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Digital Healthcare - Detailed Presentation PDF

Digital healthcare technologies are transforming healthcare delivery globally. Companies are developing technologies like mobile apps, big data analytics, and smart medical devices to improve patient monitoring and outcomes. These digital innovations extract insights from medical data to enhance healthcare provisioning, reduce costs, and support preventative care and remote patient monitoring. Emerging areas like bioinformatics and medical analytics utilize big data to provide actionable clinical insights.

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Digital Healthcare Healthcare Delivery is currently undergoing a global transformation – with Digital Healthcare Technologies leading the way. Companies such as BT Health, Blueprint Health, BUPA, Microsoft, Telefonica Digital and Rockhealth - are all shaping novel and emerging Digital Healthcare Technologies - bringing new and innovative business propositions to market.
Atlantic Force: Digital Healthcare Next-Generation Social Enterprise (NGSE) Business Models – are driving emerging Digital Healthcare service providers. The Digital Social Enterprise is all about doing things better today in order to deliver a better tomorrow. Digital Healthcare is driven by rapid response to changing social conditions so that we can create and maintain increased stakeholder value - and everyone share in a brighter future for our stakeholders to enjoy today.….
Atlantic Force: Digital Healthcare Map
Value Pathways in Digital Healthcare • One of the key obstacles to rolling out the Digital Healthcare Ecosystem is bio-medical data availability, immediacy and liquidity - the flow of clinical data to every stakeholder - including patients, clinical practitioners, service providers and fund holders. Many stakeholders are now using “Big Data” methods to overcome this challenge, as part of a modern data architecture. This section describes some example Digital Healthcare use cases, a Digital Healthcare reference architecture and how “Big Data” methods can resolve the risks, issues and problems caused by poor clinical data latency. • In January 2013, McKinsey & Company published a report entitled “The ‘Big Data’ Revolution in Healthcare”. The report points out how big data is creating value in five “new value pathways” allowing data to flow more freely between stakeholders. The Diagram below is a summary of five of these new value pathway use cases and an example of how “Big Data” can be used to address each use case. Examples are taken from the Clinical Informatics Group at UC Irvine Health - many of their use cases are described in the UCIH case study. CASE STUDY 1: – Medical Analytics Digital Healthcare Value Pathways
Pathway Benefit “Big Data” Use Case Patient Health and Wellbeing Patients can build stakeholder value by taking an active role in their own health, wellbeing and treatment, including disease prevention. Predictive Analytics: Heart patients weigh themselves at home with scales that transmit data wirelessly to their health center. Algorithms analyze the data and flag patterns that indicate a high risk of readmission, alerting a physician. Patient Monitoring Patients get the most timely and appropriate diagnoses, treatment and clinical intervention available. Real-time Monitoring: Patient vital statistics are transmitted from wireless sensors every minute. If vital signs cross certain risk thresholds, staff can attend to the patient immediately. Healthcare Provisioning Healthcare Provider capabilities matched to the complexity of the assignment— for instance, nurses or physicians’ assistants performing tasks that do not require a doctor. Also the specific selection of the provider with the best outcomes. Historical EMR Analysis: Big Data reduces the cost to store data on clinical operations, allowing longer retention of data on staffing decisions and clinical outcomes. Analysis of this data allows administrators to promote individuals and practices that achieve the best results. Patient Value Proposition Ensure cost-effectiveness of care provision, such as tying Healthcare Provider reimbursement to patient outcomes, or eliminating fraud, waste, or abuse in the system. Medical Device Management: Biomedical devices stream geo- location and biomedical sensor data to manage patient clinical outcomes from medical equipment. The biomedical team know where all the patients and equipment are, so they don’t waste time searching for a location. Over time, determine the usage of different biomedical devices, and use this information to make rational decisions about when to repair or replace equipment. Digital Innovation The identification of new therapies and approaches to delivering care, across all aspects of the system and improving Medical Analytics engines themselves. Collaborative Research : Clinical Researchers attached to hospitals can access patient data stored in Hadoop Cluster “Big Data” Stores for discovery, then present the anonymous sample data to their Internal Review Board for approval, without ever having seen uniquely identifiable information. CASE STUDY 1: – Medical Analytics Digital Healthcare Value Pathways
• Changing demographics and regulations are putting tremendous pressure on the healthcare sector to make significant improvements in care quality, cost control, clinical management, organizational efficiency and regulatory compliance. To stay viable, it is paramount to effectively address issues such as missed and mis- diagnosis, coding error, over / under treatment regimes, unnecessary procedures and medications, insurance fraud, delayed diagnosis, lack of preventive health screening and proactive health maintenance. To that end, better collaboration across and beyond the organization with improved information sharing, and a holistic approach to capture clinical insights across the organization are critical. • In an environment prevalent with multiple unstructured data silos and traditional analytics focused on structured data, healthcare organizations struggle to harness 90% of their core data - which is mostly medical images, biomedical data streams and unstructured free text found in clinical notes across multiple operational domains. Connecting healthcare providers directly with patient data reduces risk, errors and unnecessary treatments; thus enabling better understanding of how delivery affects outcomes - and uncovering actionable clinical insights in order that proactive and preventive measures decrease the incidence of avoidable diseases. Digital Healthcare Digital Healthcare
• Digital Healthcare is a cluster of new and emerging applications and technologies that exploit digital, mobile and cloud platforms for treating and supporting patients. The term is necessarily general as this novel and exciting Digital Healthcare innovation approach is being applied to a very wide range of social and health problems, ranging from monitoring patients in intensive care, general wards, in convalescence or at home – to helping doctors make better and more accurate diagnoses, improving drugs prescription and referral decisions for clinical treatment. • Digital Healthcare has evolved from the need for more proactive and efficient healthcare delivery, and seeks to offer new types of prevention and care at reduced cost – using methods that are only possible thanks to sophisticated technology. • Digital Healthcare Technologies – Bioinformatics and Medical Analytics. Novel and emerging high-impact Biomedical Health Technologies such as Bioinformatics and Medical Analytics are transforming the way that Healthcare Service Providers can deliver Digital Healthcare globally – Digital Health Technology entrepreneurs, investors and researchers becoming increasingly interested in and attracted to this important and rapidly growing Life Sciences industry sector. Bioinformatics and Medical Analytics utilises Big Data / Analytics to provide actionable Clinical insights. Bioinformatics and Medical Analytics Digital Healthcare Technologies
• Healthcare is undergoing a global transformation – with Digital Healthcare Technologies leading the way. Companies such as BT Health, Blueprint Health, BUPA, Cisco, ElationEMR , Huawei, GE Healthcare, Microsoft, Telefonica Digital and Rockhealth - are all developing novel and emerging Digital Healthcare technologies - from Mobile Devices and Smart Apps to “Big Data” Analytics - bringing new and exciting Digital Healthcare business propositions to market. • Private Equity and Corporate Investment Funds are pouring seed-money and Capital into Digital Health start-up ventures - in the hope of funding a “quick win”. Applied Proteomics has just received an investment of $28 million from Genting Berhad, Domain Associates and Vulcan Capital. The State of Essen in Germany has recently invested 55m Euros on a SAP HANA Digital Health Proof-of-concept. • Telefónica Digital is sponsoring research into Smart Wards with St. Thomas's Hospital in London. At the Institute of Digital Healthcare, part of the Science City Research Alliance, researchers are not only looking to develop biomedical technologies, but to base this firmly on a pragmatic understanding of both the benefits and limitations of integrating biomedical technologies within the existing range of commercial Digital Healthcare products and services currently on offer. Digital Healthcare Digital Healthcare Technologies
• Case Study 1 – HP Autonomy Medical Analytics. Changing healthcare service provisioning, regulation and patient demographics are putting increasing pressure on the healthcare industry to make significant improvements in care quality, cost management, organizational efficiency and compliance. Priorities include the need to address challenging issues such as misdiagnosis, coding error, over / under treatment, unnecessary procedures and medications, fraud, delayed diagnosis, lack of preventive screening and proactive health maintenance. Improved collaboration within the organization with better information sharing, and a holistic approach to capture and action medical insights across the organization are crucial to success. • Case Study 2 – Telefónica Digital was created as a Special Purpose Vehicle to lead Telefónica’s transformation into an M2M / M2C / C2C Digital Services provider - cloud computing / digital telecommunications value added network services (VANS). Telefónica Digital is the vehicle for launch / bringing to market digital products and services - which will help to improve the lives of customers by leveraging the power of digital technology. This ranges from developing new technologies for healthcare providers to communicate with other stakeholders, to helping Healthcare Providers, Life Sciences businesses and government Health Case Studies Summary – Digital Healthcare Transformation Digital Healthcare Technologies
The Cone™ – Digital Healthcare
The Cone™ – Patient Model The Cone™ - Patient Model – turning Biomedical Data Streams into Actionable Medical Insights… • Acute – (10%) Active Patient Monitoring – Alerts and Alarms • Chronic – (20%) Passive Monitoring – Biomedical Data Streaming • Casuals – (30%) Walk-in – Treat On-demand • Indifferent – (40%) See Annually – Health-check / Review
Electronic Medical Records (EMR)
The Cone™ - Patient Types Acute - 10% Chronic- 20% Casuals - 30% Indifferent - 40% The Cone™ Patient Biomedical Analytics Actionable Medical Insights Presentation Clustering Biomedical Profile Biomedical Epidemiology – Groups (Streams), Types (Segments) Hybrid Cone – 3 Dimensions Biomedical Analytics
The Cone™ - Eight Primitives Primitive Domain Function Product Who ? People - Patient EMR SalesForce.com What ? Event Appointment, Walk-in, Referral, 1st Responders and Emergency Services Primary Care, GPs Healthcare Provider Hospitals, Clinics Why ? Motivation Triage - Acute / Chronic Biomedical Analytics Where ? Places - Location GIS / GPS / Analytics Geospatial Analytics When ? Time / Date Procedure Biomedical Analytics How ? Biomedical Data Streaming Medical Data Smart Devices / Apps Mobile Platforms, IoT Which ? Clinical Procedure Investigate, Diagnose, Treatment, Follow-up Nurse, Consultant Via ? Referral Channel Delivery Partner Healthcare Service Delivery, Procedure Healthcare Provider Hospitals, Clinics
The Cone™ – EIGHT PRIMITIVES Event Dimension Party Dimension Geographic Dimension Motivation Dimension Time Dimension Data Dimension Cone™ MEDIA FACT WHO ? WHAT ? WHERE ? HOW ?WHEN ?WHY ? • Indifferent • Casuals • Chronic • Acute • Temperature • Breathing Rate • Heart Rate • Blood Pressure • Blood Sugar • Brain Activity • Consultation • Clinical Tests • Diagnosis • Treatment • Appointment • Attendance • Phone Call • Letter • Location • Attitude • Movement • Region / Country • State / County • City / Town • Street / Building • Postcode • Person • Organisation Procedure Dimension WHICH ? • Procedure • Prescription Channel Dimension VIA ? • Channel / Partner • Hospital / Clinic Patient Data Delivery Channel Environment Data Subject Location Biomedical Data Event • Referral • Walk-in Motivation Patient Time / Date Version 3 – Healthcare
CASE STUDY 1: – HP Autonomy Medical Analytics - actionable insights from clinical data • HP Healthcare Analytics delivers a robust and integrated set of core and healthcare industry specific capabilities which organises and interprets unstructured data in context - designed to harness this untapped clinical data and unlock actionable medical insights. This helps to improve care quality by connecting healthcare providers directly with their data through self-service analytics; providing intelligence for more accurate diagnoses so reducing errors, risk and unnecessary treatments; enabling better understanding of how delivery affects outcomes and uncovering insights for preventive measures to decrease the rate of avoidable diseases. • Changing demographics and regulations are putting tremendous pressure on the healthcare industry to make significant improvements in care quality, cost management, organizational efficiency and compliance. To stay viable, it is paramount to effectively address issues such as misdiagnosis, coding error, over/under treatment, unnecessary procedures and medications, fraud, delayed diagnosis, lack of preventive screening and proactive health maintenance. To that end, better collaboration within the organization with improved information sharing, and a holistic approach to capture actionable insights across the organization becomes crucial. • In an environment prevalent with multiple unstructured data silos and traditional analytics focused on structured data, healthcare organizations struggle to harness 90%* of their core data - which is mostly medical images, biomedical data streams and unstructured free text found in clinical notes across multiple operational domains. This rich and rapidly growing data asset containing significant biomedical intelligence supports actionable Clinical Insights.. CASE STUDY 1: – Medical Analytics Digital Healthcare Technologies
The Biomedical Cone™ Converting Data Streams into Actionable Insights Salesforce Anomaly 42 Cone Unica End User BIG DATA ANALYTICS BIOMEDICAL DATA Patient Monitoring Platform INTERVENTION • Treatment • Smart Apps The Cone™ Patient Biomedical Analytics Actionable Medical Insights Electronic Medical Records (EMR) • Geo-demographics • Streaming • Segmentation • Households PATIENT RECORDS • Medical History • Key Events Insights InsightsInsights Anomaly 42 Unica Biomedical Data Streaming People, Places and Events Health Campaigns • Clinical and Biomedical Data • Images – X-Ray, CTI, MRI • Procedures and Interventions • Prescriptions and Treatment Social Media EXPERIAN Mosaic
CASE STUDY 2: – Digital Healthcare SMAC – Smart, Mobile, Analytics, Cloud • Digital Healthcare is a cluster of new and emerging applications and technologies that exploit digital, mobile, analytic and cloud platforms for treating and supporting patients. Digital Healthcare is necessarily generic as this novel and exciting Digital Healthcare innovation approach is being applied to a very wide range of social and health problems, ranging from monitoring patients in intensive care, general wards, in convalescence or at home – to helping doctors make better and more accurate diagnoses, improving drugs prescription and referral decisions for clinical treatment. • Digital Healthcare has evolved from the need for more proactive and efficient healthcare delivery, and seeks to offer new types of prevention and care at reduced cost – using methods that are only possible thanks to sophisticated technology. • Telefónica Digital is sponsoring research into Smart Wards with St. Thomas's Hospital in London. At the Institute of Digital Healthcare, part of the Science City Research Alliance, researchers are not only looking to develop new technologies, but to base this firmly on a pragmatic understanding of both the benefits and limitations of integration with commercial Digital Healthcare products which are currently on offer. CASE STUDY 2: – SMAC Digital Healthcare Digital Healthcare Technologies
CASE STUDY 1: – Medical Analytics Data Science in Digital Healthcare
CASE STUDY 4: – Digital Healthcare in the Cloud • Digital Healthcare is a cluster of new and emerging applications and technologies that exploit digital, mobile, analytic and cloud platforms for treating and supporting patients. Digital Healthcare is necessarily generic as this novel and exciting Digital Healthcare innovation approach is being applied to a very wide range of social and health problems, ranging from monitoring patients in intensive care, general wards, in convalescence or at home – to helping doctors make better and more accurate diagnoses, improving drugs prescription and referral decisions for clinical treatment. • Digital Healthcare has evolved from the need for more proactive and efficient healthcare delivery, and seeks to offer new types of prevention and care at reduced cost – using methods that are only possible thanks to sophisticated technology. • Telefónica Digital is sponsoring research into Smart Wards with St. Thomas's Hospital in London. At the Institute of Digital Healthcare, part of the Science City Research Alliance, researchers are not only looking to develop new technologies, but to base this firmly on a pragmatic understanding of both the benefits and limitations of integration with commercial Digital Healthcare products which are currently on offer. CASE STUDY 4: – Digital Healthcare Digital Healthcare Technologies
CASE STUDY 5: – HP Autonomy Medical Analytics - actionable insights from clinical data • HP Healthcare Analytics delivers a robust and integrated set of core and healthcare industry specific capabilities which organises and interprets unstructured data in context - designed to harness this untapped clinical data and unlock actionable medical insights. This helps to improve care quality by connecting healthcare providers directly with their data through self-service analytics; providing intelligence for more accurate diagnoses so reducing errors, risk and unnecessary treatments; enabling better understanding of how delivery affects outcomes and uncovering insights for preventive measures to decrease the rate of avoidable diseases. • Changing demographics and regulations are putting tremendous pressure on the healthcare industry to make significant improvements in care quality, cost management, organizational efficiency and compliance. To stay viable, it is paramount to effectively address issues such as misdiagnosis, coding error, over/under treatment, unnecessary procedures and medications, fraud, delayed diagnosis, lack of preventive screening and proactive health maintenance. To that end, better collaboration within the organization with improved information sharing, and a holistic approach to capture actionable insights across the organization becomes crucial. • In an environment prevalent with multiple unstructured data silos and traditional analytics focused on structured data, healthcare organizations struggle to harness 90%* of their core data - which is mostly medical images, biomedical data streams and unstructured free text found in clinical notes across multiple operational domains. This rich and rapidly growing data asset containing significant biomedical intelligence is exploited using HP Medical Analytics,. CASE STUDY 5: – Medical Analytics Digital Healthcare Technologies
The Cone™ – Actionable Clinical Insights
Digital Healthcare
Digital Healthcare Technologies These are some of the most important DIGITAL HEALTH CATEGORIES..... • Digital Imaging – (MRI / CTI / X-Ray / Ultrasound) • Robotic Surgery – (Microsurgery / Remote Surgery) • Patient Monitoring – (Clinical Trials / Health / Wellbeing) • Biomedical Data – (Data Streaming / Biomedical Analytics) • Emergency Incident Management – (Response Team Alerts) • Epidemiology – (Disease Transmission / Contact Management) Here are some of the most important DIGITAL MONITORING SMART APPS..... • Activity Monitor – (Pedometer / GPS) • Position Monitor – (Falling / Fainting / Fitting) • Sleep Monitor – (Light Sleep / Deep Sleep / REM) • Cardiac Monitor – (Heart Rhythm / Blood Pressure) • Blood Monitor – (Glucose / Oxygen / Liver Function) • Breathing Monitor – (Breathing Rate / Blood Oxygen Level)
Digital Healthcare Technologies These are some of the most influential FUTURE DIGITAL HEALTH leaders: - – Huawei - John Frieslaar (Digital Futures) – Cisco - Andrew Green (Digital Healthcare) – ElationEMR - Kyna Fong (Digital Imaging) – Microsoft - John Coplin (Digital Healthcare) – Google - Eze Vidra (Head of Campus at Tech City) – GE Healthcare - Catherine Yang (Digital Healthcare) – MIT – Prof Alex “Sandy” Pentland (Digital Epidemiology) – Telefónica Digital – Mathew Key – CEO (Digital Healthcare) – Open University – Dr. Blain Price (Digital Patient Monitoring) – UCLA – Prof. Larry Smarr (FuturePatient – Digital Patient Monitoring) – Telefónica – Dr. Mike Short CBE (Digital Futures and the Smart Ward) – Thames Valley Health Innovation and Education Cluster – David Doughty – Department of Business, Industry & Skills – Richard Foggie, KTN Executive – Science City Research Alliance – Sarah Knaggs (Strategic Project Manager)
Digital Healthcare – Executive Summary • Digital Healthcare is a cluster of new and emerging applications and technologies that exploit digital, mobile and cloud platforms for treating and supporting patients. The term "Digital Healthcare" is necessarily broad and generic as this novel and exciting Bioinformatics and Medical Analytics innovation driven approach is applied to a very wide range of social and health problems - from monitoring patients in intensive care, general wards, in convalescence or at home – to helping general practitioners make better informed and more accurate diagnoses, improving the effect of prescription and referral decisions for clinical treatment. • Bioinformatics and Medical Analytics utilises Data Science to provide actionable clinical insights. Digital Healthcare has evolved from the need for more proactive and efficient healthcare service delivery, and seeks to offer new and improved types of pro-active and preventive monitoring and medical care at reduced cost – using methods that are only possible thanks to emerging SMAC Digital Technology. Digital Healthcare Technologies – Bioinformatics and Medical Analytics: - Digital Patient Monitoring • Biomedical Data Streaming • Biomedical Data Science and Analytics • Epidemiology, Clinical Trials, Morbidity and Actuarial Outcomes • • Novel and emerging high-impact Biomedical Health Technologies such as Bioinformatics and Medical Analytics are transforming the way that Healthcare Service Providers can deliver Digital Healthcare globally – Digital Health Technology entrepreneurs, investors and researchers becoming increasingly interested in and attracted to this important and rapidly expanding Life Sciences industry sector.
Digital Healthcare – Executive Summary • While many industries can benefit from SMAC digital technology – Smart Devices, Mobile Platforms, Analytics and the Cloud – this is especially the case for Life Sciences, Pharma and Healthcare industry sectors – resulting in more accurate diagnosis, improved treatment regimes, more reliable prognosis, better patient monitoring, care and clinical outcomes. Let’s take a look at some of the Digital Technologies that are bringing significant improvements and benefits to Healthcare • Today, thanks to the regulatory compliance requirements for HIPAA, HITEC, PCI DSS and ISO 27001, the reluctance to adopt Digital Technology has been overcome, and Digital Healthcare adoption is gaining increased traction. Many of the security features required for data protection and patient confidentiality are being addressed by Digital Healthcare service providers, therefore relieving healthcare delivery organizations from tedious and complex security and data protection frameworks. Biomedical Data Analytics: • The exploitation of data by applying analytical methods such as statistics, predictive and quantitative models to patient segments or groups of the population will provide better insights and achieve better outcomes. As far back as 2010, there was evidence that: “93 percent of healthcare providers identified the digital information explosion as the major factor which will drive organizational change over the next 5 years.” (Related article: Cloud and healthcare: A revolution is coming)
Digital Healthcare – Executive Summary Data Security and Privacy: • Today, thanks to the regulatory compliance requirements for HIPAA, HITEC, PCI DSS and ISO 27001, reluctance to adopt emerging technologies is starting to be addressed and digital technology is beginning to gain traction - bear in mind also that many of the security features required for data security and protection are addressed by the service providers, therefore relieving the healthcare organization from tedious and complex security frameworks. Mobility: • Mobility Services, where Smart Devices, Smart Apps, Mobile Platforms and Cloud Infrastructure is providing the backbone for medical personnel to access all sorts of patient information from any place, any where - and from a wide range of mobile devices. Collaboration with patients: • Mobility means that complete patient records are now available to healthcare professionals anytime, anywhere – allowing physicians to access historical patient case records , images and clinical data to fine-tune their diagnosis and make informed decisions on treatment – thus reducing diagnosis latency, increasing accuracy and improving patient care and clinical outcomes from initial consultation to specialist referrals. Some scenarios are illustrated in the following: - • Physician Collaboration Solutions (PCS) • • PCS solutions offers video conferencing to facilitate remote consultations and care continuity, allowing patients to be viewed remotely. PCS allows physicians to consult with patients and even perform remote robotic surgery. This is dubbed “tele-health solutions.”
Digital Healthcare – Executive Summary • Electronic Medical Records (EMR) • • Every piece of information pertaining to a specific is recorded and stored. The solution is designed to capture and provide a patient’s data at any time of the patient’s monitoring cycle, including the complete medical records and history. • Patient Information Exchange (PIE) • • This allows for the healthcare information to be shared electronically across organizations within a region, community or hospital system. There are currently several Digital Healthcare cloud service providers addressing this market, taking the role of collecting and distributing medical information from and among multiple organizations. • The New York Times has published an interesting article illustrating the use of the cloud in healthcare - leveraging big data in the cloud to manage patient relationships and clinical outcomes. Collaboration among peers: • Technology can provide medical assistance to doctors in the field, b e it in remote areas or in emergency relief operations through satellite communications. Refer to the Remote Assistance for Medical Teams Deployed Abroad (T4MOD project) which could easily find its place in the Digital Healthcare cloud space.
Digital Futures: - Creating new roles and value chains
Digital Healthcare - Overview Digital Futures: - Creating new roles and value chains Novel and emerging Biomedical Health Technologies are transforming the way that Healthcare Providers can deliver Healthcare globally – with Digital Health Technology entrepreneurs and investors becoming increasingly attracted to this rapidly growing industry sector. Healthcare Delivery is currently undergoing a global transformation – with Digital Healthcare Technologies leading the way. Companies such as BT Health, Blueprint Health, BUPA, Microsoft (John Coplin), Telefonica Digital (Dr. Mike Shaw) and Rockhealth - are all shaping novel and emerging Digital Healthcare Technologies - bringing new and innovative business propositions to market.
Changing the patient experience • Advances in technology are already changing patient experiences - making healthcare better, easier, more accurate and more efficient for physicians, patients, hospital staff and administrators are • These changes will no doubt affect the role of hospitals and emergency departments. As continuous monitoring of biometric data becomes the norm, the ER will be used as a dispatch center, with patients' information reaching the hospital before they do. This will eliminate wait times and decrease the risk of disease transmission, especially important when immune-compromised patients face hours in the ER. • All of these advances translate into one main objective: improving patient outcomes. With access to more powerful tools that are cheaper, faster and better than their predecessors, patient outcomes are certain to improve. People will become increasingly responsible for their own health. This will lead to more effective care, as people will be able to detect problems much earlier in the process. Patients will no longer put off appointments for years because personal health will be ever-present. This will reduce healthcare costs on several levels and change the type of medical professionals the industry needs most.
Diagnostics @ Point of Care • Point of Care Diagnostics: Technology promises to put the burden of care and diagnosis directly in the hands of patients. The Qualcomm Tricorder XPRIZE Challenge is sponsoring a $10 million race to develop a handheld, non-invasive electronic device that can diagnose 15 diseases and track 5 vital signs in the field. Patients would no longer have to go to a doctor's office or hospital. Instead, a device in their homes would analyze their data, diagnose the problem and send their information up to the cloud, where a physician could treat them remotely. Such a device could make healthcare more accessible in rural areas and developing nations. • One of the devices up for the challenge is being developed by Scanadu, which also has an electronic urinanalysis stick, similar to a pregnancy test, which performs up to 9 different tests and sends the results through the cloud to the treating physician, eliminating the need for routine lab visits.
Biomedical Robotics • Robotics: Robotics are quickly advancing medical treatment. Ekso Bionics has already launched the first version of its exoskeleton, which enables paraplegics to stand and walk independently. This revolutionary technology allows a person who has spent 20 years in a wheelchair to stand on her own. This holds huge promise for the next generation of robotics. • Robotic home health care workers are on the horizon. Honda’s robot ASIMO is a humanoid robot with the ability to navigate through crowds and objects using sensor technology. Fully autonomous, in the future, we’ll see ASIMO and similar robots in the home to help when you’re sick or elderly – or just need an extra set of hands. The possibilities for technology and healthcare really are endless. Now, just think of all the things your own personal Rosie the Robot will do …. • BCI and BBIs: As brain-computer interfaces become more advanced, healthcare will incorporate more complex human-computer connections. The uses range from helping people manage pain to controlling robotic limbs. Harvard University researchers recently created the first brain-to-brain interface that allowed a human to control a rat's tail — and another human's movements — with his mind, proving that controlled robotic limbs have far-reaching possibilities for patients.
Biomedical Robotics • Artificial intelligence: IBM's Watson Super Computer is just the first step toward using artificial intelligence in medicine. The supercomputer, which defeated two human champions on "Jeopardy!" two years ago, has gone to medical school. Watson not only gives the top 3 probabilities for a diagnosis, but what physicians most appreciate is Watson gives the evidence behind these probabilities. • IBM opened up their API for anyone to use – whether you are 2 kids in a garage or a Fortune 500 company. Why would they give their technology to their competitors? Easy. Because Watson improves with use. So the more people and organizations use Watson, the faster it learns, the better it becomes. • Biomedical 3D printing: California-based research company Organovo has printed human liver tissue to test drug toxicity on specific sections of the liver. Although printing organs for transplants may still be far off, this technology could be used in the near future with individual patients to test their toxicity reactions to specific drugs. • Recently researchers have printed out exact replicas of kidneys with tumors for simulated surgery before going into a patient. These 3D printed kidneys are transparent so the surgeons can discern where the blood vessels are located. In one case, this reduced the amount of time a patient’s blood flow to the organ was interrupted from 22 minutes to 8 minutes during surgery.
The Bacteriophage Revolution • The emergence of pathogenic bacteria resistant to many, if not most, currently available anti-microbial agents has become a critical clinical problem in modern medicine - particularly in the concomitant increase in immuno-suppressed patients. The concern that the treatment of disease is re-entering the “pre-antibiotics” era has become real, and the development of alternative anti-infection modalities is now one of the highest priorities of modern medicine and biomedical technology. • Prior to the discovery and widespread use of antibiotics, it was suggested that bacterial infections could be prevented and/or treated by the administration of viruses which attacked bacteria - bacteriophages. Although the early clinical studies with bacteriophages were not vigorously pursed in the United States and Western Europe, phages continued to be utilized in the former Soviet Union and Eastern Europe. The results of these studies were extensively published in non- English (primarily Russian, Georgian, and Polish) journals and, therefore, were not readily available to the western scientific community. In this review, we briefly describe the history of bacteriophage anti-microbial research in the former Soviet Union and the reasons that the clinical use of bacteriophages failed to take root in the West. Further, we share our thoughts about future prospects for phage therapy in biomedical research – the Bacteriophage Revolution.
Digital Healthcare – Technical Appendices
HP – Outlook for 2015 Biomedical Analytics HP Autonomy Medical Analytics - actionable insights from clinical data • HP Healthcare Analytics delivers a robust and integrated set of core and healthcare industry specific capabilities which organises and interprets unstructured data in context - designed to harness this untapped clinical data and unlock actionable medical insights. This helps to improve care quality by connecting healthcare providers directly with their data through self-service analytics; providing intelligence for more accurate diagnoses so reducing errors, risk and unnecessary treatments; enabling better understanding of how delivery affects outcomes and uncovering insights for preventive measures to decrease the rate of avoidable diseases. • Changing demographics and regulations are putting tremendous pressure on the healthcare industry to make significant improvements in care quality, cost management, organizational efficiency and compliance. To stay viable, it is paramount to effectively address issues such as misdiagnosis, coding error, over/under treatment, unnecessary procedures and medications, fraud, delayed diagnosis, lack of preventive screening and proactive health maintenance. To that end, better collaboration within the organization with improved information sharing, and a holistic approach to capture actionable insights across the organization becomes crucial. • In an environment prevalent with multiple unstructured data silos and traditional analytics focused on structured data, healthcare organizations struggle to harness 90%* of their core data - which is mostly medical images, biomedical data streams and unstructured free text found in clinical notes across multiple operational domains. This rich and rapidly growing data asset containing significant biomedical intelligence supports actionable Clinical Insights..
IBM – Outlook for 2015 Wave-form Analytics IBM Infosphere - Excel Medical Streaming Analytics Platform • Excel Medical Electronics’ BedMasterEx software is the industry leader in acquisition and storage of complex physiological data (waveforms, vital signs, and clinical alarms) acquired from hospital patient monitoring networks and medical devices. • Excel Medical Electronics has tightly integrated their BedMasterEx solution with IBM’s InfoSphere Streams to create a groundbreaking new platform to analyze volumes of unstructured clinical data in real time with the goal of creating predictive medical algorithms. In conjunction with IBM Watson Research Center, IBM and Excel Medical Engineers developed adapters to the BedMasterEx system. • These adapters feed data for both real time analytics and retrospective research databases. The Excel Medical Streaming Analytics Platform provides a common development channel among academic researchers to collaborate and speed up validation of algorithms.
IBM – Outlook for 2015 Mobile Access Platforms IBM and the Boston Children Hospital • This is exemplified by the recent announcement from IBM and the Boston Children Hospital, creating “the world’s first cloud-based global education technology platform to transform how paediatric medicine is taught and practiced around the world. The initiative aims to improve the exchange of medical knowledge on the care of critically ill children, no matter where they live.” • As with everything, you have to be aware of a few shortcomings, the most significant of all being data security and breach of confidentiality. This recurrent theme acted as an inhibitor to healthcare embracing cloud technology. While many cloud providers are now claiming to be able to ensure compliance with HIPAA, the healthcare organizations do still have to figure out how exactly to address these requirements in a cloud environment. • The organizations now entrusting their cloud providers to host sensitive data and infrastructure do need to understand that they are actually handing over sensitive data to the cloud provider. This in turn will imply the need to explore how the cloud provider will indeed provide the level of security, the quality of service and the availability of the stored information. • While the healthcare industry is starting to embrace cloud computing, we can already foresee the tremendous potential of this technology leveraging on big data and analytics and all the applications that may come from its many uses. While there might be shortcomings, these are far outweighed by the benefits for both the industry and the patients. What do you think?
Microsoft – Outlook for 2015 • Big Data in Digital Healthcare offers a path towards clinical insight and medical advances through a culture-challenging information strategy and effective data management. The global amount of data and internet content is expected to reach a staggering 5,247 gigabytes per person by 2020. Translated into physical terms, there are twice as many bytes of data in the world than there are litres of water in our oceans – that’s a lot of data out there to manage. Further fuelling the rapid increase in data abundance are falling hardware costs coupled with the proliferation of vast amounts of machine-generated data in the Cloud from fixed and mobile appliances, devices and sensors. • At Microsoft, our goal is to bring Data Science, its applications, information and Biomedical Data insights to one billion people through secure, scalable and easy- to-use enterprise-class tools. Data Science and Big Data are driving clinical insight and medical advances, are fast becoming the major factor for competitive advantage and business growth. Big Data is just one of several important trends because through the strategic use of information, businesses can innovate more quickly, lower operational costs, improve clinical outcomes and drive up patient health and wellbeing.
Oracle – Outlook for 2015 • The number of new and emerging technologies that employ ubiquitous appliances, monitors, sensors and devices in order to generate, transmit. store and analyse vast amounts of automatic machine-generated data will continue to grow as consumers embrace their new digital lifestyles. For one example, wearable digital technology will start to enter the mainstream market and begin generating vast amounts of new consumer data from which companies will be able to draw new meaningful insights. In 2015 we expect big data to finally go mainstream and emerge at a scale much more significant than just a simple tool for capturing and analysing digital consumer insights. Scientific Research • Advanced scientific research is a game played in the minutiae of life, in the place where discoveries made on the tiniest scale can have enormous implications for the entire human population. Projects are often long and labour-intensive, as researchers conduct a seemingly endless number of iterative analyses on these microscopic events as they look for trends that point to new discoveries. Health and Life Sciences • Data Science and Big Data have the potential to drive meaningful progress in the biomedical field, particularly as health experts seek cures for life-threatening illnesses that affect more and more people each year. In the medical research arena, for example, the ability to consolidate health data from patients in hospitals all over the world and trend it in real-time against demographic and geographic epidemiology, treatment and prescriptive factors - weather, local social customs and family history becomes very powerful. Armed with the new insights that big data analyses will give them, medical professionals can focus their efforts and accelerate the race to cure terminal disease.
SAP – Outlook for 2015 • SAP is a Growth Company. SAP wishes to elevate itself to become a trusted innovator for all of their customers – whether it’s achieving business outcomes, simplifying everything through the cloud or driving business efficiency and growth using Mobile and In-memory Computing. • Industry Focused. In 2013 SAP was global the market leader for supplying ERP application software across 25 different Industry Sectors – and will continue to increase its Industry Sector focus to make SAP HANA the standard business platform for world-class Industry Sector applications and process execution. • The Digital Enterprise. SAP grew its mobile, cloud and in-memory computing businesses heavily in 2013 and will continue to strengthen its transition into products supporting the Digital Enterprise area even more so in 2015. BIW (Business Information Warehouse) and ECC6 (ERP Central Components version 6) Business Suite – will ultimately be fully integrated into Cloud, Mobile and SAP HANA High-availability Analytics in-memory computing platform environments. • Key Technology Platforms and Industry Sector areas for SAP in 2015 include the following: - 1. Digital Healthcare 2. Multi-channel Retail 3. Financial Technology 1. Cloud Services 2. The Mobile Enterprise 3. In-memory Computing Industry SectorsTechnologies
Healthcare: - SAP Solution Roadmap • Patient Experience and Journey – Patient Administration and Billing – Patient Relationship Management • Clinical Delivery – Clinical Treatment and Care • Digital Imaging – (MRI / CTI / X-Ray / Ultrasound) • Robotic Surgery – (Microsurgery / Remote Surgery) • Patient Monitoring – (Clinical Trials / Health / Wellbeing) • Biomedical Data – (Data Streaming / Biomedical Analytics) • Emergency Incident Management – (Response Team Alerts) • Epidemiology – (Disease Transmission / Contact Management) – Enterprise Healthcare Mobility (Mobile Devices / Smart Apps) • Activity Monitor – (Pedometer / GPS) • Position Monitor – (Falling / Fainting / Fitting) • Sleep Monitor – (Light Sleep / Deep Sleep / REM) • Cardiac Monitor – (Heart Rhythm / Blood Pressure) • Blood Monitor – (Glucose / Oxygen / Liver Function) • Breathing Monitor – (Breathing Rate / Blood Oxygen Level) • Care Collaboration – Connected Care – Referral Management
From sports to scientific research, a surprising range of industries will begin to find value in big data.....
“Big Data” in Digital Healthcare “Big Data” in Pharma / Life Sciences • Big data now plays an important role in medical and clinical research. Digital Patient Records are now being harvested and analysed in large-scale patient population studies – which are yielding actionable clinical insights. The UK Government has made anonymised patient records from the National Health Service openly available. Medical Centres, Research Institutes and Pharma / Life Sciences funding agencies have all made major investments in this area.
Big Data” in Clinical Medicine “Big Data” in Clinical Medicine • Big data plays an important role in medical and clinical research and has been exploited in clinical data studies. Major research institute centres and funding agencies have made large investments in the arena. For example, the National Institutes of Health recently committed US $100 million for the big data to Knowledge (BD2K) initiative [40]. The BD2K defines “biomedical” big data as large datasets generated by research groups or individual investigators and as large datasets generated by aggregation of smaller datasets. The most well- known examples of medical big data are databases maintained by the Medicare and Healthcare Cost and Utilization Project (with over 100 million observations). • One of the differences between medical big data and large datasets from other disciplines is that clinical big data are often collected based on protocols (ie, fixed forms) and therefore are relatively structured, partially due to the extraction process that simplify raw data as mentioned above. This feature can be traced back to the Framingham Heart Study [41], which has followed a cohort in the town of Framingham, Massachusetts since 1948. Vast amounts of data have been collected through the Framingham Heart Study, and the analysis has informed our understanding of heart diseases, including the effects of diet, exercise, medications, and obesity on risk [42]. There are many other clinical databases with different scopes, including but not limited to, prevalence and trend studies, risk factor studies, and enotype-phenotype studies.
“Big Data” – Analysing and Informing • SENSE LAYER – Remote Monitoring and Control – WHAT and WHEN? – Remote Sensing – Sensors, Monitors, Detectors, Smart Appliances / Devices – Remote Viewing – Satellite. Airborne, Mobile and Fixed HDCCTV – Remote Monitoring, Command and Control – SCADA • GEO-DEMOGRAPHIC LAYER – People and Places – WHO and WHERE? – Person and Social Network Directories - Personal and Social Media Data – Location and Property Gazetteers - Building Information Models (BIM) – Mapping and Spatial Analysis – Landscape Imaging & mapping, Global Positioning (GPS) Data – Temporal / Geospatial data feeds –Weather and Climate, Land Usage, Topology / Topography • INFORMATION LAYER – “Big Data” and Data Set “mashing” – HOW and WHY? – Content – Structured and Unstructured Data and Content – Information – Atomic Data, Aggregated, Ordered and Ranked Information – Transactional Data Streams – Smart Devices, EPOS, Internet, Mobile Networks
“Big Data” – Analysing and Informing • SERVICE LAYER – Real-time and Predictive Analytics – WHAT / WHEN NEXT? – Global Mapping and Spatial Analysis - GIS – Service Aggregation, Intelligent Agents and Alerts – Data Analysis, Data Mining and Statistical Analysis – Optical and Wave-form Analysis and Recognition, Pattern and Trend Analysis an Extrapolation • COMMUNICATION LAYER – Mobile Enterprise Platforms and the Smart Grid – Connectivity - Smart Devices, Smart Apps, Smart Grid – Integration - Mobile Enterprise Application Platforms (MEAPs) – Backbone – Wireless and Optical Next Generation Network (NGE) Architectures • INFRASTRUCTURE LAYER – Cloud Service Platforms – Public, Mixed / Hybrid, Enterprise, Private, Secure and G-Cloud Cloud Models – Infrastructure – Network, Storage and Servers – Applications – COTS Software, Utilities, Enterprise Services – Security – Principles, Policies, Users, Profiles and Directories, Data Protection
National Institute for Medical Research • NIMR is one of the world's leading medical research institutes, dedicated to studying important questions about the life processes that are relevant to all aspects of health. Francis Crick Institute
Digital Healthcare Skills Matrix Cluster Theory – Digital
Digital Healthcare Skills Matrix Cluster Theory – Digital
Abiliti: Future Systems Slow is smooth, smooth is fast..... .....advances in “Big Data” have lead to a revolution in Chronic Patient Management, Clinical Trials, Epidemiology, Morbidity, Actuarial Science, Biomedical profiling, forecasting and predictive modelling – but it takes both human ingenuity, and time, for Biomedical and Healthcare Models to develop and mature.....
Digital Futures: - Creating new roles and value chains
Digital Healthcare • Digital Healthcare is a cluster of new and emerging applications and technologies that exploit digital, mobile and cloud platforms for treating and supporting patients. The term is necessarily general as this novel and exciting Digital Healthcare innovation approach is being applied to a very wide range of social and health problems, ranging from monitoring patients in intensive care, general wards, in convalescence or at home – to helping doctors make better and more accurate diagnoses, improving drugs prescription and referral decisions for clinical treatment. • Digital Healthcare has evolved from the need for more proactive and efficient healthcare delivery, and seeks to offer new types of prevention and care at reduced cost – using methods that are only possible thanks to sophisticated technology. • Digital Healthcare Technologies – Bioinformatics and Medical Analytics. Novel and emerging high-impact Biomedical Health Technologies such as Bioinformatics and Medical Analytics are transforming the way that Healthcare Service Providers can deliver Digital Healthcare globally – Digital Health Technology entrepreneurs, investors and researchers becoming increasingly interested in and attracted to this important and rapidly growing Life Sciences industry sector. Bioinformatics and Medical Analytics utilises Data Science to provide actionable Clinical insights.
Digital Healthcare Technologies Scalable Enterprise Waveform Analytics Platform for Pharma • Neural ID provides the only collaborative bio-signal analytics platform spanning the pharmaceutical lifecycle. From Discovery through Clinical and Health Information, Neural ID delivers a scalable enterprise solution addressing the industry’s productivity crisis. Our flagship product, IWS, delivers expert-driven machine learning, massive data reduction and an interoperable data format to help customers make better decisions, faster. • Neural ID’s enterprise software platform is used by the world's leading companies to deliver cutting-edge biosignal analytics, including 4 of the top 10 pharmaceutical companies.
Helix Health Solutions • Streaming Analytics - Physiological Wave- form Analysis Platform Excel Medical Electronics has developed a groundbreaking new research platform for analyzing volumes of unstructured data in real time by integrating their BedMasterEx data acquisition solution with IBM’s® InfoSphere™ Streams technology. Complex and high frequency medical data such as physiological waveforms have gone relatively unstudied in the healthcare industry due to substantial technology barriers.
Digital Healthcare Technologies Medical Education and Remote Diagnostics • Capabilities in Remote Diagnostics and Medical Education are evolving rapidly. Companies that are innovating on this front and encompassing solutions such as crowd-sourcing and peer-2-peer learning. Some of those companies really taking advantage of the explosion in Biomedical “Big Data' include HP, GE Healthcare, Siemens Healthcare, Boardvitals and AgileMD Secure Storage and Sharing of Biomedical Information • Box is a platform that is HIPAA and HITECH compliant for secure capture, storage and management of Protected Personal Health Information (PPHI). Medical Service Provider's Tools • More and more service providers continue to jump on board with the new Medical Service Provider's Tools that are out there. Two companies that are particularly interesting are Clinicast and Reify Health (currently in beta test)
Digital Healthcare Technologies Digital Diagnostics Tools • Researchers are now taking advantage of new and emerging biomedical technologies which integrate with Mobile Phones and other Smart Devices in order to add diagnostic capabilities to the arsenal of the general and clinical physician. One company that looks promising in the future is Cellscope - FDA approved. • Proteus Digital Health takes endoscopy to an extraordinary new level. This device is housed in a small capsule which can be swallowed - and contains a range of sensors and detectors, automatically streaming continuous digital information – and even images - to Mobile Phones and other Smart Devices. The device is capable of monitoring and tracking how the patient’s alimentary canal and digestive system behaves when an oral drug is being administered or when food or drink is being consumed. Nephosity - imaging - FDA approved. • Dexcom markets a device that monitors blood glucose levels which is tucked neatly under the skin of the patient’s abdomen - FDA approved. Google are trialling a soft contact lens with an embedded bluetooth device and a sensor that monitors blood glucose levels - which continuously streams blood glucose level data to a monitoring service in the cloud, via a bluetooth mobile phone connection.
Digital Healthcare Technologies Patient Communities – Chronic Disease Management • Reducing the cost of treating chronic illness is a major goal – because it can dramatically improve health indices in populations of individuals suffering from chronic long-term illness Focusing on those highest-cost patient population's is an exciting approach that a number of companies are exploring. Chronic Disease management can be improved by supporting care providers and extenders that take on the task of assisting with the healthcare and improving the outcomes of these high-cost patients. • Patients that have chronic illness have a variety of needs. Some patients require planned, regular interactions with support to their carers, focusing on function and prevention of acute episodes and complications. Community Healthcare Coaches can provide ongoing assessments in compliance with the treatment plan. Another important issue could be behavioural modification, and an organised support system for the patient. Planned interactions are overseen by the Primary Care Leader and any further intervention must be initiated by the medical practitioner and directed by clinically relevant information systems and continuing follow-up plans. – Companies that are providing Chronic Disease Management software for Patient Communities include: - Omada Health, Wallgreens and Safeway Health
Digital Healthcare Technologies Electronic Medical Records (EMR's) • EMR's are Active web applications that can intervene directly in order to effect positive patient outcomes. “Prioritising positive patient care becomes a natural consequence when the EMR is built with the intent of facilitating the patient- physician relationship. EMR's focus on supporting the physician – so that the physician can focus on treating the patient” - says Kyna Fong - ElationEMR • Companies developing Active Patient Management in order to promote positive Medical Outcomes include the following Digital Health Technology providers: - – ElationEMR, GEHealthcare, Curemd and Drchrono and 5 O'Clock Records, CareCloud between them offer a variety of web-based EMR‘s in addition to General Practice patient administration systems and revenue cycle management solutions – DoseSpot is an e-prescribing platform. Medopad and Practice Fusion are EMR's which are marketed to community practitioners and doctors in primary health groups.
Digital Healthcare Technologies Telemedicine • With systems such as Teladoc you can obtain an on-line consultation from a consultant physician or specialist anywhere in the world via an on-line video-link. Teladoc is bringing this facility over to the 'brick and mortar' side by working on the development of walk-in patient kiosks situated in Health Centres and high-street Pharmacies . Grid Computing World • Community Grid for grid computing applications - Mobile Phones and other smart devices will make use of sensor and imaging technology to gather passive and active data for statistical analysis and diagnosis via Remote Healthcare Monitoring and Emergency Event Management Centres. Care Delivery • Delivery of care can always be improved. Some of the winners in this category are going to be: - – One Medical, Sherpaa, Metamed (personalized medical research) and Statphone (patient transfers).
Digital Healthcare Technologies Behavioural Health Analytics • Patient Behaviour Analysis is the diagnostic tool of the future. Every patient has unique genetic characteristic and environmental exposure - habits and behaviour patterns - and any changes to those everyday habits and behaviour patterns may be an indicator of a change in health status requiring intervention or a predictive determinant of the future path a patient may take in terms of health and wellbeing. Mobile Phones and other smart devices will make use of sensor and imaging technology to gather passive and active data for statistical analysis and diagnosis. Biomedical “Big Data” Management and Analytics • Anapsis and EMBI, focus on Biomedical “Big Data” Management and Analytics. This service is highly customisable for every client. • Ginger.io is another example of a Behavioural Analytics platform. Ginger.io examines patterns of everyday activity which are used as points of entry for understanding larger issues such as paediatrics requirements, geriatrics needs and mental health care for schemes such as Care in the Community and Assisted Living at Home.
Digital Healthcare Technologies Transitional Care • "Care transitions" is a term that describes the flow of patients from clinical settings to settings in the community - which are socially more appropriate relative to their needs. Every patient's needs change over time. Patients may encounter a Primary Care Provider, a hospital physician, the nursing team and even Social Services before they are “whisked off" to a nursing facility or care home. Promising companies in the area of Care Transition include: - – Care At Hand, Independa and OpenPlacement • Companies such as these are building Smart Apps for Mobile Phones and other smart devices which will make use of sensor and imaging technology for streaming data to monitoring services that will bring new possibilities in the transition from Intensive Care Units and General Hospital Wards, into a convalescent nursing facility or care home and on into other patient care schemes such as Care in the Community and Assisted Living at Home.
Digital Healthcare Technologies Patient Management and Patient Administration Systems • Integrated new clinical and back-office Patient Management and Patient Administration Systems will be in demand to manage the changing landscape of healthcare services provisioning, funding and cross-charging. • Some of the challenges that are being addressed range from the simple capture at source of one-off chargeable consultation, medication and point medical procedures – to fully-featured clinical billing systems for managing the provision of complex multi-stage and continuous medication and clinical procedures, re-charging costs and administering payments from Primary Care budget holders and Health Insurance Companies – or patients themselves. • Solutions from those companies listed below are of interest: - • Medmonk, Medikly, Simplee, Cake Health, Castlight Healthcare, SwiftPayMD.
Digital Healthcare Technologies - Bioinformatics • Healthcare is undergoing a global transformation – with Digital Healthcare Technologies leading the way. Companies such as BT Health, Blueprint Health, BUPA, Cisco, ElationEMR , Huawei, GE Healthcare, Microsoft, Telefonica Digital and Rockhealth - are all developing novel and emerging Digital Healthcare technologies - from Mobile Devices and Smart Apps to “Big Data” Analytics - bringing new and exciting Digital Healthcare business propositions to market. • Private Equity and Corporate Investment Funds are pouring seed-money and Capital into Digital Health start-up ventures - in the hope of funding a “quick win”. Applied Proteomics has just received an investment of $28 million from Genting Berhad, Domain Associates and Vulcan Capital. The State of Essen in Germany has recently invested 55m Euros on an SAP Digital Health Proof-of-concept. • Telefónica Digital is sponsoring research into Smart Wards with St. Thomas's Hospital in London. At the Institute of Digital Healthcare, part of the Science City Research Alliance, researchers are not only looking to develop biomedical technologies, but to base this firmly on a pragmatic understanding of both the benefits and limitations of integrating biomedical technologies within the existing range of commercial Digital Healthcare products and services currently on offer.
Wave-form Analytics • • WAVE-FORM ANALYTICS • is an analytical tool based on Time-frequency Wave- form analysis – which has been “borrowed” from spectral wave frequency analysis in Physics. Deploying the Wigner-Gabor-Qian (WGQ) spectrogram – a method which exploits wave frequency and time symmetry principles – demonstrates a distinct trend forecasting and analysis capability in Wave-form Analytics. Trend-cycle wave-form decomposition is a critical technique for testing the validity of multiple (compound) dynamic wave-series models competing in a complex array of interacting and inter- dependant cyclic systems - waves driven by both deterministic (human actions) and stochastic (random, chaotic) paradigms in the study of complex cyclic phenomena. • • WAVE-FORM ANALYTICS in “BIG DATA” • is characterised as periodic alternate sequences of, high and low trends regularly recurring in a time-series – resulting in cyclic phases of increased and reduced periodic activity – Wave-form Analytics supports an integrated study of complex, compound wave forms in order to identify hidden Cycles, Patterns and Trends in Big Data. The existence of fundamental stable characteristic frequencies in large aggregations of time-series Economic data sets (“Big Data”) provides us with strong evidence and valuable information about the inherent structure of Business Cycles. The challenge found everywhere in business cycle theory is how to interpret very large scale / long period compound-wave (polyphonic) temporal data sets which are non-stationary (dynamic) in nature.
Wave-form Analytics Track and Monitor Investigate and Analyse Scan and Identify Separate and Isolate Communicate Discover Verify and Validate Disaggregate Background Noise Individual Wave Composite Waves Wave-form Characteristics
"Big Data” Analytics – Profiling and Clustering • "BIG DATA” ANALYTICS – PROFILING, CLUSTERING and 4D GEOSPATIAL ANALYSIS • • The profiling and analysis of large aggregated datasets - to determine a ‘natural’ structure of data relationships or groupings - is an important starting point forming the basis of many mapping, statistical and analytic applications. Cluster analysis of implicit similarities - such as time-series demographic or geographic distribution - is a critical technique where no prior assumptions are made concerning the number or type of groups that may be found, or their relationships, hierarchies or internal data structures. Geospatial and demographic techniques are frequently used in order to profile and segment populations by ‘natural’ groupings. Shared characteristics or common factors such as Behaviour / Propensity or Epidemiology, Clinical, Morbidity and Actuarial outcomes – allows us to discover and explore previously unknown, concealed or unrecognised insights, patterns, trends or data relationships. • "Big Data" sources include: - – Transactional Data Streams from Business Systems – Energy Consumption Data from Smart Metering Systems – SCADA and Environmental Control Data from Smart Buildings – Vehicle Telemetry Data from Passenger and Transport Vehicles – Market Data Streams – Financial, Energy and Commodities Markets – G-Cloud – NHS Communications Spine, Local and National Systems – Machine-generated Exploration / Production Data created in Digital Oilfields – Cable and Satellite Home Entertainment Systems – Channel Selection Data – Call Detail Records (CDRs) from Telco Mediation, Rating and Billing Systems – Internet Browsers, Social Media / Search Engines – User Site Navigation and Content Data – Biomedical Data Streaming – Smart Hospitals / Care in the Community / Assisted Living @ Home – Other internet click-streams – Social Media, Google Analytics, RSS News Feeds / Market Data Feeds
The Temporal Wave – 4D Geospatial Analytics • The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic) context. The problems encountered in exploring and analysing vast volumes of spatial– temporal information in today's data-rich landscape – are becoming increasingly difficult to manage effectively. In order to overcome the problem of data volume and scale in a Time (history) and Space (location) context requires not only traditional location–space and attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the additional dimension of time–space analysis. The Temporal Wave supports a new method of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context. • This time-visualisation approach integrates Geospatial (location) data within a Temporal (timeline) data along with data visualisation techniques - thus improving accessibility, exploration and analysis of the huge amounts of geo-spatial data used to support geo- visual “Big Data” analytics. The temporal wave combines the strengths of both linear timeline and cyclical wave-form analysis – and is able to represent data both within a Time (history) and Space (geographic) context simultaneously – and even at different levels of granularity. Linear and cyclic trends in space-time data may be represented in combination with other graphic representations typical for location–space and attribute–space data- types. The Temporal Wave can be used in roles as a time–space data reference system, as a time–space continuum representation tool, and as time–space interaction tool.
BIOMEDICAL DATA - CASE-BASED AND STREAM-BASED CLASSICATION Yang Hang Department of Computer and Information Science University of Macau, Macau henry.yh@gmail.com Simon Fong Department of Computer and Information Science University of Macau, Macau ccfong@umac.mo Andy Ip Faculty of Science and Technology University of Macau, Macau henry.yh@umac.mo Sabah Mohammed Department of Computer Science Lakehead University Thunder Bay, Canada sabah.mohammed@lakeheadu.ca CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau
Bioinformatics and Medical Analytics • Digital Healthcare Technologies – Bioinformatics and Medical Analytics. Novel and emerging high-impact Biomedical Health Technologies such as Bioinformatics and Medical Analytics are transforming the way that Healthcare Service Providers can deliver Digital Healthcare globally – Digital Health Technology entrepreneurs, investors and researchers becoming increasingly interested in and attracted to this important and rapidly growing Life Sciences industry sector. Bioinformatics and Medical Analytics utilises Data Science to provide actionable Clinical insights.
Bioinformatics
Bioinformatics • Advances in “Big Data” have lead to a revolution in Chronic and Acute Patient Monitoring and Management, Clinical Trials, Epidemiology, Morbidity, Actuarial Science, Biomedical profiling, forecasting and outcome predictive modelling. • There are two major families of biomedical data which are commonly to be found in Bioinformatics – firstly, case-based Biomedical data (which consist of historical record archival data sets), and secondly, stream-based Biomedical data (which are dynamic signal streams captured in real-time from Medical Equipment – scanners, sensors or monitors – or any other scientific equipment that you may care to think of..... ) • Profiling and Cluster Analysis has proven its effectiveness over traditional decision-tree classification for revealing interesting patterns and trends in data-mining of static case- based clinical data sets . These techniques are, however, used mainly for pattern and trend detection in historic case-based data - rather than classification, diagnosis or biomedical event prediction in Biomedical Metrics data which is streamed from Medical Equipment. The application of Wave-form Analytics to the data mining of dynamic real- time biomedical data streams has not previously been explored by other researchers - despite biomedical signal processing techniques having existed for several decades. CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau
Bioinformatics • Computer Science researchers at the University of Macau have examined the impact of data mining techniques against static Historic biomedical datasets and dynamic, continuous Real-time biomedical data streams. The Macau research team have demonstrated that the two very different bio-medical workflows – consisting of static case-based and dynamic stream-based data mining for diagnostics classification – both require radically different Data Mining techniques. In a Simulation Programme for conducting experiments in the analysis of these two types of biomedical data. a comparison of the two data mining techniques (case-based and stream-based), the researchers observed that case-based diagnostic classification data mining has a higher accuracy – but, because it runs in batch-mode in order to support numerous multiple database scans – it is much slower than stream-based data mining methods • Stream-based imaging and analytics has a very low latency but achieves a relatively lower accuracy - unless the dataset size reaches a critical very large-scale or size – Biomedical “Big Data”. The researchers propose a new method of Data Profiling – Cluster Analysis - to resolve the problem of needing multiple batch scanning passes or steps using classification decision trees – in the long-running multiple database scanning stages during data mining of dynamic, real-time Biomedical data-streams. CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau
Bioinformatics • Biomedical datasets pose certain challenges to bioinformatics because of their inherent natures of high-dimensionality, huge volume, and demand for extremely high accuracy (as this often involves life-and-death interventions). Recent advances in biomedical sensing and monitoring technologies further step up the challenges as datasets are generated from real-time time-series Biomedical data streams – e.g. foetal cardiograms, where multiple diagnostic features are automatically and continuously being measured through streaming processing and displaying wave-form signals and images. The problem with current data mining methods is the Medical datasets must be delimited (finite) - and the long latency to construct or even to refresh a diagnostics model. A fundamental question for the research project: - could traditional data mining methods effectively support the mining of dynamic, continuous, machine-generated, large-scale and real-time biomedical data streams? No ! • Many biomedical imaging analytics and signal processing methods currently exist which can detect anomalous patterns out of the general “noise” from the incoming data streams – but it is deemed necessary to have additionally a decision support technique that offers accurate diagnosis predication based on the latest updates of the incoming signal streams. Traditional data-mining - for example, induction-based decision-tree diagnostic taxonomy and classification, works by multiple file scanning passes – against a finite and structured set of data – repeated many times over in order to build up a taxonomic diagnosis model. CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau
Bioinformatics • The researchers from the University of Macau have generalised this method as “Historic Case-based data mining” - which has been widely applied in the following fields of bio- medical data for statistical analysis / prognosis of chronic and acute disease outcomes: - – Endocrine System metric diagnoses – Geriatric adult’s healthcare outcomes etc. – Paediatric children’s healthcare outcomes – Heart and Lung transplant patient monitoring – Traditional Chinese medicine - efficacy and effectiveness – Clinical Trials, Epidemiology, Morbidity and Actuarial Science • Recently a new group of data mining algorithms – “Real-time data-stream mining” – which developed from internet click-stream processing originated by Google – have been further developed and enhanced for handling large volumes of continuous high-speed Biomedical data-streams. Stream-based data-mining may address the challenges of processing high-volume, real-time biomedical data or signals. The main requirement - that of acquiring timely decisions for intervention from the data mining model – is the data mining run-time must be significantly less than the velocity of the incoming data streams. CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau
Bioinformatics • The other unique requirement is that we are no longer able to take for granted that a full and continuous long-timeline data is always going to be available – compared with long-exposure data collection, new and emerging data stream mining algorithms can now process relatively short-term, small and incomplete datasets in a single pass, allowing a clinical decision can be made instantaneously – within specific parameters of accuracy. These requirements fit in very well with biomedical applications - especially those that involve dynamic monitoring and real- time diagnostic analytics, and / or chronic and acute medical event and outcome prediction . • Previous Biomedical data streaming research has evaluated the differences between traditional Historic (batch) and real-time (dynamic) data mining applications - but only against non-medical (financial markets data streaming) data-streams and artificially generated medical data-streams, • To the best of the research team’s knowledge, this is the first documented attempt to exploit real- time data stream mining techniques using dynamic bio-medical datasets. The prime objective of the University of Macau research project was to investigate how well Biomedical data-stream mining performs against dynamic real-time bio-medical datasets, and to evaluate their respective diagnostic and medical event prediction accuracy – especially in the use of Wave-form and Imaging Analytics over real-time traditional diagnostic classification methods. CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau
Biomedical Data Sensors and Detectors Biomedical Data Sensors and Detectors • Data Captured via Biomedical sensors, detectors, metering (measurement), monitoring (looking for changes) and control (maintaining vital statistics) systems - can now be managed in vast “Biomedical Clouds” which exploit grid computing devices in order to capture, store and interrogate a wide spectrum of real-time Biomedical Data Types – ranging from simple measurements of patients temperature, blood oxygen, sugar and carbon dioxide levels – to the most complex Image Processing and Visual Rendering in real time using data streamed from MRI, CTI, Ultra-sound and X-ray scanning machines • There are three major areas of opportunity – these are some of the applications that Biomedical companies are currently working on: - 1. Biomedical data collection, storage and communication - from individual patients 2. Biomedical data integration – combining multiple data sets for analysis / interpretation 3. Biomedical data aggregation and summarisation – vast clinical data sets collected and integrated from thousands of patients – driving Geo-demographic clustering and statistical analysis for Clinical Trials, Epidemiology, Morbidity and Actuarial Science • Companies that have great potential in these areas include: - Sanyo Intelligence, Apple and GEHealthimagination, Cardiio, MC10, AliveCor, AgaMatrix, Proteus.
Real-time Biomedical Data Streaming Real-time Biomedical Data Streaming • Biomedical Scientists around the world are deeply committed to advanced Medical Programmes which are capable of automatically generating and processing, Exobytes (millions of Petabytes) of Biomedical Data. in real-time This data is captured via Biomedical, sensors, detectors, measurement, monitoring and control systems - and is managed in vast “Biomedical Clouds” which utilise grid computing devices in order to capture, store and analyse a wide spectrum of real-time Biomedical Data Types – ranging from simple measurements of patients temperature, blood oxygen, sugar and carbon dioxide levels – to complex Image Processing and Visual Rendering in real time using data from MRI, CTI, Ultra-sound and X-ray scanning machines
Real-tIme bioMEdical data Streaming (RIMES)
Real-time Biomedical Data Streaming Real-time Biomedical Data Streaming • Most of these Biomedical datasets are huge – potentially containing Exobytes (millions of Petabytes ) of Biomedical “Big Data”. Biomedical Data Streams are composed of machine-generated metering, sensing and monitoring data captured by scientific instruments deployed in support of large-scale Biomedical Research programs. Biomedical Software features intelligent agents and alerts which can automatically trigger alarms and interventions. Various types of biomedical data are supported by the Biomedical Cloud environment, including .pdb and .dcd files. • As Biomedical Data in the working repository is continuously updated, appended image frames may be streamed to an RBNB Data-turbine Cloud by the RIMES Synchronisation client - which ensures that data from the Biomedical Data Stream is continuously synchronized with the Biomedical Data Cloud. User Clinicians may deploy various extended user services over the core biomedical grid computing features and mass storage systems – including various Biomedical Software Portals, such as intelligent agents and alerts, visualization and analytics tools portals – which are continuously processing incoming dynamic real time biomedical data streams.
Biomedical Data and Analytics: - Management Principles
Data Management Principles • Driving economic value out of data is a complex task and one that requires sophisticated enterprise- level data management software. This is apparent right now but will become even more obvious as cloud architectural models become ever more sophisticated and ubiquitous. In the world of hybrid cloud for example, a lot of attention has been focused on the movement of workloads from one cloud to another. The ability to move an application from one service provider to another or from one private cloud to a public cloud is one of the main attractions of a hybrid cloud model. What tends to be over looked in the discussion though is the data that is associated with the workload and how that moves through this ecosystem. Data Management Principles • Data Sovereignty – Data stored in a country should be subject to the data laws prevalent in that country. This is especially acute for customer data and many countries have amended their data laws to ensure that customer data created in-country stays in-country. This can be difficult to regulate as workloads and their data are moved to the cloud, especially in a public cloud model. There is an element of trust of the service provider that is required. • Data Gravity – Moving data about from one platform to another is problematic. Data storage is persistent and resides some physical place unlike an application that is being processed at the compute layer or data that is transferred over a network. In essence, data has inertia and data movement takes time.
Data Management Principles • Data Classification – Not all data is created equal. Being able to classify data and apply suitable policies to the treatment of that data is essential. This actually is the higher order capability, and the basis for really deriving value out of the data, allowing data analysis technologies do their work. • Data Privacy – This needs little explanation. Data privacy laws are continually being updated (and usually getting tighter). Cloud service providers, whether public, private or hyperscalar need to be as cognizant of the need for data privacy just as much as enterprises running on-prem data centers. If anything they need to be even more vigilant given their systems are often multi-tenanted, storing data from a large number of customers, some of whom may even be competitors. • Data Governance, Data Ownership – All roughly the same broad topic as Data Stewardship and Data Custody. Data, especially in the context of an enterprise, needs to be governed properly. Auditable processes need to be established and individuals held responsible for following them. Phil Brotherton has written eloquently about what he calls ‘the value of data control’ in the cloud and why choosing the right partners to deliver a hybrid cloud is essential if data stewardship issues are to be fully addressed. • Data Replication – Allied to the movement of data question. Data needs to be replicated for a plethora of reasons such as backup and recovery, high availability, compliance obligations etc. The legality of where copies are data are stored is an interesting question related to the data sovereignty issue noted above..
Data Management Principles • Data Security – IT security as an overarching topic has been at the top of CIOs agenda for the last several years and I doubt it will ever drop off their lists. As we start to employ more cloud based architectural paradigms, the IT security issue will only intensify. Data protection and anti-data leakage technologies will continue to be essential in protecting the integrity of data, whether held in on-premise data centers or in the cloud. • Data Escrow – What happens to your data when your cloud service provider goes belly-up? Getting it back came be very expensive – read what happened when 2e2 shut its data center last year or Nirvanix, a cloud storage vendor who went into administration last year giving its customers two weeks to retrieve their data (at their own expense). The lesson here is if you outsource you data processing provisioning to a service provider, you do not outsource the ownership of the data nor your responsibility. As an old boss of mine used to say “there’s a fine line between delegation and abrogation of responsibility”. After looking up the word I understood what he meant about crossing that line. • Data Asset Management – Deriving value out of data is a complex task and one that requires sophisticated enterprise-level data management software. This is apparent right now but will become even more obvious as cloud architectural models become ever more sophisticated and ubiquitous. In the world of hybrid cloud for example, a lot of attention has been focused on the movement of workloads from one cloud to another. The ability to move an application from one service provider to another or from one private cloud to a public cloud is one of the main attractions of a hybrid cloud model. What tends to be over looked in the discussion though is the data that is associated with the workload and how that moves through this ecosystem.
Data Management Principles • Data Storage – The storage of data is a means to an end. Why do we implement storage arrays at all? Essentially it is to manage all the data that our stakeholders create and to do so in the most effective way possible: - ffective from both a cost and a performance perspective. The relationship between storage systems and data management is therefore intrinsic. Storage systems tend to have similar non-functional requirements. The major criteria are: - 1. Performance – will it give me the throughput and the latency that my users need in order to get access to the data they want? 2. Reliability – how often will it break down? how often will data be unavailable if at all? 3. Scalability – how many disks can I add? how much data can it store? 4. Ease of Use - how complex will it be? how can the data I store on it be tracked, backed up, restored etc? • Data storage and data management are intrinsically linked - these are complex storage issues which big storage vendors have been addressing for 30 years or more. However when I think about storage today, I am drawn much more to the latter than the former. Certainly storage hardware vendors have differentiated technologies that provide the bedrock for data management, but it is in the complexities of the data management layer where I believe the true action lies and differentiation will be observed.
Data Management Principles • In summary, Data Management is set to be an extremely critical area of IT over the next few decades. The Internet of Things is now being flooded with the ubiquitous presence of pervasive smart devices – in particular, in the Wearable Technology, Future Homes and Smart Cities categories. It isn’t just about the vast volumes of data that we are now seeing with the Internet of Things and the tsunami wave of machine-generated data from connected devices - it also about the abstraction of numerous storage capabilities from hardware into software and the emergence of the so-called software-defined Software Data Storage Platforms. As the future unfolds – data density can only get more intense.
Alex Osterwalder invented the Internet of Things Business Canvas in 2008
A Business Model for the Internet of Things • Studies from Cisco, IBM, Microsoft, McKinsey, Gartner, Forrester and other companies are now indicating a tremendous surge in growth of several consumer categories and product areas in the Internet of Things – often referred to as the Internet of Everything Everywhere. The Internet of Things is now being flooded with the ubiquitous presence of pervasive smart devices – in particular, Wearable Technology, Future Homes and Smart Cities categories. The number of internet connected devices on our bodies, in our homes and around our cities is only one example demonstrating how fast IOT / IEE technology is growing. • The Internet of Things Business Canvas splits the IOT business model into two distinct streams, the physical and the digital. Amazing new opportunities are now being created through connecting and integrating physical devices into digital communications – revealing fascinating social insights that we have never appreciated before. Connecting the unconnected, the physical and the digital streams are pivotal to the delivery of this new value proposition. Consumers are embracing for example, Wearable Technology, Future Homes and Smart Cities in almost every aspect of their daily life. Small start-ups funded by the crowd are offering all kinds of services based on connected devices - on a massive scale.
A Business Model for the Internet of Things Claropartners have developed a business model template for the Internet of Things
Digital Product Lifecycle Strategy
Digital Product Lifecycle Strategy