By Dennis Cannelis

Current Health Care Cost Climate

Health care costs continue to rise. Total health care costs per employee are expected to rise above $10k in 2010. Employers face an average year over year increase of 7%.

21% of the workforce suffers from a chronic condition, accounting for fully 75% of all medical costs. Health risks such as blood pressure and high cholesterol account for 25% of total health care costs.

However, 70% of health care claims have shown to be preventable. The key opportunity to reducing costs today is to focus on wellness, that is, to target the demand side of healthcare – identify behavioral and lifestyle factors and manage or improve risk factors to prevent more costly ongoing treatments.

Corporate wellness programs can decrease employee absenteeism by 26% and can decrease health care costs by 27%.

The key to these programs is a comprehensive approach where participation of the employee through incentive design is the key to a successful health management program.

The AIM Approach

In addition a program consisting of Assessment (through health risk assessments, biometric screenings, and Claims Analytics), Interaction (through outreach programs measuring health risk scores and achievements, and clinical compliance programs), and Measurement (tangible results of health improvement) utilizing incentives to drive participation is a key to successfully reducing costs.

In addition, the program would have to rely on analytics for population metrics.  Information Technology is being utilized to promote and manage innovative strategies and programs that make use of proactive metrics to reduce the cost of healthcare delivery.

Another key factor in wellness programs is Analytics

In fact, self insured employer groups, provider organizations, and health insurance payers that focus on state and federally reimbursed insurance programs all are keenly focused on analytics that can identify the potential for risk of illness and disease in population management profiles through identification of user defined metrics that can be defined with the help of   health care delivery professionals and insurance organizations. The patient profile is a dynamic record - a snapshot of the employee’s history, current health status, compliance, and a record of specific test results.  This information will enhance the ability to determine an individual’s risk for other problems.  Information will be collected automatically wherever possible via the Engagement Analytics engine to create a stratified layer of comprehensive information from disparate sources:

• ·     Health Risk Assessment

• ·     Claims Data

• ·     Rx

• ·     HRA

• ·     Lab Results

• ·     Patient Encounters and electronic health record information

• ·     Screening and Outreach Programs ( Wellness Management Services)

The differentiator in this approach is that the aggregation of this information creates a member profile that proactively determines the potential for risk rather than traditional disease management analytics retroactively searching for information that is either unavailable, incorrect, or lagging.   This is hit or miss at best and is one of the problems with typical disease management programs.  

Instead, this is a central repository that incorporates all of the different aspects of a case- history could be updated automatically when the record is first implemented with a retrospective claims analysis using population screening metrics.  History is an important consideration that impacts a person's risk for disease-yet, it is typically 'collected' via HRA or some sort of voluntary admission by the member/patient.  That is one way to do it, but there is also a wealth of information in the previous years’ (if available) claims data--if there are claims with diagnosis that indicate chest pain or cancer or anything that is either a serious symptom, complication or disease -chronic or acute, then it is considered history.  This eliminates the need to try to figure out how to build on those 'English' statements that can not be mapped to ICD-9 and/or CPT-4 codes.    Risk can be identified for multiple diseases if a search is performed on the member's history for smoking, high blood pressure, high cholesterol, low LDL cholesterol, diabetes, cardiovascular conditions and obesity.  If the history is positive for any of these things, that information is appended to the member profile - it remains there as a factor that determines that member's status. 

Analytics for Diabetes Example: A member being tested for glucose, who has metrics for history, risk, age, etc., would serve as a trigger to get those results.  The combination of data that is collected will reveal co- morbid conditions automatically.

One example is the glucose tolerance test, which is used to monitor diabetics, screen for pre-diabetes due to either diabetics, screen for pre-diabetes due to either risk factors or symptoms, and is also a preventive measure for everyone over 45. While the reason for the test may not be known initially,  it should be a pro-active ‘red flag’,  since it is the single most common way that pre-diabetes and diabetes are first diagnosed.  This presents an opportunity for a high value intervention since someone with impaired glucose is likely to develop diabetes II within two years without intervention.  However, in order achieve the ultimate goal of pre-diabetes intervention; results of the test would be required.  There are a couple of different scenarios that are possible:

Example: The test reveals a high glucose level and results in a diagnosis of pre-diabetic or diabetic, in which case the system would pick that up on a subsequent screening/surveillance, IF the patient returns to the doctor.  Since the lab is not the one who does the diagnostic follow up and the lab is not the entity that communicates with the payer except to send a bill with the service rendered - results will not be known, or even why the test was ordered until if or when a subsequent claim is received for that diagnosis.  This could be months later, depending on several factors including claims lag and patient's compliance with the outcome, or, the patient is complying with preventive measures and does not require follow up.  There will not be a clear difference between healthy preventive, good outcome and non-compliance, risky outcome without further intervention.

Combined with the member's demographics, the data that is accumulated in the file determines a member's health status. 

Here are other possible scenarios for engagement analytics searches possible based on metrics accumulated:

 “Anyone ≥45 and BMI= ≥26, or, anyone ≤45 and BMI= ≥ 26 and, has one or more risk factors [add dx.xx], then get tested. Then, if IFT results =100-125, or IGT results = 140-199, then re – test again in 1-2 years”.

Conclusion 

The key factors to a successful wellness program are : providing a way to identify potential problems through analytics and other forms of assessment, to enroll those employees in employer a defined wellness outreach programs, incent their behavior to participate, and to use analytics to measure these results.

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Dennis Cannelis is a Fellow of The Business Forum Institute and is recognized as a serial entrepreneur specializing in healthcare Information Technology. He currently serves as Vice President of Information Technology for Community Health Plan of Washington. Dennis has more than twenty five years experience in Information Technology, Software Development in the fields of Healthcare Services, Professional Services, Investment Banking, International Markets, and Manufacturing; and has served in several roles as either  CEO, Managing Partner, Senior Vice President and Management Consultant.  Dennis holds a B.A. from New York University and a certification in Information Systems Management from Program Systems Institute.

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