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261. Miller RH et al. 2004 "Physicians' use of electronic medical records: barriers and solutions." | Digital Healthcare Research
     digital.ahrq.gov/health-it-tools-and-resources/workflow-assessment-health-it-toolkit/research/miller-rh-et-al-2004
     January 01, 2004 - usability -especially for documenting progress notes - caused physicians to spend extra work time to learn

262. Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/machine-learning-validation-medication-regimen-complexity-critical-care/citation/machine
     January 01, 2023 - Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data. Citation Sikora A, Zhao B, Kong Y, Murray B, Shen Y. Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data. medRxiv [Preprint]. 202…

263. Building and maintaining trust in clinical decision support: Recommendations from the Patient‐Centered CDS Learning Network. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/patient-centered-outcomes-research-clinical-decision-support-learning-network/citation/building
     January 01, 2023 - Building and maintaining trust in clinical decision support: Recommendations from the Patient‐Centered CDS Learning Network. Citation Richardson JE, Middleton B, Platt JE, et al. Building and maintaining trust in clinical decision support: Recommendations from the Patient‐Centered CDS Learning Network…

264. HANDS Care Plan Tool Seeks to Improve Nurse Communication at Handoff in AHRQ-Funded Study | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/past-initiatives/transforming-healthcare-quality-through-health-it/hands-care-plan-tool-seeks
     January 01, 2023 - The nurses may ask them what their goals are for the day, learn whether they're aware of new medications

265. ML-ROVER: Machine Learning to Reduce Laboratory Test Overutilization | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/ml-rover-machine-learning-reduce-laboratory-test-overutilization
     January 01, 2025 - ML-ROVER: Machine Learning to Reduce Laboratory Test Overutilization Project Description Publications Implementing a validated machine learning based clinical decision support tool to reduce laboratory testing overutilization in pediatric intensive care unit patients will…

266. Machine Learning to Improve Patient Triage in the Emergency Department | Digital Healthcare Research
     digital.ahrq.gov/program-overview/research-stories/machine-learning-improve-patient-triage-emergency-department
     January 01, 2023 - Machine Learning to Improve Patient Triage in the Emergency Department Theme: Supporting Health Systems in Advancing Care Delivery Subtheme: Advancing Health Equity The use of an emergency department triage tool informed by machine learning has the potential to improve predictions around…

267. Synthesizing Lessons Learned Using Health Information Technology - Final Report | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/synthesizing-lessons-learned-using-health-information-technology/final-report
     January 01, 2023 - Synthesizing Lessons Learned Using Health Information Technology - Final Report Citation Nemeth L. Synthesizing Lessons Learned Using Health Information Technology - Final Report. (Prepared by the Medical University of South Carolina under Grant No. R03 HS018830). Rockville, MD: Agency for Healthcare …

268. Research Centers in Primary Care Practice Based Research and Learning - Final Report | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/research-centers-primary-care-practice-based-research-and-learning/final-report
     January 01, 2023 - Research Centers in Primary Care Practice Based Research and Learning - Final Report Citation Ornstein S. Research Centers in Primary Care Practice Based Research and Learning - Final Report. (Prepared by the Medical University of South Carolina under Grant No. P30 HS021678). Rockville, MD: Agency for…

269. Learning from Primary Care Meaningful Use Exemplars - Final Report | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/learning-primary-care-meaningful-use-exemplars/final-report
     January 01, 2023 - Learning from Primary Care Meaningful Use Exemplars - Final Report Citation Ornstein, S. Learning from Primary Care Meaningful Use Exemplars - Final Report. (Prepared by the Medical University of South Carolina under Grant No. R18 HS022701). Rockville, MD: Agency for Healthcare Research and Quality, 2…

270. Use of Push and Pull Health Information Exchange Technologies by Ambulatory Care Practices and the Impact on Potentially Avoidable Healthcare Utilization | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/use-push-and-pull-health-information-exchange-technologies-ambulatory-care
     January 01, 2023 - This approach is a mechanism for providers to learn additional information about a patient’s health and

271. Disseminating Knowledge and Research Findings at Conferences | Digital Healthcare Research
     digital.ahrq.gov/2020-year-review/research-dissemination/disseminating-knowledge-and-research-findings-conferences
     January 01, 2020 - Click on the links below in Table 3 to learn more about this research.  

272. Enhancing an EMR-Based Real-Time Sepsis Alert System Performance Through Machine Learning - Final Report | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/enhancing-emr-based-real-time-sepsis-alert-system-performance-through-machine/final-report
     January 01, 2023 - Enhancing an EMR-Based Real-Time Sepsis Alert System Performance Through Machine Learning - Final Report Citation Sherwin R. Enhancing an EMR-Based Real-Time Sepsis Alert System Performance Through Machine Learning - Final Report. (Prepared by Wayne State University under Grant No. R21 HS024750). Rock…

273. Nemeth, Lynne | Digital Healthcare Research
     digital.ahrq.gov/principal-investigator/nemeth-lynne
     January 01, 2023 - Nemeth, Lynne Synthesizing Lessons Learned Using Health Information Technology - Final Report Citation Nemeth L. Synthesizing Lessons Learned Using Health Information Technology - Final Report. (Prepared by the Medical University of South Carolina under Grant No. R03 HS018830)…

274. Blumenfeld, Barry H. | Digital Healthcare Research
     digital.ahrq.gov/principal-investigator/blumenfeld-barry-h
     January 01, 2023 - Blumenfeld, Barry H. A Stakeholder-driven Action Plan for Improving Pain Management, Opioid Use and Opioid Use Disorder Treatment Through Patient-Centered Clinical Decision Support. Citation Osheroff JA, Blumenfeld BH, Richardson JE, Lasater B and the Opioid Action Plan Worki…

275. Ornstein, Steven | Digital Healthcare Research
     digital.ahrq.gov/principal-investigator/ornstein-steven
     January 01, 2023 - Ornstein, Steven Learning From Primary Care EHR Exemplars About Health IT Safety - Final Report Citation Ornstein S. Learning From Primary Care EHR Exemplars About Health IT Safety - Final Report. (Prepared by Medical University of South Carolina under Grant No. R21 HS024327).…

276. Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/optimal-methods-notifying-clinicians-about-epilepsy-surgery-patients
     January 01, 2023 - Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients Project Final Report ( PDF , 647.83 KB) Disclaimer Disclaimer The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent…

277. Using the Electronic Health Record To Identify Children Likely To Suffer Last-Minute Surgery Cancellation | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/using-electronic-health-record-identify-children-likely-suffer-last-minute
     January 01, 2023 - Using the Electronic Health Record To Identify Children Likely To Suffer Last-Minute Surgery Cancellation Project Final Report ( PDF , 1.1 MB) Disclaimer Disclaimer The findings and conclusions in this document are those of the author(s), who are responsible for its content, and …

278. Case Study: Developing a State Medicaid Health IT Plan (SMHP): Lessons Learned From Oklahoma Medicaid | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/medicaid-and-chip/case-study-developing-state-medicaid-health-it-plan-smhp
     January 01, 2023 - Case Study: Developing a State Medicaid Health IT Plan (SMHP): Lessons Learned From Oklahoma Medicaid Case Study Developing a State Medicaid Health IT Plan (SMHP): Lessons Learned From Oklahoma Medicaid Prepared for: Agency for Healthcare Research and Quality U.S. Document Case Study: Developi…

279. Machine learning-augmented interventions in perioperative care: A systematic review and meta-analysis. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/enhanced-handoffs-echo/citation/machine
     January 01, 2024 - Machine learning-augmented interventions in perioperative care: A systematic review and meta-analysis. Citation Mehta D, Gonzalez XT, Huang G, Abraham J. Machine learning-augmented interventions in perioperative care: A systematic review and meta-analysis. Br J Anaesth. 2024 Dec;133(6):1159-1172. doi:…

280. Achieving Individualized Precision Prevention (IPP) through Scalable Infrastructure Employing the USPSTF Recommendations in Computable Form | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/achieving-individualized-precision-prevention-ipp-through-scalable
     January 01, 2023 - Achieving Individualized Precision Prevention (IPP) through Scalable Infrastructure Employing the USPSTF Recommendations in Computable Form Project Final Report ( PDF , 696.89 KB) Disclaimer Disclaimer   The findings and conclusions in this document are those of the author(s), …