Readying providers, hospitals for big data

From: EHR Intelligence

Kyle Murphy, PhD

The ability of the healthcare industry to unlock of the potential of big data depends on the way healthcare organizations implement big data solutions, according to the authors of a recent article in the Journal of AHIMA. In “Big Data, Bigger Outcomes,” authors Lorraine Ferdandes, RHIA, Michele O’Connor, MPA, RHIA, FAHIMA, and Victoria Weaver, RHIA, lay out the steps necessary for the successful implementation of big data solutions for providers.

Following information technology in other industries, the healthcare industry is setting its sights on using big data to transform quality care through analytics that provide real-time support for provide, recognize trends among populations, allow research to extend its reach, and inform decision-making by federal legislators and agencies.

At its most basic, providers will benefit most significantly from big data, but the extent of this benefit will depend on how well their organizations manage and integrate their health information systems.  “Once Big Data is managed and integrated, organizations can apply analytics to better understand the clinical and operational states of their business based on historical and current trends, and predict what might occur in the future with a trusted level of reliability,” write Fernandes et al.

According to the authors, the successful implementation of big data solutions depends on organizations following four important steps:

1. Establish data governance, define data objectives: Implementing a big data solutions begins with putting the right people in place. With an executive council of senior members in place, an organization can then move on to considering how and to what end it will leverage big data:

Special consideration must be given to the new automated processes, inferences, metrics, and monitoring tools provided by Big Data solutions. Policies and procedures will also be required that govern the use of data, define the required actions and quality control processes, and optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions.

2. Identify data and information requirements: Big data is about the data. Depending on the size of an organization, its data could be spread out among an array of systems and settings. Moreover, data exist in a number of forms, and some the most important information is unstructured (e.g., free text, imaging). Organizations must first recognize the current state of their data and anticipate future changes to the way information is capture. “Organizations need to understand what data they will use today, and any potential data that they may want to access in the future,” the authors explain. “Organizations will also need to establish a data acquisition roadmap based on business and analysis priorities.”

3. Normalize, integrate, and organize big data solutions: Normalizing a database requires health IT professionals to identify potential redundancies and dependencies in their data fields and tables. Moving to the scale of big data will only exacerbate these inefficiencies and negatively impact the value a big data solution going forward. Imperfect information leads to imperfect results.

4. Protect security and privacy of big data: In the healthcare industry, the privacy and security are major concerns for providers and patients. The idea of compiling more personally-identifiable information (PII) and sharing it with many different organizations and agencies adds to these concerns. The authors recommend following best practice when approaching these matters, such as the Federal Trade Commission’s Fair Information Practice Principles (FIPPs):

The best way to address privacy concerns or requirements is for Big Data solutions to support FIPPs. FIPPs are industry-agnostic, basic information privacy principles that can guide the thorny discussions that may be required when analytic projects cross industries, data sources, and data types.

Whether big data has a positive effect on healthcare will come down to how well healthcare organizations and health IT professionals prepare it and what measures they take to safeguard the privacy of patients and the integrity of these data.

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