Better patient outcomes through mining of biomedical big data

Abstract

Digitalization is changing healthcare today. Big data analytics of medical information allows diagnostics, therapy and development of personalized medicines, to provide unprecedented treatment. This leads to better patient outcomes, while containing costs. In this review, opportunities, challenges and solutions for this health-data revolution are discussed. Integration and near-instant-response analytics across large datasets can support care-givers and researchers to test and discard hypotheses more quickly. Physicians want to compare a patient to other similar patients, to learn and communicate about treatment best-practices with peers, across large cohorts and sets of parameters. Real-time interactions between physician and patient are becoming more important, allowing "live" support of patients instead of single interactions once every few weeks. Researchers from many disciplines (biomedical, payers, governments) want to interpret large anonymized datasets, to uncover trends in drug-candidate behavior, treatment regimens, clinical trials or reimbursements, and to act on those insights. These opportunities are however met by daunting challenges. Biomedical information is available in data silos of structured and unstructured formats (doctor letters, patient records, omics data, device data). Efficient usage of biomedical information is also hampered by data privacy concerns. This has led to a highly-regulated industry, as a result of which digitalization in healthcare has progressed slower than in other industries. This review concludes with examples of how integration and interpretation of big data can be used to break down data silos and pave the way to better patient outcomes, value-based care, and the creation of an intelligent enterprise for healthcare.