Making quality improvement programs more effective

Abstract

Purpose

In the past 25 years, and as recent as 2011, all external evaluations of the Quality Improvement Organization (QIO) Program have found its impact to be small or difficult to discern. The QIO program costs about $200 million on average to administer each year to improve quality of healthcare for people of 65 years or older. The program was created to address questionable quality of care. QIOs review how care is provided based on performance measures. The paper aims to discuss these issues.

Design/methodology/approach

In 2012, the author supported the production of quarterly reports and reviewed internal monitoring and evaluation protocols of the program. The task also required reviewing all previous program evaluations. The task involved many conversations about the complexities of the program, why impact is difficult to discern and possible ways for eventual improvement. Process flow charts were created to simulate the data life cycle and discrete event models were created based on the sequence of data collection and reporting to identify gaps in data flow.

Findings

The internal evaluation uncovered data gaps within the program. The need for a system of specification rules for data conceptualization, collection, distribution, discovery, analysis and repurposing is clear. There were data inconsistencies and difficulty of integrating data from one instance of measurement to the next. The lack of good and reliable data makes it difficult to discern true impact.

Practical implications

The prescription is for a formal data policy or data governance structure to integrate and document all aspects of the data life cycle. The specification rules for governance are exemplified by the Data Documentation Initiative and the requirements published by the Data Governance Institute. The elements are all in place for a solid foundation of the data governance structure. These recommendations will increase the value of program data.

Originality/value

The model specifies which agency units must be included in the governance authority and the data team. The model prescribes in detail a data governance model to address gaps in the life cycle. These prescriptive measures will allow the program to integrate all of its data. Without this formal data governance structure, the QIO program will be undetermined by the persistent lack of good data for monitoring and evaluation.

Citation

Shaw-Taylor, Y. (2014), "Making quality improvement programs more effective", International Journal of Health Care Quality Assurance, Vol. 27 No. 4, pp. 264-270. https://doi.org/10.1108/IJHCQA-02-2013-0017