- General theme: the healthcare system is broken, with no practical solutions for fixing it
- Politics are not helping to solve anything... government regulations hinder innovation
- Value-based care is not going away, and both providers and payers want to work under that model
Across policy and practice, health care appetites are changing. As reimbursement models shift from volume (of procedures) to value (of health outcomes), providers are expanding their menu of services to initiate broader and earlier interventions for health, to lower costs and to reach across populations.
Health and well-being are valuable societal objectives, and have consequences for our country’s bottom line. Healthy individuals report experiencing a greater quality of life, and a healthy population contributes to higher rates of economic growth. Health equity matters, too. Income and wealth disparities between and among demographic groups result in poorer health outcomes and exert negative effects on individual productivity and on national economic growth and sustainability.
Last Friday was supposed to be the shootout at the healthcare corral where Republican gunslingers were going to take the first major step toward repealing Obamacare. Instead legislators retreated to their offices with no shots fired.
Speaker Paul Ryan pulled the vote on the controversial American Health Care Act (AHCA) as moderate and conservative Republican Representatives pulled their support in droves.
The AHCA is dead for now. May it rest in peace. President Trump tried it Speaker Ryan’s way and supported a healthcare reform bill with a miniscule 17% public approval rating. By contrast and for the first time, a majority of Americans expressed approval for Obamacare.
At Katmai National Park, the Alaska brown bears know their existence depends on salmon. To maximize their chance for survival, the bears head up the Brooks River where the fish are concentrated and efforts to capture them most effective.
Health providers, too, know that as they are increasingly expected to provide value and better health for consumers, their efforts must shift upstream to maximize health outcomes – outside the four walls of the clinic, into the world of the consumer.
Take a minute to look outside your window. Are you surrounded by parks, healthy food stores, and neatly maintained sidewalks … or fast-food restaurants, liquor stores, and bowling alleys?
Your physical environment plays a significant, but undervalued, role in determining your overall health status, quality of life, and longevity. In fact, your zip code is a stronger predictor of your overall health than other factors, including race and genetics.
This Thanksgiving Week, as we ponder things to be thankful for, I am starting to build my wish list of resolutions for the New Year.
Here is the first, in the form of an excellent piece from Christianna Silva at FiveThirtyEight: "Why don't food and housing count as health care?"
The odds are good that your nearest healthcare system has conducted a Community Health Needs Assessment (CHNA) sometime within the last few years.
The odds are even better that you haven't read it.
"Healthcare is the last industry that has not adopted digital technology in any major way to help deliver its services. And it’s becoming challenging for physicians and consumers to actually manage care without those digital tools."
- David Schlanger, CEO, WebMD (2015)
Healthcare is changing - rapidly.
Telemedicine, drug pricing, and hospital system consolidation – it’s enough to make your head spin. When the market gets complicated, sometimes it’s best to go back to basics – to remember what is really important about your market and your healthcare consumers.
Forrest Gump famously said that life is like a box of chocolates. He may well be right. But predictive modeling is less a box of chocolates, and more a tube of toothpaste.
In each tube, there is a certain amount of toothpaste available. Think of this toothpaste as information or predictive capability. The same dataset may have varying amounts of "toothpaste" for different predictive purposes ... but let's ignore that for now. For a given investigation, there is only so much information to be squeezed from a dataset.