The technology disruptions set to grow in 2019

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Feb 14, 2019

Value-based care (VBC) has disrupted the healthcare industry and shifted the paradigm away from visit-based reimbursement. But, other changes on the horizon could make that disruption pale in comparison. In this first blog in our two-part series on embracing disruption, we’ll tackle the seismic shifts in technology that are impacting health systems. 


In our next post, look for our take on what the rise of newer types of competition means for health systems. In both, we examine the new opportunities for organizations looking to capitalize on change.  


Three technology trends that are disrupting but potentially improving access to the ‘last mile’ of care are:


  1. Virtual care
  2. Artificial intelligence (AI) and blockchain
  3. High-performance computing (HPC) and precision medicine

Virtual care puts the last mile in the home

Although physicians are still struggling to be duly compensated for the breadth of virtual care, consumers are embracing this technology. In 2017, 66% of adults responding to an AmericanWell poll said they would be willing to see their doctor in a virtual visit.[i] Half of large employers say that implementing virtual care solutions is a top priority for their benefits in 2019.[ii]


These newer healthcare delivery models often deliver greater convenience at lower cost, competing with care formerly provided in the emergency department or physician’s office.


And compensation is progressing. Beginning this year through the Medicare physician fee schedule, three new reimbursement codes for remote patient monitoring are available, along with expanded and unrestricted telehealth fees, the reading of patient-generated videos and images, and new fees for patient virtual check-ins and electronic peer consults, for example.


Progressive health systems are taking note. After a study conducted by Massachusetts General Hospital (MGH) and Brigham and Women’s Hospital (BWH) found no significant difference in outcomes between in-person and virtual visits for patients with hypertension, the hospitals expanded their programs to other patient populations.[iii]   


Another leader, Carolinas Hospital System, has fully launched their virtual care services to patients at their eight hospitals.[i] They also offered free telehealth visits to residents impacted by Hurricane Florence in September 2018, enabling patients to receive treatment when hospital and road closures impacted access to care.[ii] Beyond the intrinsic value of relieving suffering, this move had strategic value, helping the system to increase the acceptance of virtual care among new users. 


Virtual care enables savvy health systems to attract patients that otherwise would have gone to a competitor’s brick and mortar locations. And even without direct reimbursement, healthcare providers at risk for the cost of care can use virtual care to tackle convenience and access. Kaiser Permanente, a leading ‘payvider,’ has been a leader in virtual care, conducting over half of all visits virtually by 2016.[iii]


These movements coupled with the new CMS virtual care ecosystem fees are incenting providers that aren’t managing a fully capitated model to move towards virtual care.[iv]

AI and machine learning inform the last mile of analytics  

Although nearly two-thirds of hospital leaders view AI as a low priority today,[i] in the next five years, half of hospitals expect to be using it in patient care.[ii]  The most powerful AI solutions likely will be hybrid solutions that couple the use of machine learning with human reasoning and traditional hypotheses and models. 


AI and machine learning are increasingly being deployed in areas such as falls prevention, imaging diagnostics, and predicting health exacerbations. A few examples follow:


  1. El Camino Hospital in Silicon Valley decreased patient falls by 39% by implementing algorithms based on machine learning and analyzing years of collected data.[iii]

  2. Using AI in imaging studies is improving diagnostic accuracy. The MGH & BWH Clinical Data Science Center is using NVIDIA’s DGX-1, a supercomputer designed for AI applications that will be used to process the hospital’s vast archive of phenotype, genetic and imaging data to develop algorithms that may aid their radiologists’ interpretations.[iv]

  3. University Hospitals in northeast Ohio is using cognitive machine learning to drive down sepsis rates, applying it to workflows that make the information actionable and lifesaving, and allowing it to target interventions prior to the emergence of clinical signs.[v]

Precision medicine and HPC tailor the last mile of treatment

Precision medicine and high-performance computing (HPC) are helping to make that last mile of diagnosis and treatment more tailored and effective. Example of using precision medicine to bring better predictability to treatment include:


  1. Nicklaus Children’s Hospital, which is collaborating with Rady Children’s Institute for Genomic Medicine and Sanford Health to develop precision care plans for each child based on their underlying genetics.[i]

  2. Since 2013 the Swedish Cancer Institute has also been a leader in precision medicine. They offer gene sequencing for their cancer patients, allowing their specialists to personalize patients’ treatment plans down to the cellular level.[ii]

  3. Vanderbilt University Medical Center is working with the NIH to launch the All of Us Research Program, which is collecting patient-reported lifestyle data, EHR data, blood and urine samples, and simple biometric data.[iii] This shared data will incorporate lifestyle, socioeconomics, environment and biologic aspects of health to provide more individually appropriate therapies.

Precision medicine is not without issues. Calculating a cost-benefit ratio is complex,[iv] and it increases the burden on clinicians to understand and incorporate a wealth of treatment into their decision-making process. That makes having partnerships between providers and technology vendors to create effective clinical decision support tools more critical. Dana-Farber Cancer Institute, for example, is working with Philips to bring enhanced cancer care pathways to the oncology community.[v]


High performance computing (HPC) isn’t new, but it’s increasingly being combined with high performance data analytics to make the burgeoning complexity of data more manageable.[vi] One example is its use in the rapid processing of genomic workflows to speed more tailored care to patients with chronic or life-threatening diseases. HPC can more readily combine disparate sources and types of data into a single longitudinal perspective of patients and their care – including genomic data, EHR data, claims and clinical trials data – to foster precision medicine.

However, AI and machine learning are still largely confined to specific use cases, and until there is clearer financial gain, their adoption may be slowed by health systems’ increasingly slim margins.


Read our upcoming blog, “Embracing Competitive Disruption,” to learn how your health system can prepare for the wave of new and different competitors.

About the author

Niki Buchanan

Niki Buchanan, 
General Manager & Business Leader, Philips

Niki Buchanan is General Manager & Business Leader for Philips PHM. A dynamic and versatile healthcare executive, Niki uses her distinctive customer satisfaction and product optimization methodology to lead improvements across the Health IT spectrum.

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