Clinical Decision Support System

Considerations for using predictive models that include race as an input variable: The case study of lung cancer screening.

Citation
Stevens ER, Caverly T, Butler JM, Kukhareva P, Richardson S, Mann DM, Kawamoto K. Considerations for using predictive models that include race as an input variable: The case study of lung cancer screening. J Biomed Inform. 2023 Nov;147:104525. doi: 10.1016/j.jbi.2023.104525. Epub 2023 Oct 14. PMID: 37844677.
Principal Investigator
Kawamoto, Kensaku
Project Name
Scalable Decision Support and Shared Decision Making for Lung Cancer Screening

Methods for using race and ethnicity in prediction models for lung cancer screening eligibility.

Citation
Landy R, Gomez I, Caverly TJ, Kawamoto K, Rivera MP, Robbins HA, Young CD, Chaturvedi AK, Cheung LC, Katki HA. Methods for using race and ethnicity in prediction models for lung cancer screening eligibility. JAMA Netw Open. 2023 Sep 5;6(9):e2331155. doi: 10.1001/jamanetworkopen.2023.31155. PMID: 37721755; PMCID: PMC10507484.
Principal Investigator
Kawamoto, Kensaku
Project Name
Scalable Decision Support and Shared Decision Making for Lung Cancer Screening

A virtual dissemination framework to inform and evaluate a neonatal project ECHO (NeoECHO).

Citation
Wyles CL, Shea K, Weiss A, Gephart SM, Newnam K. A virtual dissemination framework to inform and evaluate a neonatal project ECHO (NeoECHO). J Adv Nurs. 2023 Dec 10. doi: 10.1111/jan.15986. Epub ahead of print. PMID: 38071610.
Principal Investigator
Gephart, Sheila Maria
Project Name
Clinical Decision Support Optimizing Necrotizing Enterocolitis Prevention Implementation in Neonatal Intensive Care Unit

Prediction of GutCheck NEC and its relation to severity of illness and measures of deterioration in necrotizing enterocolitis.

Citation
Gephart SM, Fleiner M, Msowoya A, Rothers J. Prediction of GutCheck NEC and its relation to severity of illness and measures of deterioration in necrotizing enterocolitis. Adv Neonatal Care. 2023 Aug 1;23(4):377-386. doi: 10.1097/ANC.0000000000001080. Epub 2023 Jun 20. PMID: 37339581; PMCID: PMC10440277.
Principal Investigator
Gephart, Sheila Maria
Project Name
Clinical Decision Support Optimizing Necrotizing Enterocolitis Prevention Implementation in Neonatal Intensive Care Unit

Digital EMS Point-of-Care Innovation to Improve Rural STEMI Outcomes

Description

This research will develop, implement, refine, and evaluate an app to support clinical decisions for ST-Elevation Myocardial Infarction care in rural areas by emergency medical services providers, reducing the time between first medical contact and reperfusion therapy to reduce morbidity and mortality, and improve health outcomes.

Grant Number
R21 HS029234
Principal Investigator(s)
Mahler, Simon A
Manning, John D.
Previous Principal Investigator(s)
Stopyra, Jason P.

An application programming interface implementing Bayesian approaches for evaluating effect of time-varying treatment with R and Python.

Citation
Chen C, Huang B, Kouril M, Liu J, Kim H, Sivaganisan S, Welge JA and DelBello MP (2023) An application programming interface implementing Bayesian approaches for evaluating effect of time-varying treatment with R and Python. Front. Comput. Sci. 5:1183380. doi: 10.3389/fcomp.2023.1183380
Principal Investigator
Morgan, Esi M.
Huang, Bin
Project Name
Inform Shared Decision Making with Advanced Bayesian Causal Inference to Improve Quality of Pediatric Rheumatology Care

Building and Implementing a Predictive Decision Support System Based on a Proactive Full Capacity Protocol to Mitigate Emergency Department Overcrowding Problems

Description

This research will use deep learning models to move a reactive full capacity protocol (FCP) for emergency department (ED) overcrowding interventions into a proactive FCP by predicting patient flow measures so that interventions may be activated to avoid ED overcrowding.

Grant Number
R21 HS029410
Principal Investigator(s)
Ahmed, Abdulaziz
Ozaydin, Bunyamin

Universal EHRs clinical decision support for thromboprophylaxis in medical inpatients: A cluster randomized trial.

Citation
Spyropoulos AC, Goldin M, Koulas I, Solomon J, Qiu M, et al. Universal EHRs clinical decision support for thromboprophylaxis in medical inpatients: A cluster randomized trial. JACC: Advances, Volume 2, Issue 8,
2023,100597, ISSN 2772-963X, https://doi.org/10.1016/j.jacadv.2023.100597.
Principal Investigator
Spyropoulos, Alex
McGinn, Thomas G.
Project Name
Implementation of a Novel Multi-Platform Evidence-Based Clinical Decision Support System