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381. 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…

382. 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…

383. 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…

384. 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 …

385. Patient Safety | Digital Healthcare Research
     digital.ahrq.gov/health-care-theme/patient-safety
     January 01, 2023 - Patient Safety Disseminating and Implementing MedSMA℞T Families in Emergency Departments: A Randomized Control Trial to Assess Effectiveness of an Evidence-Based Gaming Intervention to Reduce Opioid Misuse Description This research tests the effectiveness of MedSMA℞T Mobile, a…

386. Improving Adherence and Outcomes by Artificial Intelligence-Adapted Text Messages - Final Report (pdf file)
     digital.ahrq.gov/sites/default/files/docs/citation/r21hs022336-farris-final-report-2017.pdf
     January 01, 2017 - messages to patients’ cell phones at designated dates and times; and (e) RL engine that will learn … individual patient it is making a decision for, while sharing data across the population in order to learn … context vector was operationalized using a method known as tile coding, which allows the agent to learn … shows that even though the majority of patients were highly adherent, the agent was still able to learn

387. El Dorado County Safety Net Technology Project/Access El Dorado County (ACCEL) | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/el-dorado-county-safety-net-technology-project-accel
     January 01, 2023 - ensure that the problem or barrier to getting appropriate health care is resolved and that clients learn

388. 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 …

389. 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…

390. 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…

391. 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…

392. 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…

393. 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). …

394. 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.…

395. Engaging Patients to Enable Interoperable Lung Cancer Decision Support at Scale | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/engaging-patients-enable-interoperable-lung-cancer-decision-support-scale
     January 01, 2024 - MyLungHealth will be “prescribed” to eligible patients so they can learn about lung cancer screening;

396. Health IT as a Means, not an End: How Health IT Supports Broad Quality Improvement for Medicaid and CHIP Beneficiaries (pdf file)
     digital.ahrq.gov/sites/default/files/docs/page/health_it_as_a_means__not_an_end__how_health_it_supports_broad_quality_improvement_for_medicaid_and_chip_beneficiaries_5.pdf
     March 29, 2010 - • Subscribe to CHCS eMail Updates to learn about new programs and resources. … • Learn about cutting-edge efforts to improve care for Medicaid’s highest-need, highest-cost members … 47 http://www.mhmc.info Quality Counts • Provider-based collaborative founded to help providers learn

397. 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…

398. Devine, Emily B. | Digital Healthcare Research
     digital.ahrq.gov/principal-investigator/devine-emily-b
     January 01, 2023 - Devine, Emily B. Customizing Value-Based Methods to Prioritize Implementation of Pharmacogenomic Clinical Decision Support for Learning Health Systems - Final Report Citation Devine E. Customizing Value-Based Methods to Prioritize Implementation of Pharmacogenomic Clinical Dec…

399. Leveraging Health System Telehealth and Informatics Infrastructure to Create a Continuum of Services for COVID-19 Screening, Testing, and Treatment: A Learning Health System Approach - Final Report | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/leveraging-health-system-telehealth-and-informatics-infrastructure-create/final-report
     January 01, 2023 - Leveraging Health System Telehealth and Informatics Infrastructure to Create a Continuum of Services for COVID-19 Screening, Testing, and Treatment: A Learning Health System Approach - Final Report Citation Simpson K., Harvey J. Leveraging Health System Telehealth and Informatics Infrastructure to Cre…

400. 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
     January 01, 2024 - 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…