Cloud Computing

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.

The enterprise software vendor will also be making the case for putting ERP in the cloud at the upcoming health IT conference.

Some healthcare CIOs say it's time to move just about everything to the cloud, while others say not so fast.

More than 80 percent of those surveyed for a new report said they think ERP software processes are most likely to be totally replaced by AI or machine learning.

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.

17 of New Zealand’s district health boards (DHBs) are hosting some of their clinical and non-clinical data offsite using cloud services. The author asked all 20 DHBs what cloud services they are currently leveraging, their future plans and the barriers they see to cloud adoption. All but three (South Canterbury, Lakes and Tairāwhiti DHBs) are using or piloting cloud services already and all are considering or have definite plans to expand their use of cloud. Cloud services are supporting key hospital applications across the country, including patient administration systems, clinical portals, laboratory, picture archiving and communication, radiology, pharmacy, eReferrals and ICU systems. Some primary and community data is being hosted in the cloud via solutions such as the Indici patient management system and the Manage My Health patient portal. DHBs are also using cloud-based collaboration and communication tools, such as BoardBooks, Zoom Health (video conferencing) and Skype, eText and SafeNet, as well as hosting their corporate websites and intranet in the cloud. Webinar: Leveraging Cloud to Revolutionize Health IT and Transform Your EMR System Many boards see moving to the cloud as a key enabler for achieving their information strategy goals and expect a significant uptake over the next three years as they migrate clinical and non-clinical data from onsite infrastructure to Infrastructure as a Service. Some report that increasing mobility is driving uptake of cloud services, especially Software as a Service and Platform as a Service. All DHBs are at least exploring possibilities or have firm initiatives in place, such as Lakes DHB, which is moving to a “national infrastructure provider for computer services”. A response from healthAlliance, the shared-services agency for Waitemata, Auckland, Counties Manukau and Northland DHBs, says cloud services are a key part of the Northern Region’s combined technology strategy and that they are in the preliminary stages of adopting government-approved cloud-based solutions. “There is a desire within the region to harness the many benefits that cloud-based technologies can offer, balanced of course with a risk-based approach to security and privacy,” it says. MidCentral DHB has a new district-wide Digital Strategy and a key principle of this is to adopt cloud services first, where possible, for any new or existing initiatives for clinical and non-clinical services. Hawkes Bay DHB is developing a cloud infrastructure roadmap to increase the utilisation of private, regional and public cloud services, and South Canterbury DHB plans to use Microsoft Azure to host its Patientrack and electronic patient observation application. Other DHBs, such as Southern and South Canterbury, are also looking at leveraging the public cloud through use of Office 365 and other Microsoft services. Waikato DHB has around 22 cloud solutions, of which 11 are clinical. It “views cloud-based solutions as having the potential of providing considerable value through innovation, flexibility, speed to market and economic differentiators”. However, Waikato DHB’s executive director corporate services Maureen Chrystall says the cloud “is not a one-size fits all solution” and that “a bimodal strategy and decision process is required to ensure risks and impacts are managed and value is delivered”. The main barriers to cloud adoption cited by DHBs include data security, cost, legislation, legacy systems and resource capability.   A version of this article first appeared on eHealthNews.nz.

The time has finally come for hospitals to keep pace with other industries patient experience-wise, analyst firm says.

Its Elastic Inference and SageMaker platforms provide health systems with scalable and powerful ways to process large datasets, while the new Comprehend Medical tool delves into unstructured data.

During December, we’ll interview thought leaders and experts to determine how data is redesigning the roles CEOs, CFOs, COOs and other execs play and take a business-first perspective on tactics such as cloud computing, telehealth and patient experience