Best care practices in anesthesiology : development and evaluation of an electronic feedback system to improve physician compliance with evidence-based practices

Recently, hospitals, regulatory agencies, and insurers have renewed their focus on improving patient care and safety. Outcomes based measures are being utilized and hospitals are being asked to report on whether patients are being treated according to a standard of care or a best practice guideline. As peri-operative physicians, anesthesiologists are able to evaluate and, to a great degree, affect the pre-operative, intra-operative, and post-operative course of a patient. However, several barriers exist. Although best practice guidelines exist, current models to risk stratify patients need improvement. Individual anesthesiologists currently have no uniform way to measure patient outcomes, either in an institutional or provider specific manner, and many treat patients based on anecdotal experience rather than on evidence based medicine. We addressed these issues through development of an electronic feedback system. The demonstration system targeted the problem of postoperative nausea and vomiting (PONV) in the ambulatory surgery patient population. Because performance of existing PONV risk prediction models was poor and could not be used for educational purposes, we created a new PONV risk prediction model and compared it against existing models. The new, improved risk prediction model was incorporated into an electronic system that gathered patient outcomes data related to best care practice and then fed back the information to care providers. After implementation of the electronic feedback system, we evaluated its efficacy in improving compliance with best care practices.

Department dc:contributor.department

Harvard University--MIT Division of Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Sarin, Pankaj, M.D. University of Rochester

Advisor dc:contributor.advisor

• Lucila Ohno-Machado.

• Harvard University--MIT Division of Health Sciences and Technology.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/42215

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/42215