Any healthcare organization with a vision of the future faces one central question: How to make the best use of data to lower costs, improve quality, assess risk and better manage their patient populations.
Of course, this is anything but a “one-size-fits-all” scenario. Organizations differ in size and resources, not to mention commitment from top leadership and buy-in for the various segments of the enterprise. Yet all organizations that have embarked on the effort of making data analytics the core of patient care can learn from each other’s experiences.
That will be the topic at of the panel, “The Journey to an Analytics-Driven Organization,” at the Institute of Healthcare Technology Transformation (iHT2) Summit in San Diego, on Jan. 20-21. Participants on the panel will include Sue Schade, CIO of the University of Michigan Hospitals and Health Centers; David Chou, CIO of the University of Mississippi Medical Center; Clark Kegley, assistant vice president, information services, Scripps Health, and Carol McCall, chief product officer of GNS Healthcare. They will share their experiences on the barriers and challenges they face and what to do to overcome them. Click here to register for the San Diego Health IT Summit to see Sue Schade and other healthcare leaders. (iHT2 is the sister organization of Healthcare Informatics, under the corporate umbrella of the Vendome group.)
Healthcare Informatics recently caught up with Schade for a preview of the discussion.
Schade offers two perspectives on the biggest challenges that provider organizations face in using analytics to drive quality in patient care. For academic medical centers, the challenge is that there is probably a lot of activity in this space already going on, she says. Those efforts have probably been made out of a perceived necessity, so are most likely decentralized and siloed.
“The challenge that I see for us and our colleagues in academic medical centers is how best to leverage that and bring it together,” she says. “Not necessarily replace all of the current activities, but look at how to come together in a more federated model, address the data governance issues, and address the tools issues, so there is not so much duplication and costs going into it.” The task, she says, is looking at the cost and determining whether the organization is getting the most out of the effort as it can, in an integrated way at the enterprise level. “That is what our story is right now, what we are trying to work through.”
At community hospitals that are now in the early stages, the main task is about determining what the overall priority is, what the value is, convincing the senior leadership of it, and then making those investments, she says.
At Schade’s organization, the effort to centralize data analytics has been focused on aligning two groups: the medical school IT group, which supports research and education, and the hospitals and healthcare group, which supports the clinical mission and makes up the health system, she explains. Historically the analytics work has been separate in those organizations. Schade, as CIO of the University of Michigan Hospitals and Health Centers, has been working with the CMIO of the health system and the CIO f the medical school to look at integrating data analytics at the enterprise level, she says.
“We have gotten support for doing that from the top leadership, Schade says. She notes there are fewer issues with operational leaders and more challenges with the faculty, who have a variety of efforts going on. She adds that the team has laid out its approach, with stakeholders involved and advising them along the way. “There are huge cultural and change management components in an organization like this,” she says. “With my colleagues on the panel, that’s what I will get into.”
Schade says that a strong informatics team is essential, including IT architects, data scientists, as well as people who can do report writing and interface with the users. She adds that there s a significant total cost of ownership, in terms of breaking down silos and becoming more efficient in terms of IT tools and people.
Yet that investment should pay off. Population health and identifying risks for a population are huge drivers from a clinical perspective, “and one of the key components as to why we need to do a much better job in terms of our analytics, both historical and predictive analytics,” she says.
To learn more about strategies to make use of data and analytics, check out the Health IT Summit in San Diego, Jan. 20-21, 2014, sponsored by the Institute for Health Technology Transformation.
