Digital Healthcare - Detailed Presentation PDF

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.
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

• 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

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

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..

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
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
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
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

Data Science in Digital Healthcare

CASE STUDY 4: – Digital Healthcare in the Cloud
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
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

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.
across multiple operational domains. This rich and rapidly growing data asset containing significant
biomedical intelligence is exploited using HP Medical Analytics,.

The Cone™ – Actionable Clinical Insights

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)

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.

• 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)

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.”

• 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.
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

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 Healthcare
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
• 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.

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.

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 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.

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

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.

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).

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.

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.

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.
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
ccfong@umac.mo
Andy Ip
Faculty of Science and
Technology
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
• 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.

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.

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.

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.

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.

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
• 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)

• 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 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 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 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 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.

• 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

• Everything around us has a lifecycle. It is born, it grows, it
ages, and it ultimately dies. It’s easy to spot a lifecycle in
action everywhere you look. As a person is born, grows,
ages, and dies – then so does a star, a tree, a bee, or a
civilization – and so does a company, a product, a technology
or a market - everything goes around in a lifecycle of it own.

• Everything around us has a lifecycle. It is born, it grows, it ages, and it ultimately dies.
It’s easy to spot a lifecycle in action everywhere you look. As a person is born, grows,
ages, and dies – then so does a star, a tree, a bee, or a civilization – and so does a
company, a product, a technology or a market - everything has a lifecycle of it own.
• All lifecycles exist within a dynamic tension between system development and
system stability. When an entity is born, and during it’s early its development - it
has low stability. As it grows, both its development and stability increase until it
reaches maturity. After peaking, its ability to develop diminishes over time while its
stability keeps increasing over time. Finally, it becomes so stable that it ultimately dies
and, at that moment, it loses all stability as well.
• That’s the basics of all lifecycles. We can try to optimize the path or slow the effects of
aging, but ultimately every system makes this lifecycle progression. Of course, not
all systems follow a bell curve like the picture below. Some might die a premature
death. Others are a flash in the pan. A very few live long and prosper - but from
insects to stars and everything in between, we can say that all things comes into
being, grows, matures, ages, and ultimately fades away. Such is the way of life.

• Everything has a lifecycle. It is born, it grows, it ages, and it ultimately dies. It’s easy
to spot a lifecycle in action everywhere you look. As a person is born, grows, ages,
and dies – as does a star, a tree, a bee, or a civilization – and so does a company, a
product, or a market - everything has a lifecycle of it own.

Investment
Product
Lifecycle
Product
Launch
Product
Development
Product
Planning
Death
Plateau
Product
Maturity
Decline
Aging
Early Growth
Migrate
Customers
to new
Products
Withdraw
Innovation Prototype / Pilot / Proof-of-concept
Cash CowCease
Investment

• What do the principles of adaptation and lifecycles have to do with your business
strategy? Everything. Just as a parent wouldn’t treat her child the same way if she’s
three or thirty years old, you must treat your strategy differently depending on the
lifecycle stage. And when it comes to your business strategy, there are actually three
lifecycles you must manage. They are the product, market, and execution lifecycles: -
– The product lifecycle refers to the assets you make available for sale.
– The market lifecycle refers to the type of customers to whom you sell.
– The execution lifecycle refers to your company’s ability to execute.
• In order to execute on a successful strategy, the stages of all three lifecycles must be in
close alignment with each other. If not, like a pyramid with one side out of balance, it will
collapse on itself and your strategy will fail. Why? Because aligning the product, market,
and execution lifecycles gives your business the greatest probability of getting new
energy from the environment now and capitalizing on emerging growth opportunities in
the future. The goal of any digital product strategy is to get new energy from the
environment, now and in the future.) As we will see, aligning all three lifecycles also
decreases your probability of making major strategic product placement mistakes.

• Each lifecycle please note that each stage blends into the next. Although every
lifecycle may have distinct stages, this is really only for convenience. There’s no
real, definitive, clean and clear break where you know when one stage has ended
and another begins. In addition, there are three basic prerequisites that you must
have before you can pursue any strategy.
• First, the strategy must be aligned with the company vision and values. Second, the
company must have or be able to get the resources – including staff, technology,
and capital – to execute the strategy. Third, the company must have or be able to
develop the core capabilities to execute the strategy. For now, I am going to assume
that you have all three prerequisites in place and that you’re currently acting on, or
about to act on, a strategy that meets these basic requirements.

Digital Product Lifecycle – End-phase

Wave-
form
Analytics
• The challenge
found everywhere
in wave-form
theory is how to
interpret very large
scale / long period
compound-wave
(polyphonic) time-
series (temporal)
data sets which
are fundamentally
variable (dynamic)
in nature - waves
which are driven
by deterministic
(human actions)
and stochastic
(random, chaotic)
processes.....
deterministic stochastic

Wave-form Analytics
• The challenge found everywhere in wave-form theory is how to
interpret very large scale / long period compound-wave (polyphonic)
time-series (temporal) data sets which are radically non-stationary
(dynamic) in nature - waves which are driven by both deterministic
(human actions) and stochastic (random, chaotic) processes.....
deterministicstochastic

Wave-form Analytics in Cycles
• Wave-form Analytics is a new analytical tool “borrowed” from spectral wave
frequency analysis in Physics – and is based on Time-frequency Wave-form
analysis – a technique which exploits the wave frequency and time symmetry
principle. This is introduced here for the first time in the study of human activity
waves, and in the field of economic cycles business cycles, patterns and trends.
• Trend-cycle decomposition is a critical technique for testing the validity of multiple
(compound) dynamic wave-form models competing in a complex array of
interacting and inter-dependant cyclic systems in the study of complex cyclic
phenomena - driven by both deterministic and stochastic (probabilistic) paradigms.
• In order to study complex periodic economic phenomena there are a number of
competing analytic paradigms – which are driven by either deterministic methods
(goal-seeking - testing the validity of a range of explicit / pre-determined / pre-
selected cycle periodicity value) and stochastic (random / probabilistic / implicit -
testing every possible wave periodicity value - or by identifying actual wave
periodicity values from the “noise” – by analysing harmonic resonance and
interference patterns in order to discover the fundamental original frequencies).

Digital Healthcare - Detailed Presentation PDF

Digital Healthcare - Detailed Presentation PDF

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