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Care coordination for children with special health needs using digital personal health records: A feasibility study protocol.

Citation
Wong W, Antonelli R, King H, Gonzales S, Gujral N, Rogers U, Fee C, Pateras S, Jones KA, Ming DY. Care coordination for children with special health needs using digital personal health records: A feasibility study protocol. Poster presentation at the annual Pediatric Academic Societies (PAS) national research conference in April 2022.
Principal Investigator
Ming, David Y.
Project Name
Feasibility Study of a Mobile Digital Personal Health Record for Family- Centered Care Coordination for Children and Youth with Special Healthcare Needs

Indication alerts to improve problem list documentation.

Citation
Grauer A, Kneifati-Hayek J, Reuland B, Applebaum JR, Adelman JS, Green RA, Lisak-Phillips J, Liebovitz D, Byrd TF, Kansal P, Wilkes C, Falck S, Larson C, Shilka J, VanDril E, Schiff GD, Galanter WL, Lambert BL. Indication alerts to improve problem list documentation. J Am Med Inform Assoc. 2022 Apr 13;29(5):909-917. doi: 10.1093/jamia/ocab285. PMID: 34957491; PMCID: PMC9006708.
Principal Investigator
Lambert, Bruce
Project Name
Preventing Wrong-Drug and Wrong-Patient Errors With Indication Alerts in CPOE Systems

TREAT ECARDS: Translating Evidence into Action: Electronic Clinical Decision Support in ARDS - Final Report

Citation
Gong M. TREAT ECARDS: Translating Evidence into Action: Electronic Clinical Decision Support in ARDS - Final Report. (Prepared by Albert Einstein College of Medicine under Grant No. R18 HS026188). Rockville, MD: Agency for Healthcare Research and Quality, 2022.
Principal Investigator
Gong, Michelle
Project Name
TREAT ECARDS: Translating Evidence into Action: Electronic Clinical Decision Support in Acute Respiratory Distress Syndrome

Examining the Clinical Workflow and Outcomes of Integrating Health Information Technology to Educate and Support Dementia Caregivers - Final Report

Citation
Ruggiano N. Examining the Clinical Workflow and Outcomes of Integrating Health Information Technology to Educate and Support Dementia Caregivers - Final Report. (Prepared by the University of Alabama in Tuscaloosa under Grant No. R21 HS026571). Rockville, MD: Agency for Healthcare Research and Quality, 2022.
Principal Investigator
Ruggiano, Nicole
Project Name
Examining the Clinical Workflow and Outcomes of Integrating Health Information Technology to Educate and Support Dementia Caregivers

Utilizing the ASTHMAXcel pro mobile platform to conduct a virtual cohort study during the COVID-19 pandemic.

Citation
Cosar E, Kaur S, Singh A, Njeze O, Hsia B, Jariwala S. Utilizing the ASTHMAXcel pro mobile platform to conduct a virtual cohort study during the COVID-19 pandemic. J Allergy Clin Immunol. 2021 Feb;147(2):AB50. doi: 10.1016/j.jaci.2020.12.209. Epub 2021 Feb 1. PMCID: PMC7849423.
Principal Investigator
Jariwala, Sunit
Project Name
Adapting, Scaling, and Spreading an Algorithmic Asthma Mobile Intervention to Promote Patient-Reported Outcomes Within Primary Care Settings

Phenotyping patients based on longitudinal heterogeneity of engagement patterns with the ASTHMAXcel PRO mobile health application.

Citation
Kaur S, Cosar E, Njeze O, Singh A, Hsia B, Jariwala S. Phenotyping patients based on longitudinal heterogeneity of engagement patterns with the ASTHMAXcel PRO mobile health application. Journal of Allergy and Clinical Immunology. 2021 Feb 1;147(2):AB51.
Principal Investigator
Jariwala, Sunit
Project Name
Adapting, Scaling, and Spreading an Algorithmic Asthma Mobile Intervention to Promote Patient-Reported Outcomes Within Primary Care Settings

Evaluating the ASTHMAXcel mobile application regarding asthma knowledge and clinical outcomes.

Citation
Hsia BC, Wu S, Mowrey WB, Jariwala SP. Evaluating the ASTHMAXcel mobile application regarding asthma knowledge and clinical outcomes. Respir Care. 2020 Aug;65(8):1112-1119. doi: 10.4187/respcare.07550. Epub 2020 Jun 2. PMID: 32487751.
Principal Investigator
Jariwala, Sunit
Project Name
Adapting, Scaling, and Spreading an Algorithmic Asthma Mobile Intervention to Promote Patient-Reported Outcomes Within Primary Care Settings

Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.

Citation
Wissel BD, Greiner HM, Glauser TA, Holland-Bouley KD, Mangano FT, Santel D, Faist R, Zhang N, Pestian JP, Szczesniak RD, Dexheimer JW. Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery. Epilepsia. 2019 Nov 29. doi: 10.1111/epi.16398. [Epub ahead of print]. PMID: 31784992.
Principal Investigator
Dexheimer, Judith W.
Project Name
Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients