7 predictions for healthcare technology in 2019

Elsevier’s health leadership team forecasts key trends – from big data to small data and patient data as research data

© 2019 Elsevier

In 2018, we witnessed unexpected mergers and partnerships, robust digital health innovations and continued policy shifts in the healthcare sector. Now, we predict 2019 will bring about significant changes in health IT infrastructure, with patient data at the center of it all.

1. Analytics will be the mainstay as health systems strive to optimize in-house clinical data.

Given that the number 1 funding category in 2018 was data analytics, we anticipate that 2019 will usher in a variety of advanced analytics demonstrations. Many will be novel in the output generated, but the uncertainty of real-world clinical value will continue. So a demonstration of the computational capabilities will be most significant since that would open the door to a range of possibilities.

Additionally, there will be a shift in emphasis on big-data to “small-data” analytics as healthcare systems focus on leveraging existing data to improve clinical and operational processes.

2. AI will start moving beyond the hype with practical use cases, primarily in radiology and pathology.

The most substantial real-world AI applications will be in image processing through early stage machine learning for areas like radiology and dermatological lesions. Secondary spin-offs
could be self-monitoring/telemedicine apps for dermatology and tele-radiology.

3. The role of CDS tools will take on expanded significance to improve physician engagement.

This trend has various implications:

  • Hospital EHR (electronic health record) deployments outside the US will increasingly include evidence-based clinician decision support (CDS) tools linked to CPOE (computerized physician order entry) and care management.
  • IDNs (integrated delivery networks) will increasingly implement pathways, including treatment selection and care coordination, to reduce unwarranted variation of care.
  • Boundaries between patient-led, home-based self-care and physician-led outpatient care will increasingly blur, so these personal health logs will merge with primary care decision support tools in new and uniquely challenging ways.

4. Real-world evidence approaches will gain traction.

Through data liquidity and third-party app integration with EHRs, there will be a refinement of clinical evidence based on clinical data captured through care delivery.

5. Personal health data stores will finally take off.

Patients as consumers will take control of and store their personal health and clinical data in applications of their choice in the cloud. Since May, GDPR requires data controllers to turn over data to consumers and patients on demand and in usable electronic formats. Apple has made notable inroads with HealthKit, and Microsoft has revived HealthVault. Even in privacy-conservative Germany, a large group of insurers, hospital chains and industry partners are finally cooperating (e.g., Vivy). For patients in Denmark and Estonia, this has already been in play with online access to health records through the national health systems.

6. The cost of gene sequencing will continue to taper, and next-gen will prove value for AI.

This is an older prediction expected by many to be imminent. After years of exponential decline, the cost of high-quality whole genome sequencing has stalled. There are barriers to broad adoption and economies of scale because many consumers still have privacy concerns and are hesitant to disclose "pre-existing conditions." On the other hand, clinicians don’t have the tools to interpret complex results. This is an issue considering that rare diseases are more common that once thought. In the US, for example, up to an estimated 8 percent of the population has some form of a rare disease.

There will be an increase in next-generation sequencing (NGS), and more AI applications will take place in genomics related to oncology, with refined genetic variants and subtypes leading to more precision medicine in treatment options.

7. Research data networks will scale.

Patient data from routine clinical practice (EHRs, radiology images, genomics) and personal health devices will increasingly drive medical research. Precision medicine requires ever larger data sets and increasingly specific genomes (e.g., in the case of breast cancers). The difficulty lies in hospitals being reticent about sharing this data, since patient privacy is paramount.

However, the solution could be found in IoT technology – allowing sophisticated distributed analytics on site so that the data never leaves the virtual walls of the hospital. Microsoft Azure, Kubernetes and OpenStack are the most likely candidates for this, with AWS staking a claim as well.

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https://www.elsevier.com/__data/assets/image/0007/843352/john-danaher.jpg
Written by

John Danaher, MD

Written by

John Danaher, MD

Dr. John Danaher, President of Clinical Solutions at Elsevier, is a prominent life-long leader and expert in health and the business of health. Before joining the Clinical Solutions group, John was President of Education (Nursing and Health Education) at Elsevier.

Prior to joining Elsevier, John was President of the Schools of Health Sciences and Nursing at Kaplan University. He brings a great depth of experience in digital media in health care and education from his time at Kaplan, Discovery Communications, and as Executive VP of WebMD. John has deep domain expertise and a successful track-record in managing health information businesses. His experience, in both government and the private sector, extends beyond the US and into global markets, including assignments and experience with Japan, Pakistan, the UK and Australia.

John received his medical degree from Dartmouth Medical School and is Board Certified in Internal Medicine. He received his MBA from Stanford University and earlier in his career was Resident & Chief Resident in Internal Medicine at Stanford University Medical Center. John has served on diverse corporate and not-for-profit boards, community organizations, teaching faculties and fellowships, including a White House Fellowship.

https://www.elsevier.com/__data/assets/image/0008/843353/Olaf_Lodbrok.jpg
Written by

Olaf Lodbrok, MSc, MBA

Written by

Olaf Lodbrok, MSc, MBA

Olaf Lodbrok is the Senior VP for Precision Medicine at Elsevier. He holds a master’s degree in Electrical Engineering and Computer Science from Aachen University (Germany) and an MBA from Duke University (USA).

He has 25 years of experience in the healthcare sector. From 2000-05, he successfully launched a start-up which was acquired by Global Healthcare Exchange. He has also held marketing, sales and senior management positions at large international corporations.

At Elsevier, Olaf launched Health Analytics, which develops machine learning algorithms and mines 5 million anonymized German patient datasets for insights, and now Research Data Management in Health. His group also applies natural language processing to the medical literature and machine learning to health data for clinical decision support.

His passion is to advance data-driven innovation in healthcare.

Olaf joined Elsevier in 2007 as the Managing Director of Elsevier Germany.

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