PART Two: The Missing Link to Patient/Member Engagement to Care Management
In Part 1 of our series, we described the Member-Centric Care Optimization™ (MCCO)™ concept, the problem it solves and the potential value drivers behind it. Following is a summary of MCCO™:
The Problem: Healthcare risk adjustment models do not always completely and accurately predict valid health care expenditures and patient/member market spend. This is due to the fact that the risk models do not factor additional variables that contribute to patient/member health outcomes or costs of care such as socio-economic disparities benefit plan design, patient non-compliance with treatment plans or unresponsiveness to treatment.
The Solution: MCCO™ is part of this new frontier in stratification and automated engagement delivery by the further refining of health risk data, benefit design data and consumer behavior data, socio-economic data, health literacy data and patient non-compliance and satisfaction to return the most value out of a care engagement.
The Benefit: MCCO™ creates increased productivity and reduced clinical variation through streamlined care delivery by allocating scarce capital resources to the patients/members who need and want the services.
In Part 2 of this series, we will further define MCCO™ and how it can be used to solve problems.
Besides using health-based or claims based risk data, healthcare organizations need to also evaluate additional patient/member variables such as (see Figure 1 on page 2):
- Engagement Preferences (What is my preferred means of interaction?)
- Satisfaction (Do I like the service I am receiving?)
- Health Literacy & Numeracy (Do I know my condition and how to care for it?)
- Socio-Economic Status (What is the income, ethnicity, etc., of my populations?)
- Environmental / Contextual (What is happening or what has happened in the environment that
Figure 1: MCCO™ Visual Representation
Using these marketing and outcome-based variables provide the ability to create better “medical-social” personas for your patients/members to ensure they receive the services they want that are meeting their quality and care cost objectives.
We call this piece of MCCO™ Care Analyzer. Care Analyzer is ideal for determining cause and effect relationships and to build robust predictive models using any statistical or other quantitative techniques of choice. We have included a snapshot of Care Analyzer in Figure 2 below:
Figure 2: Care Analyzer
A good use of Care Analyzer is to provide insight into CMS STARS and NCQA HEDIS measures. Health Providers and Plans could use Care Analyzer to see the effectiveness of different campaigns across different geographic areas. For example, could it be the campaign itself that is not working, the health literacy of the population, lack of access to care, or all of the above that impact STARS and/or HEDIS measures? Traditional Business Intelligence tools do not provide a complete view across space and time to see clinical and non-clinical effectiveness of campaigns and programs.
After deciding what variables impact the quality and cost of care in your responsible community of health, one needs to take immediate action to task and configure actions according to an organization’s business processes. For example, care management processes need to be setup to handle:
- Patients/members with multiple conditions including prioritization.
- Products with benefit carve outs and opt-in/opt-out mandates.
- Patients/Members whom have already been engaged at some point in time and when to
- Hundreds of thousands of tasks in accordance with staffing levels and system capacity.
- Service level of agreements and statutory mandates for required outreach levels.
Hundreds of years of industrial manufacturing and digital transformation have taught us that removing the human element has a tremendous impact on material risk and process efficiency. Care Optimizer (see Figure 3 below) continues to follow this path and fill gaps that are not filled by risk stratification and care management platforms. Specifically, Care Optimizer allows an organization to:
- Load your health risk data (e.g., DxCG, AEG, etc.).
- Add your specific case or condition management programs.
- Throttle outreach based on risk indicators, outreach preference, and/or health literacy.
- Send tasks automatically to Integrated Voice Response (IVR), Fulfillment, and/or Care
Figure 3: Care Optimizer
The most significant factor affecting healthcare clinical operations is volatility in services and humandriven processes. These processes are notoriously complex because they present multiple challenges to manage and change such as various call types coming in from the patients, members and/or providers. The result of this volatility and variation is wasted resources and increased administrative costs. By using Care Analyzer and Care Optimizer, 1) organizations have visibility across space and time into what variables are impacting quality and cost of care and 2) a tool to automatically take action based on the organization’s business processes and capacity.
In short, MCCO™ provides an approach and tools to quickly provide real-time visibility into what is driving value and the execution capability to be proactive on maximizing that value before dissipation.
SDLC Partners, L.P., headquartered in Pittsburgh, opened its doors in 2004 as the alternative to large consulting organizations. The firm’s 375 employees take a practical and collaborative approach to deliver process improvement, analytics and technology solutions to Fortune 1000 and mid-market customers by effectively working with business and I.T. to serve as the “execution partner of choice” for improving speed to market, reducing costs and enhancing quality. SDLC’s health care solutions and tools seamlessly link business and technology to improve the quality of patient care, reduce costs, improve operational performance and support revenue growth. For more information, visit SDLC’s website at www.sdlcpartners.com.