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381. (pdf file)
     digital.ahrq.gov/sites/default/files/docs/ahrq_nrc_multi_grantee_meeting_discussion_summary_patient_recruitment.pdf
     June 01, 2010 - the NRC conducted a multi-grantee Webinar in April 2010 to provide an opportunity for grantees to learn … Patients could learn more about the study on this page and could also complete the consent form. … Effective approaches for engagement include hosting “lunch and learn” sessions, providing clinicians

382. Electronic Data Methods Forum (2010-2017) | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/past-initiatives/electronic-data-methods-edm-forum
     January 01, 2023 - Electronic Data Methods Forum (2010-2017) The Electronic Data Methods (EDM) Forum was established in 2010 as a cooperative agreement with AHRQ to advance the national dialogue on the use of electronic health data for research and quality improvement. The EDM Forum facilitated learning and co…

383. Epilepsy | Digital Healthcare Research
     digital.ahrq.gov/medical-condition/epilepsy
     January 01, 2023 - Epilepsy Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial. Citation Wissel BD, Greiner HM, Glauser TA, Mangano FT, Holland-Bouley KD, Zhang N, Szczesniak RD, Santel D, Pestian JP, Dexheimer JW. Automated, machine learn…

384. Held, Philip | Digital Healthcare Research
     digital.ahrq.gov/principal-investigator/held-philip
     January 01, 2023 - Held, Philip A Machine Learning Health System to Integrate Care for Substance Misuse and HIV Treatment and Prevention Among Hospitalized Patients - Final Report Citation Held M., Thompson H. A Machine Learning Health System to Integrate Care for Substance Misuse and HIV Treatm…

385. Thompson, Hale M. | Digital Healthcare Research
     digital.ahrq.gov/principal-investigator/thompson-hale-m
     January 01, 2023 - Thompson, Hale M. A Machine Learning Health System to Integrate Care for Substance Misuse and HIV Treatment and Prevention Among Hospitalized Patients - Final Report Citation Held M., Thompson H. A Machine Learning Health System to Integrate Care for Substance Misuse and HIV T…

386. Deep-learning model for predicting 30-day postoperative mortality. | AHRQ Digital Healthcare Research: Informing Improvement in Care Quality, Safety, and Efficiency
     digital.ahrq.gov/ahrq-funded-projects/anesthesiology-control-tower-feedback-alerts-supplement-treatment-actfast/citation/deep
     January 01, 2019 - Deep-learning model for predicting 30-day postoperative mortality. Citation: Fritz BA, Cui Z, Zhang M, He Y, Chen Y, Kronzer A, Ben Abdallah A, King CR, Avidan MS. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth. 2019 Nov;123(5):688-695. doi: 10.1016/j.bja.2019.07.025. Epub 2019 …

387. Predicting self-intercepted medication ordering errors using machine learning. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/etiology-medication-ordering-errors-computerized-provider-order-entry-systems/citation/predicting
     January 01, 2023 - Predicting self-intercepted medication ordering errors using machine learning. Citation King CR, Abraham J, Fritz BA, Cui Z, Galanter W, Chen Y, Kannampallil T. Predicting self-intercepted medication ordering errors using machine learning. PLoS One. 2021 Jul 14;16(7):e0254358. doi: 10.1371/journal.po…

388. Toward an Optimal Patient Safety Information System - 2008 | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/toward-optimal-patient-safety-information-system/annual-summary/2008
     January 01, 2008 - The ways in which hospitals learn about adverse events can impact the way in which events are addressed

389. AHRQ National Web Conference Q&A - Assessing Safety Risks Associated with EHRs (pdf file)
     digital.ahrq.gov/sites/default/files/docs/safety-risks-ehr-qa-082916.pdf
     August 29, 2016 - QUESTION: When providers catch themselves and retract an order, do they seem to learn from the experience … And when they identify a problem, it usually gets put into a learn system loop as well as an intervention

390. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/hopscore-electronic-outcomes-based-emergency-triage-system/citation/machine-learning
     January 01, 2023 - Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Citation Levin S, Toerper M, Hamrock E, et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to…

391. Applying machine learning in distributed data networks for pharmacoepidemiologic and pharmacovigilance studies: Opportunities, challenges, and considerations. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/improving-missing-data-analysis-distributed-research-networks/citation/applying
     January 01, 2023 - Applying machine learning in distributed data networks for pharmacoepidemiologic and pharmacovigilance studies: Opportunities, challenges, and considerations. Citation Wong J, Prieto-Alhambra D, Rijnbeek PR, Desai RJ, Reps JM, Toh S. Applying machine learning in distributed data networks for pharmacoe…

392. 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 risks of postoperative complications. Citation Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of machine learning to develop and evaluate models using preopera…

393. 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 prediction. Citation Keats K, Deng S, Chen X, Zhang T, Devlin JW, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, Sikora A. Unsupervised machine learning analysis to identify patterns of ICU medication …

394. The RHIO as Convener and Catalyst: The Birth of EHR of Rhode Island. (pdf file)
     digital.ahrq.gov/sites/default/files/docs/page/2006Adams_051311comp.pdf
     June 05, 2006 - guiding principles (and practice them) – Invite the early adopter/opinion leaders to participate – Learn

395. Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/improving-diabetes-and-depression-self-management-adaptive-mobile-messaging/citation/adaptive
     January 01, 2023 - Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. Citation Figueroa CA, Aguilera A, Chakraborty B, Modiri A, Aggarwal J, Deliu N, Sarkar U, Jay Williams J, Lyles CR. Adaptive learning algorithms to optimize mobile applications for beh…

396. Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/using-electronic-health-record-identify-children-likely-suffer-last-minute/citation/mining
     January 01, 2023 - Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery. Citation Liu L, Ni Y, Zhang N, Nick Pratap J. Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery. Int …

397. Early identification of epilepsy surgery candidates: A multicenter, machine learning study. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/optimal-methods-notifying-clinicians-about-epilepsy-surgery-patients/citation/early
     January 01, 2023 - Early identification of epilepsy surgery candidates: A multicenter, machine learning study. Citation Wissel BD, Greiner HM, Glauser TA, Pestian JP, Kemme AJ, Santel D, Ficker DM, Mangano FT, Szczesniak RD, Dexheimer JW. Early identification of epilepsy surgery candidates: A multicenter, machine learni…

398. Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/artificial-intelligence-based-health-it-tools-optimize-critical-care/citation/machine
     January 01, 2023 - Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU. Citation Sikora A, Zhang T, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, Chen X, Buckley MS, Rowe S, Devlin JW. Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU…

399. Fiks, Alexander | Digital Healthcare Research
     digital.ahrq.gov/principal-investigator/fiks-alexander
     January 01, 2023 - Fiks, Alexander National Center for Pediatric Practice Based Research and Learning - Final Report Citation Fiks A. National Center for Pediatric Practice Based Research and Learning - Final Report. (Prepared by the American Academy of Pediatrics under Grant No. P30 HS021645). …

400. American Academy of Pediatrics | Digital Healthcare Research
     digital.ahrq.gov/organization/american-academy-pediatrics
     January 01, 2023 - American Academy of Pediatrics National Center for Pediatric Practice Based Research and Learning - 2012 Principal Investigator Wasserman, Richard Project Name National Center for Pediatric Practice-Based Research and Learning National Cen…