National Healthcare Quality and Disparities Report
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Search All Research Studies
Topics
- Children/Adolescents (1)
- (-) Clinical Decision Support (CDS) (4)
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- Decision Making (2)
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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 4 of 4 Research Studies DisplayedRizk S, Kaelin VC, Sim JGC
Implementing an electronic patient-reported outcome and decision support tool in early intervention.
The study’s 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 = 6), service coordinators (n = 9), and program leadership (n = 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 (“outer setting”) 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’s 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 hours and inpatient care needs within 72 hours 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 = 4322) or after (n = 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, 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), Decision Making, Implementation