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Machine Learning

Optimal acute kidney injury algorithm for detecting acute kidney injury at emergency department presentation.

Citation
Ehmann MR, Hinson JS, Menez S, Smith A, Klein EY, Levin S. Optimal acute kidney injury algorithm for detecting acute kidney injury at emergency department presentation. Kidney Medicine. 2023 Feb: 5(2): 100588.
Principal Investigator
Hinson, Jeremiah S.
Levin, Scott R.
Project Name
Transforming Kidney Care in the Emergency Department Using Artificial Intelligence-Driven Clinical Decision Support

Predictive Monitoring: IMPact of Real-Time Predictive Monitoring in Acute Care Cardiology Trial (PM-IMPACCT)

Description

This research evaluates an artificial intelligence risk predictive tool called CoMET that uses visual outputs of patient data to serve as an early warning system for patients at risk of cardiac decompensation to allow for earlier intervention and reduction in morbidity and mortality.

Grant Number
R01 HS028803
Principal Investigator(s)
Keim-Malpass, Jessica
Bourque, Jamieson MacDonald

Incorporating patient-reported outcomes into shared decision-making in the management of patients with osteoarthritis of the knee: a hybrid effectiveness-implementation study protocol.

Citation
Lin E, Uhler LM, Finley EP, Jayakumar P, Rathouz PJ, Bozic KJ, Tsevat J. Incorporating patient-reported outcomes into shared decision-making in the management of patients with osteoarthritis of the knee: a hybrid effectiveness-implementation study protocol. BMJ Open. 2022 Feb 21;12(2):e055933. doi: 10.1136/bmjopen-2021-055933. PMID: 35190439; PMCID: PMC8860037.
Principal Investigator
Bozic, Kevin John
Project Name
Incorporating Patient-Reported Outcomes into Shared Decision Making with Patients with Osteoarthritis of the Knee

Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events.

Citation
Fong A, Harriott N, Walters DM, Foley H, Morrissey R, Ratwani RR. Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events. Int J Med Inform. 2017 Aug;104:120-125. doi: 10.1016/j.ijmedinf.2017.05.005. Epub 2017 May 11. PMID: 28529113.
Principal Investigator
Ratwani, Raj M.
Project Name
Developing Evidence-Based, User-Centered Design and Implementation Guidelines to Improve Health Information Technology Usability

Responding to health information technology reported safety events: Insights from patient safety event reports.

Citation
Adams K.T., Kim T.C., Fong A., Howe J.L., Kellogg K.M., Ratwani R.M. (2019) Responding to health IT reported safety events: Insights from patient safety event reports. Journal of Patient Safety and Risk Management. https://doi.org/10.1177/2516043519847330
Principal Investigator
Ratwani, Raj M.
Project Name
Developing Evidence-Based, User-Centered Design and Implementation Guidelines to Improve Health Information Technology Usability

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.0000000000000731. PMID: 32555052.
Principal Investigator
Ratwani, Raj M.
Project Name
Developing Evidence-Based, User-Centered Design and Implementation Guidelines to Improve Health Information Technology Usability

Advancing motivational interviewing training with artificial intelligence: ReadMI.

Citation
Hershberger PJ, Pei Y, Bricker DA, Crawford TN, Shivakumar A, Vasoya M, Medaramitta R, Rechtin M, Bositty A, Wilson JF. Advancing motivational interviewing training with artificial intelligence: ReadMI. Adv Med Educ Pract. 2021 Jun 4;12:613-618. doi: 10.2147/AMEP.S312373. PMID: 34113205; PMCID: PMC8186935.
Principal Investigator
Hershberger, Paul J.
Project Name
Real-time Assessment of Dialogue in Motivational Interviewing Training (ReadMI)