Clinical Variations play a major role as the healthcare industry transitions from a fee-for-service to a fee-for-value payment model, providers are increasingly taking on the financial risk of poor patient outcomes and are incented to deliver higher quality care at lower costs. Programs like the Center for Medicare and Medicaid Services (CMS) Bundled Payment for Care Improvement Initiative reward care quality instead of quantity. Healthcare providers are under immense pressure to identify and enforce best practices that efficiently deliver high-quality care to an entire patient episode.Because of problems that arise in the healthcare industry, clinical variations start to occur. There are several reasons why clinical variations occur. In summary, clinical variations cause poor quality and outcomes. Based on research professionals have identified some major reasons that lead to clinical variations.
Increasing complexity in the Healthcare Industry
Over the last 50 years, we have witnessed huge changes in how care is delivered, with massive growth in complexity. In the 1950s, physicians had a small number of medications to choose from. Now, according to the Institute for Safe Medication Practices, there are more than 10,000 prescription drugs and biologicals — and 300,000 over-the-counter products — available in the United States. There have been equally profound changes in care delivery options and environments, including modern imaging techniques, highly sophisticated intensive care units and surgical suites, catheter-based procedures, transplant services, minimally invasive techniques, and a host of other complicated options. Under the current system, care providers are being overwhelmed with complexity and so the complexity of modern American medicine exceeds the capacity of the unaided human mind. This argument brings in the relevance of artificial intelligence in the healthcare industry.
An exponential increase in medical knowledge
In 1998, Mark Chassin published an article tracking the publication of randomized controlled trials (RCTs) between 1966 and 1995. One look at the figure below and it is apparent that there has been an explosion in the production of published trials. The number of randomized clinical trials has grown to over 20,000 per year in 2010. In 2004, the U.S. National Library of Medicine added almost 11,000 new articles per week to its online archives. That represented only about 40 percent of all articles published worldwide in biomedical and clinical journals. In 2009, it was estimated that this rate of production had grown to one article every 1.29 minutes. A 1991 study published in the Journal of the American Medical Association found that approximately three to four years after board certification, general internist, and internal medicine subspecialists begin to show “significant declines in medical knowledge.” He estimated that 15 years after initial board certification approximately 68 percent of internists would not pass the American Board of Internal Medicine certification exam. He went on to estimate that to maintain current knowledge, a general internist would need to read 20 articles a day, 365 days a year. Clearly, maintaining current knowledge has become a near-impossible task for all clinicians.
Lack of valid clinical knowledge
This sounds ironic because of the points above which discuss that there has been an increase in medical knowledge. However, quantity doesn’t always mean quality and in this case also validity. There have been three published studies looking at the percentage of clinical care that is based on published scientific research. These studies have concluded that only between 10 and 20 percent of routine medical practice has a basis in scientific research. Thus, much of what we do in routine clinical practice is based on tradition or opinion. That doesn’t necessarily mean it is wrong, as much of it has likely been shown to work over time. However, it does suggest that healthcare delivery organizations should use their own data to determine the efficacy of clinical practice and to determine how to improve it over time. This implies the need to create a data-driven continuous learning environment.