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Perspectives on Big Data in Healthcare

Posted by Matt Breese on May 3, 2016

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Data acquisition and analysis is a critical component in any industry that is future oriented — including healthcare. Payers use data to manage formularies, gain a competitive edge and negotiate pricing. Providers see data as an indispensable tool to gain insight into population medicine, to standardize clinical pathways, and to evaluate treatment options and outcomes. And for pharma, data provides the foundation for research and development (R&D) decisions, as well as targeted business strategies.

Limiting factors, such as the size of data sets and the ease with which they can be studied and shared, hinder decision making for payers, providers and pharma, and can affect business or practice growth, as well as clinical outcomes.

As technology advances and more sophisticated analytical tools become available, data assumes an increased relevance in day-to-day healthcare operations, and in future planning.

Making a case for big data

Big data — very large, comprehensive data sets that can be used for complex analytics to reveal patterns, trends and relationships — is a credible tool to optimize innovation, improve efficiencies in research and clinical trials, and individualize provider, payer and pharma partnerships. At the macro level, big data can influence the care and, therefore, the overall health of large segments of society — such as geographic regions, ethnic groups or nations. The McKinsey Global Institute estimates that employing big-data strategies could generate up to $100 billion in value annually across the United States healthcare system.

The larger the data sets and the more sophisticated the analytical technology, the greater the impact. The challenge is to identify the best way to gather, house, analyze and share the data. Few individual companies, hospitals or medical practices have the resources to be the sole source of big data, but with the appropriate in-house expertise and access, they can take advantage of the work others have done.

Leveraging big data

Although big data generally refers to enormously large data sets, pharma can realize significant improvement by simply expanding the data they collect and improving their approach to managing and analyzing it. McKinsey Global suggests eight technology-enabled measures for pharma to accomplish a big-data approach to decision making.

  1. Integrate data. Ensuring data is consistent, reliable and well-linked is a huge challenge. Integrating and managing data acquisition from discovery through clinical trials, regulatory approval and delivery to the end-user is fundamental to value-added large-scale and subset analytics. This level of data integrity is the authoritative foundation of pharma R&D and business strategies.
  1. Collaborate. R&D often operates in a silo, with little internal or external collaboration. Extending knowledge and data networks requires breaking down existing silos that separate internal functions and developing external partnerships that improve access to outside data resources.
  1. Employ IT-enabled portfolio-decision support. Expedited decision-making benefits portfolio and pipeline progression. IT-enabled portfolio management encourages decision-making that is driven by reliable data and helps convert a subjective choice about which projects to pursue or reject into a dispassionate decision.
  1. Leverage new discovery technologies. The field of technology is not stagnant. New devices and applications become available at an amazing rate. Improved analytical techniques, along with more robust data, will enhance future innovation and drug development.
  1. Deploy sensors and devices. Increasingly sophisticated health-measurement devices are making it possible to gather large quantities of real-world data that can be integrated into pharma big data that can, in turn, be used for targeted marketing or development strategies.
  1. Raise clinical-trial efficiency. Smart devices for gathering data and smooth-flowing data integration improve clinical-trial design, efficiency and outcomes.
  1. Improve safety and risk management. Risk management can benefit from creative data collection. Rather than waiting for an adverse-event report, pharma can use search engines, patient Web inquiries, online physician communities, electronic medical records and consumer-generated social media to identify early signals of potential safety issues. While the main purpose of collecting data from these outlets is safety and risk management, analysis can also provide valuable data on the reach and reputation of certain drugs.
  1. Sharpen focus on real-world evidence. Payers’ value-based pricing drives pharma efforts to improve documentation of outcomes. Reliable big data can support differentiation, a clinical edge and the population-based value of various drugs.

Better outcomes through big data

Big data is changing healthcare. Providers no longer need to rely on trial and error to find treatments that produce the best outcomes — a lengthy and stressful process for patients and providers alike. For example, cancer providers order sophisticated genomic sequencing to identify a patient’s molecular abnormalities and then zero in on the treatment that most frequently offers the best outcomes by searching data sets of patients with similar abnormalities. Practices use outcomes data to develop efficient and effective clinical pathways. Big data helps researchers identify diseases that are common among geographic or ethnic segments of society in order to focus on screening and prevention. And data is the foundation of clinical trials for new medications from initial candidate selection and enrollment to post-trial outcomes monitoring.

Big data is undoubtedly a strategic tool for practices, payers and pharma to enhance their business and clinical operations. It is also an essential tool that makes it possible to identify treatment gaps and consistencies, target the research and development of new drug therapies and — in the end — to find cures.

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Topics: Industry Trends, Data & Analytics