HMT Newsletter Sign Up

 

 

 

 

 

 

 

 

 

 


 

 

 

 

 

 Big Data

Incorporating analytics into EMRs

Pushes for quality care are inspiring a deeper delving into analytics.

Email this article to a friend
  

   By Dan Hogan, August 2013

The fact is, today’s practice management solutions must evolve to meet the rapidly changing needs of healthcare. Four demands are pushing this drastic need for development:

  • Baby boomers: In January 2011, the first baby bomber turned 65. Since then, 10,000 more baby boomers qualify for social security every day. It’s no surprise that aging populations have an increased need for medical care. The healthcare system will soon be flooded with boomer patients.
  • Longer but sicker life spans: Current U.S. life expectancy is 80.6 years for women and 75.6 for men, according to the U.S. Centers for Disease Control and Prevention. In 1988, it was 78.3 for women and 71.4 for men. In the same time span, diabetes, obesity and other chronic diseases have soared.
  • Physician shortages: The Association of American Medical Colleges projects that by 2020 the U.S. will be short 45,000 primary care physicians. This assumes one physician for every 2,500 patients.   
  • Affordable Care Act: Overall, the act initiates a pay-for-performance healthcare standard. No longer will agencies be paid based on visits, but the quality of that visit.

With all of these changes, efficiency will be key in surviving the healthcare evolution. Technology is healthcare’s conduit of efficiency.

One of the primary technologies in U.S. healthcare is the electronic medical record (EMR); 70 percent of all primary care physicians use one, according to the 2012 Commonwealth Fund International Health Policy Survey. That’s up from less than 50 percent in 2009. There is little doubt this surge is directly related to the 2009 initiation of the ACA that utilized monetary incentives to propel healthcare execs to integrate technology into their practices. EMRs are playing a major role in the technological evolution of practice management.

Predictive modeling is also on the rise. Gartner projects that by 2016, 70 percent of the most profitable companies will be using this form of advanced analytics for business intelligence.

Thornberry Ltd., a healthcare technology solutions vendor,  is considering blending the two technology powerhouses by incorporating Medalogix’s predictive modeling toolset to its NDoc solutions suite.

NDoc currently serves an agency by managing patient information, billing, scheduling and reporting. The folks behind the technology have already made strides in providing analysis and alerting clinicians of avoidable events, rehospitalization risk and compliance. The addition of Medalogix’s predictive modeling toolset adds unparalleled accuracy in predicting patient risk; it’s helped our partnering agencies reduce their average rate of 30-day readmissions by nearly 36 percent. That’s an important feat, especially amidst the ACA provision that allows Medicare to withhold payment from hospitals if a patient is readmitted within 30 days. 

The Medalogix algorithm is unique in that it determines risk by considering the patient’s clinical environment in addition to the clinical diagnostic group and state/nationwide data. This is key because when you consider, for instance, a home-health patient with a history of congestive heart failure (CHF), a state/nationwide risk assessment would assert that patient is at a higher risk for readmission than a patient with a history of diabetes. The flaw in that analysis is it doesn’t account for the agency’s strengths. If the patient with CHF is being cared for at an agency with historically low readmissions for CHF, the patient may have a lower risk of readmission.

Analyzing risk is extremely important because clinicians can monitor at-risk patients more carefully and improve their conditions before they worsen. An experienced clinician may have the ability to assess a patient’s health by simply monitoring the patient, but analytics can see what the human eye sometimes misses. Additionally, analytics helps pinpoint which patients need help first. This aids clinicians in understanding who needs their immediate attention and where their time will be best used.

The pushes for quality care and efficiency, initiated by the four drivers previously listed, are inspiring those involved with healthcare to delve deeper into technology and analytics. It’s an exciting time to be in the industry.

About the author

Dan Hogan is president and CEO, Medalogix. For more on Medalogix, click here.


Tags:  Big Data  Analytics