Machine Learning with Health Information Technology: Transforming Data-Driven Healthcare Systems

Abstract

The integration of machine learning (ML) into health information technology (HIT) is revolutionizing data-driven healthcare systems, yet several key challenges and areas of focus remain. Electronic health records (EHRs) constitute most of the data source (60%), with wearable devices and interviews/focus groups comprising smaller portions. This indicates a continued reliance on traditional health records, although emerging technologies are beginning to play a role. Another key preprocessing challenge, with data cleaning consuming the most effort (40%), followed by data anonymization and feature selection, each requiring substantial effort in ensuring the accuracy and privacy of patient data. Supervised learning dominates in healthcare applications, followed by deep learning and unsupervised learning. In terms of accuracy, EHR data consistently yields the highest performance, around 85%, closely followed by wearable devices, genetic data, and lifestyle data. However, challenges remain in addressing data privacy and algorithm transparency, as highlighted by the distribution of effort in ensuring compliance and maintaining data privacy. The findings suggest a need for further exploration into wearable devices and the real-time monitoring capabilities they bring to healthcare, alongside tackling data preprocessing and ethical challenges in HIT.