How does a data-driven healthcare organization work?

As the pressure increases for accountability and transparency for healthcare organizations, the spotlight is squarely on data: how does the organization gather, validate, store and report it.  In addition, the increasing level of regulatory reporting is driving home a need for certifying data – applying rigor and measurement to its quality, audit, and lineage.  As a result, a healthcare organization must develop an Enterprise Information Management approach that zeros in on treating data as a strategic asset.  While treating data as an asset would seem to be obvious given the level of IT systems necessary to run a typical healthcare organization, the explosion of digital data collected and types of digital data (i.e. video, digital photos, audio files) has overwhelmed the ability to locate, analyze and organize it.

A typical example of this problem comes when an organization decides to implement Business Intelligence or performance indicators with an electronic dashboard.  There are many challenges in linking data sources to corporate performance measures.  When the same data element exists in multiple places, i.e. patient IDs, encounter events, then there must be a decision about the authoritative source or “single version of the truth.” Then there is the infamous data collision problem: Americans move around and organizations end up with multiple addresses for what appears to be the same person, or worse yet, multiple lists of prescribed medications that don’t match.  The need to reconcile data discrepancies requires returning to the original source of information – the patient to bring it to a current status.  Each of us can relate to filling out the form on the clipboard in the doctor’s office multiple times.  Finally, there is the problem of sparseness – we have part of the data for tracking performance but we don’t have enough for the calculation.  This problem can go on and on, but it boils down to having the right data, at the right time and using it in the right manner.

Wouldn’t the solution simply be to create an Enterprise Data Warehouse or Operational Data Store that has all of the cleansed, de-duplicated, latest data elements in it?  Certainly!  Big IF coming up: IF your organization has data governance to establish a framework for audit-ability of data; IF your organization can successfully map source application systems to the target enterprise store; IF your organization can establish master data management for all the key reference tables; IF your organization can agree on standard terminologies, and most importantly, IF you can convince every employee that creates data that quality matters, not just today but always.

One solution is to understand a key idea that made personal computers a success – build an abstraction layer.  The operating system of a personal computer established flexibility by hiding the complexity of different hardware items from the casual user through a hardware abstraction layer that most of us think of as drivers.  A video driver, a CD driver, USB driver allows the modularity and allows flexibility to adapt the usefulness of the PC.  The same principle applies to data-driven healthcare organizations.  Most healthcare applications try to tout their ability to be the data warehouse solution.  However, the need for the application to improve over time introduces change and version control issues, thus instability in the enterprise data warehouse.  In response, moving the data into an enterprise data warehouse creates the abstraction layer and the extract, transform and load (ETL) process can act like the drivers in the PC example.  Then as the healthcare applications move through time, they do not disrupt the Enterprise Data Warehouse, its related data marts and, most importantly, the performance management systems that run the business.  It is not always necessary to move the data in order to create the abstraction layer, but there are other benefits to that approach including the retirement of legacy applications.

In summary, a strong data-driven healthcare organization has to train and communicate the importance of data as a support for performance management and get the buy-in from the moment of data acquisition through the entire lifecycle of that key data element.  The pay-offs are big: revenue optimization, risk mitigation and elimination of redundant costs.  When a healthcare organization focuses on treating data as a strategic asset, then it changes the outcome for everyone in the organization, and restores trust and reliability for making key decisions.

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