Healthcare Technology Featured Article

December 12, 2016

Healthcare Technology Trends for 2017

While interoperability, predictive analytics, machine learning, and patient-generated health data will continue to be topics at the forefront of technology considerations, Loopback Analytics believes the transformative changes in 2017 will come from effectively leveraging these new capabilities to successfully drive clinical and financial improvements.

1. Acceleration of Interoperability

Historically, establishing interoperability across the healthcare continuum has been stymied by a myriad of issues ranging from data security concerns, technical standards, and misaligned motivations for participation.  As the transition to value-based payment models gains momentum, the calculus of interoperability changes. Bundled payments will be the catalyst that drives interoperability across care settings. As hospitals and payers seek to drive out unwarranted variability, networks will narrow, with referral volumes concentrated with high quality, cost-effective providers that agree to share data as part of a cohesive interoperable network. To effectively manage patient care transitions and report on key clinical and outcome measures, hospitals and PAC providers will need to have both the technology to support data interoperability and the legal framework to facilitate data sharing that protects both patient privacy and financial interests of network participants. PAC interoperability costs will be offset by higher referral rates to providers that meet quality and cost goals. Post-acute providers that do not or cannot provide data interoperability stand to lose out on referral volumes that are essential to their financial survival.

2. Prediction goes mainstream

Predictive analytics continues to hold great promise – for providers, patients, and investors.  From 2011 to 2014, nearly $2 billion in venture capital has been invested in healthcare technology companies with some focus on predictive analytics.  In the coming year, the adoption of predictive analytics will become more mainstream as these products leave the laboratory and are incorporated into integrated, action-oriented care management processes with complete feedback loops and definitive outcome measurements.

Medication adherence, the degree to which a patient is taking medications as prescribed, is a mainstream example of leveraging predictive analytics with other technologies to deliver tangible care and economic outcomes.  Poor medication adherence takes the lives of 125,000 Americans annually, and costs the healthcare system nearly $300 billion a year in additional doctor visits, emergency department visits and hospitalizations.  There are many potential failure points after a prescription is written across affordability, transportation, literacy, confusion over brand vs. generics, and duplication of therapy.  Gaps in refills of chronic maintenance medications strongly correlate with increased risk of re-hospitalization.

Predictive analytics can be used to proactively identify patients most likely to be re-hospitalized due to any combination of risk factors, and match them to a variety of interventions such as bedside delivery of medications prior to hospital discharge, analysis of medications for adverse events and personalized telehealth follow-up from a pharmacist or post-discharge clinician. Ongoing assessment of each intervention action from identification to engagement closes the loop in measuring effectiveness in improving patient outcomes.

3. Telehealth and patient-generated data fill in the gaps

Just as the transition to value-based care spurs the case for increased interoperability, Loopback anticipates a similar effect on the uptake of telehealth and patient-generated health data in 2017.  In the past, broader use of telehealth, especially for Medicare episodes, was thwarted by reimbursement.  Providers and patients were faced with a limited set of reimbursable post-acute care choices by CMS.  With providers managing the cost-care equation in bundled payments, new models will start to emerge for sub-populations of patients.  Using advanced analytics, providers may identify patients in a bundle that may be better served in a combination of ambulatory or outpatient procedures, targeted in-home intervention over the 90 day episode, supplemented by telehealth interaction with clinicians.  Broader deployment of remote monitoring and  acquisition of patient-generated data will provide insights into patient conditions that enable clinicians to better direct care during the post-acute segment of the episode.

4. Visibility and care extends beyond the provider silo

According to the National Institute of Mental Health, approximately 20 percent of adult Americans will experience a mental health episode in the course of a year.  Of that population, roughly one-quarter of the adults will experience a serious mental illness. Often, law enforcement officers are often the primary first responders for individuals experiencing a mental health crisis.  Because of the gap in quality mental health services, the criminal justice system frequently carries the burden for people with serious mental health conditions who could benefit from longer term, more focused treatment.  In fact, according to the State of Mental Health in 2017 Report by Mental Health America, there are over 1.2 million people currently residing in prisons and/or jails with a mental health condition.  Unsurprisingly, the lack of access to mental healthcare has been linked to higher rates of incarceration.

The year 2017 will see technology integration expand beyond the healthcare provider silo to manage mental health and high-utilizer populations with alternatives to incarceration.  Today, individuals are often either jailed or transported to a local ER, neither of which are adequately equipped to deliver appropriate behavioral healthcare. Once the immediate crisis passes, these individuals are released and often wind up back in the system with another ER visit or incarceration-sometimes a dozen or more per year. Connected coalitions of criminal justice, homeless shelters, hospitals, and mental health providers will provide cross-discipline care delivery based on improved longitudinal visibility.  Predictive analytics and machine learning will emerge as two tools to best direct scarce resources to serve “super-utilizers” while identifying candidate non-chronic high utilizers for alternative care such as behavioral telehealth visits.  Results from the Veterans Administration pilot behavioral telehealth in 2014 are promising but underscore the importance of accurate prediction of patients best served by a virtual intervention.

About the Author

Neil  Smiley,  a  serial  entrepreneur  with  a  passion  for  transforming  industries  with  data-driven solutions,  founded  Loopback  Analytics  in  2009  to  deliver  an  advanced  Software-as-a-Service platform  healthcare providers  can  use  to  prevent costly readmissions.  The  Loopback Analytics team  currently  works with  the  largest  pharmacy,  hospitalist  group,  health  system,  payer  and senior  housing  provider  in  the  nation,  providing  proven  intervention  solutions  that  improve clinical outcomes and reduce the total cost of care. Prior to founding Loopback Analytics, Smiley launched Phytel, a population health solutions company that was successfully sold to a VC firm. Smiley  began  his  career  as  an  Accenture  consultant  and  later  as  a  partner  with  EY,  working with Fortune 1000 clients. Smiley holds a computer science degree from Dartmouth College.

Edited by Alicia Young
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By Special Guest
Neil Smiley, CEO and Founder of Loopback Analytics ,


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