Kick up the quality of your care with tips and tactics from Caitlin Clancy MD, coauthor of ACP’s High Value Care Curriculum. We learn to define quality and value in healthcare; the most common barriers to high value care; use of probability and likelihood ratios to boost clinical reasoning and combat diagnostic uncertainty; some useful tools to estimate cost; sources of healthcare waste; and some general pearls on how the healthcare system works…or doesn’t. ACP members can claim free CME-MOC at acponline.org/curbsiders (goes live 0900 EST on podcast release date).
Written & Produced by: Matthew Watto MD
Hosts: Paul Williams MD, Stuart Brigham MD, Matthew Watto MD
Guest: Caitlin Clancy MD
Quality refers to all the benefits, both immediate and long term, that can be derived from a particular diagnostic test or treatment.
Cost refers to the financial and non-financial harms of a particular diagnostic test or treatment e.g. missed work, physical or emotional suffering, etc.
The above definitions are according to Clancy et al. ACP HVC Curriculum 4.0 Module 1.
One in three healthcare dollars are waste! (Porter ME. N Engl J Med 2010). The top three contributors to spending were unnecessary services, excess administrative costs, inefficient delivery of care. Missed prevention is an honorable mention (Clancy et al. ACP HVC Curriculum 4.0 Module 1).
Clinicians don’t intend to order unnecessary services, but several factors lead to these wasteful practices (Clancy et al. ACP HVC Curriculum 4.0 Module 1).
The uninsured patient lacks the negotiating power (leverage) of a large private insurance company or government institution. As a result, they end up getting charged the inflated face value for diagnostic tests and procedures. This is a common cause of bankruptcy (Clancy et al. ACP HVC Curriculum 4.0 Module 2).
The insured patient pays a monthly insurance premium that is often subsidized by their employer. When they need diagnostic testing or treatment their insurance company negotiates a lower reimbursement rate with the institution providing the needed services. The patient pays their deductible and the insurance company pays the difference. Government insurers can negotiate lower reimbursement than private insurers due to their larger size (Clancy et al. ACP HVC Curriculum 4.0 Module 2).
Large hospital systems often charge insurance companies more for their services than privately owned centers or independent practices because of their large market share and larger operating costs (Philly.com Article on Two Echocardiograms). “Economies of scale don’t translate to lower prices. With their market power bigger providers can simply demand more.” (Elisabeth Rosenthal in her book American Sickness, Rule #7). For example, if insurance company A refuses to pay the high prices charged by hospital system A (who has 60% market share in a region) then insurance company A may lose their customers to insurance company B who remains in good standing with hospital system A.
What we know about insured patients:
Much of the the above data comes from the Oregon Health Insurance Experiment.
Doctors and patients are doing a poor job at being conscious about the cost of care. Only about 1% of healthcare consumers are getting the best prices for “shoppable” tests e.g. an elective MRI (nytimes.com article by Austin Frakt ; http://www.nber.org/papers/w24869).
Here are two resources that allow you to enter a zip code and see the range of prices for a given diagnostic test. Use these resources to determine the fair market price.
Dr Clancy recommends using pretest probability, likelihood ratios and posttest probability to help mitigate diagnostic uncertainty (Clancy et al. ACP HVC Curriculum 4.0 Module 3). The example used on our show included a patient presenting to clinic with suspect heart failure. The pretest probability can be estimated based on a patient’s history, exam and initial data. Next, consider the likelihood ratio (LR) of a given diagnostic test. The magnitude of the likelihood ratio determines its effect on posttest probability. For example, a LR of 2 will raise the posttest probability by ~15%.
LR <5 = small effect on posttest probability
LR 5-10 = moderate effect on posttest probability
LR >10 = large effect on posttest probability
Use the likelihood ratio nomogram to draw a straight line from the pretest probability through the LR to find posttest probability. A test isn’t worth ordering if it is unlikely to change the posttest probability or patient care. Dr Clancy recommended trying the docLogica app, which can help clinicians perform these calculations without a pencil and paper.
Some of the top barriers to high value care include defensive medicine, local culture and patient expectations (Clancy et al. ACP HVC Curriculum 4.0 Module 5). On the show we discussed how to recognize these barriers and use communication and quality improvement projects to overcome them. Clinical decision tools can be added to the electronic health record to help avoid low value practices (e.g. ordering antibiotics inappropriately, making it difficult to order daily labs, or prompting the clinician with questions when ordering a blood transfusion for a hemoglobin above 7). There is some evidence that providing peer prescribing data may lower antibiotic use (Meeker et al. JAMA 2016 PMID 26864410).
Listeners will define and recognize key aspects of high value care including: cost, quality, and barriers to high value care.
After listening to this episode listeners will…
Dr Clancy reports no relevant financial disclosures. The Curbsiders report no relevant financial disclosures.
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