National Healthcare Quality and Disparities Report
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Search All Research Studies
Topics
- Adverse Events (1)
- Cardiovascular Conditions (2)
- Clinical Decision Support (CDS) (1)
- Electronic Health Records (EHRs) (4)
- Emergency Department (1)
- Emergency Medical Services (EMS) (1)
- (-) Health Information Technology (HIT) (9)
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- Quality of Care (1)
- Risk (5)
- Sepsis (2)
- Surgery (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 9 of 9 Research Studies DisplayedChandran A, Xu C, Gross J
A web-based tool for quantification of potential gains in life expectancy by preventing cause-specific mortality.
In collaboration with the Baltimore City Health Department, the authors aimed to develop a web-based tool to estimate the potential lives saved and gains in life expectance (LE) in specific neighborhoods following interventions targeting achievable reductions in preventable deaths. Using the PROLONGER (ImPROved LONGEvity through Reductions in Cause-Specific Deaths) tool, they found that, if heart disease deaths could be reduced by 20% in a given neighborhood in Baltimore City, there could be up to a 2.3-year increase in neighborhood LE. Further, the neighborhoods with highest expected LE increase are not the same as those with highest heart disease mortality burden or lowest overall life expectancies. They concluded that focusing programs based on potential LE impact at the neighborhood level could lend new information for targeting of place-based public health interventions.
AHRQ-funded; HS000046.
Citation: Chandran A, Xu C, Gross J .
A web-based tool for quantification of potential gains in life expectancy by preventing cause-specific mortality.
Front Public Health 2021 Jul 1;9:663825. doi: 10.3389/fpubh.2021.663825..
Keywords: Mortality, Health Information Technology (HIT)
Young JC, Pack C, Gibson TB
Machine learning can unlock insights into mortality.
In this study, the investigators discuss the research implications of having disparate streams of health and mortality data; introduce how machine learning can help overcome these limitations; highlight important considerations for machine learning, including the risk of algorithmic bias; and briefly discuss best practices for applying machine learning to enhance public health research.
AHRQ-funded; HS000032.
Citation: Young JC, Pack C, Gibson TB .
Machine learning can unlock insights into mortality.
Am J Public Health 2021 Jul;111(S2):S65-S68. doi: 10.2105/ajph.2021.306418..
Keywords: Health Information Technology (HIT), Mortality
Angraal S, Mortazavi BJ, Gupta A
Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction.
This study developed models to predict the risk of death and hospitalization in patients with heart failure (HF) with preserved ejection fraction (HFpEF). Data was used from the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) clinical trial. Five methods: logistic regression with a forward selection of variables; logistic regression with a lasso regularization for variable selection; random forest (RF); gradient descent boosting; and support vector machine, were used to train models for assessing risks of mortality and HF hospitalization through 3 years of follow-up and were validated using 5-fold cross-validation. RF was found to be the best performing model for predicting mortality and HF hospitalization. Blood urea nitrogen levels, body mass index, and Kansas City Cardiomyopathy Questionnaire (KCCQ) subscale scores were strongly associated with mortality, while hemoglobin level, blood urea nitrogen, time since previous HF hospitalization, and KCCQ scores were the most significant predictors of HF hospitalization.
AHRQ-funded; HS023000.
Citation: Angraal S, Mortazavi BJ, Gupta A .
Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction.
JACC Heart Fail 2020 Jan;8(1):12-21. doi: 10.1016/j.jchf.2019.06.013..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Mortality, Hospitalization, Risk, Health Status, Health Information Technology (HIT)
Fritz BA, Cui Z, Zhang M
Deep-learning model for predicting 30-day postoperative mortality.
The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. In this study the investigators sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. They concluded that a deep-learning time-series model improved prediction compared with models with simple summaries of intraoperative data.
AHRQ-funded; HS024581.
Citation: Fritz BA, Cui Z, Zhang M .
Deep-learning model for predicting 30-day postoperative mortality.
Br J Anaesth 2019 Nov;123(5):688-95. doi: 10.1016/j.bja.2019.07.025..
Keywords: Adverse Events, Health Information Technology (HIT), Mortality, Risk, Surgery
Hannan EL, Barrett SC, Samadashvili Z
Retooling of paper-based outcome measures to electronic format: comparison of the NY State public risk model and EHR-derived risk models for CABG mortality.
This study assessed the feasibility of retooling the paper-based New York State coronary artery bypass graft (CABG) surgery statistical model for mortality and readmission into a model for electronic health records (EHRs). Researchers found that only 6 data elements could be extracted from the EHR, and outlier hospitals differed for readmission but was usable for mortality. They concluded that the EHR model was inferior to the NYS model, and that simplifying the EHR risk model couldn’t capture most of the risk factors in the NYS model.
AHRQ-funded; HS022647.
Citation: Hannan EL, Barrett SC, Samadashvili Z .
Retooling of paper-based outcome measures to electronic format: comparison of the NY State public risk model and EHR-derived risk models for CABG mortality.
Med Care 2019 May;57(5):377-84. doi: 10.1097/mlr.0000000000001104..
Keywords: Surgery, Electronic Health Records (EHRs), Health Information Technology (HIT), Mortality, Outcomes, Risk, Cardiovascular Conditions
Austrian JS, Jamin CT, Doty GR
Impact of an emergency department electronic sepsis surveillance system on patient mortality and length of stay.
The goal of this study was to determine if an electronic health record (EHR) based sepsis alert system could improve quality of care and clinical outcomes for patients with sepsis. A patient-level, interrupted time series study of emergency department patients with severe sepsis or septic shock was conducted, with an intervention introduced at the approximate mid-point--a system of interruptive sepsis alerts triggered by abnormal vital signs or laboratory results. Mean length of stay for patients with sepsis decreased significantly following the introduction of the alert, but the alert system had no effect on mortality or other clinical or process measures. The researchers conclude that a more sophisticated algorithm for sepsis identification is needed to improve outcomes.
AHRQ-funded; HS023683.
Citation: Austrian JS, Jamin CT, Doty GR .
Impact of an emergency department electronic sepsis surveillance system on patient mortality and length of stay.
J Am Med Inform Assoc 2018 May;25(5):523-29. doi: 10.1093/jamia/ocx072..
Keywords: Electronic Health Records (EHRs), Emergency Department, Health Information Technology (HIT), Hospitals, Mortality, Outcomes, Quality Improvement, Quality of Care, Sepsis
Taylor RA, Pare JR, Venkatesh AK
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing clinical decision rules (CDRs) and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. It concluded that this approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis.
AHRQ-funded; HS021271.
Citation: Taylor RA, Pare JR, Venkatesh AK .
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
Acad Emerg Med 2016 Mar;23(3):269-78. doi: 10.1111/acem.12876.
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Keywords: Emergency Medical Services (EMS), Mortality, Clinical Decision Support (CDS), Sepsis, Health Information Technology (HIT)
Amarasingham R, Velasco F, Xie B
Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients: validation and comparison to existing models.
The purpose of this study was to evaluate the degree to which electronic medical record-based risk models for 30-day readmission or mortality accurately identify high risk patients and to compare these models with published claims-based models. The researchers found that a new electronic multicondition model based on information derived from the electronic medical record predicted mortality and readmission at 30 days, and was superior to previously published claims-based models
AHRQ-funded; HS022418.
Citation: Amarasingham R, Velasco F, Xie B .
Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients: validation and comparison to existing models.
BMC Med Inform Decis Mak 2015 May 20;15:39. doi: 10.1186/s12911-015-0162-6.
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Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Mortality, Hospital Readmissions, Risk
Faerber AE, Horvath R, Stillman C
Development and pilot feasibility study of a health information technology tool to calculate mortality risk for patients with asymptomatic carotid stenosis: the Carotid Risk Assessment Tool (CARAT).
The researchers describe the development of the CArotid Risk Assessment Tool (CARAT) into a 2-year mortality risk calculator within the electronic medical record. They integrated the tool into the clinical workflow, trained the clinical team to use the tool, and assessed the feasibility and acceptability of the tool in one clinic setting.
AHRQ-funded; HS021581.
Citation: Faerber AE, Horvath R, Stillman C .
Development and pilot feasibility study of a health information technology tool to calculate mortality risk for patients with asymptomatic carotid stenosis: the Carotid Risk Assessment Tool (CARAT).
BMC Med Inform Decis Mak 2015;15:20. doi: 10.1186/s12911-015-0141-y..
Keywords: Health Information Technology (HIT), Electronic Health Records (EHRs), Mortality, Risk