Guest post by Anil Jain, MD, FACP, senior vice president and chief medical officer, Explorys, and consulting staff, Department of Internal Medicine, Cleveland Clinic.
Despite advances in medical education, the proliferation of medical journals and the speed of light retrieval of information on the Internet, the lag time between when researchers identify life-saving clinical interventions and when they are put into practice ranges from 10 to 25 years, averaging 17 years. This lag time between the discovery at the “bench” and its practice at the “bedside” is even more startling when you consider the impact of care at the “bedside” to the “bottom-line.” This “bottom-line” has become increasingly important with the formation of accountable care organizations (ACOs) that aim to reward provider organizations and payers that meet the “triple-aim”: high-quality care for the population, high-quality care for the patient, at the most affordable cost. Unfortunately, current practices at the “bedside” reportedly generate approximately $700 billion in care that isn’t necessary and may even be potentially harmful to the “bottom-line.” Moreover, despite healthcare expenditures of 17 percent of our GDP, the U.S. lags behind most industrial nations when looking at composite measures of healthcare quality.
With the increasing use of health information technology and data we should be able to shorten the time between “bench” to “bedside” and improve the “bottom line.”
“Big data” is data that is of high volume, variety and of sufficient velocity that is not amenable to traditional data storage and analysis tools. This “big data” is most typically generated from health systems’ electronic health records (EHRs), laboratory, radiology, financial and billing systems, personal health records, biometrics and smart devices. In addition, patients today are oftentimes utilizing various mobile health and wellness apps and wearable devices which also collect a plethora of data, which only adds to the complexity.
The aggregation of de-identified medical information across millions of health records from varying venues of care facilitating a longitudinal view of a person can be incredibly beneficial for researchers focused on net new knowledge discovery. For data from disparate health systems to be aggregated, it is vital that it is standardized and that subjects across health systems can be matched. This harmonization of disparate data coupled with the appropriate analytics software is critical to identify patterns in the data.
In this setting, the larger the data set, the more likely that a signal can be detected through the noise, even in the rarest of conditions. Fortunately, many hypotheses can be conceived and tested through appropriate analytics within this real-world data set in a much more cost-effective manner than conducting full-scale clinical trials. Furthermore, if a signal is detected or a pattern is found, researchers can then design a more focused explanatory or pragmatic clinical trial to prospectively test the hypotheses. For example, over the past few years within the Explorys network, more than a dozen peer-reviewed abstracts and publications have been generated by leveraging a de-identified data set comprised of nearly 48 million subjects, searchable by a specialized browser-based analysis and query application.
Guest post by Anil Jain, MD, FACP, chief medical officer, Explorys, and staff, Department of Internal Medicine, Cleveland Clinic.
Nearly every aspect of our lives has been touched by advances in information technology, from searching to shopping and from calling to computing. Given the significant economic implications of spending 18 percent of our GDP, and the lack of a proportional impact on quality, there has been a concerted effort to promote the use of health information technology to drive better care at a lower cost. As part of the 2009 American Reinvestment and Recovery Act (ARRA), the Health Information Technology for Economic and Clinical Health (HITECH) Act incentivized the acquisition and adoption of the “meaningful use” of health IT.
Even prior to the HITECH Act, patient care had been profoundly impacted by the use of health informationtechnology. Over the last decade we had seen significant adoption of electronic health records (EHRs), use of patient portals, creation of clinical data repositories and deployment of population health management (PHM) platforms — this has been accelerated even more over the last several years. These health IT tools have given rise to an environment in which providers, researchers, patients and policy experts are empowered for the first time to make clinically enabled data-driven decisions that not only at the population level but also at the individual person level. Not only did the 2010 Affordable Care Act (ACA) reform insurance, but it also has created incentive structures for payment reform models for participating health systems. The ability to assume risk on reimbursement requires leveraging clinical and claims data to understand the characteristics and needs of the contracted population. With this gradual shift of risk moving from health plans and payers to the provider, the need to empower providers with health IT tools is even more critical.
Many companies such as Explorys, a big data health analytics company spun-out from the Cleveland Clinic in 2009, experienced significant growth because of the need to be able to integrate, aggregate and analyze large amounts of information to make the right decision for the right patient at the right time. While EHRs are the workflow tool of choice at the point-of-care, an organization assuming both the clinical and financial risk for their patients/members needs a platform that can aggregate data from disparate sources. The growth of value-based care arrangements is increasing at a staggering rate – many organizations estimate that by 2017, approximately 15 percent to 20 percent of their patients will be in some form of risk-sharing arrangement, such as an Accountable Care Organization (ACO). Already today, there are currently several hundred commercial and Medicare-based ACOs across the U.S.
There is no doubt that there are operational efficiencies gained in a data-driven health system, such as better documentation, streamlined coding, less manual charting, scheduling and billing, etc. But the advantages of having data exhaust from health IT systems when done with the patient in mind extend to clinical improvements with care as well. We know that data-focused health IT is a necessary component of the “triple-aim.” Coined by Dr. Donald Berwick, former administrator of the Centers for Medicare and Medicaid Services (CMS), the “triple-aim” consists of the following goals: 1) improving health and wellness of the individual; 2) improving the health and wellness of the population and 3) reducing the per-capita health care cost. To achieve these noble objectives providers need to use evidence-based guidelines to do the right thing for the right patient and the right time; provide transparency to reduce unnecessary or wasteful care across patients; provide predictive analytics to prospectively identify patients from the population that need additional resources and finally, use the big data to inform and enhance net new knowledge discovery.