AEGIS COST CONTAINMENT SOLUTIONS

The Mission of Aegis Cost Containment Solutions Inc. is to assist Health Plans and employees in achieving real Evidenced Based Quality Care by specific diagnoses. maximizing employer Health plan savings, and getting healthier employees. Provider Selection Optimization (PSO)

We work with:   

Health systems, Health Plans ,Taft-Hartley Plans, Payers, Third Party Administrators, Referral/medical/case/population/Disease Management Companies, Network Management Companies, ACOs, VBP/P4P programs, HIEs, Brokers and Reinsurers.

Here is how Our PSO program is unique:

1) Our program identifies the optimal provider for treating every illness and procedure – matching patients with specific illnesses with providers having demonstrated success for treating those specific illnesses. We provide the info for the highest probability of success in terms of price efficiency and quality. Our program provides the optimal action – not just “actionable information”

2) Our program Ranks (not just rates) hospitals and doctors for 

 Price efficiency and quality (price efficiency defined as severity-adjusted, longitudinal data, all treatment costs over a 12 month period and quality data uses 300 QIs derived from dozens of credible, objective third party organizations) All chronic illnesses (3/4 of health care costs) and episodic procedures (bundled)

3) Claims data is extracted from standard existing billing formats, our program ‘scrubs’ the data to endure definitional consistency

4) Our program provides a reporting package integrating actuarial, clinical and operational info in a pivot table format; allowing virtually instant ad hoc reporting and referral generation along with:

5) Full documentation of price efficiency and quality to foster credibility, coordination of care and care improvement

6) Our program facilitates the benefits of narrow networks (better price and quality) while maintaining full network access

7)  Our program identifies change in patient risk (from claims and/or EHRs) for early intervention and then generates optimal referral recommendations and documentation

8)  Our program measures cost and quality improvements with customer-specific data

9) Our program provides nurse navigator services to improve patient adoption and engagement

10) Our program uses the CMS claims data base as a foundation for ranking providers on cost then supplements that with 300 Quality Indicator's and client-specific data, so Our program’s rankings are statistically valid for virtually all illnesses and US providers

Named one of four finalists for the Intel Innovation Award

We can help with Evidenced based Learning objectives such as; 

• Evidence based Provider referral quality, and savings Optimization. Case study reveals that getting just 20% of employees to migrate to evidenced based higher quality providers, with proven outcomes, within your network, can save up to 30% to the health plan.  No cost review, guaranteed ROI, (Seamless Integration)

• Assistance with Virtual-Health and RPM program's for chronic conditions

• Expanding RPM to new disease states and patients 

• Understanding the challenges of using RPM for chronic disease management and how to overcome those hurdles  

• Collecting and using RPM data to enhance patient outcomes

• Determining care team workflows within RPM programs   

     "No Cost Review" ('NCR") is to uncover cost savings opportunities, and introduce new 

      strategies for Evidence Based cost savings and employee health prospectively.

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Health Systems/ACOs – Improve quality scores to mitigate CMS quality penalties, increase bonus payments/shared savings from value-based purchasing arrangements/pay for performance programs and make risk-based reimbursement profitable using our program for referral optimization.

Case Study – How Our Referral Optimization Solution Increases Profitability and Funds Shared Savings Programs Under Risk Reimbursement/ACOs While Mitigating CMS Quality Penalties 

A major health system decided to become an insurer/ACO and asked a critically important question “How does a health system generate profits under risk reimbursement?”

Answer; Just like any other business, by reducing costs of production within the revenue parameters while improving quality. Historically, health care systems looked to optimize revenues not costs. However, with the growth of bundled payments and other risk reimbursement models, optimizing costs will be necessary in order to be profitable. Furthermore, with increasingly severe financial penalties for quality deficiencies, minimizing that risk will also be required. So, optimizing cost and quality within revenue limitations is necessary.

The question then is how do we reduce cost without major organizational disruption? 

Answer; Referral Optimization – identify which providers have demonstrated superior performance in longitudinal price efficiency and quality for specific diagnoses and then fund a shared savings program to incentivize providers to change referral patterns.

What are the benefits of Referral Optimization?   

• It decreases costs by matching patients with efficient providers and therefore increases profitability
•  It improves quality and mitigates risk of CMS quality penalties. It qualifies providers for pay for performance, shared savings and other programs 
• It capitalizes on economies of scale by having highly efficient providers become “focused factories” by treating more of the same types (by diagnosis) of patients
•  It allows for documenting and publishing high performing treatment protocols so other providers can adopt “best practices” on a large scale.
• It allows creation of documented Centers of Excellence for both episodic and chronic diagnoses (chronic represents two-thirds to three-fourths of all health care revenue/cost).
• Leads with transparency that enhances integrity of network and can eliminate referral leakage
• Radically enhances the patient experience and builds long-term loyalty

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So how was this executed? 

1. Our program scored and ranked physician/facility combinations on a diagnosis-specific basis on both longitudinal price efficiency and quality. 

2. Our program quantified differences in physician/facility on a longitudinal diagnosis-specific basis as shown in Exhibit 2 A and 2 B in the link below. 

3. Then the amount of profit contribution was estimated that would fund the shared savings. This required assumptions such as what percent of patients would be referred to high performing providers and the average savings per year per diagnosis was calculated. 

4. Rules for bonus payments from the shared savings fund are customized so providers have a financial incentive to change referral patterns.

A process is established to document and publish “best practices” by diagnosis.   

https://www.dropbox.com/s/tx3m7m00vfyq7dm/Case%20Study%20-%20ACO%201pdf.pdf?dl=0 

What was the result?                       

• We found that due to wide price disparities a change of only 1 out five patients being referred to higher performing providers generated 30% overall savings, Yes, 20% change = 30% savings.
• Amplification in savings due to price/cost disparities make risk reimbursement more profitable system wide.
• The documentation and publishing of “best practices” improve profit margins and shared savings fund in subsequent years even further.
• Enables the health system to create Centers of Excellence for a number of chronic illness which differentiate the system from competitors and help grow market share and patient loyalty.

Conclusion 

Health systems must find ways to reduce costs and improve quality in the ever-increasing risk-reimbursement environment. Referral optimization is a logical tool when done right. Unfortunately, too often referral optimization is viewed primarily as a tool to keep patients within a system, not as a tool to improve cost and quality. Virtually all industries, except health care, use optimization tools to reduce costs and improve quality through efficiency. And of course, there are many other functions which make referral optimization effective (such as: integration with scheduling system for referrals and with medical management programs, effective patient communication with transparency, remote patient monitoring, etc.). 

This case study is abbreviated. Complete detail supporting this document can be shared upon request.

Chart Exhibit     https://www.dropbox.com/s/tx3m7m00vfyq7dm/Case%20Study%20-%20ACO%201pdf.pdf?dl=0