Several significant forces in the last several years have been changing the way healthcare has and will continue to be delivered. The emergence of more unique ways to deliver care such as clinics incorporated into businesses and factories, the increased use of mid-level providers (nurse practitioners & physician assistants), the increase integration of technologies such as telemedicine and robotics and the shift from interventional reimbursement to outcomes reimbursement are just a few examples.
Compounding these are the ever-increasing costs of healthcare, the strain of funding Medicare on the U.S. economy, and the complications of insurance and healthcare payments under the affordable care act, ACA.
This has led to changes in how businesses intend to interface with the IoT In healthcare system going forward. CVS’s acquisition of Aetna will try to leverage healthcare delivery through their pharmacy structure. United Healthcare’s acquisition of DaVita hopes to leverage cost containment and resource control by directly controlling physicians. And the recently announced collaboration among Berkshire Hathaway, Amazon and J.P. Morgan Chase presents a yet unknown structure whose stated goals is improved quality and less cost. How they will implement their strategy is yet to emerge.
The decline in hospital admission over the last several decades has further led to restructuring by hospital corporations such as Tenet. Premise Health has emerged as a company placing physicians and other healthcare providers directly in corporate/business offices.
The big question then with these new ventures are how do organizations know what works financially and how do they track performance… In other words, how do you track, measure and value the relationships between cost and outcomes?
How can the analyst measure which methods(s) may generate better or best outcomes?
A simple return on investment, ROI, calculation will not provide needed nor valid insights. However, the use of cost-effectiveness analysis (CEA) would provide quite useful, valid and actionable information. CEA uses decision tree models to compare not only cost outcomes but effectiveness outcomes of various treatments on patient health and even on future healthcare usage based on various current actions. It can further be used to determine how effective a set amount of money spent on a particular treatment or method will impact outcomes (i.e. willingness to pay calculation). CEA models are flexible and can incorporate a wide variety of scenarios. As opposed to Big Data, CEA makes use of Broad Data so that comparisons of treatment modalities can be evaluated using real life outcomes. It can compare effects on a discrete problem such as a cancer 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 for evaluating treatments and programs become more necessary if not essential. Methods must be implemented to evaluate these new treatments and programs once they are in place so adjustments can be made. CEA enable organizations to both initially evaluate and subsequently monitor new methods and programs in a meaningful way.
If your objective is to provide the best decision-making for your organization and take a global view of your business, expanding your sights beyond ROI, and educating other decision-makers, Cost Effectiveness Analysis can make your organization more competitive and more profitable.