Dean Koh

Last week, Parkway Pantai, one of the largest integrated private healthcare groups in Asia and UCARE.AI, a Singapore-based AI healthcare startup, announced that they have been using Artificial Intelligence (AI) to dynamically generate personalised, more accurate hospital bill estimates that vary from the actual bill by a high 82 per cent accuracy rate on average, a significant 60-percentage-point improvement over the current bill estimation system. This means that eventually, all patients would receive highly accurate bill estimates that fall within an 18 percent margin from the final bill figure. Using an advanced suite of AI and machine learning algorithms from UCARE.AI, Mount Elizabeth, Mount Elizabeth Novena, Gleneagles and Parkway East hospitals in Singapore will dynamically generate personalised bill estimates based on relevant parameters such as the patient’s medical condition and medical practices. It also takes into account the patient’s current age, revisit frequency and existing co-morbidities like high blood pressure or diabetes. “Parkway Pantai has always been committed to enhancing price transparency of hospital charges. Our investment in this new AI-powered system gives patients more accurate hospital bill estimates and empowers them to make more well-informed decisions on the medical treatment options available. More importantly, it allows patients to have greater peace of mind over their healthcare expenditure so that they can focus on getting well,” said Mr Phua Tien Beng, Chief Executive Officer, Singapore Operations Division, Parkway Pantai. The new estimation system, which has been in use since November 2018, has made more than 10,000 predictions so far. In its first two weeks of going live, the AI system has already closed the average gap between the estimated and actual bills by 60 percent. The accuracy of its predictions is expected to improve over time as the AI collects and references more data through a process of self-learning. The system analyses a multitude of dynamically changing parameters specific to the individual patient. The information is then used to automatically and quickly predict the patients’ bill size at different touchpoints, from pre-admission till their eventual recovery. As such, patients are better informed and empowered to seek the treatment option that is most cost efficient and effective for recovery. In contrast, conventional bills estimation methods are based on statistical calculation of historical hospital bill sizes from past admissions up to two years ago. They are unable to account for dynamically changing factors such as disease aggravation and unexpected complications resulting in longer length of stay or additional unplanned surgeries. Mr. Neal Liu, Founder and CTO of UCARE.AI said, “UCARE.AI was selected by Parkway Pantai for providing (1) the most accurate and precise predictions based on blind-testing, and (2) a cloud-based microservices architecture solution that offers flexibility, scalability, ease and speed in implementation. We are thrilled to work with Parkway Pantai, one of the largest global healthcare players who pride themselves on innovation and quality patient care, to debut UCARE.AI’s first revolutionary AI-powered system. Together, we seek to ride the wave of healthcare disruption and roll out more AI systems and services to benefit patients globally.”

Two of the biggest concerns that potential customers indicate when implementing Enterprise Content Management (ECM) solutions in the cloud are the migration of huge data stores to the cloud and connectivity to the cloud, says Marc Cianciolo, Director of Global Cloud Services, Hyland. In this interview, Cianciolo also addresses some misconceptions about the cloud, hybrid cloud for healthcare customers and future developments beyond Hyland’s cloud hosted OnBase application. As a professional who has been in the cloud space for some time, what do you think are some of the biggest misconceptions behind cloud services that you wish to dispel? From my perspective, there are a few misconceptions equally contributing to reluctance, albeit an easing reluctance, to embrace the cloud.  While we have seen a significant shift in organisational acceptance of the cloud because security in the cloud has been well vetted, there remains a concern among a small population of the market that believe security can still be compromised. The fact is that proven cloud vendors with a history of audited security controls are realistically more secure than a customer’s on premises installation.  The reason for this is deep expertise in areas like infrastructure and network security, combined with economies of scale that established vendors have to so they can deploy, and maintain, leading edge security protocols. In an increasingly digital world, threats have become more sophisticated and pervasive. Only those vendors and organisations capable of meeting the dynamic, evolving and often expensive security posture can adequately provide the level of sophisticated security required in today’s business climate. Additionally, there exists a misconception that IT resources will no longer be as relevant when an organization outsources to a cloud based application.  In my experience, this is untrue. Realistically, organisations moving to the Hyland Cloud have adequately taken advantage of the ability to reallocate IT resources to other value-add roles and responsibilities, rather than burdening them with ongoing infrastructure administration. What is a hybrid cloud and what are the pros and cons of adopting a hybrid cloud in healthcare? ‘Hybrid cloud’ has a different connotation depending upon whom you ask.  At a basic level, it is a solution that utilises a company’s on-premises technology to satisfy a portion of that particular solution, but also engages a third-party cloud provider for a separate portion of the same solution. Often, the cloud is used for more resource intensive elements of the solution because a cloud provider can offer those resources more cost effectively than on-premises. Hybrid is often preferred for healthcare customers when a) some personal health data must be kept on-premises, or b) application processing services are highly intensive (e.g., medical image processing) and can only guarantee mission-critical performance levels when done on-premises, . Then but other, less critical background services are pushed to the cloud. Additionally, we’ve also seen a hybrid approach work well for healthcare customers who wish to store a third copy of their on-premises content outside of their managed infrastructure.  For this, Hyland recently launched a Replicated Disk Group offering. With this option, organisations can leverage the Hyland Cloud to store an additional copy of just their content.  In the event the on-premises solution is compromised and content is lost or corrupted, the organization can rest assured knowing that an externally hosted additional copy of the content would be available.  What are some trends/developments that you observe in the global healthcare industry with regards to cloud technologies/services? Historically, healthcare enterprises had have been more inclined to embrace cloud solutions for more benign back office content like accounts payable or human resources. However, with the validation of cloud governance, risk and compliance sophistication, healthcare organisations are now more apt to trust the hosting of more mission critical clinical content. From my perspective at Hyland, that mindset, in conjunction with proven technology advancements such as robust integrations with Epic, have led healthcare enterprises to expand their cloud adoption to clinical content and operations. Are there any unique challenges/concerns in the context of healthcare organisations when they are considering implementing ECM solutions in the cloud? When we talk with potential customers, two concerns tend to permeate discussions, including sizable data stores that need to be migrated to the Hyland Cloud and concern over connectivity to the Hyland Cloud. Very large backfiles of several hundred terabytes have raised concerns among potential customers as they build a plan to migrate data from incumbent ECM applications.  Seamless orchestration between Hyland and the customer is required to ensure timely availability of content in the Hyland Cloud. In response, the Hyland Cloud has assembled a team of experts in its Data Services Group to manage and administer the process, resulting in successful migrations of large sets of data—  in some cases up to 400 TB of data. Additionally, while the vast majority of customer are well served by connecting to their hosted installation of OnBase via HTTPS over the internet, others occasionally require an alternative connection protocol like VPN or MPLS.  Hyland has accommodates these requests and maintains satisfied customers. Often, the need is not critical, but in some cases alternate connectivity can alleviate the concerns of customers who are new to exploring cloud-based applications. Hyland is a pioneer in cloud-based ECM solutions for more than a decade. Could you tell us more about some of the current product developments and perhaps a glimpse of what is to come? For many years, Hyland offered, and the Hyland Cloud hosted, only the OnBase application. As Hyland introduced new products developed organically within Hyland and acquired new applications, Hyland’s solutions portfolio grew exponentially. Several of the new or acquired products are in various stages of being available via a hosted Hyland solution.  Our short-term focus for hosted solutions include Hyland’s customer communications management (CCM) technology, Acuo vendor-neutral archive and NilRead universal medical imaging viewer. Additionally, the Hyland Cloud continues to offer its products in all regions of the world by expanding its global footprint of co-located data centers.  In 2018, Hyland launched two additional data centres in the United States and opened two facilities in Canada, increasing Hyland’s global data centre count to thirteen sites. Additional regions are under consideration in order to address dynamic and evolving global data sovereignty requirements. Could you share with us some of the success stories of end-users/customers in the healthcare sector through their use and experience of cloud-based ECM solutions from Hyland? Hyland has helped small and large healthcare customers capture efficiencies and add value both departmentally and across their enterprise by implementing OnBase in the Hyland Cloud.  Most notably, Hyland’s integration with customer implementations of Epic, both on-premises and in Epic’s cloud, has been an integral approach to reshaping and modernizing how healthcare organisation view and process clinical content. The addition of a dedicated line directly between Hyland’s primary data center in Virginia and Epic’s data center in Wisconsin is evidence of the respected partnership. Nearly 100 healthcare organisations worldwide have trusted the secure hosting of their patient information to the Hyland Cloud, including several of the world’s largest healthcare enterprises, the identity of which remains protected in accordance with Hyland’s security policy.  Cybersecurity and data protection is obviously a critical concern for many customers and end-users, given the frequency and increased complexity of cybercrimes and cyberattacks. What is Hyland’s approach to cybersecurity, especially in terms of the cloud-based ECM solutions? Security within the Hyland Cloud is of paramount importance. Nothing is implemented, strategically or operationally, without first considering the impact to the security of the overall environment and the integrity of the associated business processes. For this reason, our Governance, Risk and Compliance team has grown by 50 percent just in the past twelve months to ensure Hyland remains on the leading edge of security controls.  Given the changing landscape with GDPR, BREXIT and a host of other international and domestic data privacy and security requirements, Hyland’s ability to demonstrate a hosting environment aligned with HIPAA guidelines and other data privacy guidelines like ISO 27001 is critical to our ability to bring further value to our healthcare customers’ ECM initiatives. Our customers rely on us to be experts in hosting security and data protection, especially in a time when cyberattacks seem all too commonplace.  Hyland’s ability to provide unwavering consistency with security audits like SOC 2, ISO 27001, PCI, and more is evidence of our continued leadership position in cloud-based ECM. Hyland is a corporate member of HIMSS Asia Pacific.

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.

In the book Affordable Excellence: The Singapore Healthcare System by William A. Haseltine published in 2013, the author noted that Singapore “ranks sixth in the world” in terms of healthcare outcomes, while “spend[ing] less on healthcare than any other high-income country”. When compared with other countries, Singapore ranks high on value-based indices - A 2014 EIU white paper that looked at health outcomes and costs across 166 countries ranked Singapore second after Japan, noting that it had achieved similar outcomes to Japan’s but with a significantly lower investment. One of the key pillars behind Singapore’s remarkable success in delivering affordable and high-quality healthcare since its independence in 1965 is the government’s approach to healthcare improvement and care as an integral and inseparable part of the overall development planning for the country, Haseltine explains in the first chapter of his book. In November 2017, the Ministry of Health introduced the ‘3 Beyonds Strategy’ to keep healthcare in Singapore good and affordable. They are: (i) Beyond Healthcare to Health (ii) Beyond Hospital to Community (iii) Beyond Quality to Value With an aging population and increasing costs and burden of healthcare in Singapore, the “Beyond Quality to Value” strategy becomes essential to retain or increase quality of care while ensuring value for money. The Agency for Care Effectiveness (ACE) was set up in 2015 to research treatments that provide the best value for money. For instance, three drugs may offer the same results, but have very different prices. Or a drug may be more expensive, but offer outcomes that are far better than cheaper alternative drugs. Webinar: Compliance as code: Automate compliance using open source technology In the long run, the conventional method of fee for service-based care that works on a basis of volume and treating illnesses and injuries as they occur is also not tenable – hospitals and healthcare organisations cannot expand their capacities indefinitely and there is already an existing manpower crunch of qualified professionals in the healthcare sector.   Treating illnesses and diseases when they occur is often expensive and unpleasant for patients – therefore, the proverb, “prevention is better than cure” is especially relevant. The “Beyond Healthcare to Health” strategy has seen the Health Promotion Board (HPB) ramping up efforts for people to become healthier and more active, through initiatives such as the National Steps Challenge (currently in its forth season) and Healthier Dining Programme. Value-based care prioritises health outcomes that matter to patients relative to the cost of achieving those outcomes. This is also related to “Beyond Hospital to Community” strategy in which patients can receive appropriate care community or at home so they can stay well and avoid frequent hospital admissions. This is better for them in terms of health and convenience, and for the healthcare system too, as hospital care is very expensive. The transition from a fee for service-based to a value-based healthcare system may not be an easy journey for many healthcare providers and organisations but it also presents many opportunities to relook existing approaches to healthcare, not just in the delivery of patient-centric care but also in aspects such as financing models and leveraging technologies such as AI to provide value to both patients and clinicians while reducing costs. With the theme of “Disruptive innovation for Value-based healthcare”, the HIMSS Singapore eHealth & Health 2.0 Summit held from 23-24 April 2019 at Marina Mandarin Singapore will feature six main topics related to achieving value-based healthcare: (i) Population health (ii) Healthcare revenue cycle (iii) Patient outcomes (iv) Acute-to-community (v) Cybersecurity; and (vi) Artificial intelligence The HIMSS Singapore eHealth & Health 2.0 Summit will be a great opportunity for like-minded healthcare leaders and professionals in Singapore and abroad to come together to tackle some of the major challenges and opportunities in moving towards a value-based healthcare system.

A recent article from China Daily stated that IT investment in the country’s hospital system will reach 65.7 billion yuan ($9.47 billion) in 2022, surging 53.5 percent from 2017 and boosting the digitalisation of the Chinese medical system, based on a forecast report by Analysys. The Beijing-based research consultancy’s report also noted that the Hospital Information System has almost achieved full coverage in China’s tertiary hospitals, which is the largest in the country’s three-tier system. Coverage in primary and secondary hospitals, the lowest two tiers, is currently at 80 percent. Statistics from the Chinese Hospital Association indicated that in 2017, hardware investment accounted for 44 percent of total hospital digitalisation investment, while spending on software and services represented 56 percent. Chen Qiaoshan, a medical analyst at Analysys said that software and services as the core of hospital digitalisation will a have higher growth potential than compared to hardware. From 2017 to 2018, 17.43 percent of the country’s hospitals greatly expanded their investment in hospital digitisation, and 29.78 percent slightly increased their investment, showing that in the future, the overall investment into hospital digitisation will continue to rise, according to the same report by Analysys. Webinar: The EHR App Store Is Open - What Is on the Shelf? Based on the latest statistics from HIMSS Analytics, China has 36 EMRAM Stage 6 validated hospitals and 10 EMRAM Stage 7 validated hospitals. EMRAM is the acronym for Electronic Medical Record Adoption Model by HIMSS Analytics, which incorporates methodology and algorithms to automatically score hospitals around the world relative to their Electronic Medical Records (EMR) capabilities. This eight-stage (0-7) model measures the adoption and utilisation of electronic medical record (EMR) functions and Stage 7 represents a remarkable achievement in which paper charts are no longer used.   Huangshi General Hospital in Hubei province and Children’s Hospital of Shanghai are two such hospitals to achieve EMRAM Stage 7 validation in the first quarter of 2018. In the same China Daily article, Qu Jing, another medical analyst at Analysys, said, “Although most hospitals attach great importance to information gathering, they do not have a clear development strategy. Statistics from the Chinese Hospital Association showed that while 97.25 percent of China’s hospitals have a specialised information and technology department, 56.29 percent of them lack thorough digitalization development planning.” Qu also added that hospitals lack regional connectivity, which is important for the development of hospital information platforms, with only 49 percent having a regional information platform.

Last week, Calvary Mater Newcastle Hospital in New South Wales (NSW), Australia became the 17th Intensive Care Unit (ICU) across the state to replace paper charting with Electronic Record for Intensive Care (eRIC), which digitally integrates patient data from bedside monitors, ventilators and other specialised equipment every minute. With this latest go live, more than a third of NSW’s 44 ICU hospitals are onboard the eRIC clinical information system. The electronic Record for Intensive Care (eRIC) is an electronic clinical information system within an Intensive Care Unit (ICU) that integrates patient data every minute from multiple systems, to improve patient safety and provide better clinical decision-making. “eRIC will cut manual documentation work, which is very time consuming,” said Kelly Duff, Clinical Nurse Educator and Change Manager at Calvary Mater. “With eRIC, we expect that documentation and compliance will improve, resulting in fewer mistakes relating to these.” Webinar: Leveraging Cloud to Revolutionize Health IT and Transform Your EMR System Calvary Mater Newcastle is the major cancer care centre for the Hunter New England Local Health District, delivering more than 320,000 occasions of outpatient services and in excess of 16,000 inpatient treatments per year. In October 2016, Port Macquarie Base Hospital (PMBH) was selected as the first ICU in NSW to deploy eRIC. Subsequently, there were eight hospital deployments of eRIC in 2017 and this year, there were nine hospital deployments, including the latest deployment by Calvary Mater Newcastle Hospital. Including Calvary Mater’s ICU, 345 beds in 17 health facilities across nine Local Health Districts (LHDs) have been enabled with eRIC as part of the ongoing digital transformation of NSW Health. Deployments of eRIC will continue in 2019, starting with Gosford Hospital and Wyong Hospital in Central Coast LHD. eRIC will also be introduced next year in the ICUs of St Vincent’s Private Hospital within South Eastern Sydney LHD, Broken Hill Base Hospital in Far West LHD and Nepean Hospital in Nepean Blue Mountains LHD.

Health Minister Datuk Seri Dr Dzulkefly Ahmad also said in a recent HIMSS TV interview that even if an organisation has a good programme and system in place, digitising healthcare will not succeed if there is no clinical buy-in underpinned by training.

Biofourmis, a Singapore-based health analytics platform, has entered into a collaboration with Brigham and Women’s Hospital in Boston, US to co-develop improvements to their proprietary analytics engine, Biovitals™ for Brigham’s Home Hospital Programme. Patients are cared for in their home instead of the hospital in Brigham’s Home Hospital Programme, with the aim of providing the right care to the patient at the right time and place. The Programme started in November 2016 and about 200 patients have been cared for from home so far. The process After a patient has been discharged from the hospital, a doctor or nurse meets the patient at his or her home and all diagnostic work are performed at home, such as blood tests, X-rays and ultrasounds. The patient’s vitals including heart and respiratory rate, as well as movement are monitored 24/7 with wireless monitoring technology. The patient is given an electronic tablet that allows him or her to communicate anytime with medical staff via phone, text or on-demand video. Many treatments, including medications, are administered at a patient’s bedside. Preliminary pilot data of nine patients who were randomised to receive care at home showed that the average direct cost for acute care episodes for home patients was up to half of the cost of the control patients cared for in the hospital. Webinar: Patient Engagement: Transforming the Patient Experience with Innovative Digital Services The collaboration between Biofourmis and Brigham and Women’s Hospital will harness and clinically utilise the vast quantity of biometric data that the home hospital team collects. The team plans use the Biovitals™ analytics engine and further innovate around new predictive algorithms. Unlike traditional threshold-based physiology monitoring, Biovitals™ uses advanced machine learning to learn a patient’s physiology and then dynamically build a personalised physiology signature that can detect subtle physiological changes that may predict a patient’s health. The programme would also use Biofourmis’ RhythmAnalytics™ platform to detect dozens of different cardiac arrhythmias. “Current remote monitoring systems are based on univariate physiology analysis and have shown high false alarm burden and no early intervention, especially while monitoring patients in an ambulatory setting. This collaboration would enable us to enhance and co-develop new predictive models for monitoring acutely ill patients’ suffering from multiple conditions like heart failure, pneumonia, COPD, and atrial fibrillation at-home, enabling clinicians to intervene early and improve the level of safety of patients,” said Kuldeep Singh Rajput, Founder & CEO of Biofourmis. “Our home hospital team is hoping to improve care for our patients by creating a suite of highly clinically-useful algorithms that can predict deterioration and improvement for those who are acutely ill,” said David Levine MD, MPH, MA, researcher and lead for Brigham and Women’s Home Hospital programme. In December 2017, Biofourmis announced that it had raised US$5.0 million in a Series A round of funding from NSI Ventures and Aviva Ventures, the strategic corporate venture arm of international insurer, Aviva plc. The company also entered into a collaboration with Mayo Clinic, which would enable them to assess de-identified healthcare data from clinical trials and Mayo’s expert medical insights.

Huiyihuiying, a Chinese medical imaging artificial intelligence company, yesterday announced the launch of 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. The platforms are based on AI 2.0 technology and were introduced at Chinese Congress of Radiology 2018 (CCR 2018) in the National Congress Center on November 8, 2018. The Breast Cancer AI Full Cycle Health Management Platform is committed to the whole cycle of breast cancer diagnosis and treatment, from breast molybdenum target screening to nuclear magnetic diagnosis to pathological diagnosis. The AORTIST 2.0 Aortic AI Cloud platform is to provide a process for the identification of breaks, choosing cardiac stents, and the prognosis of follow-up treatment. WHY IT MATTERS Compared to medical imaging products based on AI 1.0 technology that only cover disease screening and auxiliary diagnosis, the biggest feature of the AI 2.0 products launched by Huiyihuiying is the deep integration of AI and healthcare in three areas: value fusion, data fusion and process fusion. According to a research article, “Heading toward Artificial Intelligence 2.0” by Yunhe Pan, AI 2.0 technology will possess distinguishing features, such as the process of combining data-driven and knowledge guidance into autonomous machine learning that is both explainable and more general. The new platforms are the result of investment from Intel Investment and Core Kinetic Energy Fund. THE LARGER TREND Based on a MIT Technology Review report in March 2018, there are some 131 companies currently working on applying AI in the country’s healthcare sector. The rapid development of AI in China is also prompted by the government’s push to transform the country into a “nation of innovation.” A year ago, China’s Ministry of Industry and Information Technology (MIIT) released the Three-Year Action Plan to Promote the Development of New-Generation Artificial Intelligence Industry (2018-2020). This new plan calls for China to achieve “major breakthroughs in a series of landmark AI products” and “establish international competitive advantage” by 2020. ON THE RECORD Founder and CEO of Huiyihuiying Xiangfei Chai said, "In order to promote the development of medical imaging AI, it is necessary to integrate multi-border integration of AI enterprises, hospitals and academic institutions so that the new technology of AI will be truly landing." "We believe that the application of multi-modal image data and the full cycle coverage of disease diagnosis and treatment are the trends of AI industry development. There will be more and more diseases in the future cured by AI applications," co-founder of Huiyihuiying, Na Guo said. Focus on Artificial Intelligence In November, we take a deep dive into AI and machine learning. .jumbotron{ background-image: url("http://www.healthcareitnews.com/sites/default/files/u2556/AIJumbotron.jpg"); background-size: cover; color: white; } .jumbotron h2{ color: white; }

Myongji Hospital, which was established in 1987 in South Korea, has recently signed an agreement with BICube, an IT company based in Korea to co-develop a blockchain-based medical information exchange system. According to the official press release by the hospital, the main purpose of the agreement is to “build a hybrid cloud that combines public and private clouds, and to secure the safety of online medical information exchange system by combining blockchain technology in the process of exchanging medical data through the cloud.” Although blockchain technologies are commonly associated with the virtual currency-based technologies, the hospital said that this project will use medical data and store new data, as well as prevent the data from being tampered. The two parties plan to use to use a virtual private network (VPN) solution to build a security system which includes features such as physical authentication and anti-piracy, anti-tampering, data protection through device authentication, remote verification, and signature codes. Webinar: IEEE: Bringing Together Blockchain, Healthcare and Emerging Technology With the patient’s agreement to release their medical information, the hospital will also provide its data exchange service through the inter-hospital blockchain to other hospitals online. During the information exchange process, the hybrid cloud plays the role of data relay, communicating between the patient and the hospital, and takes charge of various payments but does not store any medical information. Both parties plan to commercialise the blockchain-based service by 2019. Myongji Hospital Director Kim Hyung-soo said, “When a blockchain-based medical information exchange system is commercialised, it can prevent unnecessary administrative procedures as well as ensuring safety.” Earlier in June this year, Myongji Hospital became the first hospital in South Korea to join the Mayo Clinic Care Network. Launched in 2011, the Mayo Clinic Care Network consists of more than 40 member organisations in the U.S., China, Mexico, Philippines, Saudi Arabia, Singapore and the United Arab Emirates.