Use of Electronic Health Records and Administrative Data for Public Health Surveillance of Eye Health and Vision-Related Conditions in the United States

Abstract

Purpose To discuss the current trend toward greater use of electronic health records and how these records could enhance public health surveillance of eye health and vision-related conditions. Methods We describe three currently available sources of electronic health data (Kaiser Permanente, the Veterans Health Administration, and the Centers for Medicare & Medicaid Services) and how these sources can contribute to a comprehensive vision and eye health surveillance system. Results Each of the three sources of electronic health data can contribute meaningfully to a comprehensive vision and eye health surveillance system, but none currently provide all the information required. The use of electronic health records for vision and eye health surveillance has both advantages and disadvantages. Conclusions Electronic health records may provide additional information needed to create a comprehensive vision and eye health surveillance system. Recommendations for incorporating electronic health records into such a system are presented.

Uses of Electronic Health Records for Public Health Surveillance to Advance Public Health

“…Data from three different data systems, Kaiser Permanente, CMS (Medicare), and the Veterans Health Administration, were combined to detect eye disorders (19). The advantages of using EHRs for surveillance of eye disease were the real time nature of the data, the automated collection, and the ability to study incidence and prevalence over time.…”

Evaluation of the secondary use of electronic health records to detect seasonal, holiday-related, and rare events related to traumatic injury and poisoning

“…Clinical data that is collected in routine medical care can be algorithmically processed for syndromic surveillance, a passive reporting technique wherein patient cases of a particular disease or condition relevant to population health (frequently, but not exclusively infectious disease) are automatically flagged and reported to appropriate authorities in real time. EHRs have been shown to be a reliable data source capable of facilitating syndromic surveillance [7][8][9][10][11]. The prevalence estimation of EHRs have also been shown to accurately reflect the known prevalence of a served region.…”

Proceedings of BioNLP 15

“…There has been a growth in the use of more complex kernels and sophisticated parameter tuning methods to improve the results (Zhang et al, 2006;Choi and Myaeng, 2010). In PPI task, machine learning methods using rich feature vectors (Miwa et al, 2009b), edit distance kernel (Erkan et al, 2007), dependency tree kernel (Chowdhury et al, 2011), allpath graph kernel (Airola et al, 2008), or their combination and variations (Miwa et al, 2009a;Zhang et al, 2012) have been proposed. Our focus is on improving the representation of information in natural texts, rather than on developing new kernels.…”

“…Moreover, ophthalmology has been reported to be a suboptimal target of application of EHR systems in several surveys carried out in the US (Chiang et al, 2013;Redd et al, 2014;Elliott et al, 2012). The special requirements of documenting a mixture of various measurements, some of them resulting in tabular data, while others in single values or textual descriptions make the design of a usable system for storing ophthalmology reports in a structured and validated form very hard.…”