In the wake of increasingly frequent surgical interventions, it is a significant challenge for a clinical practitioner to provide proper evidence based preoperative evaluations, given the constantly changing clinical evidence and the broad realm of specialty literature pertinent to preoperative testing and risk management. There is a critical need for patients to identify their clinical risk factors to effectively identify and manage surgical risk. However, there is a paucity of tools for patient driven self-identification of clinical risk factors, as many evidence driven tools have not readily translated from disparate research studies into patient and clinician use at the point of care. With the ultimate goal of creating a patient-driven cardiac self-assessment tool for preoperative risk assessment and planning, we executed a combination of three separate research studies, leveraging clinical guidelines and best practice recommendations, informatics and usability principles. Utilizing the results and inference from these respective studies, we successfully created the foundations for a patient driven information acquisition tool based on recommendations of evidence based research. The examination and validation of patient reported self-assessment of pre-operative cardiac risk against gold standard provider assessment cardiovascular risk provided important data for objective risk assessment. The comparison of patient risk perceptions to provider perceived risk for post-operative complications created data on patient appreciation of surgical risk. The evaluation of end clinician-user attitudes, satisfaction and ease of use for using the web based decision support yielded data on clinical usefulness. The results from the observations and analysis of these three studies will serve to create and validate a patient driven health information acquisition and decision support tool, and contribute to clarify provider experience and attitudes on the use of patient reported health information.
University of Minnesota Ph.D. dissertation. April 2014. Major: Health Informatics. Advisor: Terrence J. Adam. 1 computer file (PDF); vii, 84 pages, appendix A.
Patient driven clinical decision support instrument for predicting cardiovascular complications of non-cardiac surgery - creation, implementation, and evaluation.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.