BACKGROUND: This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured information in the official databases.
METHODS: A Natural Language Processing (NLP) based pipeline has been developed to extract the waiting time information from the text of referrals for follow-up examinations in the Lombardy Region. A manually annotated dataset of 10 000 referrals has been used to develop the pipeline and another manually annotated dataset of 10 000 referrals has been used to test its performance. Subsequently, the pipeline has been used to analyze all 12 million referrals prescribed in 2021 and performed by May 2022 in the Lombardy Region.
RESULTS: The NLP-based pipeline exhibited high precision (0.999) and recall (0.973) in identifying waiting time information from referrals\' texts, with high accuracy in normalization (0.948-0.998). The overall reporting of timing indications in referrals\' texts for follow-up examinations was low (2%), showing notable variations across medical disciplines and types of prescribing physicians. Among the referrals reporting waiting times, 16% experienced delays (average delay = 19 days, standard deviation = 34 days), with significant differences observed across medical disciplines and geographical areas.
CONCLUSIONS: The use of NLP proved to be a valuable tool for assessing waiting times in follow-up examinations, which are particularly critical for the NHS due to the significant impact of chronic diseases, where follow-up exams are pivotal. Health authorities can exploit this tool to monitor the quality of NHS services and optimize resource allocation.

While much is known about governance models for research informatics programs in academic medical centers and similarly situated cancer centers, community and public health systems have been less well-characterized. As part of implementing an enterprise research governance framework, leaders in the Los Angeles County Department of Health Services established a research informatics program, including research data warehousing. The strategy is focused on high-priority, patient-centered research that leverages the investment in health IT and an efficient, sustained contribution from 2 affiliated Clinical Translational Sciences Institutes. This case study describes the foundational governance framework and policies that were developed. We share the results of several years of planning, implementation, and operations of an academically funded research informatics service core embedded in a large, multicenter county health system. We include herein a Supplementary Appendix of governance documents that may serve as pragmatic models for similar initiatives.