FASStR: a framework for ensuring high-quality operational metrics in health care.

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.

Appointment scheduling in multi-stage outpatient clinics.

Healthcare providers can benefit from adding less costly capacity to their existing resources in order to satisfy demand while maintaining the quality of patient care. The addition of mid-level service providers (MLSPs) such as physician assistants or nurse practitioners that carry out portions of patient care provides a viable alternative for adding physician capacity. This research considers the circumstances under which adding an MLSP to a single-physician outpatient office becomes the best strategy for the clinic, and determines how scheduling policies from the widely-researched single-stage environment should be adjusted for a multi-stage environment. Compared to a single-stage system where a physician completes all portions of the service, we show that adding an MLSP can reduce patient waiting time, patient flow time, and physician service time with patients. This, in turn, can enable the clinic to see more patients and/or free up physician time for other tasks. Appointment scheduling rules are developed for a multi-stage outpatient service system using a simulation optimization approach. Performance measures focus on the patient experience and clinic operation before and during each stage of service.

Rolling horizon appointment scheduling: a simulation study.

Health-care consumers continue to be frustrated with long waits, especially when an appointment has been made. However, providers who book appointments are under increasing pressure to maximize utilization so that revenues will be increased and costs reduced. Thus, scheduling appointments involves opposing forces that are difficult to manage. This challenge is addressed in a rolling-horizon environment with fluctuating demand loads. These two issues have not been explored previously in the appointment-scheduling research. Two management policies are considered: overload rules (OLR) and rule delay (RD). The former considers different scheduling methods (overtime, double booking) when demand loads are high, and the rule delay policy considers when to implement the overload rules. These methods are explored for six different demand patterns/loads and evaluated with a variety of client and server-oriented measures. The results show that managers of appointment scheduling systems must carefully consider which measures are most important to them since the best choices of OLR and RD vary substantially by measure. Good choices also depend on the general type of client demand pattern. Thus, to consider the various tradeoffs between client and server measures a matrix is developed that outlines good choices for each scenario.