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
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 3 of 3 Research Studies DisplayedHinson JS, Klein E, Smith A
Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.
This study’s objective was to develop, implement, and evaluate an electronic health record (EHR) embedded clinical decision support (CDS) system that leveraged machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24 hours and inpatient care needs within 72 hours into rapidly interpretable COVID-19 Deterioration Risk Levels made viewable within ED clinician workflow. A retrospective cohort of 21,452 ED patients who visited one of five ED study sites was used to derive ML models and were prospectively validated in 15,670 ED visits that occurred before (n = 4322) or after (n = 11,348) CDS implementation. Model performance and numerous patient-oriented outcomes including in-hospital mortality were measured across study periods. ML model performance was excellent under all conditions. AUC ranged from 0.85 to 0.91 for prediction of critical care needs and 0.80-0.90 for inpatient care needs. Total mortality was unchanged across study periods but was reduced among high-risk patients after the implementation.
AHRQ-funded; HS026640.
Citation: Hinson JS, Klein E, Smith A .
Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.
NPJ Digit Med 2022 Jul 16;5(1):94. doi: 10.1038/s41746-022-00646-1..
Keywords: COVID-19, Clinical Decision Support (CDS), Health Information Technology (HIT), Implementation, Electronic Health Records (EHRs), Emergency Department, Shared Decision Making
Kukhareva PV, Weir C, Del Fiol G
Evaluation in Life Cycle of Information Technology (ELICIT) framework: supporting the innovation life cycle from business case assessment to summative evaluation.
The authors developed an evaluation framework for electronic health record-integrated innovations to support activities at four information technology (IT) life cycle phases: planning, development, implementation, and operation. The resulting Evaluation in Life Cycle of IT (ELICIT) framework covers these four phases and three measure levels: society, user, and IT. The ELICIT framework recommends 12 evaluation steps. The authors concluded that, as health care undergoes a digital transformation, it will be critical for EHR-integrated innovations to be systematically evaluated, and their framework can facilitate such evaluations.
AHRQ-funded; HS026198.
Citation: Kukhareva PV, Weir C, Del Fiol G .
Evaluation in Life Cycle of Information Technology (ELICIT) framework: supporting the innovation life cycle from business case assessment to summative evaluation.
J Biomed Inform 2022 Mar; 127:104014. doi: 10.1016/j.jbi.2022.104014..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Implementation
Barnes GD, Sippola E, Ranusch A
Implementing an electronic health record dashboard for safe anticoagulant management: learning from qualitative interviews with existing and potential users to develop an implementation process.
This study examined the implementation of electronic dashboards and the key barriers that were found. Semi-structured interviews were conducted at the national Veterans Health Affairs (VA) following implementation of a population health tool, and in Michigan for the Michigan Anticoagulation Quality Improvement Initiative (MAQI(2) dashboard tool designed for pharmacist or nurse use to monitor safe outpatient anticoagulant prescribing by physicians and other clinicians. A total of 45 stakeholders were interviewed, 32 at the VA, and 13 at MAQI(2). Five key determinants of implementation success were identified: (1) clinician authority and autonomy, (2) clinician self-identity and job satisfaction, (3) documentation and administrative needs, (4) staffing and work schedule, and (5) integration with existing information systems. Key differences between the two contexts included concerns about IT support and prioritization within MAQI(2) prior to implementation but not VHA after implementation and also concerns about authority and autonomy.
AHRQ-funded; HS026874.
Citation: Barnes GD, Sippola E, Ranusch A .
Implementing an electronic health record dashboard for safe anticoagulant management: learning from qualitative interviews with existing and potential users to develop an implementation process.
Implement Sci Commun 2022 Feb 2;3(1):10. doi: 10.1186/s43058-022-00262-w..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Blood Thinners, Medication, Implementation