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Research Studies is a compilation of published research articles funded by 大象视频or authored by 大象视频researchers.

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1 to 6 of 6 Research Studies Displayed
Barton HJ, Maru A, Leaf MA
Academic detailing as a health information technology implementation method: supporting the design and implementation of an emergency department-based clinical decision support tool to prevent future falls.
This study investigated the effectiveness of academic detailing, a method involving personalized education sessions with clinicians, in implementing a machine learning-based clinical decision support (CDS) tool designed to prevent future falls in elderly emergency department patients. Through qualitative analysis of interviews with clinicians who had encountered the CDS tool, researchers identified several factors influencing its use, including aspects of the tool's design, clinicians' understanding of the tool and referral process, the fast-paced emergency department environment, clinicians' perception of patient fall risk, and the complexity of the referral process. Academic detailing sessions allowed for real-time clarification of misconceptions and demonstration of the tool's functionality, highlighting its potential as a valuable strategy for supporting the implementation and optimization of health information technologies. Additionally, insights gained from these sessions can inform both immediate adjustments to the implementation process and long-term redesign of the tool to better align with clinicians' needs and workflows.
AHRQ-funded; HS027735.
Citation: Barton HJ, Maru A, Leaf MA . Academic detailing as a health information technology implementation method: supporting the design and implementation of an emergency department-based clinical decision support tool to prevent future falls. JMIR Hum Factors 2024 Apr 18; 11:e52592. doi: 10.2196/52592..
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Implementation, Emergency Department, Falls, Prevention
Salwei ME, Hoonakker P, Carayon P
Usability of a human factors-based clinical decision support in the emergency department: lessons learned for design and implementation.
A human-centered design process was followed to assess the usability and adoption of human factors (HF)-based clinical decision support (CDS) in the emergency department (ED). A CDS was developed to aid in pulmonary embolism (PE) diagnosis, showing high usability in testing. However, despite positive perceptions, actual CDS usage remained low due to integration issues with clinician workflow. The findings highlight the need for ongoing refinement of CDS design to align with clinical workflows and enhance usability.
AHRQ-funded; HS026395; HS024558; HS022086. NIH 142099
Citation: Salwei ME, Hoonakker P, Carayon P . Usability of a human factors-based clinical decision support in the emergency department: lessons learned for design and implementation. Hum Factors 2024 Mar; 66(3):647-57. doi: 10.1177/00187208221078625.
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Emergency Department, Implementation
Rizk S, Kaelin VC, Sim JGC
Implementing an electronic patient-reported outcome and decision support tool in early intervention.
The study鈥檚 aim was to identify and prioritize early intervention (EI) stakeholders' perspectives of supports and barriers to implementing the Young Children's Participation and Environment Measure (YC-PEM), an electronic patient-reported outcome (e-PRO) tool, for scaling its implementation across multiple local and state EI programs. A mixed-methods study was conducted with EI families (n鈥=鈥6), service coordinators (n鈥=鈥9), and program leadership (n鈥=鈥7). Semi-structured interviews and focus groups were conducted and used to share quantitative trial results. All three stakeholder groups identified thematic supports and barriers across multiple constructs within each of four Consolidated Framework for Implementation Research (CFIR) domains: (1) Six themes for "intervention characteristics," (2) Six themes for "process," (3) Three themes for "inner setting," and (4) Four themes for "outer setting." Priorities from stakeholders included prioritized reaching families with diverse linguistic preferences and user navigation needs, further tailoring its interface with automated data capture and exchange processes ("process"); and fostering a positive implementation climate ("inner setting"). Improving EI access (鈥渙uter setting鈥) using YC-PEM e-PRO results was also articulated by service coordinators and program leadership.
AHRQ-funded; HS027583.
Citation: Rizk S, Kaelin VC, Sim JGC . Implementing an electronic patient-reported outcome and decision support tool in early intervention. Appl Clin Inform 2023 Jan; 14(1):91-107. doi: 10.1055/s-0042-1760631..
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Children/Adolescents, Evidence-Based Practice, Patient-Centered Outcomes Research, Implementation
Hinson JS, Klein E, Smith A
Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.
This study鈥檚 objective was to develop, implement, and evaluate an electronic health record (EHR) embedded clinical decision support (CDS) system that leveraged machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24鈥塰ours and inpatient care needs within 72鈥塰ours into rapidly interpretable COVID-19 Deterioration Risk Levels made viewable within ED clinician workflow. A retrospective cohort of 21,452 ED patients who visited one of five ED study sites was used to derive ML models and were prospectively validated in 15,670 ED visits that occurred before (n鈥=鈥4322) or after (n鈥=鈥11,348) CDS implementation. Model performance and numerous patient-oriented outcomes including in-hospital mortality were measured across study periods. ML model performance was excellent under all conditions. AUC ranged from 0.85 to 0.91 for prediction of critical care needs and 0.80-0.90 for inpatient care needs. Total mortality was unchanged across study periods but was reduced among high-risk patients after the implementation.
AHRQ-funded; HS026640.
Citation: Hinson JS, Klein E, Smith A . Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions. NPJ Digit Med 2022 Jul 16;5(1):94. doi: 10.1038/s41746-022-00646-1..
Keywords: COVID-19, Clinical Decision Support (CDS), Health Information Technology (HIT), Implementation, Electronic Health Records (EHRs), Emergency Department, Shared Decision Making
Salwei ME, Carayon P, Hoonakker PLT
Workflow integration analysis of a human factors-based clinical decision support in the emergency department.
Numerous challenges with the implementation, acceptance, and use of health IT are related to poor usability and a lack of integration of the technologies into clinical workflow, and have, therefore, limited the potential of these technologies to improve patient safety. In this paper, the investigators propose a definition and conceptual model of health IT workflow integration. Using interviews of 12 emergency department (ED) physicians, they identified 134 excerpts of barriers and facilitators to workflow integration of a human factors (HF)-based clinical decision support (CDS) implemented in the ED.
AHRQ-funded; HS022086.
Citation: Salwei ME, Carayon P, Hoonakker PLT . Workflow integration analysis of a human factors-based clinical decision support in the emergency department. Appl Ergon 2021 Nov;97:103498. doi: 10.1016/j.apergo.2021.103498..
Keywords: Emergency Department, Workflow, Clinical Decision Support (CDS), Health Information Technology (HIT), Implementation
Panattoni L, Stults CD, Chan AS
The human resource costs of implementing autopend clinical decision support to improve health maintenance.
This study estimated the costs of developing and implementing the Sutter Health autopend functionality within an existing electronic health maintenance (HM) reminder system. Findings showed that developing and implementing autopend took more than 3 years, involved 6 managers and 3 Epic programmers, and cost $201,500 and 2670 total hours, excluding the costs of implementing the initial HM reminder system. The autopend clinical decision support might be similarly costly for other organizations to implement if their managers need to complete comparable activities. However, electronic health record vendors could include autopend as a standard package to reduce development costs and improve the uptake of this promising clinical decision support tool.
AHRQ-funded; HS022631.
Citation: Panattoni L, Stults CD, Chan AS . The human resource costs of implementing autopend clinical decision support to improve health maintenance. Am J Manag Care 2020 Jul;26(7):e232-e36. doi: 10.37765/ajmc.2020.43766..
Keywords: Clinical Decision Support (CDS), Shared Decision Making, Implementation

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