Category Archives: Publications

Making Care Management Work for ACOs

An article in the May 2017 issue of Health Affairs, Bending The Spending Curve By Altering Care Delivery Patterns: The Role Of Care Management Within A Pioneer ACO by John Hsu and others from the Massachusetts General Hospital and Partners HealthCare in Boston, provides more evidence that care management interventions can meaningfully contribute to the performance of an ACO.  Doing so, however, is neither fast nor easy.

Here are a few key takeaways (which parallel my own experience):

1.  Target higher-risk populations with modifiable risk factors which your program is specifically designed to address
2.  Stay constant to purpose … utilization (ER visits and hospitalizations) may even go up at first … but will, as participants are in the the care management program longer, progressively decline over time (most notably at 13+ months)
3.  Sophisticated analytics and study designs are required to assess the impacts of such programs in ‘real-world’ implementations (i.e., without a randomized controlled trial)
4.  Inability to serve the entire target population should not deter continued implementation – enroll as many as current resources permit and continue to work toward expanding capacity

The care management program described in the article shares some attributes with HQP’s Advanced Preventive Care model.  One of the authors of the Health Affairs article was also an author on a 2014 issue brief for the Commonwealth Fund that identified HQP’s model as one of four (out of 18 successful models reviewed) having the highest quality evidence of effectiveness in managing populations with complex chronic disease. The details of a care management program’s design and implementation are critical to its success, with many varieties of “generic” care management have been found to be ineffective.

While not quick or easy, the path to improving population health – from both a health outcomes and cost perspective – is becoming clearer. The advantage will go to those with the know-how, relentless commitment to innovation, and constancy to purpose.

Evidence Supports Scalability of Effective Models: Enormous Possibility

In the June 2012 article in Health Affairs by Brown et al., “Six Features of Medicare Coordinated Care Demonstration Programs That Cut Hospital Admissions of High Risk Patients” a subgroup of patients was defined using criteria available in Medicare claims data;

[(HF, CAD, or COPD) AND ≥1 hospitalization in prior year]
OR [(diabetes, cancer (not skin), stroke, depression, dementia, atrial fibrillation, osteoporosis, rheumatoid arthritis/osteoarthritis, or chronic kidney disease)
AND ≥2 hospitalizations in the prior 2 years]

Members of this subgroup participating in the Health Quality Partners (HQP, program had -33% fewer hospitalizations (p=0.02), -30% lower Part A & B Medicare expenditures (with program fees excluded) (p=0.045) and -21.5% lower net costs (program fees included) (p=0.15).  All terrific stuff and since the emphasis of this particular analysis was to identify common elements of successful programs, using complex subgroup definitions for that purpose is fine.  However, there are significant real-world challenges in trying to use such a complex eligibility criteria for program implementation and scalability.

In the HQP experience, it remains hugely challenging to cobble together a patchwork of collaborative data sharing agreements with hospitals and primary care practices in order to serve a geographic region.  Complex criteria sets such as these make that job harder.  Having worked many years with the authors of this article I know that they too are fully aware of and appreciate this concern, but the inexperienced reader might confuse or meld these two separate issues: finding common elements of successful programs vs. defining the “best” target population for scaling effective care management interventions.In tables in the Appendix to the article another, simpler subgroup is defined as;


Just having one or more of these 3 conditions meets this subgroup criteria; no other prior hospitalization usage, other co-morbidities, etc.  This group is a lot easier to “find” prospectively with data readily available in primary care practices (their billing data).  In the demonstration, HQP randomized 695 individuals meeting these criteria (43% of all those in the study) vs. just 273 (17% of those enrolled) of the more complex subgroup above.  Results for this simpler, more easily identified subgroup?  For HQP’s program, not bad; -25% fewer hospitalizations (p=0.005), -20% lower Parts A & B Medicare expenditures (-$220 per person per month) (p=0.02), and -10% net savings when program fees were included (-$116 per person per month) (p=0.22).

There are plenty of challenges to scaling highly-effective care management programs like HQP’s.  One challenge we can and should avoid is making the criteria set for eligibility needlessly restrictive and difficult to implement – especially when the evidence supports a wider population of people who can benefit.  With each larger scale cycle of testing, the criteria can be further refined (and coned down, if necessary), but in the meantime, we should encourage the use of target group criteria that are feasible to implement and support system redesigns with the greatest possible chance of successfully transforming our health care system for the better.

This same blog article is also posted on the HQP blog at

Prevention So Good it Saves Lives AND Money

HQP’s nurse care management model is such an effective preventive intervention that it saves lives among chronically ill older adults.  I’m proud to be the lead author of the study undertaken by the remarkable team at HQP and published in PLoS Medicine.  It’s a compelling proof of concept for a whole new approach to Advanced Preventive Care.  Read the full report here;