Page 43 - FCW, October 2016

A GUIDE TO HEALTH IT
and healthcare functions, including clinical decisions, disease surveillance, and population health management, according to a report by the U.S. National Library of Medicine and National Institutes of Health (NIH).
The California-based health network Kaiser Permanente, for example, has more than 9 million members. It’s currently storing upwards of 44 petabytes of electronic health record data, including images and annotations, according to the NIH report. Other reports indicate aggregate data from the U.S. healthcare system reached 150 exabytes as far back as 2011. At the current growth rate, U.S. healthcare data stores are well into
the zettabyte level, perhaps even approaching the yottabyte level.
These massive healthcare data stores are overwhelming not only because of the volume, but also the diversity of data types.
These days, healthcare data comes from a dizzying array of sources, states the NIH report, including clinical data (such as physician’s written notes and prescriptions, medical imaging, laboratory, pharmacy, insurance, and other administrative data); patient data in electronic patient records (EPR); machine/sensor data such as vital signs from connected monitoring systems; social media posts such as Twitter feeds, Facebook updates, and blogs; and other less patient-specific information such as emergency care data, news feeds, and medical journals.
Much of that unstructured data likely sits unused, having been retained primarily for regulatory purposes. However, there are several valid use cases for this type of data, according to a story on CIO.com, including:
• Take a more evidence-based approach to medicine, even including on data from other institutions for clinical decision support. For example, resist the practice of applying the same test to emergency room patients exhibiting similar symptoms.
• Streamline the research process. Randomized controlled trials can last years and cost millions, while observational studies can suffer from inherent bias. Embed clinical trials with data used to augment clinical decision support.
• Develop and deploy smart healthcare data-driven apps with access to standardized data.
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• Map raw data to accepted standards to ensure widespread access and acceptance.
• Ensure healthcare IT vendors articulate their strategy to better aggregate and standardize clinical data.
• Make use of public health data and statistics to refine strategic planning.
Truly transforming raw data into actionable intelligence requires informed standardization, processing and advanced analytics. The data is there; and now the practices and technologies are evolving to derive valuable guidance from aggregated healthcare data.
Three Steps to Transform Data into Value
In his post on Health Catalyst, data analysis expert Russell Staheli identifies the three stages data must go through to be truly usable and valuable in a healthcare environment.
Data Capture: Successful analysis depends on the people, processes, and devices produce and capture data. Ask did they capture the right data, did they capture
it in the right format, and was the data captured in an accessible way?
Data Provisioning: Data has to come from multiple sources to generate meaningful insights. An analyst assisting a team of clinicians, for example, needs a variety of data from multiple source systems, such as EMR data, billing data, and cost data.
Data Analysis: This process begins once the appropriate data has been captured, pulled into a single location, and aggregated. The analysis process itself includes several components, including evaluating data quality, data discovery, interpretation and presentation.
Ensuring these three stages of insightful data analysis are followed will drive meaningful healthcare analytics. Be sure to focus on the data analysis process; not just data capture and provisioning, writes Staheli.
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