The changing face of health care

Several major forces over the last several years have changed the way health care has and will continue to be delivered. The emergence of more unique ways of providing care, such as clinics incorporated in businesses and factories, the increased use of mid-level providers (nurses and medical assistants), the increased integration of technologies such as telemedicine and robotics, and the shift from intervention reimbursement to reimbursement of results are just a few examples.

Compounding these are the ever-rising healthcare costs, the strain of funding for Medicare for the U.S. economy, and the complications of insurance and healthcare services under the Affordable Care Act, ACA.

This has led to changes in how companies intend to collaborate with the healthcare system in the future. CVS’s acquisition of Aetna will seek to leverage healthcare delivery through their pharmacy structure. United Healthcare’s acquisition of DaVita hopes to capitalize on cost containment and resource control through direct medical supervision. And it recently announced collaboration between Berkshire Hathaway, Amazon and J.P. Morgan Chase presents an as yet unknown structure whose stated goals are improved quality and less cost. How they will implement their strategy has not yet emerged.

The decline in hospital admissions over the last several decades has further led to the restructuring of hospital companies such as Tenet. Premise Health has emerged as a company that places doctors and other health care providers directly in corporate offices.

The big question with these new ventures is how do organizations know what works financially and how do they track results … In other words, how do you track, measure and value the relationship between cost and performance?

How can the analyst measure which method (s) can generate better or best results?

A single return on investment (ROI) does not provide necessary or valid insight. However, using cost-effectiveness analysis (CEA) would provide quite useful, valid and actionable information. CEA uses decision tree models to compare not only cost outcomes, but efficacy outcomes of various patient health treatments and even future health care use based on various current actions. It can also be used to determine how effectively a lump sum spent on a particular treatment or method will affect the results (ie willingness to pay calculation). CEA models are flexible and can contain a variety of scenarios. Unlike Big Data, CEA uses broad data, so comparisons of treatment approaches can be evaluated using real-life results. It can compare effects on a discrete problem, such as a cancerous tumor, or on chronic ongoing diseases such as COPD or CHF.

As the delivery of effective, yet profitable, or at least cost-effective, healthcare becomes more challenging, methods of evaluating treatments and programs become more necessary, if not necessary. Methods must be implemented to evaluate these new treatments and programs once in place so that adjustments can be made. CEA allows organizations to both initially evaluate and then monitor new methods and programs in a meaningful way.

If your goal is to provide the best decision-making for your organization and take a global view of your business, expand your sights beyond ROI and educate other decision makers, cost-effectiveness analysis can make your organization more competitive and more profitable.