proven to be an eff ective component of industry-leading population health management programs.
Eric Mueller, Director, Product Management, Lumeris
Leveraging clinical data analytics to predict outcomes, mea-
sure trends and establish correlations that drive quality care at lower costs means that a patient’s primary care physician, spe- cialist and ancillary service providers have a complete view of a patient’s health status informed by claims, EMR, lab, pharmacy and other data. When we are able to use data to make better- informed decisions, we can impact quality, cost and utilization for patients and populations, which is critical to achieving success in value-based care.
Bonnie Cassidy, Senior Director of HIM Innovation, Nuance
Clinical data analytics, if used to its fullest potential, can en- able the healthcare industry to create a true partnership between patients, providers and payers. Personal health information sup- plies patients with the knowledge they need to make informed health decisions. Additionally, it serves as a set of metrics that physicians can use to discuss lifestyle improvements that can help their patients better manage their own health and well-being. A knowledgeable and well-informed patient population will make better health decisions, which leads to improved health out- comes. T is means that payers will also see the impact of analytics through reduced claims information, as engaged patients take a more proactive approach to managing their health. Analyzing accurate clinical data also off ers a clear snapshot of the larger patient population that a particular healthcare organiza- tion or facility is serving. Armed with such insights, providers can tailor their health strategies to their community, off ering health education courses or other health services that will benefi t their patient population.
Anil Jain, M.D., FACP, Senior Vice President & Chief Medical Information Offi cer, Explorys Inc.
Simply put, it means that we ought to look at all the informa- tion that we already collect and record about our patients over a period of time such as their habits, vital signs, diagnoses, labora- tory test results and medication use to identify patterns that tell us which patients are going to do well and those that will not do
well. We can use similar information to show what procedures and medications physicians prescribe appear to be necessary for the patient’s well being and those procedures or medications that don’t appear to benefi t the patient or even harm them. Avoiding unnecessary services reduces the overall cost while maintaining the highest levels of quality and patient satisfaction.
atisfaction. Dan Riskin, M.D., CEO, Health Fidelity y To date, the U.S. has aimed to capture healthcare information
electronically. T e goal in doing so was to improve outcomes and reduce costs. T us far, we have created the foundation for accomplishing these goals, but not yet created value. Leveraging clinical data requires eff orts at the point of care and at the population level. At the point of care, we must provide clinical decision and interoperable patient information to reduce errors. At the population level, we must fi nd areas of low perfor- mance and ensure these areas and instances are addressed. We must perform predictive analytics to fi nd patients at risk and drive resources their way in a preventative approach. We must also leverage technology to assure the standard of care is followed not just for sickness, but also for wellness.
Tony Jones, M.D., CMO, Philips Healthcare’s Patient Care and Clinical Informatics’ Business Unit
Most healthcare organizations today are using two sets of data: 1. Retrospective – basic event-based information collected from medical records or insurance claims; and
2. Real-time clinical – the information captured and presented at the point of care (imaging, blood pressure, oxygen saturation, heart rate, etc.).
Predictive analytics is combining these two data sources so that clinicians can access the relevant information they need to identify trends that will impact the decisions they make at the point of care. Much of what’s needed for predictive analytics is already
available – labs, images, patient history and vital signs. What’s changing is the ability to capture and store this data, and then apply new tools and algorithms to analyze the data. T ese analyses can detect patterns, and if the patterns repeat over and over, this provides confi dence that it may be useful in predicting a future event rather than waiting for that event to occur before initiating treatment. In many cases, a life-threatening, expensive medical event can be avoided or easily managed if the signs and symptoms (i.e., the pattern) can be detected earlier than we often do today. For example, if a diabetic patient enters the hospital with numbness in their toes, instead of immediately assuming the cause is their diabetes, the clinician might monitor their blood fl ow and oxygen saturation, and potentially determine if there’s
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