Submission Type



quality improvement, Learning Health System, Research Networks


Introduction: There are many benefits of multistate collaboratives or networks to states, but at the center is that they allow for the opportunity to learn from other states and experts about the practices and policies states have implemented without the significant time lag of published research. This commentary examines these benefits and illustrates the importance of quality improvement collaborations to decision-making in state Medicaid programs.

Background: In 2007, the Medicaid Medical Directors Learning Network (MMDLN) began conducting quality improvement studies using their own state-level administrative data to better understand the major clinical issues facing the Medicaid populations and to work together on policies to improve outcomes.

Rationale and Results: The three issues selected by MMDs for quality improvement monitoring to date involved an important national problem – including both morbidity and cost – and were amenable to policy solutions. The studies examined the use of antipsychotic medication in children, hospital admissions and readmissions, and early elective deliveries (i.e., elective deliveries occurring before 39 weeks).

Importance and Utility: The multistate clinical quality projects conducted offer a key mechanism for achieving the goal of helping the Medicaid program deliver value-driven, high-quality, cost-effective health care in an efficient manner. These projects also provide the participating states with data to inform policies internally.

Conclusions: In order for the quality of health care to improve, the system needs to be structured as a learning health care system; one that is always accessing evidence, implementing a variation of it (i.e., with new data sources or tools such as electronic clinical data), assessing effectiveness, and sharing results for others to repeat the cycle.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.