Text Mining and Automation for Processing of Patient Referrals

Abstract

Background Various tasks within health care processes are repetitive and time-consuming, requiring personnel who could be better utilized elsewhere. The task of assigning clinical urgency categories to internal patient referrals is one such case of a time-consuming process, which may be amenable to automation through the application of text mining and natural language processing (NLP) techniques.

Objective This article aims to trial and evaluate a pilot study for the first component of the task—determining reasons for referrals.

Methods Text is extracted from scanned patient referrals before being processed to remove nonsensical symbols and identify key information. The processed data are compared against a list of conditions that represent possible reasons for referral. Similarity scores are used as a measure of overlap in terms used in the processed data and the condition list.

Results This pilot study was successful, and results indicate that it would be valuable for future research to develop a more sophisticated classification model for determining reasons for referrals. Issues encountered in the pilot study and methods of addressing them were outlined and should be of use to researchers working on similar problems.

Conclusion This pilot study successfully demonstrated that there is potential for automating the assignment of reasons for referrals and provides a foundation for further work to build on. This study also outlined a potential application of text mining and NLP to automating a manual task in hospitals to save time of human resources.

Keywords

natural language processing - process improvement - efficiency improvement - cost reduction and return on investment - quantitative methodology

Note

All codes used in this study are available on BitBucket, and can be accessed through the following link: https://bitbucket.org/JTodd_Bond/patientreferrals/. The actual patient referrals are not available as they contain private information.

Protection of Human and Animal Subjects

Ethics approval has been obtained for this project. The referral documents were deidentified by redacting names and information of medical professionals and patients, and date information before being provided.

• References

• 1 Chapman WW, Nadkarni PM, Hirschman L, D'Avolio LW, Savova GK, Uzuner O. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions. J Am Med Inform Assoc 2011; 18 (05) 540-543
• 2 Wang Y, Wang L, Rastegar-Mojarad M. , et al. Clinical information extraction applications: a literature review. J Biomed Inform 2018; 77: 34-49
• 3 Cai T, Giannopoulos AA, Yu S. , et al. Natural language processing technologies in radiology research and clinical applications. Radiographics 2016; 36 (01) 176-191
• 4 Meystre SM, Haug PJ. Comparing natural language processing tools to extract medical problems from narrative text. AMIA Annu Symp Proc 2005; 525-529
• 5 Schadow G, McDonald CJ. Extracting structured information from free text pathology reports. AMIA Annu Symp Proc 2003; 584-588
• 6 Warren J, White S, Day KJ, Gu Y, Pollock M. Introduction of electronic referral from community associated with more timely review by secondary services. Appl Clin Inform 2011; 2 (04) 546-564
• 7 Moon S, Liu S, Kingsbury P. , et al. Medical concept intersection between outside medical records and consultant notes-a case study in transferred cardiovascular patients. 2017 IEEE International Conference on Bioinformatics and Biomedicine. Kansas City, MO; November 13–16, 2017:1495–1500
• 8 R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: 2016
• 9 Australian Institute of Health and Welfare. Gold Coast University Hospital . 2017. Available at: https://www.myhospitals.gov.au/hospital/310000050/gold-coast-university-hospital . Accessed September 18, 2017
• 10 Queensland Health. Clinical Prioritisation Criteria. 2016. Available at: https://cpc.health.qld.gov.au/ . Accessed August 10, 2017