Law firms spend vast amounts of time and resources to ensure they are extracting the right information from medical records to best represent their clients in mass tort litigations. Unfortunately, most of the common tools used to assist in the process rely on simple keyword search, which has many limitations. This article will explore why an AI-based contextual search can be superior to a simple keyword search for extracting the most relevant information from medical records that ensures the best outcome for clients.
The problems with “control-f”
We’re all familiar with the simple “find” function in popular software such as Adobe Acrobat or Microsoft Word. Though easy to use, this search method only finds exactly what you typed. There are two major reasons why this simple keyword search falls short when dealing with medical records:
- Fails to Account for Huge Clinical Variance: A simple keyword search is limited in its ability to account for the huge clinical variance in medical records. A robust medical search word set for a single medical concept requires hundreds of variants, thousands if you want to be resilient to all common misspellings. This limitation can result in a high false positive rate mixed with false negatives from limitations of word matching, making it an unreliable process.
- Blind to Context: A simple keyword search can’t distinguish between good and bad findings. It can’t take into account important factors such as record types, family history, etc. If you search for “diabetes” or “heart disease” on an individual with a family history of that disease, your search becomes useless. To ensure that proof of medication use, diagnosis, procedures, etc., is context-aware, it’s important to know if it’s on a pathology report, operative report, etc.
The benefits of AI-based contextual search
Modern software, like Pattern Data, uses artificial intelligence (AI) and natural language processing (NLP) to not just search for the key word, but also do its best to understand the context and learn from previous search results. Here are three ways why AI-based contextual search models can be a better option for identifying the best data from medical records.
- Allows Findings with the Highest Relevance to be Elevated: AI-based contextual search models can surface findings with the highest relevance based on context and surrounding content. This means that findings with low relevance, such as patient education or negative diagnostics, can be demoted. This approach provides a more efficient and effective way to extract relevant information from medical records.
- Provides Resilience to New Terminology and Misspellings: AI-based contextual search models are resilient to new terminology, misspellings, and ambiguous shorthand. Medical terminology is constantly evolving, and drugs have generic names and shorthand. Diseases also have many different names. For example, Sturge Weber syndrome has over a dozen synonyms (e.g. encephalotrigeminal angiomatosis, angiomatosis encephalofacial, dimitri disease), and depending on what the doctor writes, an AI-based contextual search would surface all the results.
- Allows Findings to be Coupled Together in Meaningful Ways: AI-based contextual search models can couple findings together in meaningful ways. This means that the context of the query can help the results greatly. For example, when processing cases for a settlement, an AI-based contextual search can find cases where there is evidence of the target product usage and injury that align with the settlement matrix. Additionally, it can also determine if the claimant’s age or geography could lead to a different settlement score. All of this can be done with one query, and the AI software takes care of the rest.
Due to the complexity of medical records and product usage, mass tort litigation requires a more advanced search approach than simple keyword search. AI-based contextual search models are more efficient and effective in extracting relevant information from medical records. They are resilient to new terminology, misspellings, and ambiguous shorthand, and they can couple findings together in meaningful ways. By using an AI-based contextual search, legal teams can save time, reduce costs, and achieve better outcomes for their clients.