The OpenLabs Project

Supporting clinical laboratory test-ordering protocol specification, execution and management: an event-condition-action rule and database approach

The cost of clinical laboratory testing has increased considerably during the past decade. This has prompted research and initiatives aimed at controlling clinical laboratory utilization without affecting the continued improvement of the quality of patient care. One of the most effective and proven approaches to clinical laboratory utilization management is the use of clinical test-ordering protocols. The major challenges being faced are those of modelling and specifying clinical guidelines and protocols in a manner that facilitates their integration into information systems, their subsequent management and their linking with electronic healthcare records. It is the authors’ belief that the event-condition-action (ECA) rule paradigm and modern database technology are of great potential in this area, and their applications here should be vigorously researched. This paper describes a generic model and language, called PLAN, based on the ECA rule paradigm for supporting clinical test-ordering protocols. The paper also discusses a database-based approach to the specification, execution and management of test ordering protocols.

A test ordering system with automated reminders for primary care based on practice guidelines

In this article we describe a real-time automated reminder system that has been developed to change Family Physicians' (FP) test ordering behavior. The system focuses on the appropriateness of test requests. We aim at using the system as a substitute for written feedback by human experts. The reminder system consists of a knowledge base, an order entry system and modules to provide passive and active support in the form of reminders to FPs. The system generates critical comments about the rationality of the test requests at the moment the FP orders a test that is not in line with national or regional guidelines. For the first validation of the knowledge base we compared the comments of a human expert to the comments of the reminder system on three random samples of test requests. The overall agreement in the subsequent validation rounds was 46, 60 and 69%. The corrections made in the knowledge base after each validation round resulted in a reminder system with 149 reminders concerning various medical problems. Due to the corrections in the knowledge base the reminder system reacts better over the subsequent validation rounds.

Open laboratory information systems: a case study

The EU-AIM project "OpenLabs" specified an open systems architecture for a clinical laboratory information system (LIS) environment and coined the term "open LIS". Since this conception has not yet been realized into systems and modules commercially available, two university hospitals in Norway had to specify an alternative model for openness when they, in a joint project, screened the market for a new LIS. To be open, a LIS must be (1) based on main-trend hardware and system software running under a standardized operating system, (2) designed for network-based distributed computing, (3) have sufficient documentation available to local staff, (4) be configurable through parameters, tables and scripts, and (5) have general and configurable access mechanisms both to real-time processes and to the database. The present study describes the selection process and examines the compliance of the selected LIS (NetLab) with this model of openness. It is shown that local staff have been able to adapt this LIS to various and changing requirements in the laboratory.

Specifying an open clinical laboratory information system

This paper presents an overview of the architectural infrastructure in which existing laboratory information systems can be made to interoperate with additional modules offering a range of advanced clinical laboratory functionalities. The infrastructure is based on an open distributed computing platform, and its specification is described using the open distributed processing reference model.