Achieving real-world quality improvement in healthcare delivery benefits everyone. Patients have improved outcomes and a higher quality of life. Physicians use evidence-based insights to make decisions and advance care. Hospitals and payers achieve efficiencies and greater savings.
This win-win-win of quality improvement can be achieved locally with the right infrastructure, data, processes, and people involved. But what happens when many institutions come together to collaborate, share data, and learn from one another?
Improvements are achieved at greater scale. More people benefit. Today's biggest issues facing patient safety and outcomes are addressed.
Let’s look at the top examples of quality improvement at scale and how they achieved it: One from a regional surgical quality collaborative and another from a national network of pediatric hospitals with cardiac ICUs. Both use healthcare analytics and clinical registries to achieve significant results.
The opioid epidemic is a top priority for physicians and healthcare providers across the continuum of care. This is especially true in surgery. Surgeons prescribe 10% of opioids across the United States, and research shows “the vast majority of these pills are not used, potentially leading to opioid dependence, misuse, or diversion into the community."1
With this issue top of mind, in partnership with Michigan OPEN, the Michigan Surgical Quality Collaborative (MSQC) successfully launched a program to reduce the number of opioids prescribed to surgical patients over a 16-month time frame.2
The project involved researchers at Michigan OPEN developing evidence-based prescribing guidelines for five operations, based on MSQC-collected data on patient-reported opioid consumption after surgery. MSQC’s registry collected opioid prescribing, consumption, and satisfaction data for more than 10,000 patients over several months. They transformed this data into knowledge and defined and disseminated new opioid prescribing guidelines. Then they continued to collect data, and track and analyze outcomes.
Did the new guidelines make a difference? The results published in the New England Journal of Medicine were clear. They found:
Reaching these outcomes required MSQC’s deliberate planning, coordination and collaboration, and information and data sharing.
MSQC organized this initiative and leveraged their infrastructure exceptionally well:
Their post-surgical opioid prescribing reduction program is best summarized an excerpt from a recent paper:
“Opioids prescribed after surgery have gained national attention for their role in the escalating opioid epidemic.14, 15 To learn about this problem, the MSQC team sought feedback from the collaborative sites, reviewed scientific and practice‐based research, and solicited patient experiences. With this knowledge, the MSQC clinical leadership designated postsurgical opioid prescribing as a quality improvement focus area with high priority. The coordinating center team added additional data collection variables to the MSQC data platform to determine the amount of opioids prescribed after surgery. They also added patient‐reported opioid consumption, and other patient‐reported outcomes including satisfaction with care and postoperative pain. Data were collected on a subset of patients undergoing five of the most commonly performed operations in the MSQC database.”2
MSQC’s clinical data registry on the ArborMetrix platform plays a central role in all of its quality improvement programs.
Along with its community 70 hospitals, MSQC uses real-world data to focus relentlessly on achieving real-world quality improvement and make Michigan the best place for surgery in the country. Their approach and proven results have made them a national example of how to advance care and reduce costs using high integrity data.3
The post-surgical opioid prescribing reduction program is no different. For this initiative, MSQC leveraged registry technology that:
Another central part of MSQC’s model is sharing performance feedback with surgeons and sites through the registry, and giving participants the tools they need to be successful.
Nearly 40,000 infants born in the United States each year have some form of congenital heart disease (CHD), making it the most common birth defect affecting 1 in every 110 babies.
Pediatric cardiologists and researchers have improved outcomes considerably over the past few decades. Yet many children still experience significant health issues over the course of their lifetimes, according to Michigan Medicine C.S. Mott Children’s Hospital.4
New approaches are necessary to make the next leap in CHD care.
A group of physicians and researchers, led by Jeffrey Anderson, M.D., M.B.A., Michael Gaies, M.D., M.P.H., and Sara K. Pasquali M.D., M.H.S., organized Cardiac Networks United to address these challenges. Member institutions span more than two thirds of all hospitals caring for congenital heart patients in the United States.5
The Pediatric Cardiac Critical Care Consortium (PC4) is one of five founding organizations of CNU. PC4 aims to improve the quality of care for pediatric heart patients through transparent data sharing that allows hospitals to evaluate their own outcomes and learn best practices.6
Their efforts are proving effective and the results are outstanding.
Eighteen hospitals significantly reduced mortality and improved care for children with critical heart conditions, according to a paper published in the December 2019 edition of the Journal of the American College of Cardiology. The study analyzed more than 19,000 hospitalizations that included cardiac surgery at the participating sites in the PC4 registry.7
Published results include:
The core principles of collaborative quality improvement drive PC4 and its member community:
Member organizations are committed to sharing data and expertise with one another to accelerate discovery and improvement in the care of patients with pediatric and congenital heart disease.
In order to achieve the level of trust and accuracy needed for collaborative quality improvement, PC4 puts a huge emphasis on the quality of the data in its registry. It focuses on three key aspects:
PC4’s registry provides 24/7 access to real-time data to be used for local quality improvement. Participating hospitals use the registry to support their own physicians and care teams. Importantly, PC4 offers access to unblinded center data to facilitate identification of top performing hospitals and stimulate collaboration among sites to improve patient outcomes.
Another key aspect driving PC4’s success are the powerful analytics behind its registry. Participants have access to risk- and reliability-adjusted comparative analyses on quality metrics selected by the consortium.
Recently of note, ArborMetrix helped develop a program-level risk adjusted metric on post-operative mechanical ventilation, measuring quality of care from the time a patient enters the OR to the end of their critical care period(s). This metric is part of PC4’s initiative to liberate children from the ventilator.
Organizations like PC4 and MSQC are the best examples of quality improvement through clinical registries. They show what is possible when you make a clinical data registry rooted in data science your hub of information to support collaborative data sharing, learning, and progress.
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