Business Intelligence Overview

At Integration Management, we believe technology is only an enabler.  True value comes from recognizing and harnessing the power of technology to help change the way you do business.  This is why we offer Business Intelligence Services along with our Decision Support tools.  After all – unless there are real decisions made with the information provided by a data warehouse, all you have is a data warehouse, not a decision support application.

Our Business Intelligence Services utilize the decision support model to ensure that you are getting the most out of your decision support application investment.  By utilizing information and insight gained from your data warehouse and analysis tools, our goal is to help you keep track of the key performance indicators in your business, and help you put into action changes that will improve your business.

The Business Intelligence Life Cycle

Raw Data Capture

Instead of using assumptions based on anecdotal feelings or rumors, our decision cycle starts with raw data – the piles of data that is captured as part of your day to day operations.  As the normal (and sometimes abnormal) processes of your business are being executed, data such as claims, contracts, authorizations and member contacts are generated from within your organization. 

Also, data from providers, regulatory institutions, hospitals, and vendors is also accumulated as you interact with your business partners.  Throughout the value chain that under girds your business model – countless transactions are taking place each hour.

Deriving Information from Business Rules

Next, using input from across your management team and our industry experience, we identify key performance indicators that help measure how well your business is executing.  While many of these metrics are consistent across the healthcare industry, how they are calculated may differ from company to company based on different regulator requirements, variations in business models, and unique organizational mission. 

We understand that what may be critical to one company may not be so important to another.  Our goal would be to find the critical 10-15 measures that indicate how your organization is doing.  This “dashboard” of measures would help provide a comprehensive view of the effectiveness of the business model and operations.  Examples include: Per Member/Per Month calculations, inpatient days, and claim expense.

Once the key measures are identified, as well as an understanding of how they are to be calculated is defined and agreed upon, the IHDS system can be configured to provide them in ways that make most sense to the management team. 

Transactional data that is captured in the first stage of the decision cycle can be transformed, aggregated and presented using the IHDS utilities and methodology.

From the information base, questions about the measures can be asked. The resultant answers allow business analysts, managers and executives to look for trends, top performers, bottom performers, segmentations, correlations, etc.

Business User Oriented Analysis

At this point, the key is providing decision makers with the analytical tools necessary to gain insight from the data warehouse information.  Trends, anomalies, and the statistical distribution of these measures can be researched and understood.  With the summary and detail information from the data warehouse at their fingertips, management can efficiently monitor the performance of the business.

Developing and Executing Smart Initiatives

Making decisions that truly change the ways your business operates is the goal of our Business Intelligence Services.  With our understanding of managed care operations and business model, we can help your management team come up with new initiatives or business practices that will clearly affect your performance, and ultimately your bottom line. 

As these changes take place, actual results can be monitored as new data is collected from ongoing business operations.  With the ability to monitor the results, initiatives can be evaluated, tweaked, and even scrapped as necessary.

Let’s look at a meaningful claims expense example.

Data accumulation during normal business operations.  Whether via EDI or manual entry, claims are entered and adjudicated in your managed care transaction system all day and every day.  In order to perform the adjudication process correctly, many data elements must be included on the claim as the claim system matches various benefit, contract, provider and other reference information.  All this raw data is accumulated during the normal processing of claims, one of the primary business practices of every health plan.

Transformation of raw data into information.  Each night or weekend, pertinent claim data, captured above, flows into the data warehouse.  During this process, many business rules execute to transform the raw data into an analysis oriented structure. 

Data Scrubs:   Data scrubs do the work of ensuring the raw data’s integrity.  For example, let’s assume the provider on some claims is not in the master provider table.  Instead of leaving this field blank, we can assign a value called “provider not found”, which ensures that the claim information is still considered in the final warehouse measures.  Another scrub might be for dates of service that are in the future, produced by a typographical error in the claims department.  Instead, this date is changed to the current date.

Categorization:  From the claim data, data points are added, adjusted, changed, realigned, or deleted to “categorize” claims the way you need to analyze them.  For example, you may want to determine whether the claim is in-network, or out-of-network.  However, the process to determine subtypes of out-of-network claims is complicated, based on a series of special codes and a vendor or provider list.  These rules can be modeled as business rules in the data warehouse, and executed consistently on each claim. 

Mapping:  Finally, the raw data must be linked together and mapped to the dimensional structure.  While your managed care system manages professional, institutional, pharmacy, and dental claims differently, they can all be combined in the data warehouse to provide a complete picture of member claim costs.

Analysis leads to Insight.  Once data is in the dimensional structure, powerful On-Line Analytical Processing tools can be used to find trends and seek out anomalies.  For example, you may find that your out-of-network providers claim costs for a specific procedure are increasing.  Further investigation points to an increase in the incidence of members seeking medical care from a set of providers in a specific sub-specialty not covered by any in-network contracts.

Making decisions to produce change.  Using the insight gleaned from the above analysis, the contracts department may decide to review contracts with existing providers to include the sub-specialty getting the increased attention.  Alternatively, they may decide to develop contracts with the group of physicians that are currently out-of-network and performing these specific sub-specialty services.

Lance Dowling, Principal
Integration Management, Inc.