Cutting Edge Data Quality Transforms Real-World Clinical Data into a Future-Proofed, Research-Ready Data Goldmine for Better Patient Care
Melissa, a leading provider of global contact data quality and identity verification solutions, announced advanced artificial intelligence (AI) solutions that combine machine reasoning, natural language processing, and machine learning to tackle one of healthcare’s biggest problems: the time and cost of data harmonization and integration.
Hospitals, clinical care, and clinical research organizations are sitting on a veritable data goldmine, based on data gathered for years from electronic medical records (EMR), electronic health records (EHR), and laboratory information management systems (LIMS). While this real-world clinical data is unusually valuable, it is also unusually complex and diverse. Melissa Informatics’ Sentient (MIS) solution is a new and unique set of clinical data quality and integration tools that quickly and easily turn diverse, dirty, and disconnected data into a clean, research-ready data resource. Melissa Informatics has laid out the company’s easy-to-follow steps to turn clinical data into a future-proofed, AI-enabled “knowledgebase” in its newest paper, “The Six-Step Guide to Turn Clinical Data into Gold,”.
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“Too often, clinical data is expensively gathered and under-valued,” said Bob Stanley, senior director, customer projects, Melissa Informatics. “When you apply machine learning and machine reasoning to access, curate, and integrate this data, it becomes ready for rewarding new uses in patient care, precision medicine research, intellectual property, and unexpected new revenue.”
Melissa Informatics further demonstrates the value of AI-enabled data quality by providing real-world use cases from world-renowned clinics including Parkinson’s Institute and Clinical Center (PICC) and PROOF Centre.
Using Melissa Informatics MIS technology, PICC transformed data such as unstructured text, XML, tables, tsv, image content and other data formats into a research quality, well-managed data resource. This helped the organization meet its technical goals including creating a new, unified “Parkinson’s Insight” data resource – as well as its business goals, including researching and publishing discoveries from that data, and engaging in revenue-generating partnerships based on the new data resource.
PROOF Centre used MIS tools to better understand and model connections between desired data sources, working under PIPEDA, HIPAA, Safe Harbor and EMEA requirements for handling of confidential patient data. MIS enabled a global platform for integrated search and reporting, analytics, and knowledge creation and sharing. This project integrated diseases, treatments, outcomes, tissue bank information, laboratory data, molecular data from blood (gene expression, proteins, metabolites), and published data.