Life Cycle of CIHI’s Briefing Report: “Seniors’ Experiences with Chronic Disease Prevention and Management in Primary Health Care across Canada”
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The scope of the project was to highlight the areas of improvement in management of chronic condition outcomes in elderly patients, so that appropriate policies could be implemented to overcome any gaps between the patients and the healthcare providers. Chronic conditions are the conditions which require the management of the disease for a longer period of time. Most of the time elderly people suffer from multiple chronic conditions that may require the primary care physician to make a tailored, self management program approach according to the needs of the patients. According to our findings from the project analysis, we gathered that the primary care physician does not typically provide enough guidance to the patients on managing their own health conditions in order to improve their health outcomes. Also, almost half of the elderly patients neither receive a medication review nor are explained the side effects of the prescribed medication adequately. The AIR team members under the supervision of project lead Brenda Palmer, developed the Analysis in Brief (AiB) using the “Canadian Survey of Experiences with Primary Health Care, 2008 (CSE-PHC)” survey data set. The project was divided into three phases; Phase one required every member of the team to perform an environmental scan, phase two comprised of literature review and presentations. The rough draft of the AiB was developed based on the rationale provided by the each member of the team and was based on the Evidence Based Literature (EBL). In the third phase each author was required to perform an analysis for the AiB. The authors used SAS tool for performing the analysis. For calculating the Confidence Limits and p-value the authors used boot strapping techniques and they used Excel for generating the figures. After the selection of the outputs to be incorporated in the AiB, the author checked for 5 the reported percentages to qualify for reporting based on the sample size and the coefficient of variance as recommended by Statistics Canada in the micro user data guide. In the latter half of his job term, he was assigned to come up with ten potential topics for AiB in winter 2011. For this purpose, the author mapped all the 105 primary healthcare indicators against the indicators developed by World Health Organization (WHO), Agency for Healthcare Research and Quality (AHRQ), National Clearing House, and Health at Glance 2009 etc. The author then listed the top ten indicators with the highest matching. He also provided the rationale for each suggested topic based on a thorough literature review along with the details of the survey data and the methodology used. The results from this project would also give an insight to the healthcare research community regarding the prevailing economic burden of the chronic conditions and the risk of medication error among the seniors with the multiple chronic conditions (co-morbidities), its prevention and adequate health risk guidance. The outcome of this project would greatly improve the management of chronic conditions in elderly patients in Canada, by advising the policy makers, researchers and other stakeholders to take concrete steps in this direction and make appropriate health policies based upon the findings in the report. This analysis was conducted using survey data provided by “Canadian Survey of Experiences with Primary Health Care, 2008 (CSE-PHC)” however, during the analysis, the team felt that it would have helped if more details on variables were covered in the survey. Sometimes, the data could not be readily mapped to another data set due to different coding systems among different provinces across Canada. Development of common standards for capturing the data at a primary level would enable better data quality and easier mapping. 6 Another recommendation suggested that our project survey data was a very clean data set without any missing values and data mining tool such as Waikato Environment for Knowledge Analysis (WEKA) for reporting the future trends in chronic condition could be used. Using the data mining for prediction of the trend would not only reflect the present scenario but will also reflect the future health outcomes. It would also indicate if proper policies were not made at the right time, what the future trend might be and how it could affect us in terms of chronic disease outcomes, health budget etc. in times to come.