The US healthcare system’s myriad of problems again seized the headlines recently with the release of an Institute of Medicine report, which found that 30 percent of healthcare spending in 2009 – around $750 billion – was wasted. Citing the “urgent need for a system-wide transformation,” the report blamed the lack of coordination at every point in the system for the massive amount of money wasted in healthcare each year.
One critical area in particular need of transformation is the business and operating model that drives healthcare in the US. There is broad-based agreement across the healthcare industry that the current fee-for-service model does not work, and needs to be changed. The sweeping health reform law enacted in 2010 included a range of more holistic, value-based payment structures that are now being referred to as “population health.”
Population health is an integrated care model that incentivizes the healthcare system to keep patients healthy, thus lowering costs and increasing quality. In this value-based healthcare approach, patient care is better coordinated and shared between different providers. Key population health models include:
· Bundled/Episodic Payments – This is where provider groups are reimbursed based on an expected cost for a clinically defined episode of care.
· Accountable Care Organizations (ACOs) – This new model ties provider reimbursement to quality and reduction in the total cost of care for a population of patients.
Continue reading “How Using a ‘Scorecard’ Can Smooth Your Hospital’s Transition to a Population Health-based Reimbursement Model”
Filed Under: Uncategorized
Tagged: ACOs, avoidable readmissions, bundled payments, coordinated care, Data, Fee-for-service, IOM, Population Health, reimbursements, Russ Richmond, Scorecards, The ACA
Dec 4, 2012
In an article posted earlier this year on this blog I argued that hospitals have traditionally done a sub-par job of leveraging what has now been dubbed “big data.” Effectively mining and managing the ever rising oceans of data presents both a major challenge – and a significant opportunity – for hospitals.
By doing a better of job connecting the dots of their big data assets, hospital management teams can start to develop the crucial insights that enable them to make the right and timely decisions that are vital to success today. And, better, timelier decisions lead to improved results and a higher level of quality patient care.
That’s the good news. The less than positive story is that hospitals are still way behind in using the mountains of data that are being generated within their institutions every day. Nowhere is this more apparent than in the advanced data management practice of predictive modeling.
At its most basic, predictive modeling is the process by which data models are created and used to try to predict the probability of an outcome. The exciting promise of predictive modeling is that it literally gives hospitals the ability to see into (and predict) the future. Given the massive changes and continuing uncertainty that are buffeting all sectors of the healthcare industry (and especially healthcare providers), having a clearer future view represents an important strategic advantage for any hospital leader.
Continue reading “Using Predictive Modeling to Make Better Decisions”
Filed Under: THCB
Tagged: analytics, Big Data, Data, Decision-making, Digital Divide, Infectious Disease, McKinsey Global Institute, Medicaid Expansion, predictive modeling, Russ Richmond
Aug 17, 2012