A Systematic Mechanism for the Ethical Collection and Interpretation of Display Format Pathology Test Results from Australian Primary Care Records (2009)

Objectives: To demonstrate ethical access to primary care patient data and the use and systematic interpretation of display format textual pathology test results held in primary care - in this case for Chlamydia surveillance. Background: In Australia, it is common for pathology test results to be stored electronically in primary care medical record systems. These records have the potential to be used in many ways across an integrated healthcare system for example in the monitoring of STI testing rates, monitoring prevalence and incidence rates, general service audit and research. Access to such data can potentially be obtained from pathology laboratories but when denominators such as practice consultation rates are required, and where practices use multiple pathology providers, the only access to systematic data is often the GP computer system. At this time, there are few options for wide-scale access to such data that complies with privacy legislation, that supports record-linkage to other clinical databases and that can properly ensure textual results are correctly interpreted. Methods: Using the GRHANITE TM software suite, a protocol was designed for the extraction of clinical consultations, pathology test requests and associated pathology test results for Chlamydia. GRHANITE TM ensures that patient identifying information such as name and date of birth are removed, but replaced with hashed identifiers used later for pseudonymous record linkage. GRHANITE TM automates the secure extraction of the data on a regular basis and the data is collected at a project data repository. In a sample of 10 practices (two different GP computer systems), results from 16 separate laboratories were obtained - all of which report test results differently. A rule-set for the parsing of this data was created and a C# program created to auto-generate rule-testing and rule interpretation SQL code. Results: Utilising the rule-set interpreter, we were able to systematically verify that: All laboratories supplying data to the practices were included in the analysis, all tests for Chlamydia were identified (often several tests from each laboratory), all specimen types were accounted for and all results were correctly interpreted. It is an iterative process with the results of the rule-set tests determining further rules that must be applied. In total 7,072 test results were analysed producing 236 distinct rules for the interpretation of the data from 16 laboratories. The rule-set generator created 3,102 lines of SQL code for rule verification and 9,135 lines for data consolidation. Implications: GRHANITE TM has shown itself capable of ethically extracting data at a patient level (that is record-linkable) from a number of practices across two distinct general practice systems. The diversity of recording formats utilised by laboratories makes the electronic interpretation of textual pathology data difficult. Using a rule-set interpreter, typographical errors in applying 236 distinct parsing rules were avoided. As more data arrives, the rule-set interpreter can be re-applied to identify new labs, new tests, new specimen types and new results. With the rule-set generator, we now have a sustainable and fast mechanism for future interpretation of any textual pathology data as held in Australian GP computer systems.