IoT And Personalized Medicine: Digital Transformation Is Creating New Business Models For Life Science

From electronic record systems (EHR) to the Internet of Medical Things (Healthcare IoT), the digital revolution has already brought disruptive changes to the healthcare sector. Even bigger changes are on the way, thanks to advances in networking and in-memory computing. Powered by IoT, personalized medicine is creating new business opportunities for pharmaceutical drugs, medical devices, and patient services that will dramatically improve therapeutic outcomes. Digital disruption has the potential to unlock $ 100 billion in commercial value, reports Accenture. With the life sciences industry poised for change, companies that take move to capitalize on new business will gain a critical, first-mover advantage.

A more than $ 100 billion opportunity: Life science digital transformation

Life science companies that embrace digital transformation are shifting value within their industry. These companies successfully unlock new revenue streams by providing a substitute treatment or medication, enabling the sharing economy, converting healthy activities into currency, or setting new standards for treatment and personalized care monitoring. For example, Accenture reports that remote monitoring for Type 2 Diabetes has the potential to shift more than $ 100 billion in value from traditional to emerging business models.

Healthcare IoT and analytics processing are coming together to enable this digital shift. IoT uses real-time data feeds from sensors and devices to enable machine-to-machine interactions. Data is now available through remote tracking, electronic medical records, diagnostic information and hand-held personal devices. Advanced analytics processing analyses this data in real time, providing actionable insights that enhance the decision-making powers of professionals and enables patients to take a more active role in managing their personal health. These innovations are transforming not just how we care for the chronically ill, but also how we empower individual wellness and proactively work to prevent disease.

In addition to the benefits of IoT for personalized health care, IoT is also making it easier for life science companies that produce equipment or medication to proactively mitigate machine failure. This helps life sciences companies improve reliability and quality. Patients benefit from a responsive supply chain and companies benefit from efficiency gains that lower production costs.

IoT digital transformation in action: Cold chain supply for biologics and smart pills

The impact of IoT on the life science industry is significant, particularly in terms of how these businesses interact with their B2B customers and, even more importantly, their consumers. Cold chain supply for biologics and consumer smart pills are two examples of how IoT is improving therapeutic outcomes through personalized medicine.

Cold chain supply for biologics

Pharmaceutical companies that manufacture environmentally sensitive drugs face several key challenges. First, these manufactures need to improve the safety and efficacy of drug production. Second, these companies are working to reduce theft and lost drugs. Finally, these companies are seeking to reduce incidental spoilage and decrease inventory requirements. IoT tracking and sensors addresses these key challenges.

By 2020, IDC predicts that more than half of all top-selling drugs will be biopharmaceutical or biologic products requiring temperature controlled transportation and storage, usually 2–8°C, but sometimes frozen or cryogenic. This requires a huge network of time/temperature sensors in factories, warehouses, trucks, labs, and pharmacies that can monitor and send this information, for both clinical trial supplies and approved products. IoT tracking sensors and networks help life sciences companies ensure the safety and efficacy of their products in transit and in storage. Investment in cold chain IoT networks will be driven by safety and compliance concerns; these investments will also contribute to savings from lower inventory and spoilage costs.

Smart pill for personalized medicine

Health care providers struggle with prescription non-adherence, especially among patients with chronic diseases. Since patients are reluctant to tell their health care providers that they are not taking their medications, the American Medical Association reports that providers may needlessly escalate treatment. IoT powered innovations like the “smart pill” may improve patient compliance. Key benefits include maximizing drug effectiveness, reducing medical costs due to improper drug usage and decrease incidental spoilage and supply chain waste.

The Proteus pill by Proteus Digital Health contains a tiny ingestible sensor that can communicate to a wearable patch on a patient’s skin when the pill has reached the patient’s stomach. The patch then sends a status update to a mobile device. The technology can be helpful for conditions where adherence to taking prescriptions has traditionally been poor. Related technology includes “smart” pill bottles that can send signals to portable devices when opened or altered, thereby improving safety and reducing fraud.

Three steps to prepare your life science company for digital transformation

Innovate or be left behind: digital transformation is contemporary imperative for today’s life sciences companies. Whether a scenario can be implemented now or in the future, your company must have the right technology and IT infrastructure in place. Otherwise, your company risks losing out on first-mover advantage. These three steps will position your business for success:

  1. Conduct a risk-benefit assessment. Define strategic and tactical goals, including high-level benchmarks against key industry competitors, both traditional and emerging. Align efforts with customer needs, key business goals, and the likelihood of market disruptions.
  1. Be “digital ready.” Start modernizing systems and business processes in alignment with future opportunities.
  1. Form strategic partnerships. Identify the partnership ecosystem that can best support your business on its path towards digital transformation.

Taking these steps today will prepare your life science company to capitalize on the disruptive IoT innovations that are essential for the next generation of personalized medicine.

Learn how to bring new technologies and services together to power digital transformation by downloading “The IoT Imperative for Consumer Industries.” Explore how to bring Industry 4.0 insights into your business today by reading “Industry 4.0: What’s Next?

Internet of Things – Digitalist Magazine

Cisco and Manchester Science Partnership open Mi-IDEA centre

Cisco and Manchester Science Partnership open Mi-IDEA centre

Manchester’s new 70,000 square foot Bright Building in Manchester Science Park (MSP) is host to Mi-IDEA, a new post-accelerator jointly run by MSP and Cisco.

The centre is part of a global network of Cisco innovation centres which also includes IDEALondon.

Mi-IDEA stands for Manchester-Inspired Innovation Digital Enterprise Alliance. The Centre aims to help digital tech start-ups flourish and to act as a catalyst for co-innovation between businesses, academia and government agencies in the North of England.

In addition to Mi-IDEA, the Bright Building houses six innovative startups:

  • Hark – maker of an all-in-one monitoring system
  • Wattl – maker of a video discovery app
  • PlaceDashboard – location-based data for retailers and others
  • Steamaco – automation for modern utilities companies
  • KMS – connected healthcare products
  • Malinko – scheduling for mobile service businesses

A smart building in itself, the facility is also a centre of operations for CityVerve, the UK’s IoT smart city demonstrator, as well as the home of the Cisco UK and Ireland Innovation team.

Read more: Manchester Uni plans for robots to work on nuclear clean-ups

Bees with backpacks

In addition to announcing Mi-IDEA, Cisco confirmed its support for the ‘bees with backpacks’ initiative. By putting trackable RFID ‘backpacks’ on bees, Cisco is working with Data61|CSIRO to build on a project already underway in Sydney, Australia and join the Global Initiative for Honey bee Health (GIHH).

The decline of bees is of worldwide concern, and this project is helping to identify potential stressors to their health. The RFID-enabled ‘backpacks’ allow bee movement to be tracked.

In conjunction with local organisations in Manchester, the programme will use innovative technology to conduct research into bee habitats, pollination and sustainability in the UK.

Read more: Manchester CityVerve is like a neural pathway

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Internet of Business

Laboratory-based IoT: is connectivity new in the realm of science?

Laboratory-based IoT: is connectivity new in the realm of science?

The Internet of Things is broadly defined as “the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data.” But is this capability really a new phenomenon in the scientific laboratory?

Firms have been working with instruments and hardware for decades to define and structure assays that are then used to drive plate readers and other instruments. These instruments in turn produce data that is sent back to the system for further analysis and data storage, or pushed into data warehouses for future use and later analysis. The intersection of informatics, networks and robotics is not new in laboratories.

So, what is new? Where things are changing at pace and scale is the number, diversity and availability of devices that can now be deployed into a laboratory that has IoT capabilities. Where in the past these systems were discrete and expensive, they are now cheaper, broader and more accessible. This brings enormous benefits, but also come with risks and wasted opportunities if things are not considered properly as part of a comprehensive strategy.

IoT in the laboratory

The primary benefit of the IoT is automation. A smart warehouse with Radio Frequency Identification (RFID) tags in boxes means people no longer need to scan all boxes that come into a warehouse as the system can automatically track and update inventories based on the information it reads from the chips. These and similar experiences open a whole world of possibilities for consumers and businesses alike.

The laboratory is no different. For instance, balances and bench-top instruments can notify systems if they are out of calibration, preventing valuable time and materials being wasted. In addition, if a hazardous material is being used, a container could automatically let the user know the correct procedure or handling method for that material by updating the end users’ screen immediately. Combining many of the different examples above can create a streamlined and highly efficient working experience, potentially improving lab safety and efficiency.

Many labs would baulk at the cost of replacing all devices with newer smarter options. Yes, new systems are often easier to use and, with the right infrastructure, can be easily added into the business operation. But in most cases, businesses are in a transitional phase requiring them to update current systems, procedures and infrastructure to really use the new capabilities of these devices and services.

This is often challenging as it requires a root and branch analysis of existing laboratory systems to work out where these new pieces can be added. If this isn’t done then often you will have an IoT-enabled set of devices and services that can’t communicate with the rest of your infrastructure, providing another island of data.

Read more: Elemental Machines uses AT&T IoT tech to make lab equipment smarter

Cost versus benefit

Just about every process or device could be upgraded or made “smarter,” but the cost-benefit of doing this varies significantly and to really unlock the experience all parts need to be considered. In some cases the whole process needs to be upgraded to provide value. When considering IoT you must consider the whole picture and work through the most valuable pieces to you as a business.

Below are some important aspects to consider when it comes to implementing IoT in the laboratory:

Cost: The cost doesn’t always justify the investment, from lighting and security systems to robots and wearables. Be very clear on where the value will come from and measure the ROI.

Vision: It is important to understand what you want. The options are vast, and once you have the vision you can then look at the costs and roadmap that can deliver the value.

Users: In most cases, there will still be the human element somewhere in the process. Bring the users into the process and ensure what you have created genuinely adds value. For instance, giving someone an iPad to walk around a busy lab may not actually be the best approach (from either a safety or productivity perspective).

Networks: In most cases this gets overlooked, but a strong network is critical to the success of an IoT strategy. Without robust connectivity information gets lost, processes get delayed and investment is wasted.

Security: This was a major concern, but the effort and awareness of IoT security is high and businesses now have access to expertise and technologies that can better manage the security of their IoT devices and environment.

Informatic: Devices are useless if you don’t have a modern informatics platform in which the data can be analysed, disseminated, shared and managed. The informatics platform also needs to be able to drive decisions and data across the network.

Integrations: It is unlikely that you’ll buy everything from a single vendor, so it is vital that systems do not become siloed.

Hardware: Many providers can now supply upgrades to older technologies that can make them “smarter” at a fraction of the cost of replacing expensive capital equipment. It is important to assess the correct path for each instrument and to weigh this against the cost of replacement/upgrade.

Communication and remote working: With the explosion of external collaborative working it is important to consider the remote working aspects of the lab.

While the IoT has been a feature in laboratories for over a decade, developments in diversity and availability of devices is opening potential benefits for research organizations. However, this also brings challenges and considerations – which require comprehensive strategic assessments for the IoT strategy to be successful.

Laurence Painell, is VP of Marketing, at IDBS.

Read more: Sensor tech researchers head for Liverpool Science Park

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Internet of Business

The Digital Athlete Brings Science To Sports

Sports are beautiful. Visually striking, poetic, and lyrical.

While there will always be art in sports, today it is science that is dominating the genre. Science is shaping how athletes train and perform. Science is guiding how teams select players, develop game plans, run practices, and scout opponents.

The digital transformation seen in so many areas of business and society is coming to athletics. Digital sports is playing an ever-increasing role in how athletes game and keep a competitive advantage.

What’s driving digital?

To understand how the digital athlete is possible today, it’s important to understand the trends that allow the transformation to happen.

Hyperconnectivity allows us all to be connected at anytime from anywhere via mobile devices. Cloud computing and supercomputing make it easier to collect, store, analyze, and retrieve vast quantities of data. Analytics programs can interpret information and offer athletes and coaches insights, all in real time.

All that data is made possible by the Internet of Things (IoT), the vast network of objects connected to each other. These objects can detect, collect, store, and send data thanks to embedded sensors, software, and wireless connectivity. Lastly, advances in cybersecurity keep data on athletes protected.

The digital athlete

What does the digital athlete look like? First, she is outfitted with wearables that track performance measures such as speed, agility, respiration, and heart rate. This information is fed in real time to coaches and trainers.

Mobile apps let her and her coaches review data and recommendations on the fly. Platforms collect the data from myriad sources, the athlete, and her teammates. Structured data, such as that from wearables, and unstructured data, such as video footage, can be captured and analyzed.

The collected data gives a comprehensive, 360-degree view of the athlete. Her trainers can identify the strain of workouts or potential damage due to improper form. Doing so can prevent injuries, or help injured athletes return to competition sooner. Coaches can pinpoint advantages that can be exploited during competition. Training regimens can be created to suit specific conditions, opponents, or competitions.

Our digital athlete can gain insights far faster than before. Video footage does not need to be broken down. Instead, insights are delivered in real time. The same immediacy is possible with data, collected either from practices or even within a contest, allowing for immediate adjustments.

The athlete also has more insights on her competitors. Scouting reports on opponents can contain richer arrays of information. Data interpretation happens faster. Virtual reality and gamification let athletes simulate situations without risking injury, getting more practice without fatigue.

The digital athlete can relay information about opponents during the competition to staff. Acting on that information allows coaches to recommend new strategies in-game.

Barriers to usage

The art of sport will certainly continue. There are nuances and intuition that will guide many decisions. But incorporating science into sports may take some time, particularly among teams and coaches who may prefer traditional approaches. That reluctance is an advantage to early adopters who seize upon the opportunity in these early stages of digital athletics.

Athletes, coaches, and owners may resist these innovations for other reasons. Jobs, revenue, and public opinion are on the line each season. Sports receive far more media attention than most any other industry. Many sports professionals may prefer to take a wait-and-see approach and reduce perceived risk.

Digital athletes will benefit most when teams, leagues, and owners focus on simplifying procedures. Complexity impedes adoption when data cannot be interpreted and used without complicated steps in conversion, uploading, and storage. To work effectively, data collected from multiple sources in different formats needs to be reconciled quickly to be useful.

In many cases, athletes do not have the luxury of time. The next competition is often just days or hours away. Preparation and practice time is limited and must be maximized. Opponents may not be known until the last minute, leaving little time to create, implement, and perfect a strategy. Collection, analysis, and retrieval of data needs to happen quickly.

The future is here

In many cases, digital athletics and athletes are already here. In a 90-minute singles tennis match, technologies can record 60,000 to 70,000 discrete records. During an hour of football (soccer) practice, 77.7 million data points are generated. By 2019, IDC projects there will be 111.9 million smartbands sold worldwide. One Major League baseball game produces 7 terabytes of uncompressed data.

Consider a recent decision by the Women’s Tennis Association. Players can now access real-time performance data during matches. Coaches can instruct players during match play based on the provided information.

In coming years, more athletes will be wearing sensors during practice and competition. Sensors connected to baseball bats, tennis racquets, and polo mallets will tell coaches how strong players are striking balls and how accurate their impacts are.


Athletes are always looking for a competitive edge. Today, digital transformation provides advantages to athletes that were the stuff of science fiction earlier. Armed with detailed information collected from multiple sources and analyzed in real time, digital athletes will soon be the norm. Those athletes and sports organizations that see the potential possible through digital innovation will remain a step ahead, a few seconds faster, and have more wins in their record book.

To learn more about digital transformation in the sports industry, click here.

Internet of Things – Digitalist Magazine

Scaling Data Science: How to Deal with Growing Volume, Complexity, and Speed

Machines generate data constantly, from their components, RFID tags, applications, servers, and sensors. If this data could be collected and analyzed, it would be of immense help in enabling business decisions.

Much of this machine data was originally generated for local and specific uses, such as troubleshooting, monitoring, debugging, compliance, and fraud protection. As a result, the protocols and formats are often idiosyncratic and proprietary.

What is needed is a way for professionals who aren’t analytics experts, and who have specific business goals, to interact with the massive amounts of data, and generate needed information from it, in a reasonable amount of time at a sensible cost.

Fortunately, a number of ThingWorx partners are finding ways to support their clients with analytics at scale.

Machine learning for machine data

Machine learning algorithms learn from the data they process and get better at extracting useful information without explicit instruction from the programmer. But machine learning isn’t a simple universal solution: different machine learning techniques work best on different kinds of data.

Machine logs are sometimes called “unstructured” data, which can be misleading. What data is considered “structured” or “unstructured” varies from domain to domain—and the IoT is unifying a wide range of different domains and industries, reducing the value of the older terminology.

Machine logs have a structure. It just isn’t a database-ready structure—and it varies from one type of log to another. Taking that structure and making it more widely usable plays to machine learning’s strengths.

Usability is key

Analytics skills at the required level will be in short supply for the foreseeable future. Fortunately, the world of IoT is one of partnership: no one will solve every problem with internal resources.

Partnership with an IoT platform can supply the analytics support needed, while allowing the business to focus on its own goals.

And, in addition, other ecosystem partners can turn the work of designing and deploying analytics solutions into something non-analytics staff can use and improve. There are many different ways to approach these problems.

Consider the choices of two different companies, National Instruments and Glassbeam.

National Instruments helps scientists and engineers without much programming experience manage and analyze the data from large networks of sensors. How much processing should occur at the sensor itself? What features should be extracted and sent on? NI provides a development environment with analytic functions that allows the user to focus on desired end results.

Glassbeam automates the process of converting machine logs into analytics-ready format, again allowing the user to focus on the problem to be solved, rather than the mechanics of analyzing it. Its analytics can compare fields and determine file structure without the need for human intervention.

Scale will only continue to grow

Several years ago Gartner noted that, while data will grow at 40 percent per year, IT resources will grow only by five percent per year. Simply throwing more resources at the problem will show limited returns. Partnering with someone who can provide smarter machine learning algorithms will allow analytics capabilities to keep up with data.

This report from O’Reilly discusses some of the techniques used at ThingWorx and two of its partners—Glassbeam and National Instruments—to automate and speed up analytics on IoT projects.

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Thingworx Blog – ThingWorx