As healthcare organizations work feverishly to convert patient records, prescriptions and complex diagnostic information to electronic format, patients continue to generate more and more data daily. The problem is also retrospective, getting paper records online and current, storing new information into nascent systems. This is an enormous project. In the US healthcare system alone, the volume of electronic data roughly doubles every two years.
Healthcare data comes in many forms – electronic charts, audio files, word document reports, image files from diagnostics, emails and more. The rich media files now created by imaging diagnostics increase the data load. A large hospital system will maintain many different electronic systems. Patient records are stored in one system, hematology reports in another, diagnostic images somewhere else. No matter where the data resides, the sheer volume of data has driven up storage costs and healthcare organizations need to devise strategies that minimize storage, optimize access and ensure business continuity. This is where a good data governance strategy can help.
Getting a grip: data governance
Data governance is the practice of defining standards, processes and technology which organizations can rely upon to manage data. A comprehensive data governance strategy satisfies regulatory requirements, ensures business continuity and empowers search and retrieval of all data. It occurs at the cross section of technology, people, policies and processes.
Some of the data governance decisions your organization will make include: How critical is the data to your organization? Is it something that you need to store based solely on a regulatory requirement? How might your data affect health outcomes in the future? How might it contribute to the larger healthcare system? What do you need to keep for eDiscovery purposes or to meet regulatory requirements? For how long will you keep each kind of data?
Piling on: legal requirements and e-discovery
To add to the complexity, eDiscovery requirements make data retention and accessibility important from a legal point of view.
Regrettably, healthcare providers at every level are often the target of litigation. Under the legal ‘duty to preserve’ information, organizations must store data when litigation can be reasonably anticipated. This sounds simple, but the application is more than ambiguous. Common sense might dictate that the duty to preserve evidence is triggered when a lawsuit is filed. The reality is that the duty to preserve can arise even before a lawsuit especially if a party is notified that future ligation is likely. Many organizations simply make the assumption that all records must be retained and available on a moving forward basis. This leads to massive data storage volumes increasing storage costs and making data more difficult to sift through looking for evidence.
Divorce policy from technology
To make good data governance decisions, it is necessary to separate technology considerations from the actual rules you put in place. Make the rules first, and then implement a technology to enforce them. While IT will be involved in building systems to support the policy, decision making should be up to management. In most healthcare organizations, the chief compliance officer heads the task of developing a data governance policy – along with a team of stakeholders, including IT, legal and operating unit managers.
Once you have decided how to classify your data, you need to determine how long to store it. So, for example, maybe you decide to keep only three years email communication, but six years of patient records. Though storage costs are coming down, the sheer volume of data your organization produces necessitates decisions about which data to delete and which to keep. In healthcare, like many other industries, there are no universally accepted standards for data governance policies, which means each organization must work toward a policy on a case-by-case basis, using healthcare laws and regulations as a guide.
Policies must be enforceable, auditable, discoverable
There are three key criteria to keep in mind when developing a data governance policy.
First, the policy must be enforceable. It makes little sense to develop policies that simply cannot be implemented because while they may be comprehensive, they become too complicated. Organizations are far better off to take an incremental approach. Start with some basic classifications and implement those.
Second, the policy must be auditable. A third party must be able to determine that the policy is being implemented satisfactorily.
Finally, the policy must lead to discoverability. Can you easily find the data you need to contribute to research, tune organizational performance or for legal reasons?
Implementing the policy with technology
Once your organization has developed a data governance policy, it’s time to determine how best to implement it. Many organizations have tried to use backup technologies alone to meet the criteria. However, while backup is an important pillar of a comprehensive data governance strategy, and is essential to business continuity needs, it must be partnered with archiving technology in order to meet discoverability requirements.
Data archiving: Your silent partner
An archive system indexes the data stored in disparate systems, including databases, file servers, email applications and whatever else might lurk on servers. Indexing creates a searchable list of what is stored in different systems. The power of indexing is that files can remain in their existing system, but still be discoverable and accessible from a single interface. The index simply points to the information you are searching. Search and retrieval can be simplifiedusing predictive coding and conceptual searches.
The beauty of indexing is that you are not creating a separate, redundant data warehouse.In reality, the opposite occurs when organizations index complex systems. Almost invariably after indexing, IT finds that up to 70% of the data stored on corporate servers is actually redundant. When IT professionals learn that they can save 70% on data storage, they quickly weed out duplicates. This leads to improved system performance and dramatic storage cost reductions.
The other notable feature of archiving systems is that they don’t inhibit end-user productivity on the systems they index. A medical records clerk simply carries out their work without any thought to how records or documents may be retrieved in the future. In many cases, a healthcare system may comprise multiple hospital campuses, each campus may have a different EHR system, separate systems for diagnostic information and so on. An archival system can consolidate information across any number of underlying data stores no matter what their format. So, rather than having to train people in new processes or consolidate systems, the data archive acts as a super library of all the data your organization stores across locations.
Better medical insights
An archive will organize information into topic areas, no matter where the data originates. Qualified personnel can access all the information you have stored regarding “heart disease” for example, including case histories, imagery, blood tests and all related communications. Previously that data was locked inside multiple systems. Or it was stored on backup tapes locked inside a vault making it essentially irretrievable. Now the information can contribute to clinical practice and research.
Data goverance and archiving
Together, a solid data governance policy coupled with an archiving system can reduce storage volumes and associated costs, help enforce retention policies, create a complete audit trail of all data, and facilitatee Discovery.
Along the way, they contribute to the larger healthcare endeavour of enabling better treatments for improved patient outcomes.