Hayes' Healthcare Blog

An article in Inc. Magazine outlined five reasons why collaboration is crucial. While the article was focused mainly on business organizations, the concept is just as important for those in healthcare generally and for compliance and auditing professionals specifically.

The key reasons for working with peers in your industry are:

• Self-awareness – Collaboration forces you to clearly describe areas of strength and weakness
• Scale – “Two heads are better than one”
• Creative Abrasion – Converting energy from people who are different into positive action
• Take the long view – Ideas may not have an immediate impact but could be beneficial down the road
• Learn, learn, and learn some more – Collaboration sets an expectation of continuous learning to expand the knowledge base of the organization.[1]

No person – or organization – has all the answers. The more you can work with peers at similar institutions facing similar challenges, the better the chances you can effectively address and successfully solve them. There are three things to consider that can help energize peer collaboration in your organization.

Rapidly changing payment methods. Shift from fee-for-service to value-based care. Declining insurance reimbursements. Increased government regulations.

Sound familiar? The litany of changes impacting the healthcare industry appears to grow longer every day. Taken together, they add up to one thing: more stress on your revenue cycle. Organizations need to adapt to this new reality to survive the upheaval.

There is a way for you to not only address these issues, but also to thrive in the turbulent times ahead. The key to solving the dilemma is close at hand: the data that resides in your EHR and other IT systems and applications. Harnessing this treasure trove of information and converting it into actionable data can help you meet the growing demands you face. But to access this big data, you need to develop a comprehensive data analytics program.

Chris Harper, Director Business Architecture and Analytics, University of Kansas Hospital

There’s no doubt that advanced analytics has become an integral component for business decision making today and the healthcare industry is no exception. The problem is that we can sometimes be hypnotized by buzzwords like “big data” and “predictive analytics” and lose sight of the real goal of an effective analytics strategy. When that happens, organizations can end up throwing dollars and investment assets at a program without investing the necessary time and thought up front to build a solid strategy for their analytics initiatives.

Data, Analytics, and the Emerging Role of the CMIO

By Gregory Ator, MD., CMIO, of the University of Kansas Hospital (Kansas City), University of Kansas Physicians Inc.

The role of data and analytics in healthcare especially as it relates to transparency has been discussed widely. What is your view of how data and analytics has changed and will continue to change the way healthcare is delivered?

It is well known that healthcare is moving from volume to value - value defined as optimal outcomes for the least cost. And with some urgency, many organizations are trying to figure out how to successfully navigate this transition. At the crux of this shift, is the availability of data. Data is crucial because you can’t achieve optimal quality and outcomes without it. Accurate clinician documentation in the EMR is more important than ever because this is what generates process improvement data points, hopefully, as a by-product of the care process. In order for all this to work, improved clinician-friendly EMR functionality is desperately needed.

For years, we have seen discussions about Business Intelligence (BI), critical indicator dashboards and other data tools for decision making. It all sounds good in theory, but I don’t see a lot of effective BI in the real world. So what is the problem? The largest problem is and always has been a lack of structure in the underlying data.

Nearly every large health network has made an attempt to consolidate data from multiple sources into some type of data warehouse. However, having the data in one place does not make it useful or trusted as a source for business intelligence. There is usually an assumption made that if the data is together in one place (data repository or data warehouse), it can be used to perform a lot of wonderful analysis and become the basis for some great BI tools. The problem with this approach is that it lacks structure. You would not build a boat by strapping together anything that floats and hoping that you can figure out how to put them work together later. So why is there a tendency to think that it will work with business intelligence?