July 14, 2017 | Written by: Anil Jain
Categorized: Blog Post | Value-Based Care
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Anil Jain, MD, is Chief Health Informatics Officer, Value-Based Care, IBM Watson Health
The passage of the American Reinvestment and Recovery Act (ARRA) and the accompanying Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 initiated a remarkable step forward in healthcare reform. Among other objectives, a significant portion of these bills was focused on a series of incentives and penalties around a staged approach to the meaningful implementation of certified electronic health record (EHR) technology. And while the intentions were good, hospitals and eligible physicians have seemed to struggle over the years with the adoption and optimization of EHRs, including claims of productivity losses, high costs, interoperability issues and population health and disease management challenges.
Despite progress and a movement towards value-based care, the U.S. healthcare system still tends to struggle with acquiring and analyzing the varied data sets needed to meaningfully improve health care. In many instances, health data remains siloed and fragmented by hospital boundaries, incompatible vendor systems, interoperability issues and complex regulatory challenges. These hurdles often mean that a complete picture of care across the continuum for a patient or a population is challenging if not often impossible.
Today, the need for complete patient and population health profiles is growing, under the demand of many stakeholders. One motivating factor is the shift to value-based care, supported by the Centers for Medicare and Medicaid Services’ goal to have half of its payments in alternative payment models by the end of 2018. Another factor is the growing number of accountable care organizations and clinically integrated networks that often need access to interoperable systems across multiple business entities and providers with different EHRs.
So how can healthcare systems create the interoperability needed to aggregate and analyze data from disparate sources to effectively manage population health?
There is good news: we are making steps toward sustainable and scalable interoperability. It is possible to aggregate and analyze data from many different sources in near-real time, using a platform that uses a flexible data model to standardize, store and report patient data.
To begin, look for population health management technology that can acquire and synthesize data from a variety of different sources, including:
- Clinical registries
- Clinical settings
- Social determinants of health
- Post-acute and outpatient care
- Patient-generated health data, such as health risk assessments and functional status surveys
- Mobile health and monitoring
- Lab and imaging
- Behavioral health
Also important is a care collaboration platform that draws data from a comprehensive data lake, which is an advanced type of data warehouse designed to aggregate data much more quickly than traditional warehouses and handle a multitude of data enquiries from a variety of stakeholders. These data lakes can then be the platforms underneath the robust analytics and monitoring needed for the population health insights that care managers, providers, health agencies and payers need to advance towards value-based care.
Take a deeper look into the history, current climate and challenges affecting the healthcare industry’s shift to value, and how technology can help, in the whitepaper, Population Health Management: Beyond the EHR.