Harnessing real-time data to give a holistic picture of patient health

Every day, vast quantities of data are collected about patients as they pass through health service organizations—from operational data such as treatment history and medications to physiological data captured by medical devices. The insights hidden within this treasure trove of data can be used to support more personalized treatments, more accurate diagnosis and more advanced preparative care. But since the information is generated faster than most organizations can consume it, unlocking the power of this big data can be a struggle. This type of predictive approach not only improves patient care—it also helps to reduce costs, because in the healthcare industry, prevention is almost always more cost-effective than treatment. However, collecting, analyzing and presenting these data-streams in a way that clinicians can easily understand can pose a significant technical challenge.

  • IBM Watson (IBM)
    Watson is a question answering computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy!. In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings. Watson received the first place prize of $1 million. Watson had access to 200 million pages of structured and unstructured content consuming four terabytes of disk storage including the full text of Wikipedia, but was not connected to the Internet during the game. For each clue, Watson's three most probable responses were displayed on the television screen. Watson consistently outperformed its human opponents on the game's signaling device, but had trouble in a few categories, notably those having short clues containing only a few words. In February 2013, IBM announced that Watson software system's first commercial application would be for utilization management decisions in lung cancer treatment at Memorial Sloan Kettering Cancer Center in conjunction with health insurance company WellPoint. IBM Watson's former business chief Manoj Saxena says that 90% of nurses in the field who use Watson now follow its guidance.
  • Analytics & Modeling - Big Data Analytics
  • Healthcare & Hospitals
  • Business Operation
  • Remote Patient Monitoring
  • CléMetric's Q2Care software harnesses, manages, and stores patient data produced by medical systems across a provider's enterprise network.
  • CléMetric
  • IBM Streams serves as a cornerstone of the CléMetric application suite—enabling the company to collect, analyze and correlate information as it arrives from a variety of medical monitoring devices, such as electrocardiograms, pulse oximeters and pulmonary artery catheters. CléMetric combines the power of IBM stream and cognitive computing with a range of analytics, data management and visual discovery tools to enable active patient monitoring, as well as retrospective analysis. Data is pulled from patient monitors in near real time and integrated into a single dashboard, giving clinicians a holistic view of each patient.

  • Electrocardiography (ECG), Electronic Medical Record, Health Parameters, Medical Diagnostic Instruments, Personal Medical
  • Impact #1
    [Process Optimization - Real Time Monitoring]
    With a near real-time, holistic view of patient health, caregivers can make smarter, faster decisions about how best to treat patients.
    Impact #2
    [Efficiency Improvement - Operation]
    CléMetric sees huge potential for bringing together near real-time physiological data with the latest research data from medical journals to open up new insight into patients, conditions and treatment patterns.
    Impact #3
  • Benefit #1

    1,000+ data points are analyzed per patient per second by IBM Watson, which delivers fast health insight.

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