Rachel Madley in The Scientist:
Over the past decade, Americans have debated the best way to fix our broken healthcare system, one that allows 35,000 Americans to die each year because they don’t have health insurance and many more to forego necessary treatment or go bankrupt paying for care. This debate has intensified in recent months due to the increasing popularity of Medicare for All, a proposal to create a publicly funded single-payer health system, and its central role in the Democratic presidential race. First, let’s define what these terms mean: Single-payer Medicare for All would establish a public funding mechanism for healthcare that covers everyone for all medically necessary treatment, including dental, vision, and hearing care. This care would be free to everyone at the point of service, regardless of income, age, employment, or immigration status. Medicare for All changes how care is financed, but not how it’s delivered, thus patients would have free choice of any doctor or hospital. Besides the benefits to patients, Medicare for All would save approximately $500 billion annually in healthcare costs, according to one estimate, by, among other things, cutting out thousands of insurance middlemen and negotiating drug prices at the national level.
Many health professionals support Medicare for All, including a majority of doctors and the largest nurses union in the US, as do a majority of registered voters in the US overall, but biomedical scientists have so far been silent. Yet they do have a stake in the outcome of healthcare reform. Medicare for All would increase the clinical data available for research and allow all patients to benefit from scientific innovation. Scratch the surface of our health system and we find that it hurts patients directly and indirectly—not just by keeping medical care out of reach, but by hindering the kind of medical research that drives innovation and benefits everyone. Today’s fractured system sequesters patient data within millions of different hospital and insurance databases, with little cross-institutional data sharing. When data are shared by multiple institutions, the coding and format are not standardized, making research on those cohorts difficult or impossible.
More here.