How to Incorporate Big Data Into the Diagnosis of Diseases

Across the world, big data sources for healthcare are being created and made available for integration into existing processes. Clinical trial data, genetics and genetic mutation data, protein therapeutics data, and many other new sources of information can be harvested to improve daily healthcare processes.

Social media can and will be used to augment existing data and processes to provide more personalized views of treatment and therapies. New medical devices will control treatments and transmit telemetry data for real-time and other kinds of analytics. The task ahead is to understand these new sources of data and complement the existing data and processes with the new big data types.

So, what would the healthcare process look like with the introduction of big data into the operational process of identifying and managing patient health? Here is an example of what the future might look like:

  1. Understand the problem we are trying to solve:

    • Need to treat a patient with a specific type of cancer

  2. Identify the processes involved:

    • Diagnosis and testing (identify genetic mutation)

    • Results analysis including researching treatment options, clinical trial analysis, genetic analysis, and protein analysis

    • Definition of treatment protocol, possibly including gene or protein therapy

    • Monitor patient and adjust treatment as needed using new wireless device for personalized treatment delivery and monitoring. Patient uses social media to document overall experience.

  3. Identify the information required to solve the problem:

    • Patient history

    • Blood, tissue, test results, and so on

    • Statistical results of treatment options

    • Clinical trial data

    • Genetics data

    • Protein data

    • Social media data

  4. Gather the data, process it, and analyze the results:

    • Commence treatment

    • Monitor patient and adjust treatment as needed

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This represents the optimal case where no new processes need to be created to support big data integrations. While the processes are relatively unchanged, the underlying technologies include the applications that will need to be altered to accommodate the impact of characteristics of big data, including the volume of data, the variety of data sources, and the speed or velocity required to process that data.

The introduction of big data into the process of managing healthcare will make a big difference in effectiveness to diagnosing and managing healthcare in the future. This same operational approach process can be applied to a variety of industries. What are the keys to successfully applying big data to operational processes? Here are some of the most important issues to consider:

  • Fully understand the current process.

  • Fully understand where gaps exist in information.

  • Identify relevant big data sources.

  • Design a process to seamlessly integrate the data now and as it changes.

  • Modify analysis and decision-making processes to incorporate the use of big data.

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