National Healthcare Quality and Disparities Report
Latest available findings on quality of and access to health care
Data
- Data Infographics
- Data Visualizations
- Data Tools
- Data Innovations
- All-Payer Claims Database
- Healthcare Cost and Utilization Project (HCUP)
- Medical Expenditure Panel Survey (MEPS)
- AHRQ Quality Indicator Tools for Data Analytics
- State Snapshots
- United States Health Information Knowledgebase (USHIK)
- Data Sources Available from AHRQ
Search All Research Studies
AHRQ Research Studies Date
Topics
- Adverse Events (4)
- Ambulatory Care and Surgery (1)
- Cardiovascular Conditions (1)
- Children/Adolescents (1)
- Community-Acquired Infections (1)
- Elderly (5)
- Electronic Health Records (EHRs) (2)
- (-) Emergency Department (9)
- Falls (2)
- Health Information Technology (HIT) (2)
- Heart Disease and Health (1)
- Home Healthcare (2)
- Hospital Discharge (1)
- Hospitalization (2)
- Hospital Readmissions (1)
- Hospitals (1)
- Infectious Diseases (1)
- Medical Errors (2)
- Nursing Homes (1)
- Outcomes (1)
- Patient Safety (4)
- Quality of Care (1)
- (-) Risk (9)
- Sepsis (1)
- Shared Decision Making (1)
AHRQ Research Studies
Sign up: AHRQ Research Studies Email updates
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 DisplayedTopaz M, Woo K, Ryvicker M
Home healthcare clinical notes predict patient hospitalization and emergency department visits.
About 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. The aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes).
AHRQ-funded; HS027742.
Citation: Topaz M, Woo K, Ryvicker M .
Home healthcare clinical notes predict patient hospitalization and emergency department visits.
Nurs Res 2020 Nov/Dec;69(6):448-54. doi: 10.1097/nnr.0000000000000470..
Keywords: Elderly, Home Healthcare, Emergency Department, Hospitalization, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Griffey RT, Schneider RM, Todorov AA
The emergency department trigger tool: a novel approach to screening for quality and safety events.
The goal of this study was to develop an automated version of a previously developed emergency department (ED) trigger tool to track the likelihood of an adverse event. Thirty triggers were associated with risk of harm. The authors identified 1,726 records out of 76,894 ED visits with greater than or equal to 1 trigger. They compared the results of the automated tool to the previous version and found it performed well. They began with a broad set of candidate triggers and validated a computerized query that eliminates the need for manual screening of triggers and also identified a refined set of triggers associated with adverse events in the ED.
AHRQ-funded; HS025052.
Citation: Griffey RT, Schneider RM, Todorov AA .
The emergency department trigger tool: a novel approach to screening for quality and safety events.
Ann Emerg Med 2020 Aug;76(2):230-40. doi: 10.1016/j.annemergmed.2019.07.032..
Keywords: Emergency Department, Patient Safety, Adverse Events, Medical Errors, Quality of Care, Risk
Shang J, Russell D, Dowding D
A predictive risk model for infection-related hospitalization among home healthcare patients.
Infection prevention is a high priority for home healthcare (HHC), but tools are lacking to identify patients at highest risk of developing infections. The purpose of this study was to develop and test a predictive risk model to identify HHC patients at risk of an infection-related hospitalization or emergency department visit. A nonexperimental study using secondary data was conducted.
AHRQ-funded; HS024723.
Citation: Shang J, Russell D, Dowding D .
A predictive risk model for infection-related hospitalization among home healthcare patients.
J Healthc Qual 2020 May/Jun;42(3):136-47. doi: 10.1097/jhq.0000000000000214..
Keywords: Elderly, Home Healthcare, Infectious Diseases, Community-Acquired Infections, Risk, Hospitalization, Emergency Department
Scott HF, Colborn KL, Sevick CJ
Development and validation of a predictive model of the risk of pediatric septic shock using data known at the time of hospital arrival.
The purpose of this observational cohort study was to derive and validate a model of risk of septic shock among children with suspected sepsis, using data known in the electronic health record at hospital arrival. The investigators concluded that their model estimated the risk of septic shock in children at hospital arrival earlier than existing models. They indicate it leveraged the predictive value of routine electronic health record data through a modern predictive algorithm and suggest it has the potential to enhance clinical risk stratification in the critical moments before deterioration.
AHRQ-funded; HS025696.
Citation: Scott HF, Colborn KL, Sevick CJ .
Development and validation of a predictive model of the risk of pediatric septic shock using data known at the time of hospital arrival.
J Pediatr 2020 Feb;217:145-51.e6. doi: 10.1016/j.jpeds.2019.09.079..
Keywords: Children/Adolescents, Sepsis, Emergency Department, Hospitals, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Paredes 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, Shared 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