Shahid Akhter, editor, ETHealthworld spoke to Dr. John Danaher, President, Clinical Solutions, Elsevier, to know what role artificial intelligence plays in healthcare and how Elsevier plans to improve diagnostic outcomes by way of AI and machine learning. Clinical errors and role of AI and health analytics There are three examples. The first one is making an initial diagnosis. What can be achieved with artificial intelligence, machine learning and actual language processing is the ability to assist doctors to make more accurate initial diagnosis. Second is the work being done in the area of image recognition with radiology and pathology.

Artificial Intelligence (AI) has been around a long time, but it is a newer concept within healthcare. AI holds a lot of promise, particularly in the areas of population health management, healthcare access and quality. At Becker's Hospital Review Health IT Clinical Leadership 2018 event, I served on a panel that talked about the promise and possibilities of AI. AI and machine learning are hot topics in healthcare that are often used interchangeably, but they mean different things. AI is about making our technology "smarter," so that it uses curated knowledge to automate and improve function.

Artificial Intelligence: Arterys AI has not had a bad year yet. Between breakthrough technologies and soaring funding rounds, there was no shortage of strong candidates to choose from in 2017. Ambra Health CEO Morris Panner, JD, gave the nod to Arterys. The 10-year-old San Francisco, California, company both started and ended 2017 in style. In January, it received a first-of-its-kind FDA approval for its cloud-based technology, which applies AI and deep learning to medical imaging analysis.

AI is not a "platform," It's an enabling technology. Many "X-with-ML" startup business plans (where X is some category of software) …but not so simple. The Ironman Suit 4. Replacing Humans 4 Models (Not Equally Common Today) 7. Model #1: Tell Me Something New Improve customer experience Data: Collect surveys, reviews/social, transactions, call logs, etc. ML: NLP on customer interactions Insight Workflow: What (concretely) makes customers happy? Extract useful data from cheap, frequent satellite images ML: Computer vision to recognize, count, measure, track objects Find use cases: government, finance, oil & gas, etc. Improve construction efficiency Data: Collect timesheets, geo, cost codes, orders, notes, etc. ML: Computer vision to tag images, NLP on notes and orders Insight Worflow: What impacts our productivity? Problem--first: Data--first: 8. Model #1: Tell Me Something New Questions to Consider… Do you have advantaged access to the data?

IBM Watson Health has announced a joint initiative with the US Food and Drug Administration to study the use of blockchain technology to share health data to ultimately improve public health. At first, the two-year collaboration will focus on oncology data, pulling together and exchanging data from a variety of sources including that from clinical trials, genomic data, EMRs, and from miscellaneous Internet of Things data from wearables, apps and connected devices. IBM and the FDA will look at how the technology can facilitate information exchange across a spectrum of data types, including clinical trials and real world data. For example, patient-generated data from connected devices could provide clinicians with more insights into population health, potentially offering up research opportunities and ways to leverage large quantities of data into biomedical and healthcare industries. At the core of the collaboration is blockchain technology, which allows secure data sharing between organizations more freely and has been increasingly favored among industry leaders.

Natural language processing (NLP) is quickly becoming one of the foundational big data technologies that will allow healthcare to move forward with complex analytics, according to a series of market reports predicting significant growth for NLP products over the next few years. As healthcare organizations seek new strategies for extracting insights from unstructured data from electronic health records, Internet of Things devices, imaging studies, and elsewhere, they will create an NLP marketplace worth $2.65 billion by 2021, says ReportsnReports. "The market is growing rapidly because of the huge surge in clinical data, increasing use of connected devices, and evolving consumer needs," the report says. Natural language processing may play an instrumental role in precision medicine, predictive analytics, population health management, clinical decision support, and EHR documentation improvement. The NLP market is divided into several segments: interactive voice response and speech analytics technologies, optical character recognition (OCR), automatic coding, text analytics, and pattern and image recognition.

As the health care industry shifts from fee-for-service to value-based care, the ability to deploy effective population health initiatives has emerged as a critical factor for the success of organizations. With more risk shifting to providers, organizations need to go beyond merely analyzing data to stratify patients into condition-specific cohorts; they need to develop sophisticated approaches for weighing a wide range of variables to selectively deliver interventions and guidance to caregivers that help improve outcomes while lowering costs. Population health has the promise to deliver big results, but many providers are facing roadblocks along the way. In order to successfully engage in population health, providers should focus on five critical areas. Successful population health initiatives need access to relevant, accurate data.