CMS is excited about the success of their next generation Accountable Care Organizations, reporting savings of $18/member/month which resulted in an annual savings of $62 million dollars. Next generation ACOs are taking 80% to 100% downside risk, so these organizations are extremely motivated to generate savings. Seema Verma is encouraging shared savings ACOs to move more rapidly to this model.
So what can we learn from the CMS 2016 report of these ACOs?
1 – Only 50% were able to create technology-enabled alerting systems to monitor real-time admission and discharges from hospitals. Real-time alerts and work lists are a basic building block for care management so I would encourage ACO leaders to explore partnerships with HIEs involved in the Patient-Centered Data Home network which is built on over a decade of experience ADT feed triggered alerts, sometimes called ED alerts. I lead teams in Indiana and South Carolina who established alerts using different people, process, and technology.
2 – Avoid overly centralized care management workers. The experience of multiple communities including Grand Junction, CO; Cincinnati, OH, and Community Care of North Carolina strongly support co-location of care management and medication management staff. Co-location is a core principle of the Patient-Centered Medical Home and can be more challenging to administer as it requires deeper collaboration across separate providers and systems. LabCorp has employed the model of co-location for its phlebotomists very effectively during the past several decades.
3 – Curate data from hospitals and medical practices IT systems. Data analytics with visual dashboards are key to risk stratification and strategy deployment. The majority of next-generation ACOs struggled to deploy effective analytics and risk management processes. One of the fundamental challenges to this effort is an incomplete evaluation of data quality from multiple sources. Any data analytics process should incorporate standard work focused on the quality of patient identity matching, accurate diagnosis coding (ICD10, SnoMed), and timeliness of information flow. This is important for each organization and is essential to data re-use.