Disease diagnostic coding to facilitate evidence-based medicine: current and future perspectives

Abstract

Technologic advances in information management have rapidly changed laboratory testing and the practice of veterinary medicine. Timely and strategic sampling, same-day assays, and 24-h access to laboratory results allow for rapid implementation of intervention and treatment protocols. Although agent detection and monitoring systems have progressed, and wider tracking of diseases across veterinary diagnostic laboratories exists, such as by the National Animal Health Laboratory Network (NAHLN), the distinction between detection of agent and manifestation of disease is critical to improved disease management. The implementation of a consistent, intuitive, and useful disease diagnosis coding system, specific for veterinary medicine and applicable to multiple animal species within and between veterinary diagnostic laboratories, is the first phase of disease data aggregation. Feedback loops for continuous improvement that could aggregate existing clinical and laboratory databases to improve the value and applications of diagnostic processes and clinical interventions, with interactive capabilities between clinicians and diagnosticians, and that differentiate disease causation from mere agent detection, remain incomplete. Creating an interface that allows aggregation of existing data from clinicians, including final diagnosis, interventions, or treatments applied, and measures of outcomes, is the second phase. Prototypes for stakeholder cooperation, collaboration, and beta testing of this vision are in development and becoming a reality. We focus here on how such a system is being developed and utilized at the Iowa State University Veterinary Diagnostic Laboratory to facilitate evidence-based medicine and utilize diagnostic coding for continuous improvement of animal health and welfare.

Keywords: data warehouse; diagnosis; diagnostic codes; diagnostic coding.

Figure 1.

The ISU-VDL diagnostic process and relevant feedback loops. From the top, examples of datasets submitted by a veterinarian and generated by the laboratory are combined to create a case report with associated diagnostic codes. This single case report is utilized to inform appropriate interventions, but also is stored in the data warehouse, which can be queried to retrieve output data. IAV = influenza A virus; MHP = Mycoplasma hyopneumoniae; P. multocida = Pasteurella multocida; PCV2 = porcine circovirus 2; PRRSV = porcine reproductive and respiratory syndrome virus.

Figure 2.

Examples of diagnostic (Dx) code entry within the LIMS at the ISU-VDL. In this case, Dx codes for influenza A virus infection and bronchopneumonia associated with Streptococcus suis and Actinobacillus sp. have already been applied (outlined in blue) and the diagnostician is applying a code for pleuritis associated with Glaesserella parasuis infection. A. The diagnostician has entered text into the search box (red oval) reflecting the first 3 aspects of the 4-part code (system, insult, and lesion) and a pop-up window displays all possible etiologies available in the Dx code database (outlined in yellow). B. The diagnostician has entered the etiology of interest (Glaesserella, red oval), and the pop-up window displays all available codes in the database related to this etiology (outlined in yellow). The code RESP BACT PLEU GPS is retrieved via either query route.