[co-authors: Kyle Letner, MBA & Brandon Danz, M.H.A., M.P.A.]
This is the second in a two-part article on healthcare data transparency and how providers and payers can leverage data as a population health opportunity. Part one, TRANSPARENCY: Embracing CMS’s Push To Publicize Your Organization’s Performance, reported on ways federal and state agencies are advancing healthcare data transparency as a vehicle to transition to value-based reimbursement. This follow-up article shares promising practices being developed by forward-thinking healthcare organizations to use data in new ways to improve patient care.
New data sources and novel analytics capabilities applied to existing data are being used by providers, payers, and technology firms to innovate in competitive market environments. Ochsner Health System, a large non-profit academic health system in Louisiana, is using cloud-based patient data to predict and prevent inpatient clinical deterioration. By bringing together multiple data sources and applying machine learning and artificial intelligence technology, Ochsner detects potential adverse events more quickly and accurately. Ochsner’s Rapid Response Team reduced adverse events outside of the Intensive Care Unit by 44% in a 90-day pilot.
Data innovation is also a necessary component of successful pursuit of the Triple Aim and value-based reimbursement. Merged claims and clinical data are yielding valuable new predictive analytics capabilities for health systems that are unified under a single EHR system and have access to payor claims data. (Note: it was recently reported that the Trump administration may be issuing an Executive Order mandating the sharing of such information.) WellSpan Health, an integrated health system in central Pennsylvania and northern Maryland (and employer of the authors), is applying predictive analytics to these data sets to identify patients who are most at risk of adverse health scenarios and enroll them in targeted disease management programs. Once enrolled, patients’ historical data is combined with real-time biometric data sourced from “Connected Care at Home” devices sent home with patients. A Connected Care at Home team of nurses then remotely monitors patient vital signs, reviews the patient’s responses to daily questions provided through an issued tablet device, and offers video chats to address identified risk indicators prior to an avoidable trip to the hospital. This orchestra of predictive claims and clinical data analytics that identify targeted populations upstream of preventable health problems, and patient-sourced data to real-time monitor their risks is becoming widely-adopted by health systems as a means to prevent avoidable hospitalizations.
It is no surprise that the U.S. Department of Veterans Affairs is invested in leveraging data and technology to transform healthcare. The Veterans Health Administration (VHA) Office of Connected Care is using telehealth, a personal health record, and mobile app development to “make it easier for veterans to be more actively involved in their health care and [give] VA care teams true mobility of patient data for the first time.” A variety of innovative VHA programs leverage connected data sources, analytics, and personal technology to provide quicker interventions and access to the right care at the right time for veterans. One program tracks and monitors critical lab and imaging tests to standardize follow-up care and ensure quality improvement. This work is a first-generation proof of concept for the types of innovation likely to come from healthcare providers and payers using CMS’s new Blue Button 2.0 (discussed in the previous article.)
Numerous examples of pioneering applications of healthcare data are reported on a regular basis and many players are entering this nascent market. Apple is now hiring doctors to support its data-fueled wellness programs. Comcast recently announced that it is developing an in-home device to monitor patients’ health and will take on financial risk for its ability to reduce E.R. visits. Amazon’s new venture with Berkshire Hathaway and JPMorgan Chase, newly named “Haven”, is using big data analytics to identify and eliminate healthcare waste and inefficiencies. These and other population health initiatives are driven by innovative uses of clinical, claims, pharmaceutical, social determinant of health, biometric, and device-sourced data to deliver on Triple Aim goals.
Important ethical considerations play into new uses of health data including what data to collect, how to use it, when and how to intervene, and how to monitor the efficacy and execution of predictive analytics. The healthcare industry can learn lessons from other industries as they traveled this path. In 2012 Target infamously predicted a teenage girl’s pregnancy by analyzing the data sourced from her shopping habits and in doing so, let her parents in on the secret before she did. Healthcare organizations looking to take advantage of new big data opportunities should include medical ethicists in the development of strategies to use data in new ways that improve patient care while maintaining proper respect for the privacy of patients and populations.