Six of the country’s leading health care systems recently announced a collaboration to improve health care quality while reducing costs. Cleveland Clinic, Dartmouth-Hitchcock, Denver Health, Geisinger Health System, Intermountain Healthcare, and Mayo Clinic will join The Dartmouth Institute for Health Policy and Clinical Practice to share data on outcomes, quality, and costs across a range of common and costly conditions and treatments.
The six health care systems, with a combined patient population of more than 10 million people, will determine best practices for delivering care for these conditions and will rapidly disseminate actionable recommendations to providers and health systems across the United States. In addition to achieving better quality and outcomes, the collaborative intends to improve the efficiency of standard clinical care delivery to reduce the per capita cost in these conditions and to keep costs in pace with the consumer price index.
Initially, the collaborative will focus on eight conditions and treatments for which costs have been increasing rapidly in recent years and for which there are wide variations in quality and outcomes across the country. The conditions and treatments include: knee replacement, diabetes, heart failure, asthma, weight loss surgery, labor and delivery, spine surgery, and depression.
“If we know that the treatment path for diabetes at one institution results in better clinical outcomes, higher patient satisfaction, and lower overall costs, then there is knowledge to be shared and replicated in other institutions,” said Robert Nesse, MD, chief executive officer of Mayo Clinical Health System and member of Mayo Clinic’s Board of Trustees. “We need to learn from each other and put systems in place that ensure that every patient gets the very best, most appropriate care, every time.”
The Dartmouth Institute will coordinate data sharing and analysis, and report results back to the collaborative members to inform development of best practices.
Source: Geisinger Health System
