Validation of text-mining and content analysis techniques using data collected from veterinary practice management software systems in the UK.

Electronic patient records from practice management software systems have been used extensively in medicine for the investigation of clinical problems leading to the creation of decision support frameworks. To date, technologies that have been utilised for this purpose such as text mining and content analysis have not been employed significantly in veterinary medicine. The aim of this research was to pilot the use of content analysis and text-mining software for the synthesis and analysis of information extracted from veterinary electronic patient records. The purpose of the work was to be able to validate this approach for future employment across a number of practices for the purposes of practice based research. The approach utilised content analysis (Prosuite) and text mining (WordStat) software to aggregate the extracted text. Text mining tools such as Keyword in Context (KWIC) and Keyword Retrieval (KR) were employed to identify specific occurrences of data across the records. Two different datasets were interrogated, a bespoke test dataset that had been set up specifically for the purpose of the research, and a functioning veterinary clinic dataset that had been extracted from one veterinary practice. Across both datasets, the KWIC analysis was found to have a high level of accuracy with the search resulting in a sensitivity of between 85.3-100%, a specificity of between 99.1-99.7%, a positive predictive value between 93.5-95.8% and a negative predictive value between 97.7-100%. The KR search, based on machine learning, was utilised for the clinic-based dataset and was found to perform slightly better than the KWIC analysis. This study is the first to demonstrate the application of content analysis and text mining software for validation purposes across a number of different datasets for the purpose of search and recall of specific information across electronic patient records. This has not been demonstrated previously for small animal veterinary epidemiological research for the purposes of large scale analysis for practice-based research. Extension of this work to investigate more complex diseases across larger populations is required to fully explore the use of this approach in veterinary practice.

A method for extracting electronic patient record data from practice management software systems used in veterinary practice.

BACKGROUND: Data extracted from electronic patient records (EPRs) within practice management software systems are increasingly used in veterinary research. The use of real patient data gives the potential to generate research that can readily be applied to clinical practice. The use of veterinary EPRs for research in the United Kingdom is hindered by the number of different Practice Management System (PMS) providers used by practices, as obtaining and combining data from different systems electronically can be problematic. The use of extensible mark up language (XML) to extract clinical data for research would potentially resolve the compatibility issues between systems. The aim of this study was to establish and validate a method for the extraction of small animal patient records from a veterinary PMS that could potentially be used across multiple systems. An XML schema was designed to extract clinical information from EPRs. The schema was tested and validated in a test system, and was then tested in a real small animal practice where data was extracted for 16 weeks. A 10 % sample of the extracted records was then compared to paper copies provided by the practice. RESULTS: All 21 fields encoded by the XML schema, from all of the records in the test system, were extracted with 100 % accuracy. Over the 18 week data collection period 4946 records, from 1279 patients, were extracted from the small animal practice. The 10 % printed records checked and compared with the XML extracted records demonstrated all required data was present. No unrequired, sensitive information e.g. costs or services/products or personal client information was extracted. CONCLUSIONS: This is the first time a method for data extraction from EPRs in veterinary practice using an XML schema has been reported in the United Kingdom. This is an efficient and accurate way of extracting data which could be applied to all PMSs nationally and internationally.