National Healthcare Quality and Disparities Report
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AHRQ Research Studies Date
Topics
- Adverse Events (3)
- Ambulatory Care and Surgery (1)
- Cardiovascular Conditions (1)
- Decision Making (1)
- Elderly (3)
- (-) Emergency Department (5)
- Falls (2)
- Heart Disease and Health (1)
- Hospital Discharge (1)
- Hospital Readmissions (1)
- Medical Errors (1)
- Nursing Homes (1)
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- Patient Safety (3)
- (-) Risk (5)
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 DisplayedParedes AZ, Malik AT, Cluse M
Discharge disposition to skilled nursing facility after emergent general surgery predicts a poor prognosis.
Emergency general surgery can have a profound impact on the functional status of even previously independent patients. In this study, the investigators examined the role and influence of discharging a patient to a skilled nursing facility. They concluded that after accounting for patient severity and perioperative course, discharge to a skilled nursing facility was an independent risk factor for death, readmission, and postdischarge complications.
AHRQ-funded; HS022694.
Citation: Paredes AZ, Malik AT, Cluse M .
Discharge disposition to skilled nursing facility after emergent general surgery predicts a poor prognosis.
Surgery 2019 Oct;166(4):489-95. doi: 10.1016/j.surg.2019.04.034..
Keywords: Nursing Homes, Hospital Discharge, Elderly, Ambulatory Care and Surgery, Emergency Department, Outcomes, Hospital Readmissions, Outcomes, Risk
Patterson BW, Jacobsohn GC, Shah MN
Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.
This study examined development and validation of a pragmatic natural language processing (NLP) approach to identify fall risk in older adults after emergency department (ED) visits. A single center retrospective review using data from 500 emergency department provider notes on older adults age 65 and older were random selected for analysis. The NLP algorithm successfully identified falls in ED notes with over 90% precision, and looks promising to reduce labor-intensive manual abstraction.
AHRQ-funded; HS024558.
Citation: Patterson BW, Jacobsohn GC, Shah MN .
Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.
BMC Med Inform Decis Mak 2019 Jul 22;19(1):138. doi: 10.1186/s12911-019-0843-7..
Keywords: Adverse Events, Elderly, Emergency Department, Falls, Risk, Patient Safety
Pang PS, Fermann GJ, Hunter BR
TACIT (High Sensitivity Troponin T Rules Out Acute Cardiac Insufficiency Trial).
This study examined the use of high-sensitivity troponin assays to determine whether a patient presenting in the emergency department with chest pains is safe for discharge. An observational study called High Sensitivity Troponin T Rules Out Acute Cardiac Insufficiency Trial (TACIT) explored whether serial high-sensitivity troponin (hsTnT) might aid in making diagnosis of acute heart failure faster. The presence of hsTnT above the 99th percentile usually indicates acute heart failure. Patients in the cohort with hsTnT at or above the 99th percentile were older, more often male, less often black, and more likely to have chronic kidney disease. The study found no difference in risk for 90-day death or rehospitalization or return ED visits in the group with hsTnT above the 99th percentile than those with levels below the 99th percentile so hsTnT would not be considered useful.
AHRQ-funded; HS025411.
Citation: Pang PS, Fermann GJ, Hunter BR .
TACIT (High Sensitivity Troponin T Rules Out Acute Cardiac Insufficiency Trial).
Circ Heart Fail 2019 Jul;12(7):e005931. doi: 10.1161/circheartfailure.119.005931..
Keywords: Cardiovascular Conditions, Heart Disease and Health, Emergency Department, Risk, Decision Making
Patterson BW, Engstrom CJ, Sah V
Training and interpreting machine learning algorithms to evaluate fall risk after emergency department visits.
This study examined the potential of using machine learning algorithms to evaluate fall risk after an emergency department (ED) visit. They compared several machine learning methodologies for creation of a risk stratification algorithm to predict the outcome of a return visit for a fall within 6 months of an ED visit.
AHRQ-funded; HS024558; HS024342.
Citation: Patterson BW, Engstrom CJ, Sah V .
Training and interpreting machine learning algorithms to evaluate fall risk after emergency department visits.
Med Care 2019 Jul;57(7):560-66. doi: 10.1097/mlr.0000000000001140..
Keywords: Adverse Events, Elderly, Emergency Department, Falls, Risk, Patient Safety
Griffey RT, Schneider RM, Todorov AA
Critical review, development, and testing of a taxonomy for adverse events and near misses in the emergency department.
Researchers created and tested a taxonomy for adverse events (AEs) and near misses for use in the emergency department (ED). This taxonomy is patient-centered, as opposed to most taxonomies which fail to describe harm experienced by patients and focus instead on errors and uses too broad categorizations. The authors reviewed candidate taxonomies using an iterative process and selected the Adventist Health Systems AE taxonomy and modified it for use in the ED. After testing with reviewers, agreement with the criterion standard was 92% at the category level and 88% at the subcategory level. Performance from individual raters ranged from very good (88%) to near perfect (98%) at the main category level.
AHRQ-funded; HS025052.
Citation: Griffey RT, Schneider RM, Todorov AA .
Critical review, development, and testing of a taxonomy for adverse events and near misses in the emergency department.
Acad Emerg Med 2019 Jun;26(6):670-79. doi: 10.1111/acem.13724..
Keywords: Adverse Events, Emergency Department, Medical Errors, Patient Safety, Risk