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Reasons for Developing Healthcare Analytics and Business Intelligence

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Industries with broad and deep data collections, such as healthcare, require extensive business intelligence and analytical solutions to address their decision-making needs.

Most healthcare providers (hospitals and insurers) are building, installing, or enhancing data capture systems.  Many are focused on developing, implementing, or revising an Electronic Medical Record (EMR) that can be a composite repository of much of the data accumulated for any individual patient.  At the same time, most organizations are starving for access to information that they can use to improve their business, processes, or techniques. 

The common technical solution across all industries for providing data for reporting and analytics is a data warehouse.  Most enterprise data warehouses are a combination of internal development and components for specific solutions to address analytics of varying kinds. 

It is important to examine the analytical needs found in a data-intensive industry such as healthcare.

Analytical Needs

Healthcare is faced with broad and deep analytical data requirements.  Here are a few of the most visible needs: 

  1. Complex operations:  Their business and financial operations can be more complex than a standard product or services company.  To process billing adequately and satisfy requests for payments from a payor, healthcare has to capture a large volume of information and associate that data to the necessary standards.  How well this is done can have a significant effect on their financial return.
  2. External reporting requirements:  This is a significant challenge, since many organizations do not have access to the appropriate data and metadata to produce these reports in an automated fashion.  Often, this inability results in the alignment of resources to gathering and sorting data to produce simple reports at great financial expense.
  3. The need for a proactive business environment:  A large healthcare organization reported financials that had them greatly worried.  They found that the number of patients with no insurance had gone up significantly and those with only Medicare had risen significantly, results that affected their financial standing.  Unfortunately, without access to track this information through BI dashboards and scorecards that would inform executives regularly of these material changes, they were able to react only at the end of the year.  This reactive mode made budgeting and planning complex and backward-looking, and placed the organization into a state where they could no longer build the types of solutions that will have strong long term value balanced against immediate cost concerns.  
  4. Operations management is enhanced by the ability of any organization to have access to performance criteria.  Having the right measurements to watch enables a business to see how they are doing, make adjustments, and continue process improvement.  Whether this relates to patient flows, nursing staffing, or management of patient meals, efficiency and effectiveness effect profitability and patient satisfaction.
  5. Medical and clinical research are critical parts of continuing to improve the practice of healthcare, the overall health of our population, and addressing the escalating costs of providing healthcare.  Using business intelligence and analytics effectively to provide the right data at the right time to the right people to make informed decisions can reduce costs and improve processes.

Role of the EMR

Electronic Medical Records (EMR) play a major role in the development and usage of business intelligence and analytics for  healthcare.  Therefore, EMR is an essential component of every healthcare organization’s approach to data management.

The EMR is one large system that is a capture and consolidation repository of patient data.  While the promise of the EMR is that all patient data is contained there, too often that is not realized.  Most organizations capture some percentage of their data via their EMR.  Additionally, some of their other data capture systems flow into the EMR so that it can house that data and provide a consolidated view of a patient to a provider, such as bringing in data from labs and registration, etc.…  Thus, the EMR requires the ability to capture, view, and process individual transactions with crucial system response time.  EMRs were not designed for reporting and analytics, so an organization cannot have staff querying or running reporting against the operational EMR while the doctor is using the EMR with a patient.  A complex or long running query could negatively affect the time it takes to capture or access patient data while they are in the office with a provider (remember, this is the number one reason a data warehouse exists – separate data facility for analysis).

Since the EMR is not an option for providing analytics to healthcare, what other possibilities other than a data warehouse could quench this thirst for information?


Brainstorming alternatives for collecting and distributing analytical data for healthcare organizations would deliver a number of technical solutions that could provide this type of access.  Most of those would have some challenges that would inhibit success.  Some generally accepted alternatives include:

  1. Direct Reporting:  The most common method of providing access to data is to write reports individually against a specific system.  This gives the ability to report on what is happening for the data in that system.  In healthcare, this is a great challenge because each application only contains data for that system. Typically, that is a fraction of the data accumulated, limiting the value and the reach of analytics.  If that system is a mission critical system, then it exposes the reporting to only running during off hours to limit the effect to the users of the system.
  2. Canned enterprise data warehouse solutions exist across most industries, but many have had serious challenges when attempted in healthcare.  One of the biggest challenges with this is related to the uniqueness in subject area terminology.  Most vendor solutions are focused on abstract terminology, enabling them to demonstrate how the data can fit.  If the organization has an agreed upon vocabulary and terminology, a canned solution will not be viable.
  3. Canned departmental or specific usage solutions exist for many aspects of healthcare analytics.  For those that have built an enterprise data warehouse, it is reasonable to map data from the data warehouse to one of these solutions and get immediate value.  For those that have not consolidated their data into an enterprise class warehouse, many of these solutions are complicated by the need to gather, cleanse, and govern their data.
  4. Federation is a concept that many vendors are promoting to provide access to all data no matter where it resides.  In theory, federation allows database connectivity across many platforms and technology layers, providing access to all data no matter where it resides, including cloud storage.  The promise of this technology sounds great.  However, in relation to analytics, the execution of the solution is an exercise in futility.  Remember, the original purpose of a data warehouse is to alleviate the burden of large scale queries on systems that have to capture and process individual data in a mission critical fashion.  The concept of federation does not address that issue, in fact, it complicates it further.  However, some organizations may have 3 or 4 data warehouses that are all based on the same model, vocabulary, and/or terminology.  In that case, federation or other storage options would be a great way to connect data across those solutions.


Over the years, organizations have found that those who can gather and provide access to comprehensive information in a timely manner are the ones best able to set themselves apart from their competitors.  It is well recognized that healthcare providers are on the cusp of a changing landscape that will challenge them in many ways:

  • Shortages of knowledge workers
  • Increasing Medicare patients, which leads to decreasing profits
  • A competitive landscape with new providers, techniques, and methods for care
  • Costs that continue to escalate

 In the face of all of this, they need access to information about their business that will:

  • Allow them to keep their finger on the pulse of their financial health
  • Satisfy requirements for producing reporting to external governing bodies
  • Improve their own internal process and operations
  • Enhance healthcare knowledge and advance discovery
  • Provide a vehicle to enable medical research to have appropriate access to clear, comprehensive data

Understanding that the challenges faced by healthcare providers are not unique from most other industries, the question is not whether or not healthcare providers need to have a data warehouse / business intelligence / analytics.  It is how to approach and implement the solution.  The key is coming up with a sound approach and appropriate plan, then leverage common tools, standards, technologies, and expert resources.


Bruce D. Johnson

Bruce D. Johnson is an experienced IT consultant focused on data / application architecture, and IT management, mostly relating to Data Warehousing. His work spans the industries of healthcare, finance, travel, transportation, and retailing. Bruce has successfully engaged business leadership in understanding the value of enterprise data management and establishing the backing and funding to build enterprise data architecture programs for large organizations. He has taught classes to business and IT resources and speaks at conferences on a variety of data management, data architecture, and data warehousing topics.

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