Monthly Archives: January 2013

Realizing the Future of Decision Support Today

Post by Ed Barthell, MD

Medical Director, Americas, Caradigm

Ask physicians about decision support systems, and you’ll probably get some groans and rolling eyes. Traditionally, decision support systems have amounted to little more than a set of annoying alerts, flagging issues that clinicians are already aware of and wasting precious time.

Early efforts to provide decision support have rarely surfaced the real insights that are needed for improving patient care, whether for an individual patient, or for a population of patients served by a practice or hospital. In fact, the National Research Council has concluded that the institution of healthcare is not adequately structured to help clinicians systematically improve their decision-making and practice, due in part to difficulties with data integration and sharing. Physicians develop a high comfort level with their decision-making processes, but they simply do not know what they do not know.

This no longer has to be the case because modern information technology has the potential to not just digitize record-keeping and paper-based processes, but to enable medical professionals and patients to leverage the wealth of available electronic medical information in a way that supports a new standard of analysis and decision-making.

The era of big data is no longer approaching; it is here now. An EMC-sponsored study found the world’s information is doubling every two years, with a colossal 1.8 zettabytes to be created and replicated in 2011. This is the equivalent of every person in the world having over 215 million high-resolution MRI scans per day.  In medicine, the PubMed database sponsored by the National Institutes of Health now covers 4,600 journals that have pertinence for medical research publishing, and includes more than 22 million citations.

The volume of available medical data clearly exceeds the ability of individual humans to effectively process the information and consistently make superior decisions.

To be worth the time and effort of implementation, however, intelligent decision support technology must be able to use the analytical and predictive capabilities of high-power computing to help medical decision-makers go beyond record management to real insight and discovery. Enabling intelligent insight and discovery requires a robust integrated system capable of overcoming the challenges that exist in health IT today. We can understand the key components of such a system by outlining its five pillars:

  • Using real-time data feeds and predictive analytics to facilitate advanced decision support across the healthcare spectrum, including both clinical and non-clinical settings.
  • Facilitating the transition from legacy systems to modern integrated health data systems by creating a framework that provides a sensible, cost-effective, surround-and-supplement migration strategy and enhancing rather than interfering with workflows.
  • Using metadata-based approaches to manage the massive data free gems clash royale no survey volumes available and to enable flexible data-management strategies that easily adapt to change.
  • Encouraging innovation by leveraging a modern services-oriented architecture and through the use of a robust platform supporting multiple modular and portable applications.
  • Ensuring high-level performance and results through comprehensive evaluation and control mechanisms.

In my next posts here I will explore these five pillars in turn, starting with the first pillar, real-time data and predictive analytics.

I’d also like to hear from readers who have been working with decision support systems, to know what has been successful for you and what has simply increased your workload without extending clinical insights. What has your experience been?

Achieving the National Quality Strategy

Post by Dana Alexander, RN, FAAN

Chief Nursing Officer, Caradigm

In 2011 the National Quality Strategy was released, outlining the strategy for the triple aim of “Better Care, Healthier Communities and Affordable Care.” Prior to the release of the NQS, the U.S. did not have a formal strategy for quality and access to care in the United States.

While on any given day providers, hospitals and vendors are focused on achieving Meaningful Use (MU) for the EHR incentive program, MU in itself is not the goal. The real goal and guiding star is the National Quality Strategy and achieving the triple aim.

In addition, as a nation we are now focusing on Accountable Care and Population Health Management, which is much broader in focus than MU but very important to achieving the triple aim. I believe we need an integrated approach to support collaboration, learning and innovation to generate new knowledge and insights into new models of care. Clinical business intelligence and data analytics have been called the next evolution in informatics and healthcare IT to support new models of care delivery.

A recent IOM report, “Best Care at Lower Cost: The Path to Continuously Learning Health Care in America” (September 2012) defines the foundational elements of a learning system for health care that will allow the industry to make strides in the quality of care. The report states that advances in computing power, connectivity, team-based care, and systems engineering techniques are promoting a culture and systems of continuous learning and improvement. While the IOM report had many findings and recommendations, it emphasized the need for a “digital infrastructure to improve the capacity to capture clinical, delivery process and financial data for better care, system improvement and creating new knowledge.”

I agree there is a critical need for a platform and infrastructure that is open, flexible, and integrated to allow for data analytics, collaboration, learning and innovation to address the healthcare challenges of today and tomorrow. The industry needs an open and flexible platform that allows for integration with providers and vendor applications and the ability to re-purpose data for continuous learning and improvement cr generator online across all facets of healthcare. These are the building blocks for achieving integrated, accountable care.

I believe that having the infrastructure and tools that support analytics, surveillance, and care management are critical to the achievement of the National Quality strategy—for clinicians and administrators to manage patient care more effectively, and for patients to more actively engage in their own care and self-management. The future of our healthcare lies in moving beyond the digitization, exchange and reporting of data to the meaningful analysis, collaboration and sharing of data.  What do you think?