As the country moves toward value-based care, artificial intelligence and machine learning – paired with data interoperability – have the potential to improve patient outcomes while driving operational efficiency to lower the overall cost of care. By enabling interoperability securely and supporting healthcare providers with predictive machine learning models and insights afforded by genomic research, clinicians will be able to seamlessly forecast clinical events – such as strokes, cancer or heart attacks – and intervene early with personalized care and access to curated information to support a superior patient experience. Further powering these predictive capabilities with location-agnostic, voice-enabled, accessible modalities of providing care advances the practice of medicine to align with what is most convenient, affordable and targeted for the specific needs of patients. Healthcare IT News sat down with Phoebe Yang, general manager for non-profit healthcare at Amazon Web Services, to discuss these subjects, offering healthcare CIOs and other health IT leaders lessons in state-of-the-art technologies. How can AI and machine learning combined with data interoperability enhance patient outcomes and operational efficiency to lower care costs? A. Interoperability among medical information systems is foundational – or should be – because without it, physicians don't have ready access to their patients' complete medical histories.

AI Policy Matters is a regular column in the ACM SIGAI AI Matters newsletter featuring summaries and commentary based on postings that appear twice a month in the AI Matters blog. Facial recognition (FR) issues continue to appear in the news, as well as in scholarly journal articles, while FR systems are being banned and some research is shown to be bad science. AI system researchers who try to associate facial technology output with human characteristics are sometimes referred to as machine-assisted phrenologists. Problems with FR research have been demonstrated in machine learning research such as work by Steed and Caliskan in "A set of distinct facial traits learned by machines is not predictive of appearance bias in the wild." Meanwhile many examples of harmful products and misuses have been identified in areas such as criminality, video interviewing, and many others. Some communities have considered bans.

Enterprise analytics company Clarify Health has secured $115 million in series C funding to scale its self-service healthcare analytics cloud and business software. Clarify Health combines longitudinal data for more than 300 million "unique patient lives" from government and commercial claims, electronic health records (EHRs) and prescriptions, according to the company. These data can help healthcare professionals manage population health and commercialize pharmaceutical and biotechnology products. "By linking CMS claims data with commercial claims, EHR, prescription and socioeconomic data, our models are trained on large cohorts and a more complete picture of each patient's longitudinal healthcare journey," Clarify Health CEO Jean Drouin, M.D., told Fierce Healthcare. The San Francisco-based company was launched in 2015 and has raised $178 million to date, according to Crunchbase.

On behalf of Dimensional Concepts LLC (DCL) of Reston, Virginia I attended a meeting sponsored by the DC-based think tank Center for Data Innovation titled U.S. Data Innovation Day 2018: The Future of Data-Driven Medicine. What data governance challenges are associated with applying AI techniques to new drug discovery and development? What data governance challenges are specific to medical AI applications? Which data governance challenges are generic? What are the implications of using AI techniques for the business processes and regulations associated with new drug development and medical treatment delivery?

There have been some exciting developments of late in the Semantic Web and Technology space. Semantic Technology trends in 2018 will continue to advance many of the trends discussed in 2017 and build upon a number of new changes just entering the marketplace. This fall, in fact, the Elsevier 2017 Semantic Web challenge focused on Knowledge Graphs. The winner was IBM Socrates by Michael Glass, Nandanda Mihindukulasooriya, Oktie Hassanzadeh, and Alfio Gliozzo of IBM Research AI. "Knowledge Graphs are currently among the most prominent implementations of Semantic Web technologies," an Elsevier press release stated. "Innovative integration of additional Artificial Intelligence techniques such as Natural Language Processing (NLP) and Deep Learning over multiple web sources to find and check facts. Their knowledge graph outperformed the state of the art."

In Part I of this two-part series, Fred Giasson and I looked back over a decade of working within the semantic Web and found it partially successful but really the wrong question moving forward. The inadequacies of the semantic Web to date reside in its lack of attention to practical data interoperability across organizational or community boundaries. An emphasis on linked data has created an illusion that questions of data integration are being effectively addressed. Linked data is hard to publish and not the only useful form for consuming data; linked data quality is often unreliable; the linking predicates for relating disparate data sources to one another may be inadequate or wrong; and, there are no reference groundings for relating data values across datasets. Neither the semantic Web nor linked data has developed the practices, tooling or experience to actually interoperate data across the Web.