Abstract
BackgroundThe aim of this project is to describe the quality of assessment data regularly collected in home and community, with techniques adapted from an evaluation of the quality of long-term care data in Canada.MethodsData collected using the Resident Assessment Instrument – Home Care (RAI-HC) in Ontario and British Columbia (BC) as well as the interRAI Community Health Assessment (CHA) in Ontario were analyzed using descriptive statistics, Pearson’s r correlation, and Cronbach’s alpha in order to assess trends in population characteristics, convergent validity, and scale reliability.ResultsResults indicate that RAI-HC data from Ontario and BC behave in a consistent manner, with stable trends in internal consistency providing evidence of good reliability (alpha values range from 0.72-0.94, depending on the scale and province). The associations between various scales, such as those reflecting functional status and cognition, were found to be as expected and stable over time within each setting (r values range from 0.42-0.45 in Ontario and 0.41-0.43 in BC). These trends in convergent validity demonstrate that constructs in the data behave as they should, providing evidence of good data quality. In most cases, CHA data quality matches that of RAI-HC data quality and shows evidence of good validity and reliability. The findings are comparable to the findings observed in the evaluation of data from the long-term care sector.ConclusionsDespite an increasingly complex client population in the home and community care sectors, the results from this work indicate that data collected using the RAI-HC and the CHA are of an overall quality that may be trusted when used to inform decision-making at the organizational- or policy-level. High quality data and information are vital when used to inform steps taken to improve quality of care and enhance quality of life. This work also provides evidence that a method used to evaluate the quality of data obtained in the long-term care setting may be used to evaluate the quality of data obtained through community-based measures.
Highlights
The aim of this project is to describe the quality of assessment data regularly collected in home and community, with techniques adapted from an evaluation of the quality of long-term care data in Canada
The RAI-Home care (HC) data from Ontario and British Columbia (BC) behave in a consistent manner, despite the changing and increasingly complex home care population in Ontario
The associations within and between scales were generally stable and consistent across provinces and sectors. This indicates good data quality, despite the challenges associated with doing assessments in the community, combined with changes toward increased complexity of home care clients, at least in Ontario
Summary
The aim of this project is to describe the quality of assessment data regularly collected in home and community, with techniques adapted from an evaluation of the quality of long-term care data in Canada. Random error is an inherent part of all health care data reflecting chance variations that result in a disagreement between observed and “true” Another threat to the quality of assessment data is the practice of using prior records to automatically complete an assessment without further examination of the person’s current status based on other more up to date sources of information. The effect of such auto-population can be to negate detection of true change in the person’s health, Hogeveen et al BMC Medical Informatics and Decision Making (2017) 17:150 potentially masking evidence of the impact of care provided. Missing values and coding inconsistencies leading to logical errors are further concerns as they may make observations unusable, thereby decreasing sample size
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