Decision Support Systems Improves User-Centered Design. Surgery 2022; 172:1537-1548. [PMID: 36031451 DOI: 10.1016/j.surg.2022.05.037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/11/2022] [Accepted: 05/30/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND Trauma clinical decision support systems improve adherence with evidence-based practice but suffer from poor usability and the lack of a user-centered design. The objective of this study was to compare the effectiveness of user and expert-driven usability testing methods to detect usability issues in a rib fracture clinical decision support system and identify guiding principles for trauma clinical decision support systems. METHODS A user-driven and expert-driven usability investigation was conducted using a clinical decision support system developed for patients with rib fractures. The user-driven usability evaluation was as follows: 10 clinicians were selected for simulation-based usability testing using snowball sampling, and each clinician completed 3 simulations using a video-conferencing platform. End-users participated in a novel team-based approach that simulated realistic clinical workflows. The expert-driven heuristic evaluation was as follows: 2 usability experts conducted a heuristic evaluation of the clinical decision support system using 10 common usability heuristics. Usability issues were identified, cataloged, and ranked for severity using a 4-level ordinal scale. Thematic analysis was utilized to categorize the identified usability issues. RESULTS Seventy-nine usability issues were identified; 63% were identified by experts and 48% by end-users. Notably, 58% of severe usability issues were identified by experts alone. Only 11% of issues were identified by both methods. Five themes were identified that could guide the design of clinical decision support systems-transparency, functionality and integration into workflow, automated and noninterruptive, flexibility, and layout and appearance. Themes were preferentially identified by different methods. CONCLUSION We found that a dual-method usability evaluation involving usability experts and end-users drastically improved detection of usability issues over single-method alone. We identified 5 themes to guide trauma clinical decision support system design. Performing usability testing via a remote video-conferencing platform facilitated multi-site involvement despite a global pandemic.
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Affiliation(s)
- Emma K Jones
- Department of Surgery, University of Minnesota, Minneapolis, MN.
| | - Gretchen Hultman
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN
| | - Kristine Schmoke
- Veterans Health Administration, Department of Veterans Affairs, Washington, DC
| | | | - Sarah Dodge
- Fairview Health Services IT, Minneapolis, MN
| | - Matthew Bahr
- Trauma Services, Fairview Health Services, Minneapolis, MN
| | - Genevieve B Melton
- Department of Surgery, University of Minnesota, Minneapolis, MN; Institute for Health Informatics, University of Minnesota, Minneapolis, MN; Fairview Health Services IT, Minneapolis, MN; Center for Learning Health System Sciences, University of Minnesota, Minneapolis, MN
| | - Jenna Marquard
- School of Nursing, University of Minnesota, Minneapolis, MN
| | - Christopher J Tignanelli
- Department of Surgery, University of Minnesota, Minneapolis, MN; Institute for Health Informatics, University of Minnesota, Minneapolis, MN; Center for Learning Health System Sciences, University of Minnesota, Minneapolis, MN
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Jenssen BP, Karavite DJ, Kelleher S, Nekrasova E, Thayer JG, Ratwani R, Shea J, Nabi-Burza E, Drehmer JE, Winickoff JP, Grundmeier RW, Schnoll RA, Fiks AG. Electronic Health Record-Embedded, Behavioral Science-Informed System for Smoking Cessation for the Parents of Pediatric Patients. Appl Clin Inform 2022; 13:504-515. [PMID: 35584789 DOI: 10.1055/s-0042-1748148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Helping parents quit smoking is a public health priority. However, parents are rarely, if ever, offered tobacco use treatment through pediatric settings. Clinical decision support (CDS) systems developed for the workflows of pediatric primary care may support consistent screening, treatment, and referral. OBJECTIVES This study aimed to develop a CDS system by using human-centered design (HCD) that identifies parents who smoke, provides motivational messages to quit smoking (informed by behavioral science), and supports delivery of evidence-based tobacco treatment. METHODS Our multidisciplinary team applied a rigorous HCD process involving analysis of the work environment, user involvement in formative design, iterative improvements, and evaluation of the system's use in context with the following three cohorts: (1) parents who smoke, (2) pediatric clinicians, and (3) clinic staff. Participants from each cohort were presented with scenario-based, high-fidelity mockups of system components and then provided input related to their role in using the CDS system. RESULTS We engaged 70 representative participants including 30 parents, 30 clinicians, and 10 clinic staff. A key theme of the design review sessions across all cohorts was the need to automate functions of the system. Parents emphasized a system that presented information in a simple way, highlighted benefits of quitting smoking, and allowed direct connection to treatment. Pediatric clinicians emphasized automating tobacco treatment. Clinical staff emphasized screening for parent smoking via several modalities prior to the patient's visit. Once the system was developed, most parents (80%) reported that it was easy to use, and the majority of pediatricians reported that they would use the system (97%) and were satisfied with it (97%). CONCLUSION A CDS system to support parental tobacco cessation in pediatric primary care, developed through an HCD process, proved easy to use and acceptable to parents, clinicians, and office staff. This preliminary work justifies evaluating the impact of the system on helping parents quit smoking.
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Affiliation(s)
- Brian P Jenssen
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Center for Pediatric Clinical Effectiveness, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States.,Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Dean J Karavite
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Shannon Kelleher
- Center for Pediatric Clinical Effectiveness, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Ekaterina Nekrasova
- Center for Pediatric Clinical Effectiveness, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Jeritt G Thayer
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Raj Ratwani
- MedStar Health National Center for Human Factors in Healthcare, Washington, Dist. of Columbia, United States
| | - Judy Shea
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Emara Nabi-Burza
- Division of General Academic Pediatrics and Tobacco Research and Treatment Center, Massachusetts General Hospital for Children, Boston, Massachusetts, United States
| | - Jeremy E Drehmer
- Division of General Academic Pediatrics and Tobacco Research and Treatment Center, Massachusetts General Hospital for Children, Boston, Massachusetts, United States
| | - Jonathan P Winickoff
- Division of General Academic Pediatrics and Tobacco Research and Treatment Center, Massachusetts General Hospital for Children, Boston, Massachusetts, United States
| | - Robert W Grundmeier
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Robert A Schnoll
- Department of Psychiatry and Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Alexander G Fiks
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Center for Pediatric Clinical Effectiveness, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States.,Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
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