Quality Care Requires High Quality Evidence

What if instead of incentivizing healthcare providers with complicated alternative payment models and "quality care" payments--you are a dear UnitedHealthcare--we examine the elephant in the room?

Why is high value care such a mystery? Perhaps it is because of the business of medical publications. Like it or not the pharmaceutical industry has a tight grasp on which algorithms are annointed  "evidence-based" care.

Why does high-value care include more screening and more interventions? Why aren't we talking more openly about NNT, NNH, and NNS measures?

​*number needed to treat, harm, or screen

If we didn't rely on a distorted lens to view quality care we wouldn't be left with the reality--our medical professionals are not able to rely on biomedical evidence.

"Why Most Published Research Findings are False"--John P.A. Ioannidis, MD

• The smaller the studies conducted in a scientific field, the less likely the research findings are to be true
• The smaller the effect sizes in a scientific field, the less likely the research findings are to be true
  
• The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true
  
• The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true
  
• The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true
  
• ​The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true
  

Before we can impact the quality of data informing decisions at the point of care--we need to acknowledge the permeating bias influencing the research results.

There are potential solutions and Dr Ioannidis provides actionable steps. 

• Large-scale collaborative research
• Adoption of replication culture
• Registration (of studies, protocols, analysis codes, datasets, raw data, and results)
• Sharing (of data, protocols, materials, software, and other tools)
• Reproducibility practices
• Containment of conflicted sponsors and authors
• More appropriate statistical methods
• Standardization of definitions and analyses
• More stringent thresholds for claiming discoveries or ‘‘successes’’
• Improvement of study design standards
• Improvements in peer review, reporting, and dissemination of research
• Better training of scientific workforce in methods and statistical literacy

The red highlights are the topics we tackle in discussions here at data & donuts. I refuse to believe that professionals in medicine need to have bright shiny targets and gobs of cash to be concerned about the outcomes of their patients.

They just need a reliable and transparent network aligned with truthfullness and improving patient well-being.

Thoughtful discussions about content development and outcomes analytics that apply the principles and frameworks of health policy and economics to persistent and perplexing health and health care problems...