clinical documentation also may be a more trustworthy source for public health research. The diagnostic codes used for insurance and billing – sometimes the only data that some of today’s EHRs will spit out – can be unreli- able for research and highly variable. The physicians’ notes provide context for the quantitative data found elsewhere in the health record.
Mining unstructured data
No doubt, incorporating physicians’ notes into the EHR and then into public-health research proves chal- lenging – but it is possible. The fi rst requirement is a robust electronic clinical documentation system that allows clinicians to capture their experiences treating patients. Next, researchers Scott Spangler and Jeffrey Kreulen at IBM describe a three-step process for mining that unstructured data based on exploring, understanding and analyzing. The process starts by exploring the data to fi nd relevant information, such as by using a keyword search or by selecting particular structured fi elds to limit the amount of data that ultimately needs to be parsed. This alone, though, lacks the ability to understand context, such as the difference between confi rming and negating a potential diagnosis.
The next phase addresses this by understanding the selected information to create an analyzable structure. This could include NLPs, which rely on various methods such as pattern matching or rule-based techniques. Some early adopters such as New York-Presbyterian Hospital- Columbia University Medical Center and the National Cancer Institute already have this in place. Spangler and Kreulen also suggest using taxonomic methods, partition- ing and clustering as potential methods. After this process is complete, then the data can begin to be analyzed, looking for trends and correlations appropriate for the research. As EHRs become increasingly widespread due to the billions of dollars in federal stimulus incentives, harness- ing unstructured clinicians’ notes gives us the power to yield valuable patient data. With each year of data, more information will be gathered that could be used to fi nd predictors for diseases or adverse effects of treatment that would otherwise have gone unnoticed by most tradi- tional research studies. Though challenging, capturing and delving into this data will be worth the effort, and could potentially help healthcare institutions meet requirements for CMS reporting and for meaningful use, access fund- ing and, most importantly, improve the health of entire populations.
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