Using Data in AICU/IOCP Models

By Patty Jones

11 October 2013

The Patient-Centered Medical Home (PCMH) model emerged in response to the recognized need for more systematic and evidence-based approaches to ensuring access to, and coordinating care for, an entire population. Within that framework an additional focus has developed that recognizes the specific needs of members with chronic and complex conditions. In order to respond to that population, additional models have emerged that focus on proactive identification of chronically ill members using claims-based predictive model scoring as well as clinically based referrals and information. These models are often referred to as Ambulatory Intensive Care Unit (AICU) or Intensive Outpatient Care Program (IOCP).

The AICU and IOCP models, now in pilot testing and operation in a number of sites and with different Milliman clients, have the following core design characteristics:

  • Payer and medical group partnerships that emphasize clinician leadership and active involvement in design and implementation;
  • Program designs that promote patient-centered process changes and result in enhanced roles for nursing and clinic administrative staff;
  • Proactive identification of members with chronic or complex illnesses using claims-based risk scoring  tools such as the Milliman Advanced Risk Adjuster (MARA) coupled with clinic and clinician-based identification of candidates for the program;
  • Clinic-based outreach to members to encourage active engagement;
  • Personalized care plans that address the specific needs and priorities of the members; and
  • Recognition of psychosocial barriers to adequate medical care resulting in innovative approaches to removing these barriers.

There are a number of good articles written about these initiatives including “Enhancing Quality of Primary Care Using an Ambulatory ICU to Achieve a Patient-Centered Medical Home”  Lewis, Hoyt and Kakoza, Journal of Primary Care & Community Health May 27, 2011 and “American Medical Home Runs” Arnold Milstein and Elizabeth Gilbertson, Health Affairs, 28, no. 5 (2009) pages 1317-1326”. These and other articles provide good information on the results of these programs.

Most of these models involve hiring clinic-based case managers or imbedding health plan case managers in clinical sites. Working with the clinical teams and health plans involved in these projects has provided a number of insights into using risk-scoring information in supporting the needs of case managers in these new models.

These observations include the following.

  • Risk scoring data can be interesting but overwhelming to clinical team members – it is best to design reports and portals with early input from the people who will ultimately use it. As fascinating as claims data may be to some of us, these clinicians have an important perspective on what they are likely to use when and how.
  • Clinical user training can help  them interpret and use risk scoring data  more effectively–focus on the basics and priorities (eg.  the difference between prospective and concurrent scores) and include case studies that lead participants through practice sessions and use of the information.
  • Clinical insight and patient knowledge is a valuable addition to the use of data – encourage care managers to use risk scoring data to find potential candidates and as a tool for adding a broader clinical, utilization and cost perspective to the information in a medical record. Clinicians and clinical teams can then provide a unique and highly useful perspective on what the information may indicate about a population and what can be done about it.
  • Managers can use summarized risk scoring data analysis to focus on a reasonable number of cases for review – risk scoring analysis can end up identifying large numbers of candidates.

Additional analysis of risk scores can be used to identify a more manageable list of candidates for care management. In the example below a client distribution analysis showed the number of members by risk score level  both concurrently and prospectively for members with a score greater than one. This analysis helped identify patterns and clusters of members by risk score range. Further analysis isolated a smaller population (approximately 7% of the total) with concurrent and prospective scores that remained level or increased. This allowed the management team to focus on new candidates more quickly.

Client Example – Using Population Risk Score Distributions to Identify Care Management Candidates

The AICU and IOCP models, viewed as specialized programs designed to support the needs of chronically ill members, may in fact be good blueprints for all care management programs. The concept of integrating payer and provider data and expertise more directly can be rewarding for care management teams and beneficial to members.Claims analysis and risk scoring tools can be key tools in identifying opportunities and targeting interventions in these models.

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