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● Roundup: Analytics

something more threatening, like an aneurism or stroke, around the corner. T at data is entered into the EMR as part of the retrospective data for that specifi c patient. If more diabetic patients enter the hospital and present a similar

trend of numbness in their toes, the coupling of real-time data (these new patients entering the hospital) and retrospective data (which was collected and stored when the fi rst patient came in) can potentially help doctors reach a treatment decision more quickly (potentially saving costs), and over time can allow them to analyze how certain treatments will work on other diabetic patients.


Karen Handmaker, MPP, Vice President, Population Health Strategies, Phytel

Most analytics solutions use medical, ancillary, lab and phar- macy claims as the primary sources of data for the reports and tools they off er health systems. Claims data provide utilization, cost and diagnosis information that can be several months old by the time they are adjudicated by payers and available in reports. Clinical data, on the other hand, has the twin advantages of being current and more specifi c. For example, claims data can tell us that a patient had an offi ce visit associated with a diabetes diagnosis and an HbA1c test, but only clinical data in the health system’s electronic medical record (EMR) can tell us that this patient’s HbA1c result was 10.2. With this information “teed up” for the doctor or care manager, they can intervene readily to help the patient improve. T erefore, “le- veraging clinical data” means incorporating current and pertinent information from the EMR to make the patient’s profi le more

“actionable” for care teams charged with “driving quality care.” Going further, analytics that incorporate clinical data off er health systems longitudinal and comparative performance reports on key quality measures they are required to track across their entire organization as well as by practice and physician. T ese reports give providers and care teams valuable information on an ongoing basis about how they are doing on key quality indicators across the board and for each individual patient, enabling them to implement improvement strategies readily.

HMT: Are healthcare organizations collecting too much data to make meaningful decisions, thereby generating waste? Why?

Todd Rothenhaus, Chief Medical Information Offi cer, athenahealth

Medical leadership and technology leadership should be listening to the CFO. Getting into the data and analyzing risk should never be at the expense of the bottom line. Leadership needs to always be asking itself, “What business problems are we trying to solve, what clinical goals are we trying to achieve, and are they aligned?” If clinical goals and the bottom line aren’t aligned, risk analysis falls off the ROI chain. I deeply believe that if an organization is actively managing patients in a risk-based contract that the next dollar they spend

10 December 2013

should not be on anything but understanding the total cost and the total picture of the population health through the claims-based analysis. If they don’t have that, they can’t succeed. No amount of other technology will help them succeed. I believe that the addition of clinical data to the claims data assets is an essential next step, but I think that sucking data out of antiquated EHR systems that are under the desk of a doctor’s offi ce or in a data center is a really nasty business. I am hopeful that there will be some standards-based integration solutions that will help them do this without breaking the bank. We at athenahealth, of course, support standards-based inter- faces for free, but asking us to suck data out of the back end of another EHR isn’t necessarily a best use of everybody’s money. So the fi rst dollar must go to claims, the second dollar to clinical data. For most providers, regular data – not big data – is enough. A lot of high-investment tech solutions aren’t profi table. It’s a sad fact that many organizations who try to tackle predictive analytics run into cash problems.

Anil Jain, M.D., FACP, Senior Vice President & Chief Medical Information Offi cer, Explorys Inc.

Most healthcare systems vary in the amount of data they collect. With storage and computing costs being relatively low, the cost of collecting data is mainly labor either through abstraction or by providers at the point of care in electronic health records. In ad- dition, health systems are dealing with a host of new data sources including imaging data, genomics, telemetry, billing and claims data, as well as smart devices. What we see with our clients at Explorys is that although most health systems collect quite a bit of data, the data is often not standardized, not collected consistently, not aligned to strategic imperatives or not analyzed due to a lack of data science skills. Data without a strategy is just data. At best, it is wasted eff ort.

At worst, it will lead to poor decisions. In a well-designed, sustain- able analytics strategy, collecting data is never the end goal – it is simply the raw material used to build the fi nal product: actionable information to drive the best decisions.

Dan Riskin, M.D., CEO, Health Fidelity

Healthcare organizations are collecting the wrong data. T at’s why the data we have currently is so hard to use to make mean- ingful decisions. One more template that supports the doubling of the documentation volume for a patient encounter does not actually improve care. Healthcare organizations should focus on getting the highest quality content in the record, whether through typing, template or dictation. Next, fi nd ways to use the high- quality content to improve care. High-quality data combined with well-designed technology can provide answers and opportunities. Doctors know what’s important – that high-quality clinical data is most valuable in a high-powered, well-designed analytics system.

Eric Mueller, Director, Product Management, Lumeris

T ere’s really no such thing as too much data. I think people may get hung up on that concept. If the right tools, information and incentives are not provided, there is no motivation or way to use data, no matter the amount. Incentives are not enough. Tools are not enough. A massive


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