Results

Total Results: 3,134 records

Showing results for "identify".

1. Using the Electronic Medical Record to Identify and Screen Patients at Risk for Delirium - Final Report | Digital Healthcare Research
   digital.ahrq.gov/ahrq-funded-projects/using-electronic-medical-record-identify-and-screen-patients-risk-delirium/final-report
   January 01, 2023 - Using the Electronic Medical Record to Identify and Screen Patients at Risk for Delirium - Final Report … Using the Electronic Medical Record to Identify and Screen Patients at Risk for Delirium - Final Report … PDF Using the Electronic Medical Record to Identify and Screen Patients at Risk for Delirium … Investigator Weckmann, Michelle Project Name Using the Electronic Medical Record to Identify

2. Using the Electronic Health Record To Identify Children Likely To Suffer Last-Minute Surgery Cancellation - Final Report | Digital Healthcare Research
   digital.ahrq.gov/ahrq-funded-projects/using-electronic-health-record-identify-children-likely-suffer-last-minute/final-report
   January 01, 2023 - Using the Electronic Health Record To Identify Children Likely To Suffer Last-Minute Surgery Cancellation … Using the Electronic Health Record To Identify Children Likely To Suffer Last-Minute Surgery Cancellation … Principal Investigator Pratap, Jayant Project Name Using the Electronic Health Record To Identify

3. Natural Language Processing to Identify and Rank Clinically Relevant Information for EHRs in the Emergency Department - Final Report | Digital Healthcare Research
   digital.ahrq.gov/ahrq-funded-projects/natural-language-processing-identify-and-rank-clinically-relevant-information/final-report
   January 01, 2023 - Natural Language Processing to Identify and Rank Clinically Relevant Information for EHRs in the Emergency … Natural Language Processing to Identify and Rank Clinically Relevant Information for EHRs in the Emergency … Project Name Natural Language Processing To Identify and Rank Clinically Relevant Information for

4. Natural language processing to identify adverse drug events. | Digital Healthcare Research
   digital.ahrq.gov/ahrq-funded-projects/surveillance-adverse-drug-events-ambulatory-pediatrics/citation/natural
   January 01, 2023 - Natural language processing to identify adverse drug events. … Natural language processing to identify adverse drug events.

5. The GuideLine Implementability Appraisal (GLIA): development of an instrument to identify obstacles to guideline implementation. | Digital Healthcare Research
   digital.ahrq.gov/ahrq-funded-projects/improving-guideline-development-and-implementation/citation/guideline
   January 01, 2023 - The GuideLine Implementability Appraisal (GLIA): development of an instrument to identify obstacles to … The GuideLine Implementability Appraisal (GLIA): development of an instrument to identify obstacles to

6. Machine learning approaches to identify nicknames from a statewide Health Information Exchange. | Digital Healthcare Research
   digital.ahrq.gov/ahrq-funded-projects/enhancing-patient-matching-support-operational-health-information-exchange/citation/machine
   January 01, 2023 - Machine learning approaches to identify nicknames from a statewide Health Information Exchange. … Machine learning approaches to identify nicknames from a statewide Health Information Exchange.

7. Using active learning to identify health Information technology related patient safety events. | Digital Healthcare Research
   digital.ahrq.gov/ahrq-funded-projects/developing-evidence-based-user-centered-design-and-implementation-guidelines/citation/using
   January 01, 2023 - Using active learning to identify health Information technology related patient safety events. … Using active learning to identify health Information technology related patient safety events.

8. Validated risk rule using computerized data to identify males at high risk for fracture. | Digital Healthcare Research
   digital.ahrq.gov/ahrq-funded-projects/knowledge-engineering-decision-support-osteoporosis/citation/validated-risk
   January 01, 2023 - Validated risk rule using computerized data to identify males at high risk for fracture. … Validated risk rule using computerized data to identify males at high risk for fracture.

9. Using language models to identify relevant new information in inpatient clinical notes. | Digital Healthcare Research
   digital.ahrq.gov/ahrq-funded-projects/discovery-and-visualization-new-information-clinical-reports-electronic-health/citation/using
   January 01, 2023 - Using language models to identify relevant new information in inpatient clinical notes. … Using language models to identify relevant new information in inpatient clinical notes.

10. Application of electronic trigger tools to identify targets for improving diagnostic safety. | Digital Healthcare Research
    digital.ahrq.gov/ahrq-funded-projects/using-electronic-data-improve-care-patients-known-or-suspected-cancer/citation/application
    January 01, 2023 - Application of electronic trigger tools to identify targets for improving diagnostic safety. … Application of electronic trigger tools to identify targets for improving diagnostic safety.

11. Safety huddles to proactively identify and address electronic health record safety. | Digital Healthcare Research
    digital.ahrq.gov/ahrq-funded-projects/decision-making-and-clinical-work-test-result-followup-health-information/citation/safety
    January 01, 2023 - Safety huddles to proactively identify and address electronic health record safety. … Safety huddles to proactively identify and address electronic health record safety.

12. Grading the evidence to identify strategies to modify risk for necrotizing enterocolitis. | Digital Healthcare Research
    digital.ahrq.gov/ahrq-funded-projects/clinical-decision-support-optimizing-necrotizing-enterocolitis-prevention/citation/grading
    January 01, 2023 - Grading the evidence to identify strategies to modify risk for necrotizing enterocolitis. … Grading the evidence to identify strategies to modify risk for necrotizing enterocolitis.

13. Overlaying multiple sources of data to identify bottlenecks in clinical workflow. | Digital Healthcare Research
    digital.ahrq.gov/ahrq-funded-projects/impact-meaningful-use-clinical-workflow-emergency-departments/citation/overlaying
    January 01, 2023 - Overlaying multiple sources of data to identify bottlenecks in clinical workflow. … Overlaying multiple sources of data to identify bottlenecks in clinical workflow.

14. Using workflow modeling to identify areas to improve genetic test processes in the University of Maryland Translational Pharmacogenomics Project. | Digital Healthcare Research
    digital.ahrq.gov/ahrq-funded-projects/electronic-health-record-linked-decision-support-communicating-genomic-data/citation/workflow
    January 01, 2023 - Using workflow modeling to identify areas to improve genetic test processes in the University of Maryland … Using workflow modeling to identify areas to improve genetic test processes in the University of Maryland

15. Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients. | Digital Healthcare Research
    digital.ahrq.gov/ahrq-funded-projects/machine-learning-validation-medication-regimen-complexity-critical-care/citation/cluster
    January 01, 2023 - Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes … Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes

16. Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction. | Digital Healthcare Research
    digital.ahrq.gov/ahrq-funded-projects/artificial-intelligence-based-health-it-tools-optimize-critical-care/citation/unsupervised
    January 01, 2023 - Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload … Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload

17. Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction. | Digital Healthcare Research
    digital.ahrq.gov/ahrq-funded-projects/machine-learning-validation-medication-regimen-complexity-critical-care/citation/unsupervised
    January 01, 2023 - Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload … Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload

18. Supporting patient-provider collaboration to identify individual triggers using food and symptom journals. | Digital Healthcare Research
    digital.ahrq.gov/ahrq-funded-projects/sharing-patient-lifelog-data-primary-care-team-two-patient-populations/citation/supporting
    January 01, 2023 - Supporting patient-provider collaboration to identify individual triggers using food and symptom journals … Supporting patient-provider collaboration to identify individual triggers using food and symptom journals

19. Mind the gap. A systematic review to identify usability and safety challenges and practices during electronic health record implementation. | Digital Healthcare Research
    digital.ahrq.gov/ahrq-funded-projects/developing-evidence-based-user-centered-design-and-implementation-guidelines/citation/mind
    January 01, 2023 - A systematic review to identify usability and safety challenges and practices during electronic health … A systematic review to identify usability and safety challenges and practices during electronic health

20. Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. | Digital Healthcare Research
    digital.ahrq.gov/ahrq-funded-projects/anesthesiology-control-tower-feedback-alerts-supplement-treatment-actfast/citation/use
    January 01, 2023 - Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify … Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify