Thursday, July 12, 2007
This presentation is part of : Chronic Care Initiatives
Evidence- Based Clinical Decision Making through Integration of a Mandated Resident Assessment Instrument and Transdisciplinary Team Effort
Clara Nisan, RN, BScN, GNC, (C), MN, Nursing, Baycrest , Affiliation with University of Toronto, Toronto, ON, Canada
Learning Objective #1: understand an evidence based team approach to problem identification, decision-making, quality improvement and outcome evaluation.
Learning Objective #2: understand the aggregate data through outcomes such as quality indicators, and decision support for the transdisciplinary team.

Purpose: To illustrate the importance of integration of transdisciplinary teamwork, technology and informatics to realize and maximize the benefits derived from the evidence based Resident Assessment Instrument (RAI). 
Baycrest is a large geriatric facility with 248 designated complex continuing care beds, 479 long-term care beds, and 220 assisted living beds with extensive clinic activity and community-based programs.  Baycrest is internationally known for research, with clinical foci in Alzheimer’s, memory and aging. The presentation will show Baycrest's RAI data, the use of internal and external benchmarking as well as the use of key quality indicators, clinical utilization, outcomes and a tradsdiciplinary  team effort.  
This session will highlight the data collection process and critical elements related to data quality and timeliness of collection and describe the techno-socio challenges encountered and solutions implemented.
The presentation will illustrate how informatics can be useful for enhancing the use of the data collected through RAI/MDS for decision-making, by describing how data is processed and resulting information disseminated. The presentation will describe the benefits of utilizing a web-based reporting tool to organize and analyze evidence-based integrated data and create customized reports, and will also describe how individuals, teams, and the organization use these reports for decision-making and to realize the benefits for collecting RAI/MDS data to anable  best practice.   
Summary:  The success of the project was achieved through integration of evidence based practice, transdisciplinary team effort and organizational ongoing support for technological changes that were required for RAI implementation.