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321. 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…

322. AHRQ Patient-Centered Outcomes Research Clinical Decision Support Learning Network (U18) | Digital Healthcare Research
     digital.ahrq.gov/funding-mechanism/ahrq-patient-centered-outcomes-research-clinical-decision-support-learning-network
     January 01, 2023 - AHRQ Patient-Centered Outcomes Research Clinical Decision Support Learning Network (U18) Patient-Centered Outcomes Research Clinical Decision Support Learning Network Description The Patient-Centered Clinical Decision Support Learning Network was created as a multistakeholder …

323. 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…

324. 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…

325. 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…

326. 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…

327. 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…

328. Enabling Shared Decision Making to Reduce Harm from Drug Interactions: An End-to-End Demonstration - Final Report (pdf file)
     digital.ahrq.gov/sites/default/files/docs/citation/u18hs027099-malone-final-report-2022.pdf
     January 01, 2022 - questionnaire (Table 2) showed that participants felt much more confident using the tool and would be able to learn … need the support of a technical person to use this system 3.17 I think that most people would learn … the system very cumbersome to use 2.17 I felt very confident using the system 3.33 I needed to learn … understandable 6 (100%) 4 (67%) 1 (17%) 1 (17%) Easy to become skillful 6 (100%) 5 (83%) 1 (17%) Easy to learn … “I learn better by what I see.”

329. 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). …

330. Identifying high need primary care patients using nursing knowledge and machine learning methods. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/implementing-personalized-cross-sector-transitional-care-management-promote/citation/identifying
     January 01, 2023 - Identifying high need primary care patients using nursing knowledge and machine learning methods. Citation Hewner S, Smith E, Sullivan SS. CIC 2022: Identifying high need primary care patients using nursing knowledge and machine learning methods. Appl Clin Inform. 2023 Mar 7. doi: 10.1055/a-2048-7343.…

331. Customizing Value-Based Methods to Prioritize Implementation of Pharmacogenomic Clinical Decision Support for Learning Health Systems | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/customizing-value-based-methods-prioritize-implementation-pharmacogenomic
     January 01, 2023 - Customizing Value-Based Methods to Prioritize Implementation of Pharmacogenomic Clinical Decision Support for Learning Health Systems Project Final Report ( PDF , 818.86 KB) Disclaimer Disclaimer The findings and conclusions in this document are those of the author(s), who are re…

332. Building and Implementing a Predictive Decision Support System Based on a Proactive Full Capacity Protocol to Mitigate Emergency Department Overcrowding Problems | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/building-and-implementing-predictive-decision-support-system-based-proactive
     September 29, 2025 - Building and Implementing a Predictive Decision Support System Based on a Proactive Full Capacity Protocol to Mitigate Emergency Department Overcrowding Problems Project Description Publications Using deep learning and predictive analytics, this research has the potential…

333. 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…

334. 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 …

335. 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…

336. 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…

337. 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…

338. 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…

339. 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 …

340. 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…