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value-based care models will be better prepared to provide targeted care to patients in the future.

Matthew Sappern, CEO, PeriGen Inc. Analytics is empowering frontline clinicians to distill actionable insights from abundant clinical data and to improve our healthcare system. With innovations in technology made over the past decade, we have unprec- edented opportunities to use analytics to improve patient care – and even to broaden

our thinking beyond prevailing medical opinion. In fact, analytics are making inroads in the perinatal

specialty. Prevailing medical opinion has paired excessive uterine contractions with high rates of neonatal depression in newborn babies. Using analytics, medical professionals at MedStar Franklin Square Medical Center in Baltimore have examined a large data set and demonstrated that this asso- ciation is not so simple. A four-year study published in T e Journal of Maternal-Fetal & Neonatal Medicine revealed that a better identifi er of the risk of serious neonatal depression at birth is the presence of fetal heart rate decelerations in con- junction with excessive contractions, or uterine tachysystole (UT). Although UT occurred in approximately 20 percent of deliveries, only 1 percent of babies with UT developed neonatal depression. How did these professionals reach this conclusion? By analyzing data gathered from thousands of electronic fetal strip tracings, equivalent to 27 miles of paper documentation. Research of this scale would be almost impossible without technologies for capturing and analyzing the data. T is is just one case where analytics is helping provide

better, safer care to women and babies. What is even better? Innovative perinatal tracing technology can help obstetrics physicians and nurses distinguish babies at serious risk of neo- natal depression – and get them the attention they need fast.

Ken Yale, DDS, JD, Vice President, Clinical Solutions, ActiveHealth Management When we think about analytics, it’s often within the context of technology, comput- ers and spreadsheets. However, advanced analytics actually have the power to predict the future and identify problems before

they happen. For example, analytics can predict how a patient will respond to a given treatment. As a result, adoption of advanced analytics will benefi t all stakeholders within our industry. Key to these new capabilities is the growing volume, veloc-

ity and variety of data (including new EMR data) and value created by analytics. New systems and algorithms are being created to leverage data. For example, one system currently in production can query administrative data, generate dynamic

care plans and empower providers with clinical decision sup- port (CDS). Some systems can also apply “advanced” clinical decision support that includes the latest medical fi ndings from clinical trials and scientifi c journals. T is information can be used to alert care managers about gaps in care or op- portunities to align treatment with best practices. T is analytics-based approach helps organizations:

• Predict which patients to contact, and why. • Close the loop after a health event to help prevent avoidable readmissions.

• Eliminate patient safety issues and duplicate testing. • Streamline provider workfl ow. • Improve communication and collaboration among a patient’s care team.

• Enable patient engagement. As a result, advanced analytics gives organizations unique ways to achieve the triple aim of improved care, reduced costs and a better patient experience.

Barry P. Chaiken, M.D., MPH, Chief Medi- cal Information Offi cer, Infor T e digital age is the age of Big Data where every piece of technology captures data available for later use. T e McKinsey Global Institute (MGI) describes data generated in this way as digital “exhaust data,” data that are created as a by-product of other activities

(Manyika, 2011). T e rapid expansion in the use of EMRs and digitally

driven and connected technology such as MRI scanners, body sensors and automated lab tests, brings the era of Big Data to healthcare. As these technologies evolve and become more widely utilized, the data collected becomes more expansive and granular, yet insuffi ciently utilized. T e fi ve broad areas to deliver that value are clinical op- erations, payment/pricing, R&D, new business models and public health. Sub-areas include comparative eff ectiveness research (CER), clinical decision support, remote patient monitoring, health economics and personalized medicine. T e four large data sources for healthcare include clinical (e.g., EMR, images), pharmaceutical (e.g., clinical trials), administrative (e.g., utilization, claims), and consumer (e.g., home monitoring, retail purchases). New analytic tools such as Semantic Web 3.0 - linked

data - off er ways for machines to analyze these disparate data sources leveraging approaches impossible using standard relational databases and statistical methodologies. T e uses of Big Data are numerous and far-reaching. Only

through innovative analytical techniques will we be able to truly leverage the healthcare data collected and improve the way we deliver care. Organizations that properly collect, ana- lyze and utilize Big Data will achieve a signifi cant competitive advantage over those organizations that fail to recognize the opportunity Big Data presents.


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