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Search All Research Studies
Topics
- Adverse Events (1)
- Ambulatory Care and Surgery (1)
- Antibiotics (1)
- Burnout (1)
- Children/Adolescents (1)
- (-) Clinical Decision Support (CDS) (11)
- Electronic Health Records (EHRs) (2)
- Emergency Medical Services (EMS) (1)
- Evidence-Based Practice (2)
- Falls (2)
- Healthcare Costs (1)
- Health Information Technology (HIT) (4)
- Health Services Research (HSR) (1)
- Hospitalization (1)
- Hospital Readmissions (1)
- (-) Hospitals (11)
- Injuries and Wounds (2)
- Inpatient Care (1)
- Medication (1)
- Medication: Safety (1)
- Patient-Centered Healthcare (1)
- Patient and Family Engagement (1)
- Patient Safety (5)
- Pregnancy (1)
- Pressure Ulcers (1)
- Prevention (3)
- Quality Improvement (1)
- Quality Indicators (QIs) (1)
- Quality Measures (1)
- Quality of Care (2)
- Risk (2)
- Shared Decision Making (5)
- Tools & Toolkits (2)
- Women (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 11 of 11 Research Studies DisplayedBui LN, Marshall C, Miller-Rosales C
Hospital adoption of electronic decision support tools for preeclampsia management.
Maternal morbidity and mortality can be reduced by the utilization of evidence-based clinical guidelines for preeclampsia management. Electronic health record (EHR)-based clinical decision support tools can improve the use of those guidelines. The purpose of this study was to investigate the organizational capabilities and hospital adoption of HER-based decision tools for preeclampsia management. The researchers conducted a cross-sectional analysis of hospitals that provided obstetric care in 2017. A total of 739 hospitals that responded to the 2017-2018 National Survey of Healthcare Organizations and Systems (NSHOS) and their results were linked to the 2017 Area Health Resources File (AHRF) and the American Hospital Association (AHA) Annual Survey Database. A final total of 425 hospitals from 49 states were analyzed. The primary outcome of the analysis was whether a hospital adopted EHR-based clinical decision support tools for preeclampsia management. The study found that 68% of the hospitals utilized EHR-based decision support tools for preeclampsia, and that hospitals with a single EHR system were more likely to adopt EHR-based decision support tools for preeclampsia than hospitals with multiple systems, including a combination of EHR and paper-based systems. The researchers also determined that hospitals with more processes to disseminate best patient care practices were more likely to adopt EHR-based decision support tools for preeclampsia management. The study concluded that having standardized EHRs and policies to disseminate evidence can help hospitals advance the use of EHR-based decision support tools for preeclampsia management in those hospitals that have not yet adopted them.
AHRQ-funded; HS024075.
Citation: Bui LN, Marshall C, Miller-Rosales C .
Hospital adoption of electronic decision support tools for preeclampsia management.
Qual Manag Health Care 2022 Apr-Jun;31(2):59-67. doi: 10.1097/qmh.0000000000000328..
Keywords: Clinical Decision Support (CDS), Electronic Health Records (EHRs), Health Information Technology (HIT), Hospitals, Pregnancy, Women
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
Dykes PC, Burns Z, Adelman J
Evaluation of a patient-centered fall-prevention tool kit to reduce falls and injuries: a nonrandomized controlled trial.
The purpose of this study was to assess whether a fall-prevention tool kit that engages patients and families in the fall-prevention process throughout hospitalization is associated with reduced falls and injurious falls. Findings showed that, in this nonrandomized controlled trial, implementation of a fall-prevention tool kit was associated with a significant reduction in falls and related injuries. A patient-care team partnership appeared to be beneficial for prevention of falls and fall-related injuries.
AHRQ-funded; HS023535.
Citation: Dykes PC, Burns Z, Adelman J .
Evaluation of a patient-centered fall-prevention tool kit to reduce falls and injuries: a nonrandomized controlled trial.
JAMA Netw Open 2020 Nov 2;3(11):e2025889. doi: 10.1001/jamanetworkopen.2020.25889..
Keywords: Falls, Injuries and Wounds, Prevention, Tools & Toolkits, Patient and Family Engagement, Patient-Centered Healthcare, Clinical Decision Support (CDS), Hospitalization, Hospitals
Co Z, Holmgren AJ, Classen DC
The tradeoffs between safety and alert fatigue: data from a national evaluation of hospital medication-related clinical decision support.
This study evaluated the overall performance of hospitals that used the Computerized Physician Order Entry Evaluation Tool in 2017 and 2018 and compared performances for fatal orders and nuisance orders each year. The authors evaluated 1599 hospitals that took the test by using their overall percentage scores along with the percentage of fatal orders appropriately alerted on and the percentage of nuisance orders incorrectly alerted on. Overall hospital scores improved from 58.1% in 2017 to 66.2% in 2018. Fatal order performance improved slightly from 78.8% to 83.0%, but there no very little change in nuisance order performance (89.0% to 89.7%). Conclusions were that perhaps hospitals are not targeting the deadliest orders first and some hospitals may be achieving higher scores by over-alerting. This has the potential to cause clinician burnout and even worsen patient safety.
AHRQ-funded; HS023696.
Citation: Co Z, Holmgren AJ, Classen DC .
The tradeoffs between safety and alert fatigue: data from a national evaluation of hospital medication-related clinical decision support.
J Am Med Inform Assoc 2020 Aug;27(8):1252-58. doi: 10.1093/jamia/ocaa098..
Keywords: Medication: Safety, Medication, Patient Safety, Clinical Decision Support (CDS), Shared Decision Making, Burnout, Hospitals, Health Information Technology (HIT), Quality of Care
Classen DC, Holmgren AJ, Co Z
National trends in the safety performance of electronic health record systems from 2009 to 2018.
This study examined trends in the safety performance of electronic health records (EHRs) in hospitals from 2009 to 2018. The Leapfrog Health IT Safety Measure test was administered by the Leapfrog Group from July 2018 to December 1, 2019. Overall mean performance scores increased from 53.9% in 2009 to 65.6% in 2018. Mean hospital scores for categories representing basic clinical decision support increased from 69.8% in 2009 to 85.6% in 2018. Advanced decision clinical support also increased from 29.5% in 2009 to 46.1%. These results showed great improvement, but there is still substantial safety risk in current hospital EHR systems.
AHRQ-funded; HS023696.
Citation: Classen DC, Holmgren AJ, Co Z .
National trends in the safety performance of electronic health record systems from 2009 to 2018.
JAMA Netw Open 2020 May;3(5):e205547. doi: 10.1001/jamanetworkopen.2020.5547..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Hospitals, Patient Safety, Quality Measures, Clinical Decision Support (CDS), Quality Indicators (QIs)
Patterson ES, Dewart CM, Stevenson K
A mixed methods approach to tailoring evidence-based guidance for antibiotic stewardship to one medical system.
The purpose of the study is to operationalize a novel antibiotic advisor, called the personalized weighted incidence syndromic combination antibiogram (pWISCA), intended to help physicians with initial antibiotic choice in hospitals. This paper presents findings from applying a mixed methods approach to identifying and prioritizing antibiotic medications and associated contextual data to display in a clinical decision support tailored to the local hospital.
AHRQ-funded; HS024379.
Citation: Patterson ES, Dewart CM, Stevenson K .
A mixed methods approach to tailoring evidence-based guidance for antibiotic stewardship to one medical system.
Proc Int Symp Hum Factors Ergon Healthc 2018 Jun;7(1):224-31. doi: 10.1177/2327857918071053..
Keywords: Antibiotics, Clinical Decision Support (CDS), Evidence-Based Practice, Health Services Research (HSR), Hospitals
Aldina S, Goldhaber-Fiebert SN, Hannenberg AA
Factors associated with the use of cognitive aids in operating room crises: a cross-sectional study of US hospitals and ambulatory surgical centers.
This study examined organizational context and implementation process factors influencing the use of cognitive aids for OR crises. It found that small facility size was associated with a fourfold increase in the odds of a facility reporting more successful implementation. Completing more implementation steps was also significantly associated with more successful implementation.
AHRQ-funded; HS024235.
Citation: Aldina S, Goldhaber-Fiebert SN, Hannenberg AA .
Factors associated with the use of cognitive aids in operating room crises: a cross-sectional study of US hospitals and ambulatory surgical centers.
Implement Sci 2018 Mar 26;13(1):50. doi: 10.1186/s13012-018-0739-4.
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Keywords: Adverse Events, Ambulatory Care and Surgery, Patient Safety, Quality Improvement, Quality of Care, Hospitals, Shared Decision Making, Clinical Decision Support (CDS)
Wang J, Gong Y
Potential of decision support in preventing pressure ulcers in hospitals.
The development of hospital-acquired pressure ulcers signals low quality of care. To meet the challenges of consistently translating best practices into effective clinical practices and promote effective teamwork communication and interprofessional collaboration, the authors consider the failure of consistent care delivery as loss of information and reveal the opportunities of informatics methods to reinforce information delivery, evidenced by typical cases. They then explain and summarize information-related issues existing at the initial assessment upon hospital admission, routine treatments, and team communication.
AHRQ-funded; HS022895.
Citation: Wang J, Gong Y .
Potential of decision support in preventing pressure ulcers in hospitals.
Stud Health Technol Inform 2017;241:15-20.
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Keywords: Clinical Decision Support (CDS), Shared Decision Making, Hospitals, Patient Safety, Pressure Ulcers, Prevention
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
Bonafide CP, Roland D, Brady PW
Rapid response systems 20 years later: new approaches, old challenges.
In this article, the authors propose a set of recommendations for a research agenda aimed at pursuing the work of optimizing the identification of deteriorating children. They recommend that the second generation of pediatric rapid response systems continue to build on past achievements while further optimizing use of the data, tools, and people available at the bedside to take the next leap forward.
AHRQ-funded; HS023827.
Citation: Bonafide CP, Roland D, Brady PW .
Rapid response systems 20 years later: new approaches, old challenges.
JAMA Pediatr 2016 Aug;170(8):729-30. doi: 10.1001/jamapediatrics.2016.0398.
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Keywords: Children/Adolescents, Clinical Decision Support (CDS), Shared Decision Making, Emergency Medical Services (EMS), Hospitals
Nuckols TK, Asch SM, Patel V
Implementing Computerized Provider Order Entry In Acute Care Hospitals in the United States could generate substantial savings to society.
This study was conducted to evaluate from the societal perspective the cost-utility of implementing computerized physician order entry (CPOE) in acute care hospitals in the United States. It found that relative to paper ordering and using typical estimates of implementation costs, CPOE had, on average, a 99 percent probability of yielding savings to society and improving health.
AHRQ-funded; HS017954.
Citation: Nuckols TK, Asch SM, Patel V .
Implementing Computerized Provider Order Entry In Acute Care Hospitals in the United States could generate substantial savings to society.
Jt Comm J Qual Patient Saf 2015 Aug;41(8):341-50..
Keywords: Health Information Technology (HIT), Hospitals, Clinical Decision Support (CDS), Healthcare Costs