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health care professional examining data

by Cerner Corporation
Published on April 22, 2019

There is no shortage of data in the health care industry, but many health systems face the challenge of turning this wealth of data into actionable insights that can transform care delivery in a meaningful way. Below, five health care and technology thought leaders offer their perspectives on how organizations can get the greatest value from their data analytics efforts.

#1 Data science should augment physicians with information and tools to enhance care.

Dr. William Feaster, chief health information officer, CHOC Children’s Hospital of Orange County

“The potential for data to transform and improve care is indeed exciting, but by the same turn can become overwhelming without a plan for turning data into knowledge, and knowledge into action. Hospitals and health systems have an overabundance of data, with more than a decade of documentation and patient data housed in the electronic health record (EHR). For health systems to fully realize the potential and power of their EHR, and see a return on investment, leaders — from the C-Suite to physician end users — must not only derive intelligence from these large stores of data but translate that intelligence into prescriptive action that can deliver better outcomes for patients, help manage the health of populations and improve results for their organizations. 

Utilizing data to augment clinical decision-making represents the real potential of data science and machine learning to transform health care. After years of dutifully documenting in the EHR, our clinicians are eager to see higher-level value emerge out of the system. Advances in medical research will continue to produce an explosion of data that will have the potential to inform care and will require tools and expertise to make it usable by clinicians. For health systems to keep pace and reap the benefits of this new wave of data, engaging the skills of a data science expert can help to incorporate new information that can inform better care protocols and outcomes.”

#2 Predictive analytics play a pivotal role in nursing staffing, scheduling and result in better patient care.

Dr. Bob Dent, senior vice president, chief operating officer, chief nursing officer, Midland Memorial Hospital

“For health care executives seeking to deliver value back to their organizations, value can mean different things and take on different forms. Workforce satisfaction may not always be the first area where leaders seek to derive value, but research shows that an engaged and effective workforce can have a positive cascade effect into other areas such as outcomes and financial performance. As health care organizations are under increasing pressure to deliver value in patient outcomes and budgetary measures, it is worth making sure that front-line employees are working in the most effective way possible.

Data mining and predictive analytics tools can now provide health care organizations with advanced insight into their workforce and the ability to act on that understanding to enhance employee performance and satisfaction.

The nursing workforce wants to show up every day and give their best, deliver positive outcomes for their patients and create a great patient experience. This means providing compassionate care and giving nurses time with their patients. Using predictive analytics, we can make sure we have the right people assigned every day and that we are staffing by acuity to make sure nurses aren't overloaded. We have a clinical operations team to watch and understand what is happening at the hospital any hour of the day and to deploy resources to assist where it's needed. When doing rounds, we can know ahead of time what kind of day staff might be having. By creating synergy between the nursing staff and the patient's needs, we can respond to the patient quickly and skillfully, and provide a higher quality of care.”

#3 Outcomes data is necessary to influence behavior.

Dr. Earl Steinberg, CEO, xG Health Solutions

“Getting this data to a point where we can use it productively is a tremendous challenge. Once we’ve received data, it undergoes a number of process to normalize it into a standard format before we can even analyze it. This is not an easy or simple task, and even today, most providers are not equipped to handle the influx of data that we face. However, this task is critically important to population health management. 


There are many opportunities. In the short term, access new types of data such as the social determinants of health, patient preferences and patient reported outcomes can provide us with critical information, but we need access to this as unstructured data within the EHR. Over the next 3-5 years, we’ll see a greater impact from artificial intelligence, deep learning and the massive amount of data that we are compiling due to recent advancements in genomics. 


Outcomes data is increasingly necessary to manage the populations we’re trying to provide care for. To influence behavior, we have to have actionable insights stemming from data that is integrated within the clinician’s workflow. High-level, descriptive analyses are incredibly valuable, but they won’t change behavior without this ability to pull insights. We can measure outcomes all we want, but merely providing dashboards within the workflow and portals with access to reports is not enough to drive change in behavior.”

#4 A robust data governance program helps create clear definitions.

Paul Lampi, director of technical services, Memorial Hermann Health System

Health care data – consisting of lab tests, medical images, clinical trials and more – make up 30 percent of the world’s data production. As our industry increasingly focuses on analytics, the quality and longevity of data is of upmost importance to cultivate an information-rich organization. For health care organizations, creating this information-rich culture requires a robust data governance strategy, ensuring that data assets are cleansed, managed and protected throughout the enterprise.

Data must be governed to ensure everyone is playing on the same field. In health care, there are so many interrelated data points, but the way people look at these data points from different views can cause varying interpretations which can lead to misunderstandings, inaccurate decisions or distrust of data. A robust data governance program helps create clear definitions so that everyone is aware of the way in which data is being utilized and what it actually means.

A robust program includes not only providing definitions, but also diving down to the data level – locating the source or sources, its table and field locations, acceptable values for the data, abbreviations or alternative names and deriving how the data was calculated.”

#5 Analytics should inform organizational strategic decision making.

Nate Kelly, senior director and general manager, Hospital Operations, Cerner

“The key is to build accurate assumptions in models that exist somewhere in the confluence between the outcomes of current daily decisions and the way future investments will affect those outcomes. In other words: One must simulate reality as much as possible to predict the future. The tools used for simulation run the gamut between Microsoft Excel and expensive proprietary software. The tool is less important than the validity of the assumptions and the accurate understanding of how multiple variables work together to form an outcome.

Simulation advancements combined with increased processing power are fueling AI’s ability to aid in decision making at the point-of-care delivery or in the moment decisions need to be made, as well as in the longer term, informing strategic decision making.”

At Cerner, we believe analytics is a multi-dimensional strategy that should be integrated within an organization’s daily processes and drive long-term, strategic decision-making. Learn more here.