INTRODUCTION: Skin and subcutaneous tissue infections (SSTI) are common conditions that cause avoidable hospitalisation in New Zealand. As part of a program to improve the management of SSTI in primary care, electronic medical records (EMR) of four Auckland general practices were analysed to identify SSTI occurrences in the last three years. METHODS: An ontology for SSTI risks, manifestation and treatment was created based on literature and guidelines. An SSTI identification algorithm was developed examining EMR data for skin swab tests, diagnoses (READ codes) and textual clinical notes. RESULTS: High occurrence and recurrence rates in those aged 20 or younger were found. Due to low usage of READ coding and laboratory tests, 65% of SSTI occurrences were identified by notes. However, 91% of all identified SSTI occurrences were appropriately treated with oral/topical antibiotics according to prescription records in the EMR. The F1 score of the analysis algorithm is 0.76 using manual review as gold standard. DISCUSSION AND CONCLUSION: The SSTI identification algorithm shows a reasonable accuracy suggesting the feasibility of automatic detecting SSTI occurrences using clinical data that are routinely collected in healthcare delivery.
INTRODUCTION: Skin and subcutaneous tissue infections (SSTI) are common conditions that cause avoidable hospitalisation in New Zealand. As part of a program to improve the management of SSTI in primary care, electronic medical records (EMR) of four Auckland general practices were analysed to identify SSTI occurrences in the last three years. METHODS: An ontology for SSTI risks, manifestation and treatment was created based on literature and guidelines. An SSTI identification algorithm was developed examining EMR data for skin swab tests, diagnoses (READ codes) and textual clinical notes. RESULTS: High occurrence and recurrence rates in those aged 20 or younger were found. Due to low usage of READ coding and laboratory tests, 65% of SSTI occurrences were identified by notes. However, 91% of all identified SSTI occurrences were appropriately treated with oral/topical antibiotics according to prescription records in the EMR. The F1 score of the analysis algorithm is 0.76 using manual review as gold standard. DISCUSSION AND CONCLUSION: The SSTI identification algorithm shows a reasonable accuracy suggesting the feasibility of automatic detecting SSTI occurrences using clinical data that are routinely collected in healthcare delivery.
Authors: Simon de Lusignan; Stacy Shinneman; Ivelina Yonova; Jeremy van Vlymen; Alex J Elliot; Frederick Bolton; Gillian E Smith; Sarah O'Brien Journal: JMIR Med Inform Date: 2017-09-28