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Tag: Predictive analytics

Big Data in Healthcare: Good or Evil? Depends on the Dollars

flying cadeuciiAn organization’s “business model” means: How does it make a living? What revenue streams sustain it? How it does that makes all the difference in the world.

Saturday, Natasha Singer wrote in the New York Times about health plans and healthcare providers using “big data,” including your shopping patterns, car ownership and Internet usage, to segment their markets.

The beginning of the article featured the University of Pittsburgh Medical Center (UPMC) using “predictive health analytics” to target people who would benefit the most from intervention so that they would not need expensive emergency services and surgery. The later part of the article mentioned organizations that used big data to find their best customers among the worried well and get them in for more tests and procedures. The article quoted experts fretting that this would just lead to more unnecessary and unhelpful care just to fatten the providers’ bottom lines.

The article missed the real news here: Why is one organization (UPMC) using big data so that people end up using fewer expensive healthcare resources, while others use it to get people to use more healthcare, even if they don’t really need it?

Because they are paid differently. They have different business models.

UPMC is an integrated system with its own insurance arm covering 2.4 million people. As a system it has largely found a way out of the fee-for-service model. It has a healthier bottom line if its customers are healthier and so need fewer acute and emergency services. The other organizations are fee-for-service. Getting people in for more tests and biopsies is a revenue stream. For UPMC it would just be a cost.

The evil here is not using predictive modeling to segment the market. The evil here is the fee-for-service system that rewards waste and profiteering in medicine.

Very Big Data

The field of analytics has fallen into a few big holes lately that represent both its promise and its peril.  These holes pertain to privacy, policy, and predictions.

Policy.  2.2/7. The biggest analytics project in recent history is the $6 billion federal investment in the health exchanges.  The goals of the health exchanges are to enroll people in the health insurance plans of their choice, determine insurance subsidies for individuals, and inform insurance companies so that they could issue policies and bills.

The project touches on all the requisites of analytics including big data collection, multiple sources, integration, embedded algorithms, real time reporting, and state of the art software and hardware.  As everyone knows, the implementation was a terrible failure.

The CBO’s conservative estimate was that 7 million individuals would enroll in the exchanges.  Only 2.2 million did so by the end of 2013.  (This does not include Medicaid enrollment which had its own projections.)  The big federal vendor, CGI, is being blamed for the mess.

Note that CGI was also the vendor for the Commonwealth of Massachusetts which had the worst performance of all states in meeting enrollment numbers despite its long head start as the Romney reform state and its groundbreaking exchange called the Connector. New analytics vendors, including Accenture and Optum, have been brought in for the rescue.

Was it really a result of bad software, hardware, and coding?   Was it  that the design to enroll and determine subsidies had “complexity built-in” because of the legislation that cobbled together existing cumbersome systems, e.g. private health insurance systems?  Was it because of the incessant politics of repeal that distracted policy implementation?  Yes, all of the above.

The big “hole”, in my view, was the lack of communications between the policy makers (the business) and the technology people.  The technologists complained that the business could not make decisions and provide clear guidance.  The business expected the technology companies to know all about the complicated analytics and get the job done, on time.

This ensuing rift where each group did not know how to talk with the other is recognized as a critical failure point.  In fact, those who are stepping into the rescue role have emphasized that there will be management status checks daily “at 9 AM and 5 PM” to bring people together, know the plan, manage the project, stay focused, and solve problems.

Walking around the hole will require a better understanding as to why the business and the technology folks do not communicate well and to recognize that soft people skills can avert hard technical catastrophes.

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Five Reasons Americans Should Want Electronic Health Records

Although healthcare providers are making progress in adopting health IT, Americans seem to be resistant to change to Electronic Health Records (EHRs). In fact, only 26 percent of Americans want their medical records to be digital, according to findings from the third annual EHR online survey of 2,147 U.S. adults, conducted for Xerox by Harris Interactive in May 2012.

Last month the Institute of Medicine issued a seminal report entitled “Best Care at Lower Cost: The Path to Continuously Learning Health in America.” The report estimates the American healthcare system suffered a $750 billion loss in 2009 from inefficient services and administrative expenditures. The report is grounded on the principle that effective, real-time insights for providers and patients which result in collaborative and efficient care depend on the adoption and use of digital records.

As people are naturally resistant to change, education will be key in gaining support among Americans for the transition to EHRs. If providers can help patients understand “what’s in it for me,” that will likely go a long way in making Americans feel more comfortable with the switch to digital.

Let’s take a look at five ways EHRs directly impact the patient. For these examples, we’ll use a fictitious patient named “Joe”:

  • Health Information Exchanges (HIE): HIEs work on the principle of a network – they grow stronger as more participants join. If Joe’s primary care doctor switches to digital, that’s a great step in the right direction. However, it isn’t truly meaningful until his primary care doctor joins an HIE and begins sharing Joe’s patient health history, medication history, lab results, family and social history and vital statistics with his specialists, emergency care providers, and so on. This sharing of information helps ensure that Joe gets the best quality of care, because all of his providers will be in sync and have the most up-to-date information. It also helps reduce the amount of duplicate exams and labs Joe will be asked to give.
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