A process model to support automated measurement and detection of out-of-bounds events in a hospital laboratory process

Abstract

Business Activity Monitoring (BAM) allows organizations to capture enterprise events from their source systems and utilize these to detect non-compliant business situations. Similar concepts may be leveraged in the healthcare domain to improve the quality of patient care and the efficiency of clinical processes. This paper introduces a generic set of constructs for formally specifying threshold values relevant for cycle time and utilization calculations. It also describes a mechanism to capture information, including thresholds, about important business parameters for Six Sigma measurement. This full set of constructs are the basis for automated measurement and monitoring and are incorporated into the process model during the definition or capture phase thereby linking the definition and monitoring phases through a common underlying process model. Bespoke software is also described which uses the constructs contributed by this research to manage and monitor process models and enterprise events. A process performance module provides automated measurement and monitoring capabilities. At an aggregate level, this is achieved through the provision of process cycle time data for selected time periods on demand and the examination of business processes at frequent intervals with alerts generated for exceptional scenarios. At a more granular level, this solution uses a rules-based approach to evaluate individual events and generate alerts for out-of-bounds business parameters. This paper demonstrates the benefits of these capabilities for health informatics through application to a Laboratory Testing process observed at a local hospital. The paper also suggests recommendations for the extension of current modelling languages with respect to the constructs detailed herein.

Leveraging big-data for business process analytics

“…Event correlation refers to the determination of the sequence of events produced by the execution of interrelated and consecutive BPs or activities, and this is essential to generate metrics per process instance or activity (Costello & Molloy, 2009), and consequently it is a key-driven at identifying exceptional situations and potential improvement opportunities.…”