Waimei Tai, MD

Lich KH, Tian Y, Beadles CA, Williams LS, Bravata DM, Cheng EM, et al. Strategic Planning to Reduce the Burden of Stroke Among Veterans:Using Simulation Modeling to Inform Decision Making. Stroke. 2014

Lich et. al. developed an interesting way to look at the stroke systems of care problem: applying engineering approach to identify areas of highest potential impact in an all inclusive system of care- the national Veterans Affairs health system. 


I have had the opportunity to speak with some of the authors of this paper from prior work in stroke systems of care. I think what they’re doing makes a lot of sense. If you have to oversee a large initiative (the entire VA health system’s stroke care program) with limited resources, it makes sense to look at what you can get for “greatest bang for your buck.” And hopefully with this tool, they’ve identified hypotheses that are worth pursuing in a prospective manner to gain more health quality for their patients.

The interventions they identified as potentially most efficient in deriving more QALYs via number needed to treat (faster tpa), or size of overall cumulative effect (hypertension control in high risk patients), and better care early care for vets with TIA, and transition to rehab. These are similar to interventions suggested by literature review or by expert opinion. It does go to show that the clinicians’ sense tends to hone in on achievable gains that can potentially dramatically improve quality of life.

One limitation (or strength, one could argue) is that the data is largely VA specific, but they’re answering a question that is unique to their population. Given the VA’s large presence in the healthcare system, their inclusive nature, they have a unique ability to analyze health statistics for a large population of patients.

In this day and age, when so much attention has been drawn on some negative aspects of VA health care system, I think this study highlights the great attention and care that the VA health care providers and staff are offering to our veterans and their plan to maximize the utility of the taxpayers’ dollars by focusing on highest impact, highest efficiency interventions. I’m grateful for these colleagues. I believe while this data set maybe limited to the VA population, that lessons learned from this modeling is applicable in other resource constrained environments such as other county or public health systems, as well as older medicare population as well.