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321. Case Study: Developing an Electronic Prescribing Incentive Program: Lessons Learned from New York Medicaid | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/medicaid-and-chip/case-study-developing-electronic-prescribing-incentive
     January 01, 2023 - Case Study: Developing an Electronic Prescribing Incentive Program: Lessons Learned from New York Medicaid Case Study Developing an Electronic Prescribing Incentive Pr ogram: Lessons Learned From New York Medicaid Prepared for: Agency for Healthcare Research and Quality U.S. Department Document …

322. Case Study: Leveraging Existing Leadership to Support Health IT and HIE: Lessons Learned from Minnesota’s Medical Assistance Program | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/medicaid-and-chip/case-study-leveraging-existing-leadership-support-health-it
     January 01, 2023 - Case Study: Leveraging Existing Leadership to Support Health IT and HIE: Lessons Learned from Minnesota’s Medical Assistance Program Case Study Leveraging Existing Leadership to Support Health IT and HIE: Lessons Learned From Minnesota’s Medical Assistance Program Prepared for: Agency for Healthcare Resea…

323. 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:…

324. Improving Diabetes and Depression Self-Management Via Adaptive Mobile Messaging | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/improving-diabetes-and-depression-self-management-adaptive-mobile-messaging
     January 01, 2024 - Improving Diabetes and Depression Self-Management Via Adaptive Mobile Messaging Project Final Report ( PDF , 544.36 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 repr…

325. 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…

326. 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 of critically ill patients. Citation Sikora A, Jeong H, Yu M, Chen X, Murray B, Kamaleswaran R. Cluster analysis driven by unsupervised latent feature learning of medications to identify…

327. 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 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 …

328. 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).…

329. 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 …

330. 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…

331. Learning from primary care meaningful use exemplars. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/learning-primary-care-meaningful-use-exemplars/citation/learning-primary-care
     January 01, 2023 - Learning from primary care meaningful use exemplars. Citation Ornstein SM, Nemeth LS, Nietert PJ, et al. Learning from primary care meaningful use exemplars. J Am Board Fam Med 2015;28(3):360-70. PMID: 25957369. Link https://www.ncbi.nlm.nih.gov/pubmed/25957369 Principal Investigator …

332. 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. Citation Kasthurirathne SN, Grannis SJ. Machine learning approaches to identify nicknames from a statewide Health Information Exchange. AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:639-647. PMID: 312590…

333. 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), …

334. Comparison of supervised machine learning and probabilistic approaches for record linkage. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/enhancing-patient-matching-support-operational-health-information-exchange/citation/comparison
     January 01, 2023 - Comparison of supervised machine learning and probabilistic approaches for record linkage. Citation McNutt, A.T., Grannis, S.J., Bo, N., Xu, H., Kasthurirathne, S. N.(2020, March). Comparison of supervised machine learning and probabilistic approaches for record linkage. AMIA Informatics summit 2020 C…

335. Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/anesthesiology-control-tower-feedback-alerts-supplement-treatment-actfast/citation/machine
     January 01, 2023 - Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study. Citation Fritz BA, Chen Y, Murray-Torres TM, et al. Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol fo…

336. 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. Citation Fong A, Howe JL, Adams KT, et al. Using active learning to identify health Information technology related patient safety events. Appl Clin Inform 2017 Jan 18;8(1):35-46. PMID: 28097287. Link …

337. Learning from Primary Care Meaningful Use Exemplars | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/learning-primary-care-meaningful-use-exemplars
     January 01, 2023 - Learning from Primary Care Meaningful Use Exemplars Project Final Report ( PDF , 358.17 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 the views of AHRQ. N…

338. A machine learning approach to reclassifying miscellaneous patient safety event reports. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/developing-evidence-based-user-centered-design-and-implementation-guidelines/citation/machine
     January 01, 2023 - A machine learning approach to reclassifying miscellaneous patient safety event reports. Citation Fong A, Behzad S, Pruitt Z, Ratwani RM. A machine learning approach to reclassifying miscellaneous patient safety event reports. J Patient Saf. 2021 Dec 1;17(8):e829-e833. doi: 10.1097/PTS.000000000000073…

339. National Center for Pediatric Practice Based Research and Learning - Final Report | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/national-center-pediatric-practice-based-research-and-learning/final-report
     January 01, 2023 - 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). Rockville, MD: Agency for Healthcare Rese…

340. Lessons learned from implementation of a computerized application for pending tests at hospital discharge. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/health-information-technology-and-improving-medication-use/citation/lessons
     January 01, 2023 - Lessons learned from implementation of a computerized application for pending tests at hospital discharge. Citation Dalal AK, Poon EG, Karson AS, et al. Lessons learned from implementation of a computerized application for pending tests at hospital discharge. J Hosp Med 2011 Jan;6(1):16-21. Link…