From Jorge Sepulveda and Donald Young: The Ideal Laboratory Information System

http://www.archivesofpathology.org/doi/pdf/10.5858/arpa.2012-0362-RA

Jorge L. Sepulveda, Donald S. Young, (2012) The Ideal Laboratory Information System. Archives of Pathology & Laboratory Medicine In-Press.

Early Online Release

The Ideal Laboratory Information System

Jorge L. Sepulveda, MD, PhD; Donald S. Young, MD, PhD

From the Department of Pathology & Cell Biology, Columbia University, New York, New York (Dr Sepulveda) and the Department of Pathology & Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (Dr Young).

Context.—Laboratory information systems (LIS) are critical components of the operation of clinical laboratories. However, the functionalities of LIS have lagged significantly behind the capacities of current hardware and software technologies, while the complexity of the information produced by clinical laboratories has been increasing over time and will soon undergo rapid expansion with the use of new, high-throughput and high-dimensionality laboratory tests. In the broadest sense, LIS are essential to manage the flow of information between health care providers, patients, and laboratories and should be designed to optimize not only laboratory operations but also personalized clinical care.

Objective.—To list suggestions for designing LIS with the goal of optimizing the operation of clinical laboratories while improving clinical care by intelligent management of laboratory information.

Data Sources.—Literature review, interviews with laboratory users, and personal experience and opinion.

Conclusions.—Laboratory information systems can improve laboratory operations and improve patient care. Specific suggestions for improving the function of LIS are listed under the following sections: (1) Information Security, (2) Test Ordering, (3) Specimen Collection, Accessioning, and Processing, (4) Analytic Phase, (5) Result Entry and Validation, (6) Result Reporting, (7) Notification Management, (8) Data Mining and Cross-sectional Reports, (9) Method Validation, (10) Quality Management, (11) Administrative and Financial Issues, and (12) Other Operational Issues.