ABSTRACT
Clinical free-text data represent a vast, untapped source of rich information. If more accessible for research it would supplement information captured in structured fields. Data need to be de-identified prior to being reused for research. However, a lack of transparency with existing de-identification software tools makes it difficult for data custodians to assess potential risks associated with the release of de-identified clinical free-text data. This case study describes the development of a framework for releasing de-identified clinical free-text data in two local health districts in NSW, Australia. A sample of clinical documents (n = 14 768 965), including progress notes, nursing and medical assessments and discharge summaries, were used for development. An algorithm was designed to identify and mask patient names without damaging data utility. For each note, the algorithm output the (i) note length before and after de-identification, (ii) the number of patient names and (iii) the number of common words. These outputs were used to iteratively refine the algorithm performance. This was followed by manual review of a random subset of records by a health information manager. Notes that were not correctly de-identified were fixed, and performance was reassessed until resolution. All notes in this sample were suitably de-identified using this method. Developing a transparent method for de-identifying clinical free-text data enables informed-decision making by data custodians and the safe re-use of clinical free-text data for research and public benefit.
Subject(s)
Data Anonymization , Electronic Health Records , Algorithms , Australia , Humans , SoftwareABSTRACT
This study developed a model for using Google Analytics (GA) data to evaluate utilisation trends of the Sydney North HealthPathways portal. HealthPathways GA data merged with dates of page localisations and promotional events from March 2017 through June 2018 were analysed to evaluate engagement and use of HealthPathways, integration into clinical practice and how HealthPathways is used. Descriptive statistics and plots were generated for each clinical stream and page for the number of users per month (total, new and return users), mean time on page, navigation and search terms. The number of page views, new users and return users increased during the study period. Each clinical stream had between 26 and 2508 views, with a median of 199 views (interquartile range 84-461 views). Individual pages had 0-12388 total views. Return users visited seven times on average. Most usage occurred between mid-morning and mid-afternoon. Diabetes was the most frequently viewed and searched clinical stream, followed by palliative care. These streams had the greatest number of promotional events. Increasing use of and interaction with HealthPathways suggests that it is a useful tool to support clinical practice among northern Sydney primary care providers.
