An Up and Coming Practice in Healthcare IT: Enterprise Data Warehouse
Healthcare has been slow to recognize the value that data and analytics could have on both the clinical and the business sides of the house – the hospital and integrated delivery network, or the physician practice when compared with other industries – financial, retail and manufacturing for example. “Health care systems are facing multiple pressures now that really make analytics move from a nice-to-have to really, truly an imperative for survival in the coming years.” Brett Davis, Deloitte consulting principal and general manager of ConvergeHEALTH told Healthcare IT News in a recent interview.
Once, all things data related revolved around the EMR and it held a prime position at the center of a healthcare organization’s universe. Now the EMR is just one source of collected data supporting a particular kind of medical work. By itself it is insufficient for the sophisticated data analytics the healthcare industry needs today (Pal, 2016).
If you’re going to achieve high performance analytics, the EMR alone won’t cut it. You need an enterprise data warehouse (EDW). In fact, there is no viable alternative to an EDW if you want to successfully use analytics to improve the cost and quality of care. By incorporating the EMR’s and other systems’ data into an EDW, you create a database that enables health systems, accountable care organizations (ACOs), physician groups and others to predict and manage patient care and improve cost and quality (Big Data and the Future of Healthcare, 2015).
Essentially, the utility of an enterprise data warehouse (EDW) that saves a copy of data collected from all internal sources as well as external registries is limitless. Reports generated and the addition of graphical formats, eye-catching charts, and comparisons allows teams across the healthcare organization to see and prioritize areas with the greatest opportunities for improving outcomes. Everyone from clinicians to analysts benefits from an enterprise data warehouse. Highlighting specific measures for improvement, and EDW helps an organization critically evaluate not only care processes for chronic diseases but procedure-specific processes as well (Lamble, 2015).
As a baseline the following characteristics should comprise any well-designed EDW:
Dependent on Multiple Source Systems. An enterprise data warehouse (EDW) is populated by at least two source systems. Examples include EHRs, billing systems, registration systems and scheduling systems (Lamble, 2015).
Cross-Organizational Analysis. An enterprise data warehouse is designed specifically to enable data analysis across business and clinical processes – that is, the ability to analyze and link data across multiple source systems that support various business processes. For example, a EDW could enable the analysis of data from an HER coded in SNOMED and data from a billing system coded in ICD by aggregating the key elements required for the analysis from each system, regardless of the terminology used (Lamble).
Trends, Metrics, and Reports. An enterprise data warehouse helps identify trends and previously unknown relationships in business processes. The data output is characterized by metrics and reports (Lamble).
Large. Enterprise data warehouses in today’s information intensive organizations may contain billions or records constituting dozens and even hundreds of terabytes of data (Lamble).
Historical. A data warehouse stores many years of data, typically at least five and sometimes as much as 30-years’ worth (Lamble).
Beyond these key, common components, there are structural differences and functional nuances that will turn the EDW your organization chooses into an indispensable tool. Arming yourself up front with knowledge about the platforms, options, vendors and unique needs of the health care industry will help you ensure the EDW you select will lead you directly to success (Pal, 2016).
Frank A. Valier, DBA
Assistant Director, Health Information Management Program
References
Bergeron, Bryan (2013) Developing a Data Warehouse Guide for the Health Care Enterprise, Chicago, IL, HIMSS.
Big Data and the Future of Healthcare. (December 3, 2015). Retrieved from http://www.computerworld.com/
Evans, R. Scott, Lloyd, James F., and Pierce, Lee A. (November 3, 2012) Clinical Use of Enterprise Data Warehouse. Retrieved from http://www.ncbi.nlm.nih.gov/
Jiang, Bin (July 12, 2012) Redefining the Enterprise Data Warehouse by Requirements Categorization. Retrieved from http://www.b-eye-network.com/
Kobielus, James (April 8, 2008) The Enterprise Data Warehouse, Defined, Redefined, Evolving with the Times . Retrieved from http://blogs.forrester.com/
Lamble, Mike (March 9, 2015) Modern Enterprise Data Warehouses: What is under the Hood? Retrieved from http://www.cio.com/article/
Pal, Kaushik (January 6, 2016) How Big Data Can Optimize IT Performance. Retrieved from https://www.techopedia.com/2/
Pal, Kaushik (December 5, 2015) Why Data Quality is Crucial to an Integrated Analytics Platform - A Health Care Example. Retrieved from https://www.techopedia.com/2/
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