FASStR: A Framework for Ensuring High-Quality Operational Metrics in Health Care - AJMC.com Managed Markets Network

Elham Torabi, PhD, MSIE; Tugba Cayirli, PhD; Craig M. Froehle, PhD; Kenneth J. Klassen, PhD; Michael Magazine, PhD; Denise L. White, PhD, MBA; and Michael J. Ward, MD, PhD, MBA

Through literature review and collaborative design, we propose the Focus, Activity, Statistic, Scale type, and Reference (FASStR) framework to provide a systematic approach to health care operation metric definition and use.

ABSTRACT

Objectives:

Poorly defined measurement impairs interinstitutional comparison, interpretation of results, and process improvement in health care operations. We sought to develop a unifying framework that could be used by administrators, practitioners, and investigators to help define and document operational performance measures that are comparable and reproducible.

Study Design: Retrospective analysis.

Methods: Health care operations and clinical investigators used an iterative process consisting of (1) literature review, (2) expert assessment and collaborative design, and (3) end-user feedback. We sampled the literature from the medical, health systems research, and health care operations (business and engineering) disciplines to assemble a representative sample of studies in which outpatient health care performance metrics were used to describe the primary or secondary outcome of the research.

Results: We identified 2 primary deficiencies in outpatient performance metric definitions: incompletion and inconsistency. From our review of performance metrics, we propose the FASStR framework for the Focus, Activity, Statistic, Scale type, and Reference dimensions of a performance metric. The FASStR framework is a method by which performance metrics can be developed and examined from a multidimensional perspective to evaluate their comprehensiveness and clarity. The framework was tested and revised in an iterative process with both practitioners and investigators.

Conclusions: The FASStR framework can guide the design, development, and implementation of operational metrics in outpatient health care settings. Further, this framework can assist investigators in the evaluation of the metrics that they are using. Overall, the FASStR framework can result in clearer, more consistent use and evaluation of outpatient performance metrics.

Am J Manag Care. 2020;26(6):In Press

Takeaway Points

When measurements are dissimilar or inadequately defined, comparison of operational performance and interpretation of research is impaired. We propose the Focus, Activity, Statistic, Scale type, and Reference (FASStR) framework. The FASStR framework can be used to provide an objective, systematic approach to ensure that metrics are defined and documented in a clear, consistent manner.

• Poorly defined measurement impairs interinstitutional comparison, interpretation of results, and process improvement in health care operations.
• Inconsistent and incomplete performance metrics are common deficiencies in health care operations metrics.
• The FASStR framework provides a systematic approach to metric development, definition, and evaluation.

Performance measurement is fundamental to health care, from clinical operations to research. The Agency for Healthcare Research and Quality recognizes this in its mission statement, which is to “develop and spread evidence and tools to measure and enhance the efficiency of health systems—the capacity to produce better quality and outcomes.”¹

Measurement of operational performance in health care is ubiquitous. We measure patient waiting, clinic durations, staff overtime, costs, patient satisfaction, no-shows, and numerous other metrics. Measures of operational performance provide a basis for evaluating and comparing the performance of different health care institutions, operating practices, and policies. Although they are seemingly easy to create, developing usable and comprehensible metrics is quite challenging. Inadequately designed and documented operational metrics create ambiguity and confusion and can lead to incorrect managerial decisions that are potentially at odds with the objectives of high-quality and safe patient care.²

To be maximally useful, a metric needs several important characteristics. First, it should be reliable and valid, measuring its true target consistently across time and contexts.³ Second, it needs to be consistent with the goals, standards, and practices of the organization within which it is applied. Third, a metric must be clear and unambiguous in its definition and use. Fourth, it should be as generalizable as possible without reducing its utility or specificity. Finally, it must be relevant to practice and aid in managerial decision-making; metrics that do not directly contribute to the management of the organization or are not sufficiently sensitive to detect meaningful operational changes are, at best, potential distractions from more critical information.

Inadequately defined and documented metrics can degrade the consistency of metric application from one instance to the next (eg, facility, time period). This is the essence of test-retest reliability. A lack of reliability undermines the sharing and acceptance of critical information, slowing the spread of vital data for efforts such as interventions and improvement collaboratives. Without clear and thorough definitions, we are unable to adequately assess the relevance or utility of a particular metric to any specific environment or situation, thus diminishing the metric’s generalizability. For example, if hospital A defines patient waiting differently than hospital B does, but both assume identical meanings, they can (wrongly) arrive at quite different conclusions about the efficacy of a particular policy. This can manifest even within an organization because of definitional “drift” as employees turn over, rendering historical comparisons meaningless. Finally, organizations may not understand or be able to communicate what is being measured and/or why it changed, thereby potentially reducing buy-in from staff and stakeholders.

Some health care organizations have begun to standardize measurement of key performance metrics.⁴ The National Quality Forum (NQF) has established a system of metric evaluation and stewardship.⁵ However, even widely adopted metrics may use a variety of definitions and may then be collected and reported along a spectrum of interpretations. Inadequacies in 1 or more of the characteristics listed above, such as consistency or generalizability, can undermine even well-supported national efforts at improving and standardizing operational metrics in health care. NQF’s Measure Evaluation Criteria evaluate the suitability of measures on importance, scientific acceptability of measure properties, feasibility, usability, and related/competing measures. We sought to develop a unifying framework focused on the second property (acceptability of measure properties) to improve the development, deployment, and spread of consistent, well-defined metrics and accelerate translation across organizations and research. Adoption and use of such a framework by investigators and organizations could make study results more comparable, repeatable, and, most importantly, more applicable to practice. This creates a “virtuous cycle,” enabling faster and more complete operational improvement.

There is a need for a framework that helps investigators and practitioners define their metrics of interest more clearly. To develop such a measurement framework, we used an iterative process consisting of (1) examination of the literature and (2) expert assessment and collaborative design. We brought together a team of international experts in health care operations and clinical research with MD and/or PhD degrees in operations management and decades of experience in interdisciplinary research to examine a metrics database and collaboratively design an organizing framework.

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FASStR: A Framework for Ensuring High-Quality Operational Metrics in Health Care - AJMC.com Managed Markets Network
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