Usage of primary and administrative data to measure the economic impact of quality improvement projects

• Published in: BMJ open quality 2020-04, Vol.9 (2), p.e000712
• Main Authors: Mendlowitz, Andrew, Croxford, Ruth, MacLagan, Laura, Ritcey, Gillian, Isaranuwatchai, Wanrudee
• Format: Article
• Language: English
• Subjects: Ambulatory care; Costs; Data Accuracy; Economic analysis; Economic Factors; Economic impact; Falls; Health administration; Health care access; Health care policy; Health economics; Health insurance; health services research; healthcare quality improvement; Hospital Costs - standards; Hospital Costs - statistics & numerical data; Humans; Long term health care; Narrative Review; Personal health; Prescription drug plans; Quality control; Quality improvement; Quality Improvement - economics; Quality Improvement - standards; Quality Improvement - statistics & numerical data; quality improvement methodologies; Quality of care; Quality standards; Rehabilitation; Teams
• ISSN: 2399-6641; 2399-6641
• DOI: 10.1136/bmjoq-2019-000712

In healthcare, quality improvement (QI) aims to improve patient outcomes and fight inefficiencies.1 2 Inefficiency is associated with limited access to care and premature death.3 QI is not a costless endeavour,1 so healthcare providers and decision-makers must quantify both potential health and economic impacts that result from QI projects.Economic analysis supports decision-makers by estimating the value associated with a programme. In QI, this includes studying the costs of implementing a project as well as evaluating any incremental changes in healthcare costs that occur as a result of the project. In tandem with an evaluation of the project’s impact on patient and provider outcomes, economic analysis provides another value dimension. Demonstrating added value can further incentivise resourcing for the implementation of a QI project to decision-makers. Despite the potential benefits of performing economic analyses on QI projects, little guidance exists on how to empirically evaluate their potential costs.4Primary and administrative data each have unique and complementary strengths. Primary data, collected during a QI project, measures processes and outcomes which are important for evaluation, and are often not captured in administrative data.5 In contrast, administrative data represents a secondary data source which, although routinely collected for purposes other than research, can serve as a source of readily available information that lends itself to further analyses. Primary data are collected immediately, without the delays inherent in accessing and analysing administrative data. Administrative data offers linkable, comprehensive records of health system resource utilisation at both the population and the individual level,6 and provides a way to follow patients over time and outside the facility conducting the project. Measures of performance (eg, changes in the utilisation of healthcare services) can be operationalised to quantify the impact of the project. The objective of this paper is to describe a methodology, using administrative and primary data to inform measures of economic impact using three QI project case studies.