National Healthcare Quality and Disparities Report
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Search All Research Studies
AHRQ Research Studies Date
Topics
- Antibiotics (1)
- Antimicrobial Stewardship (1)
- Cardiovascular Conditions (2)
- (-) Clinical Decision Support (CDS) (5)
- Diagnostic Safety and Quality (1)
- Elderly (1)
- Emergency Department (1)
- Falls (1)
- Health Information Technology (HIT) (2)
- Heart Disease and Health (2)
- Hospital Readmissions (1)
- Hospitals (1)
- Imaging (1)
- Medication (1)
- Provider: Clinician (1)
- (-) Risk (5)
- Shared Decision Making (2)
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 5 of 5 Research Studies DisplayedGallo T, Heise CW, Woosley RL
Clinician satisfaction with advanced clinical decision support to reduce the risk of torsades de pointes.
The purpose of this study was to create an advanced torsades de pointes (TdP) clinical decision support (CDS) advisory that provides relevant, patient-specific information, including 1-click management options, and to evaluate clinician satisfaction with the CDS. The researchers implemented the advanced TdP CDS across a health system comprising 29 hospitals. A brief electronic survey was developed to collect clinician feedback on the advisory and was emailed to 442 clinicians who received the advisory. Feedback was generally positive across the 38 responding providers, with 79% of respondents reporting that the advisory assisted with their care for their patients and 87% responding that the alerts clearly specified alternative actions. The researchers concluded that providers who receive an advanced TdP risk CDS alert generally view the alert favorably.
AHRQ-funded; HS026662.
Citation: Gallo T, Heise CW, Woosley RL .
Clinician satisfaction with advanced clinical decision support to reduce the risk of torsades de pointes.
J Patient Saf 2022 Sep 1;18(6):e1010-e13. doi: 10.1097/pts.0000000000000996..
Keywords: Clinical Decision Support (CDS), Shared Decision Making, Risk, Provider: Clinician, Heart Disease and Health, Cardiovascular Conditions
Jacobsohn GC, Leaf M, Liao F
Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.
The authors used a collaborative and iterative approach to design and implement an automated clinical decision support system (CDS) for Emergency Department (ED) providers to identify and refer older adult ED patients at high risk of future falls. The system was developed using collaborative input from an interdisciplinary design team and integrated seamlessly into existing ED workflows. A key feature of development was the unique combination of patient experience strategies, human-centered design, and implementation science, which allowed for the CDS tool and intervention implementation strategies to be designed simultaneously. Challenges included: usability problems, data inaccessibility, time constraints, low appointment availability, high volume of patients, and others. The study concluded that using the collaborative, iterative approach was successful in achieving all project goals, and could be applied to other cases.
AHRQ-funded; HS024558.
Citation: Jacobsohn GC, Leaf M, Liao F .
Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.
Healthc 2022 Mar;10(1):100598. doi: 10.1016/j.hjdsi.2021.100598..
Keywords: Elderly, Clinical Decision Support (CDS), Shared Decision Making, Falls, Risk, Emergency Department, Health Information Technology (HIT)
Marafino BJ, Schuler A, Liu VX
Predicting preventable hospital readmissions with causal machine learning.
This study’s goal was to assess the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention called the Transitions Program, which used electronic health records from Kaiser Permanent Northern California (KPNC). A total of 1,539,285 index hospitalizations meeting the inclusion criteria and occurring between June 2010 and December 2010 at 21 KPNC hospitals were analyzed. There was substantial heterogeneity in patients’ response to the intervention, with patients at somewhat lower risk appearing to have the largest predicted effects. The estimates appeared to be well calibrated. The results did suggest a mismatch between risk and treatment effects.
AHRQ-funded; HS022192.
Citation: Marafino BJ, Schuler A, Liu VX .
Predicting preventable hospital readmissions with causal machine learning.
Health Serv Res 2020 Dec;55(6):993-1002. doi: 10.1111/1475-6773.13586..
Keywords: Hospital Readmissions, Hospitals, Clinical Decision Support (CDS), Risk
Trubiano JA, Vogrin S, Chua KYL
Development and validation of a penicillin allergy clinical decision rule.
Penicillin allergy is a significant public health issue for patients, antimicrobial stewardship programs, and health services. Validated clinical decision rules are urgently needed to identify low-risk penicillin allergies that potentially do not require penicillin skin testing by a specialist. The objective of this study was to develop and validate a penicillin allergy clinical decision rule that enables point-of-care risk assessment of patient-reported penicillin allergies.
AHRQ-funded; HS026395.
Citation: Trubiano JA, Vogrin S, Chua KYL .
Development and validation of a penicillin allergy clinical decision rule.
JAMA Intern Med 2020 May;180(5):745-52. doi: 10.1001/jamainternmed.2020.0403..
Keywords: Antimicrobial Stewardship, Antibiotics, Medication, Clinical Decision Support (CDS), Risk
Levy AE, Shah NR, Matheny ME
Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: implications for natural language processing tools.
The authors investigated whether Natural Language Processing (NLP) tools could potentially help estimate myocardial perfusion imaging (MPI) risk. Subjects were VA patients who underwent stress MPI and coronary angiography 2009-11; stress test reports were randomly selected for analysis. The authors found that post-test ischemic risk was determinable but rarely reported in this sample of stress MPI reports. They conclude that this supports the potential use of NLP to help clarify risk and recommend further study of NLP in this context.
AHRQ-funded; HS022998.
Citation: Levy AE, Shah NR, Matheny ME .
Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: implications for natural language processing tools.
J Nucl Cardiol 2019 Dec;26(6):1878-85. doi: 10.1007/s12350-018-1275-y..
Keywords: Imaging, Risk, Clinical Decision Support (CDS), Health Information Technology (HIT), Diagnostic Safety and Quality, Cardiovascular Conditions, Heart Disease and Health