Infobesity in the Healthcare Industry: A Well-Balanced Diet of Predictive Governance is needed


Fat TwitterWith the rapid advances in healthcare technology, the movement to electronic health records, and the relentless accumulation of regulatory requirements, the shift from records management to information governance is increasingly becoming a necessary reality.

In a 2012 CGOC (Compliance, Governance and Oversight Counsel) Summit survey, it was found that on the average 1% of an organization’s data is subject to legal hold, 5% falls under regulatory retention requirements and 25% has business value. This means that 69% of an organization’s ESI is not needed and could be disposed of without impact to the organization. I would argue that for the healthcare industry, especially for covered entities with medical record stewardship, those retention percentages are somewhat higher, especially the regulatory retention requirements.

According to an April 9, 2013 article on ZDNet.com, by 2015, 80% of new healthcare information will be composed of unstructured information; information that’s much harder to classify and manage because it doesn’t conform to the “rows & columns” format used in the past. Examples of unstructured information include clinical notes, emails & attachments, scanned lab reports, office work documents, radiology images, SMS, and instant messages. Despite a push for more organization and process in managing unstructured data, healthcare organizations continue to binge on unstructured data with little regard to the overall health of their enterprises.

So how does this info-gluttony, (the unrestricted saving of unstructured data because data storage is cheap and saving everything is just easier), affect the health of the organization? Obviously you’ll look terrible in horizontal stripes, but also finding specific information quickly (or at all) is impossible, you’ll spend more on storage, data breaches will could occur more often, litigation/eDiscovery expenses will rise, and you won’t want to go to your 15th high school reunion…

To combat this unstructured info-gain, we need an intelligent information governance solution – STAT!  And that solution must include a defensible process to systematically dispose of information that’s no longer subject to regulatory requirements, litigation hold requirements or because it no longer has business value.

To enable this information governance/defensible disposal Infobesity cure, healthcare information governance solutions must be able to extract meaning from all of this unstructured content, or in other words understand and differentiate content conceptually. The automated classification/categorization of unstructured content based on content meaning cannot accurately or consistently differentiate the meaning in electronic content by simply relying on simple rules or keywords. To accurately automate the categorization and management of unstructured content, a machine learning capability to “train by example” is a precondition. This ability to systematically derive meaning from unstructured content as well as machine learning to accurately automate information governance is something we call “Predictive Governance”.

A side benefit of Predictive Governance is (you’ll actually look taller) previously lost organizational knowledge and business intelligence can be automatically compiled and made available throughout the organization.

The Dangers of Infobesity at LegalTech


LegalTech just concluded in New York and one of the popular hot buttons many vendors were talking about was the idea that too much corporate, especially valueless, ungoverned, unstructured information is both risky as well as costly to organizations… I agree. The answer to this “infobesity” (the unrestricted saving of ESI because storage is supposedly cheap and saving everything is easier than checking with others to see if its ok to delete) is a defensible process to systematically dispose of information that’s not subject to regulatory requirements, litigation hold requirements or because it still has business value. In a 2012 CGOC (Compliance, Governance and Oversight Counsel) Summit survey, it was found that on the average 1% of an organization’s data is subject to legal hold, 5% falls under regulatory retention requirements and 25% has business value. This means that 69% of an organization’s ESI can be disposed of.

Several vendors at LegalTech were highlighting Defensible Disposal solutions, also known as defensible disposition and defensible deletion, as the answer to the problem of infobesity. Defensible Disposal is defined by many as a process (manual, automated or both) of identifying and permanently disposing of unneeded or valueless data in a way that will standup in court as reasonable and consistent. The key to this process is to be able to identify valueless information (not subject to regulatory retention or legal hold) with enough certainty to be able to actually follow through and delete the data. This may sound easy… its not. Many organizations are sitting on huge amounts of data because their legal department doesn’t want to be accused of spoliation, so has standing orders to “keep everything forever”. Corporate legal has to be convinced that the defensible disposal processes and solutions billed as being the answer to infogluttony can actually tell the difference, accurately and consistently, between information that should be kept and that information that’s truly valueless.

To automate this defensible disposal process, the solution needs to be able to be able to understand and differentiate content conceptually; that an apple is a fruit as well as a huge high tech company. The automated classification/categorization of content cannot accurately or consistently differentiate the meaning in unstructured content by just relying on keywords or simple rules.

An even less consistent approach to categorization is to base it on simple rules such as “delete everything from/to Bill immediately” or “keep everything to/from any accounting employee for 3 years”. This kind of rules based retention/disposition process will quickly have your GC explaining to a Judge why data that should have been retained was “inadvertently” deleted.

To truly automate disposal of valueless information in a consistently defensible manner, categorization applications must have the ability to first, conceptually understand the meaning in unstructured content so that only content meeting your intended intentions, regardless of language, is classified as “of value” to the organization not because it shares a keyword with other records but because it truly meets your definition of content that needs to be kept. Second, because unstructured data by definition is “free-flowing” (not structured into specific rows and columns) extremely high categorization accuracy rates and defensibly can only be achieved with defensible disposal solutions which incorporate an iterative training processes including “train by example” in a human supervised workflow.