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Reducing Medication Errors Through Clinical Simulation
Participants:
Springhill Hospital
USA School of Computing
USA College of Nursing

Objectives:

Determine factors that lead to real-life clinical medication errors; Design and build a simulation that is able replication known factors as well as test the impact of proposed factors; Use findings to create a real-time decision support tool for clinicians; Use the simulator to train and evaluate student nurses.

Participants:
Mitchell Cancer Institute
USA School of Computing
USA College of Nursing

Objectives:

Monitor patients’ health after receiving chemotherapy and radiation treatments; Track patient symptoms and provide support; Alert physicians to changes in the patient’s condition

Participants:
USA College of Medicine
USA School of Computing

Project Objectives:

Develop an application that produces a virtual community with controllable attributes of age, ethnic, gender and other parameters. Model epidemiological, clinical, prophylactic and treatment parameters of obesity, hypertension, diabetes and atherosclerosis, including coronary artery disease and stroke, among appropriate number of individuals of the virtual patient community. Allow all virtual patients to progress based on natural history, prophylaxis and treatment of diseases so that each student can follow a group of virtual patients throughout their lifetimes.

Participants:
USA School of Computing
USA College of Nursing
USA Center for Strategic Health Innovation

Data Source: CMS - 2011 Medicare Claims Data, 5% sample

Project Objectives:

Identify factors leading to patient readmission for a number of common diseases; suggest actions that providers can take to mitigate the negative impact of these factors and reduce readmission events.

Participants:
USA School of Computing
USA College of Nursing

Objectives:

Examine the electronic devices used in both training healthcare workers and providing treatment to patients. Identify security vulnerabilities in medical devices and work with healthcare providers and manufacturers to mitigate risk using various sources and a risk management perspective, we developed a health information security and privacy threat tree. We defined 25 nodes (threats), breaking them down into key risk-related data attributes: threat source and action, the health information asset and its vulnerability, and potential controls. The identified threats related to the disclosure of health information by insiders and outsiders, and the manipulation of health information by vandalism, loss, or corruption of data. The construction of such a threat catalog is argued to be useful for risk assessment and to inform public healthcare policy.