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Clinical Staff/Clinician

A socio-technical systems approach to the use of health IT for patient reported outcomes: Patient and healthcare provider perspectives.

Citation
Wesley DB, Schubel L, Hsiao CJ, Burn S, Howe J, Kellogg K, Lincoln A, Kim B, Ratwani R. A socio-technical systems approach to the use of health IT for patient reported outcomes: Patient and healthcare provider perspectives. J Biomed Inform. 2019;100S:100048. doi: 10.1016/j.yjbinx.2019.100048. Epub 2019 Sep 24. PMID: 34384570.
Principal Investigator
Wesley, Deliya
Ratwani, Raj M.
Project Name
Advancing the Collection and Use of Patient-Reported Outcomes Through Health Information Technology

Effect of restricting electronic health records on clinician efficiency: Substudy of a randomized clinical trial.

Citation
Kneifati-Hayek JZ, Applebaum JR, Schechter CB, Dal Col A, Salmasian H, Southern WN, Adelman JS. Effect of restricting electronic health records on clinician efficiency: Substudy of a randomized clinical trial. J Am Med Inform Assoc. 2023 Apr 3:ocad025. doi: 10.1093/jamia/ocad025. Epub ahead of print. PMID: 37011638.
Principal Investigator
Adelman, Jason Stuart
Project Name
Assess Risk of Wrong-Patient Errors in an EMR That Allows Multiple Records Open

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

ACHIEVE: Successfully Achieving and Maintaining Euglycemia During Pregnancy for Type 2 Diabetes Through Technology and Coaching

Description

To assist pregnant individuals with pre-pregnancy type 2 diabetes with Medicaid coverage in reaching and maintaining normal blood sugars, this research will develop, test, and evaluate a digital health solution called ACHIEVE that includes a mobile health application, a provider dashboard, continuous glucose monitoring, and team-based coaching for medical needs and nonmedical health-related social needs.

Grant Number
R01 HS028822
Principal Investigator(s)
Fareed, Naleef
Joseph, Joshua J
Venkatesh, Kartik Kailas

Medication safety in the perioperative setting: A comparison of methods for detecting medication errors and adverse medication events.

Citation
Stipp MM, Deng H, Kong K, Moore S, Hickman RL Jr, Nanji KC. Medication safety in the perioperative setting: A comparison of methods for detecting medication errors and adverse medication events. Medicine (Baltimore). 2022 Nov 4;101(44):e31432. doi: 10.1097/MD.0000000000031432. PMID: 36343025; PMCID: PMC9646678.
Principal Investigator
Nanji, Karen C.
Project Name
Preventing Perioperative Medication Errors and Adverse Drug Events Through the Use of Clinical Decision Support

User-centered design of a clinical tool for shared decision-making about diet in primary care.

Citation
Tierney WM, Henning JM, Altillo BS, Rosenthal M, Nordquist E, Copelin K, Li J, Enriquez C, Lange J, Larson D, Burgermaster M. User-centered design of a clinical tool for shared decision-making about diet in primary care. J Gen Intern Med. 2022 Sep 20. doi: 10.1007/s11606-022-07804-x. Epub ahead of print. PMID: 36127543.
Principal Investigator
Burgermaster, Marissa
Project Name
Clinical Decision Support for Collaborative Diet Goal Setting in Primary Care

Neonatal intensive care workflow analysis informing NEC-zero clinical decision support design.

Citation
Gephart SM, Tolentino DA, Quinn MC, Wyles C. Neonatal intensive care workflow analysis informing NEC-zero clinical decision support design. Comput Inform Nurs. 2023 Feb 1;41(2):94-101. doi: 10.1097/CIN.0000000000000929. PMID: 35470306.
Principal Investigator
Gephart, Sheila Maria
Project Name
Clinical Decision Support Optimizing Necrotizing Enterocolitis Prevention Implementation in Neonatal Intensive Care Unit

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