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By Monica E. Oss

Earlier this month, I gave the keynote address, Emerging Technology & The Future Of Community-Based Care, at the Innovative Technology in Community Health Care Conference, at the Joseph B. Martin Conference Center at Harvard Medical School, in Boston. The day was an exciting one – reinforcing both the potential of technology in the field to change the field and the slow path to using that potential in improving consumer service.

The conference featured some exciting presentations of new-to-market technology. Chief Business Development Officer Rini Gahir from Mozzaz talked about using technology to promote collaboration for better care coordination and collaboration for consumer with disabilities; Chief Medical Director Trishan Panch, M.D. from Wellframe, talked about their mobile care management system (see Vinfen Wins 2015 Innovation Award); MyStrength Chief Executive Officer Scott Cousino, discussed the expansion of their online consumer self-care program (see Study Shows Users Of Digital Mental Health Experience Eighteen Times Greater Reduction In Depression Within Only Two Weeks); and Vice President of Clinical Development of Pear Therapeutics, Yuri Maricich, M.D. talked about the power of combining technology and pharmaceutical agents (see Following Hospital Discharge, Individuals With Schizophrenia Had High Engagement Rates With Mobile Relapse Prevention Intervention).

Also on the panel, Amar Das, M.D., Ph.D., the Healthcare Effectiveness Research Director at IBM, spoke about IBM’s investment in cognitive computing. While his presentation was the most futuristic and not tied to a particular application, I was struck by how the development of tech applications is creating the platform for leveraging the power of cognitive computing. All the applications are gathering vast amounts of previously unavailable information – the “big data” that we so frequently reference. The next step is to analyze that big data to facilitate tech-enabled decision support. Even more exciting is advancing those tech-enabled decisions using cognitive computing or machine learning – with the ability of the tools to make increasingly better decisions through self-learning thought processes like pattern recognition and natural language processing.

The applied use of these “smart decision” technologies are starting to hit the field. Just in the past year, we’ve reported on some of these initial applications.

AI for schizophrenia diagnoses – The University of Alberta is testing the use of algorithms to diagnose schizophrenia – and running a 75% accuracy rate (see University Of Alberta & IBM Using Artificial Intelligence To Diagnose Schizophrenia).

AI-supported physician collaboration – The Tiatros platform is being used to allow physicians to create private social networks around each consumer to foster collaboration and build longitudinal relationships among the entire care team. The platform uses natural language analytics to analyze the unstructured data, identifying when and how to best add behavioral health and social support services (see Tiatros Consumer-Centered Social Network Platform To Use IBM Watson).

AI-facilitated juvenile court decisionmaking – A judge in Ohio is now using IBM Watson to power digital case file management. The tool displays a dashboard of summary “essential” information for each juvenile case (see Montgomery County Ohio Judge First To Use IBM’s Watson Supercomputer On Juvenile Cases).

AI tools to discover new medications – The Mayo clinic has created partnership using artificial intelligence (AI) to aid in the discovery of new medicines, by mining large volumes of the medical literature and clinical data to identify opportunities for the development of new drugs (see New Mayo Clinic Startup Will Use AI To Discover New Medicines).

AI prediction of relapse – The start up Behaivior will merge artificial intelligence with wearable devices to predict relapse danger for recovering opioid users. The device will gather data, such as elevated body responses or notifications of missed counseling appointments, to determine when a candidate might be in danger of a relapse using predictive models (see Pittsburgh Start-Up Behaivior To Predict Relapse Through Artificial Intelligence & Wearable).

AI-managed robots to monitor consumers in their homes – A robot prototype using a number of technologies, including AI, will monitor consumers’ vitals at regular intervals. In addition, the robot can detect if they’ve fallen and can’t get up – and monitor the consumers’ living space for potentially problematic changes in physical and environmental conditions (see IBM Eldercare Robot Could Help Seniors Age In Place).

While that is the potential, my presentation also referenced the long path we’re on to wide adoption of these technologies. Executives of provider organizations are still struggling with electronic health record (EHR) implementation, creating tech-enabled consumer communication channels, and using web-enabled service delivery (see EHR ‘Square One’ and Do You Need An EHR ‘Makeover’?).

PowerPoint Presentation

But, artificial intelligence and cognitive computing is slowly (in some cases, very slowly) remaking the health and human service markets by changing both systems and the expectations of consumers and professionals alike. It’s hard to tell the exact form that tech will take. But I’m reminded of the Bill Gates quote, “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. Don’t let yourself be lulled into inaction.”

For more, join me on November 8 at The 2017 OPEN MINDS Technology & Informatics Institute for my closing plenary session, “Mastering Technology To Stay Competitive: How To Build A Tech-Enabled Organization.”

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