National Healthcare Quality and Disparities Report
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AHRQ Research Studies Date
Topics
- Adverse Events (1)
- Antibiotics (1)
- Antimicrobial Stewardship (1)
- Cardiovascular Conditions (2)
- (-) Clinical Decision Support (CDS) (12)
- Clinician-Patient Communication (1)
- Communication (1)
- Diagnostic Safety and Quality (1)
- Education: Continuing Medical Education (1)
- Elderly (2)
- Emergency Department (1)
- Evidence-Based Practice (1)
- Falls (3)
- Health Information Technology (HIT) (3)
- Heart Disease and Health (2)
- Home Healthcare (1)
- Hospital Readmissions (1)
- Hospitals (2)
- Human Immunodeficiency Virus (HIV) (1)
- Imaging (1)
- Injuries and Wounds (2)
- Inpatient Care (2)
- Medication (2)
- Patient Safety (2)
- Prevention (2)
- Primary Care (1)
- Provider: Clinician (1)
- (-) Risk (12)
- Shared Decision Making (6)
- Surgery (1)
- Tools & Toolkits (1)
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 12 of 12 Research Studies DisplayedShear K, Rice H, Garabedian PM
Usability testing of an interoperable computerized clinical decision support tool for fall risk management in primary care.
The purpose of this study was to conduct usability testing of the ASPIRE fall risk management tool for use in divergent primary care clinics. Participants recruited from two sites with different electronic health records and clinical organizations used ASPIRE across two clinical scenarios; they rated ASPIRE usability as above average, based on usability benchmarks. Time spent on tasks decreased significantly between the first and second scenarios, indicating ease of learnability. The authors conclude that ASPIRE could be integrated into diverse organizations, since it allows a tailored implementation without the need to build a new system for each organization. ASPIRE is therefore well positioned to impact the challenge of falls at scale.
AHRQ-funded; HS027557.
Citation: Shear K, Rice H, Garabedian PM .
Usability testing of an interoperable computerized clinical decision support tool for fall risk management in primary care.
Appl Clin Inform 2023 Mar;14(2):212-26. doi: 10.1055/a-2006-4936.
Keywords: Clinical Decision Support (CDS), Shared Decision Making, Health Information Technology (HIT), Falls, Primary Care, Risk, Prevention
Gallo 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)
Kostick KM, Blumenthal-Barby JS
Avoiding "toxic knowledge": the importance of framing personalized risk information in clinical decision-making.
In this article, the authors discuss personalized risk information in clinical decision making, concluding that the framing of this information’s intended purpose at the patient level should be tailored to the decision-making context as a patient perceives it, which may vary from patient to patient.
AHRQ-funded; HS027784.
Citation: Kostick KM, Blumenthal-Barby JS .
Avoiding "toxic knowledge": the importance of framing personalized risk information in clinical decision-making.
Per Med 2021 Mar;18(2):91-95. doi: 10.2217/pme-2020-0174..
Keywords: Clinical Decision Support (CDS), Shared Decision Making, Risk, Clinician-Patient Communication, Communication
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
Leeds IL, Rosenblum AJ, Wise PE
Eye of the beholder: risk calculators and barriers to adoption in surgical trainees.
This study examined barriers to surgical trainees in using risk calculator tools before surgery. A total of 124 surgical residents responded to a survey and most still favored more traditional methods for risk calculation including direct verbal communication, sketch diagrams, and brochures. Only about half or less were familiar with more contemporary tools such as best-worst case scenario framing, case-specific risk calculators, and all-procedure calculators.
AHRQ-funded; HS024736.
Citation: Leeds IL, Rosenblum AJ, Wise PE .
Eye of the beholder: risk calculators and barriers to adoption in surgical trainees.
Surgery 2018 Nov;164(5):1117-23. doi: 10.1016/j.surg.2018.07.002..
Keywords: Clinical Decision Support (CDS), Shared Decision Making, Education: Continuing Medical Education, Risk, Surgery
Dykes PC, Duckworth M, Cunningham S
Pilot testing Fall TIPS (Tailoring Interventions for Patient Safety): a patient-centered fall prevention toolkit.
Patient falls during an acute hospitalization cause injury, reduced mobility, and increased costs. The laminated paper Fall TIPS Toolkit (Fall TIPS) provides clinical decision support at the bedside by linking each patient's fall risk assessment with evidence-based interventions. The investigators examined strategies to integrate this evidence into clinical practice. They concluded that engaging hospital and clinical leadership is critical in translating evidence-based care into clinical practice. They address and detail barriers to adoption of the protocol to provide guidance for spread to other institutions.
AHRQ-funded; HS025128.
Citation: Dykes PC, Duckworth M, Cunningham S .
Pilot testing Fall TIPS (Tailoring Interventions for Patient Safety): a patient-centered fall prevention toolkit.
Jt Comm J Qual Patient Saf 2017 Aug;43(8):403-13. doi: 10.1016/j.jcjq.2017.05.002..
Keywords: Clinical Decision Support (CDS), Shared Decision Making, Evidence-Based Practice, Falls, Hospitals, Injuries and Wounds, Inpatient Care, Patient Safety, Prevention, Risk, Tools & Toolkits
Le P, Martinez KA, Pappas MA
A decision model to estimate a risk threshold for venous thromboembolism prophylaxis in hospitalized medical patients.
To determine a threshold for prophylaxis based on risk of venous thromboembolism, the researchers constructed a decision model with a decision-tree following patients for 3 months after hospitalization, and a lifetime Markov model with 3-month cycles. They found that the prophylaxis threshold was relatively insensitive to low-molecular-weight heparin cost and bleeding risk, but very sensitive to patient age and life expectancy.
AHRQ-funded; HS022883.
Citation: Le P, Martinez KA, Pappas MA .
A decision model to estimate a risk threshold for venous thromboembolism prophylaxis in hospitalized medical patients.
J Thromb Haemost 2017 Jun;15(6):1132-41. doi: 10.1111/jth.13687.
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Keywords: Adverse Events, Clinical Decision Support (CDS), Inpatient Care, Patient Safety, Risk
Yin MT, Shiau S, Rimland D
Fracture prediction with modified-FRAX in older HIV-infected and uninfected men.
The authors investigated considering HIV as a cause of secondary osteoporosis when calculating FRAX, a clinical fracture risk calculator, in HIV-infected individuals. They found that modified-FRAX underestimated the fracture rates more in older HIV-infected than in otherwise similar uninfected men. and they recommend further studies to determine how to risk stratify for screening and treatment in older HIV-infected individuals.
AHRQ-funded; HS018372.
Citation: Yin MT, Shiau S, Rimland D .
Fracture prediction with modified-FRAX in older HIV-infected and uninfected men.
J Acquir Immune Defic Syndr 2016 Aug 15;72(5):513-20. doi: 10.1097/qai.0000000000000998.
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Keywords: Clinical Decision Support (CDS), Elderly, Injuries and Wounds, Human Immunodeficiency Virus (HIV), Risk
McDonald MV, Feldman PH, Barron-Vaya Y
Outcomes of clinical decision support (CDS) and correlates of CDS use for home care patients with high medication regimen complexity: a randomized trial.
The researchers assessed the outcomes of a clinical decision support (CDS) intervention designed for home care patients with high medication regimen complexity (MRC) and examined correlates of CDS use. They found that eighty-two percent of intervention nurses used the CDS but for only 42 percent of their patients. Among intervention patients, CDS use (vs. non-use) was associated with reduced MRC and hospitalization.
AHRQ-funded; HS017837.
Citation: McDonald MV, Feldman PH, Barron-Vaya Y .
Outcomes of clinical decision support (CDS) and correlates of CDS use for home care patients with high medication regimen complexity: a randomized trial.
J Eval Clin Pract 2015 May 26;22(1):10-19. doi: 10.1111/jep.12383.
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Keywords: Clinical Decision Support (CDS), Home Healthcare, Medication, Risk