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AHRQ Research Studies Date
Topics
- Clinical Decision Support (CDS) (2)
- Communication (1)
- (-) Data (6)
- Electronic Health Records (EHRs) (1)
- Healthcare Delivery (1)
- Health Information Technology (HIT) (1)
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- Infectious Diseases (1)
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- (-) Shared Decision Making (6)
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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 6 of 6 Research Studies DisplayedYang Y, Bass EJ, Sockolow PS
Knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization.
Researchers elicit knowledge related to expert decision-making processes to inform information technology design and related interventions. In this study, the investigators examine knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization. The investigators concluded that the data collection and validation methodology showed promise for knowledge elicitation in time-constrained situations.
AHRQ-funded; HS024537.
Citation: Yang Y, Bass EJ, Sockolow PS .
Knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization.
AMIA Annu Symp Proc 2018 Dec 5;2018:1127-36..
Keywords: Home Healthcare, Shared Decision Making, Health Information Technology (HIT), Data
Sockolow PS, Yang Y, Bass EJ
Data visualization of home care admission nurses' decision-making.
This study investigated nurses’ decision making regarding hospital to home care admissions. They conducted a focus group case study with six admitting home health nurses at a rural agency in Pennsylvania and analyzed the data using thematic analysis.
AHRQ-funded; HS024537.
Citation: Sockolow PS, Yang Y, Bass EJ .
Data visualization of home care admission nurses' decision-making.
AMIA Annu Symp Proc 2018 Apr 16;2017:1597-606..
Keywords: Data, Shared Decision Making, Home Healthcare, Nursing, Transitions of Care
Arcia A, Woollen J, Bakken S
A systematic method for exploring data attributes in preparation for designing tailored infographics of patient reported outcomes.
Tailored visualizations of patient reported outcomes (PROs) are valuable health communication tools to support shared decision making, health self-management, and engagement with research participants, such as cohorts in the NIH Precision Medicine Initiative. The authors of the study present a systematic method to exploring data attributes, with a specific focus on application to self-reported health data. They present two case studies to illustrate how this method affected design decisions particularly with respect to outlier and non-missing zero values.
AHRQ-funded; HS019853; HS022961.
Citation: Arcia A, Woollen J, Bakken S .
A systematic method for exploring data attributes in preparation for designing tailored infographics of patient reported outcomes.
eGEMS 2018 Jan 24;6(1):2. doi: 10.5334/egems.190..
Keywords: Communication, Shared Decision Making, Patient-Centered Healthcare, Patient-Centered Outcomes Research, Outcomes, Data
Roosan D, Samore M, Jones M
Big-data based decision-support systems to improve clinicians' cognition.
This study focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. It found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians' cognition to deal with complex problems. These cognitive strategies all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records.
AHRQ-funded; HS023349.
Citation: Roosan D, Samore M, Jones M .
Big-data based decision-support systems to improve clinicians' cognition.
IEEE Int Conf Healthc Inform 2016;2016:285-88. doi: 10.1109/ichi.2016.39.
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Keywords: Clinical Decision Support (CDS), Shared Decision Making, Data, Electronic Health Records (EHRs)
Roosan D, Del Fiol G, Butler J
Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain: a pilot study.
The objectives of this study were: 1) to explore the feasibility of extracting and displaying population-based information from an actual clinical population's database records, 2) to explore specific design features for improving population display, 3) to explore perceptions of population information displays, and 4) to explore the impact of population information display on cognitive outcomes. It concluded that a population database has great potential for reducing complexity and uncertainty in medicine to improve clinical care.
AHRQ-funded; HS023349.
Citation: Roosan D, Del Fiol G, Butler J .
Feasibility of population health analytics and data visualization for decision support in the infectious diseases domain: a pilot study.
Appl Clin Inform 2016 Jun 29;7(2):604-23. doi: 10.4338/aci-2015-12-ra-0182.
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Keywords: Clinical Decision Support (CDS), Data, Shared Decision Making, Infectious Diseases, Public Health
Marshall DA, Burgos-Liz L, Pasupathy KS
Transforming healthcare delivery: integrating dynamic simulation modelling and big data in health economics and outcomes research.
The authors discussed the synergies between big data and dynamic simulation modelling (DSM), practical considerations and challenges, and how integrating big data and DSM can be useful to decision makers to address complex, systemic health economics and outcomes questions and to transform healthcare delivery.
AHRQ-funded; HS023710.
Citation: Marshall DA, Burgos-Liz L, Pasupathy KS .
Transforming healthcare delivery: integrating dynamic simulation modelling and big data in health economics and outcomes research.
Pharmacoeconomics 2016 Feb;34(2):115-26. doi: 10.1007/s40273-015-0330-7.
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Keywords: Data, Shared Decision Making, Healthcare Delivery, Patient-Centered Healthcare, Patient-Centered Outcomes Research