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SDOH will require a new education approach, platform and principles akin to personalized medicine.

With a tele-ICU program, A/V tech and predictive analytics, the system reported 297,613 fewer ICU days and saved an estimated 15,000 lives.

The MasterCare EMR solution will be used by Bass Coast Health clinicians to govern the assessment and management plans of clients, along with the monitoring and measuring of on-going outcomes. It comprises of a complete EMR and Practice Management solution, with software supporting Minimum Data Set (MDS) collection required for funding, including: Commonwealth Home Support Program (CHSP); Home and Community Care (HACC); Community Health (CH) MDS; Victorian Alcohol and Drug Collection (VADC) and Victorian Integrated Non-Admitted Health (VINAH) data sets. MasterCare EMR Product Manager Kye Cherian told HITNA that the decision behind using EMR was to have an integrated clinical and administrative system with a collection of reporting data built into administrative and clinical workflows. “The solution is integrated with a number of other Australian funding and billing types. Many other Victorian services are either using paper-based records and administrative systems with stand-alone applications to collect their reporting data,” he said. According to Cherian, more Federal and State funded programs becoming available resulted in Bass Coast Health requiring an efficient data collection software solution, in addition to fundamental features in an EMR. Having initially used PBJ Software Australia technology, Cherian said the move to MasterCare EMR’s intelligent commissioning framework allowed much of the new program data capture and reporting requirements to be supported through software configuration rather than new development. The three-month implementation involved interfacing MasterCare EMR to the existing DXC iPM Patient Management System, currently used in acute services around Victoria. “Global Health… brought a deep understanding of Victorian program workflow and data capture requirements. It resulted in a project where both parties took the time to understand the product framework, business needs and data capture requirements,” he said. “The outcome was a solution that utilises the product’s configurability to support new programs and streamline workflows with minimal software enhancements required. “A big challenge was migrating five core and high volume programs and their reporting requirements into the new application. With that being said, items such as data migration, integration and change management had to be amplified resulting in the solution having great end-user acceptance and adoption.”  Cherian said the increased efficiency of data collection in MasterCare EMR has resulted in “a wide range of benefits” including ease of reporting and a decreased duplication of work. “It future-proofs Bass Coast Health on a number of levels. The system has framework to support a number of anticipated program structure changes and being integrated with core e-health foundation services has improved processes.” Moving forward, Bass Coast Health has planned subsequent phases of the project to enhance the applications’ use as an EMR within the service and to integrate additional features within the application. Cherian said there is also potential for some planned product improvements to strengthen the product’s support for certain programs and services. “This will enhance MasterCare EMR’s credentials around the state. Both parties remain committed to working collaboratively in order to become a luminary site for Victorian outpatients and community health organisations.” This article first appeared on Healthcare IT News Australia.

With EHR frustrations at a boiling point and physician burnout at epidemic levels, it's time to rethink the way IT is designed, developed and deployed to better enable a human touch, says one clinician.

System deployed clinical decision support alerts along with its EHR to lead the country with the least amount of patient falls.

Recently the first digital currency – redeemable for U.S. dollars – was deposited in physicians’ wallets for improving care and patient satisfaction.

Three winners and one honorable mention have been announced in the Office of the National Coordinator for Health IT's Certified Health IT Product List Data Challenge. The competitors were tasked to find ways for users of the Certified Health IT Product List to make better use of the granular data it keeps on every technology product that has been approved by ONC. The winners include: ResearchAE. First place winners ResearchAE took their background in developing health IT dataset search engines to map out many of the different realms of data CHPL contains, enabling users to analyze how ONC certified health IT is being used and by whom. The prize was $20,000 Shiro Labs. Shiro Labs claimed runner-up for an application enabling providers to cross-analyze their Medicare Quality Payment Program and CHPL data, helping them see how they are meeting their reporting requirements. The prize was $10,000. Darena Solutions. Darena Solutions also won runner-up for CheckEHR, a program that lets users view which certified criteria different certified health IT modules meet, allowing them to better reach goals like patient engagement or care coordination. The prize was $10,000. Tom Nguyen. Nguyen won an honorable mention for a Google Chrome extension that helps users more easily navigate the CHPL listings. WHY IT MATTERS The CHPL contains a massive amount of very granular data which has only recently been made available in a structured and actionable way. Providers already have to abide by a wide range of standards and regulations, so having easier access to the CHPL data and finding ways to make more efficient use of it will help enable organizations to better use their health IT. Webinar: The EHR App Store Is Open - What Is on the Shelf? THE LARGER TREND As the healthcare technology marketplace evolves, new to health IT start to take bigger roles in the space, providers will continue to roll out new IT services to meet those demands and remain competitive. Being able to make intelligent searches of CHPL data means providers can chart a more informed course though the array of technology options available. ON THE RECORD "This challenge shows that there is more to the CHPL data than meets the eye. It is great to the see the creativity and ingenuity each participant put into their submissions," said Steve Posnack, executive director, office of technology, ONC. Benjamin Harris is a Maine-based freelance writer and and former new media producer for HIMSS Media. Twitter: @BenzoHarris.

In a span of about three years, Huiyihuiying (HY) has become a leading company in the development and implementation of AI in the medical sector in China. The enterprise, which focuses on AI for medical imaging, recently launched a new product at the Radiological Society of North America's Annual Meeting (RSNA 2018), which can intelligently screen for tuberculosis and quantify the location and shape of tuberculosis texture by combining X-rays and CT analysis. In an email interview with Healthcare IT News Asia Pacific, Xiangfei Chai, CEO and founder of HY, shared on his journey behind starting the company, some observations in the key trends in the developments of AI technologies in healthcare within China and abroad, as well as some of the obstacles in the developments of AI in healthcare. You have been a medical imaging researcher and developer for almost a decade, working in the department of radiotherapy/radiology at well-known academic hospitals. How did the idea to start Huiyihuiying (HY) in 2015 come about? Since my time as a graduate student, I have been with the hospital and also working with medical image industry for more than ten years. I had been involved in the development of imaging applications, which includes guided radiotherapy systems, image cloud platform, radiotherapy cloud platform, etc. in the Netherlands Cancer Research Centre and the Stanford University School of Cancer Radiotherapy Centre. I may have continued my post-doctoral and research working in the medical imaging field if I didn’t start the business. If so, this is how I see my life will be like decades later. The laboratory is the cradle of AI. Stanford University is the cradle of AI entrepreneurs and the main battlefield of the global artificial intelligence. For a long time, Stanford University has a very good environment that fosters innovation and entrepreneurship, encourages bold ventures, with a freedom to explore atmosphere and multiculturalism that tolerates failure. For example, teachers can manage one day a week freely that does not require them to engage in school teaching and research. They are allowed to work as a consultant or an independent director. How to turn scientific research results into use results is what I want to verify from the postgraduate era. Although it is not easy to productise and commercialise the theory, it is worthwhile to do so. At the beginning of 2015, I left the Stanford University Medical College Affiliated Hospital and ended my 12-year medical imaging academic career. I founded Huiyihuiying (HY) and wanted to explore further. HY recently launched their new AI Full Cycle Health Management Cloud Platform, which consists of two separate platforms for different health concerns: the Breast Cancer AI Full Cycle Health Management Platform and the AORTIST 2.0 Aorta AI Cloud Platform. Both platforms are based on the AI 2.0 technology. Could you tell us what AI 2.0 tech is in a nutshell and its main advantages over ‘conventional’ AI? Webinar: How AI Will Revolutionize Precision Medicine For AI1.0, we use Convolutional Neural Networks (CNN), Fast Region-based CNN (RCNN), Residual Networks (ResNet) and other technologies to identify lesions, assist imaging and screening diagnosis, improve the efficiency of images for doctors and reduce misdiagnosis, which is the solution for main AI products. An example would be AI lung nodule screening applications. AI2.0 is based on image data, clinical data, pathological data, etc., combined with follow-up information, we use natural semantic recognition technology, use AI to empower the whole process of medical treatment, from pre-diagnosis to participation in treatment decision-making, prognosis prediction and follow-up monitoring to achieve evidence-based medicine. At present, some of the operations in many top hospitals are prosthetic ones with high proportion of postoperative recurrence. Prognosis prediction and follow-up is a challenge of this type of complex disease. We are targeting to design a patient-centered product that covers the patient's entire medical cycle. Besides improve the surgeon's surgical accuracy, the AORTIST system integrates the radcloud platform developed by HY and embeds a prognostic prediction model that will provide the prediction after surgery of B-type dissection. What are some key trends that you observe in the developments and applications of AI in healthcare in China and more broadly, world-wide? Patient-centred applications are promising. Since 2010, improving patient experience has become the mainstream of the US medical community. We believe that the ultimate goal of both doctors and patients is the same that is to cure the disease. So we adjusted the entire product design logic to patient-centered six months ago to improve the patient experience. Entering the era of data-driven precision medicine: From 1898 onwards, we have experienced the era of physical driven represented by X-ray, ultrasound, nuclear magnetic, etc., and application driven represented by image guidance and treatment plans. After 2010, we have entered the era of data-driven precision medicine. Its typical feature is to mine effective information in massive data and optimise diagnosis and treatment methods. Artificial intelligence participates in the medical cycle management: In many complicated diseases, prognosis prediction and follow-up are big challenges. AI can be integrated with multi-dimensional data such as imaging, genetics, pathology and clinical, to provide individual medical solutions for patients, recommend surgical plans for clinicians and provide medication guidance. AI can play a greater value in the medical cycle by providing patients with reasonable examination, treatment, follow-up and rehabilitation programmes, provide comprehensive monitoring and management of the entire disease, optimise the diagnosis and treatment process and reduce medical expenses overall. What do you feel are obstacles or roadblocks to AI development in healthcare? First of all, compared with US-European countries, there is a large number of interdisciplinary talents especially in the medical imaging AI industry which is an interdisciplinary industry. Therefore, it needs diverse and interdisciplinary portfolio with both technical and marketing teams. With that, people with different knowledge and experience backgrounds can gather wisdom in different fields and eventually form a closed loop of productivity that can break through the limitations of a single discipline. The reality is that doctors have a relative lack of understanding of technology and it is difficult for technical talents to have a deep understanding of the medical field. Second, data is the key. Medical big data is very special that it doesn’t have big volume, even image data is very limited, especially in a single disease. Normally each of us do not even take one film scan per year, such as for interstitial pneumonia or fractures. There are only several thousands of patients in the country every year and they are scattered in various hospitals. Data acquisition is very difficult. In addition, the data collection standards between hospitals are not uniform and there is a large amount of unstructured data. Third, in the development and deployment of AI applications, there are different brands and models of equipment used in different hospitals, resulting in differences in image layer thickness, layer spacing, etc., there is a need to optimise the image and normalise the processing to ensure the validity of the data. It is also necessary to interface with the existing data systems of the hospital according to the specific conditions of the hospital to ensure the stability and safety of the operation. Fourth, this is a Chinese characteristic - the demand and supply of medical resources in China has long been an unbalanced “mismatched” situation. In the context of the Chinese government’s implementation of grading diagnosis and treatment, artificial intelligence applications have entered medical care, especially the grassroots also face some fundamental problems and medical informationisation has become a rift in the field of artificial intelligence. Although there are many Chinese medical information companies, the standards are not uniform, including all interfaces, specific implementation of each hospital and each hospital has done a lot of personalised localisation improvements which leads to great progress in medical informationisation. The direction is more structured, more standardised and more unified. Informatisation solves not only the efficiency problem, but also makes the overall information flow better form the basis and data source of artificial intelligence. HY is collaborating with more than 800 medical institutions in China in clinical applications and scientific research projects, including the Chinese PLA General Hospital, Peking Union Medical College Hospital, Beijing Friendship Hospital and several medical associations. The company also plans to expand its business to the other parts of the world – what are HY’s plans for the Asia-Pacific market? Huiyihuiying is actively developing overseas markets and has set up branches in the United States. Currently, we are covering Japan, France, Kazakhstan, the United States, India, Israel, etc. For example, we signed a contract with Kazakhstan's largest private hospital chain group, established cooperation with Japan's largest cloud PACS company on radcloud platform, cooperated with France largest oncology company and developed US market with US medical AI companies, etc. In the future, besides strengthening cooperation with countries along the “Belt and Road” initiative, HY will collaborate with more partners around the world and strive to make medical AI another beautiful business card in China. In a relatively short period of about 3 years, HY has emerged to become a leading company in the development and implementation of AI in the medical sector. What do you think are some of the main factors for HY’s success and what do you hope for HY to achieve in the long-term? First of all, it is very important to condense a large number of outstanding interdisciplinary talents. HY is constantly improving the introduction and training mechanism of outstanding talents. Second, medical treatment is a very complicated matter, especially medical AI. It is not a single breakthrough. HY is building a team culture where everyone is a product manager. Everyone is a team manager of customer managers, able to bring products, technology, sales are always in sync and balanced. Third, HY has established a full-cycle data intelligence platform to build a full-cycle, high-value database with large hospitals through NLP intelligent extraction, structured reporting, and intelligent follow-up. High-quality data is based on the labeling of a large number of professional doctors. HY uses three-blind labeling instead of double-blind labeling. Each case is marked by at least 3 professional imaging doctors. We have obtained millions of cases. Fourth, we adopted migration learning last year. We combined image data with clinical data, test data, and genetic data on a self-built full-scale data platform to build AI models in multi-dimensional data to achieve small data sets. Accurate modeling on the surface overcomes many problems of disease dispersal and less complete data, ensuring good model training results. Lastly, in terms of computational power, we take the lead in using Intel's EXON scalable processor to enable its latest scalable computational resources to converge into the medical image, which surpasses the memory limitation of GPU and it can conduct unsupervised learning on three-dimensional CT and MRI data and U-Net segmentation without manual labeling data, directly use PACS and RIS data to score that greatly improves the efficiency of modeling. In the future, we hope to break through the barriers of data, combine genomics, proteomics, molecularomics, metabolomics and imaging-omics, etc. to build a full-scale data centre and then model, mine the greater value behind the data, assist clinical decision-making and promote personalised diagnosis and treatment. This is the biggest vision of my ten years and one of our biggest dreams.

Neck-and-neck, the two rideshare companies are making big moves to assert themselves in the space, hiring health IT talent as providers and other vendors continue to partner.

Many EHR and health IT vendors have eliminated obstacles to participation, and most have made connection to the network plug-and-play, a new report says.