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Digital Health

Big Data, AI and Coronavirus COVID-19 (NCov-2019)

Coronavirus COVID-19 (NCov-2019) has tested some of the digital health capabilities such as AI-based predicitive models and real-time big data visualization. As a positive side effect, it has also allowed public to learn about epidemiology via video games.

 

AI-based predictive models caught COVID-19 faster than us

According to the news reports, two AI-based and one human volunteer-based warning systems were first to alert humanity about the threat coming from Wuhan.

HealthMap Project Website

The first to react was the automated HealthMap system at Boston Children’s Hospital, which scans online news and social media reports for signals of spreading disease. Its warning was very quick and accurate (pneumonia cases in Wuhan) – raised at 11:12PM local time on December 30, but it did not assign significance high enough to the message.

The second report came from a human. Marjorie Pollack from the Program for Monitoring Emerging Diseases (ProMed)  has based on similar social media reports received about 4 hours before the HealthMap warning. ProMed team’s analysis was more detailed than the first warning from AI but came about half an hour later.

BlueDot Explorer (screenshot from the website)

The third and most publicized report came from another AI-based model called BlueDot. BlueDot first became aware of the pneumonia cases in Wuhan on December 31st, and in addition to notifying their clients and government stakeholders directly, they publicly released their findings in the Journal of Travel Medicine on January 14th. While it was not truly the fastest, it is worth hearing how Kamran Khan, a Canadian MD, and founder of BlueDot explains the process behind.

Big Data Visualization to track Coronavirus COVID-19 (NCov-2019)

Dashboards showing the number of infected people, geographical spread and trends of the disease are useful to HCPs but also journalists and the public. This use case, although unfortunate, shows how important it is to be able to see and not only read data.

coronavirus covid19 dashboard
Screenshot of the COVID-19 dashboard by Johns Hopkins CSSE

The first and most known dashboard came from the team at Johns Hopkins University. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It has been accompanied by an article in The Lancet.

Currently, there are multiple dashboards showing diverse aspects of the epidemic of COVID-19. The list of dashboards with working links can be found at ESRI website.

Coronavirus virally spreads a game for people want to know

Due to the virus business slows down. Except for Ndemic Creations, a studio that in 2012 developed Plague Inc. A game that simulates a viral epidemic.

Plague Inc. is a game, but it is based on science and realistically shows the spread of viral infections amongst the human population. In the game, the player is supposed to infect all humans before the cure is available. It is so successful in teaching about epidemiology, that it has been endorsed by the CDC. During the COVID-19 outbreak, it has reached the top of charts on the Apple Store. According to its developers, similar peaks in popularity have accompanied the Ebola outbreak in Africa in 2014-2016.

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Digital Health MedTech Pharma Marketing

MoovCare – a clinically proven mHealth app in lung cancer treatment

MoovCare, a mHealth app to help patients with lung cancer presented at the annual American Society of Clinical Oncology (ASCO) meeting.

moovcare on tablet
MoovCare tablet interface

An Israeli company Sivan Innovation and the Cancer Institute of Western France are behind MoovCare – a mHealth application presented at the ASCO annual meeting in Chicago.


MoovCare claims to be the first application of its kind. It allows controlling lung cancer treatment based on reports on outcomes submitted by patients via web or mobile-connected devices. It enables the early identification of relapse or complications requiring rapid and specific care.
According to the clinical data from the III phase randomized study on 300 patients as presented at ASCO, this mHealth application provides direct benefit in terms of prolonged survival.


The main advantage of using the app in lung cancer therapy is early detection of relapse, which is symptomatic and typical for lung cancer. This allows optimal treatment and in turn increases of survival rate among patients.


An additional effect of the app is improved treatment compliance (observance of scheduled visits, lower number of inopportune phone calls, lower number of imaging). All this at a comparatively very low cost of less than 10 000 USD versus 265 000 USD per one CT-scan for Lung Cancer.

How does MoovCare work?

MoovCare mHealth app lung cancer Mode of Action – Sivan Innovation
MoovCare mHealth app lung cancer Mode of Action – Sivan Innovation


MoovCare is being “prescribed” to ambulatory patients. Patients are asked to fill in the web-based form each week, self-assessing 12 clinical parameters and having a free text field to enter any information they consider of importance. Data are securely passed and processed within the application. An algorithm behind MoovCare analyses the data provided and in case of any anomaly detected reports it to the oncologist and hospital dashboard. Based on the alert from Moovcare healthcare providers can contact patients and take any necessary action.
Company and research behind MoovCare.


MoovCare is a product of Sivan Innovation. Founded in 2014 in Jerusalem by Daniel Israel, Sivan Innovation is an Israeli E-health start-up and R&D company.

The research presented at ASCO has been conducted by Dr. Fabrice Denis at the Cancer Institute of Western France


MoovCare is a perfect example of how digital innovation, mHealth, and IoT trends are positively impacting healthcare and patient outcomes. We look forward to seeing more of such innovations coming not only from startups such as Sivan innovation but also from Big Pharma companies with their vast R&D resources.
 

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Digital Health MedTech Pharma Marketing

Internet of Things in Healthcare and Pharma

Internet of Things becomes one of the most impactful trends in Healthcare and Pharma industry. According to the Big Data in Internet of Things (IoT): Key Trends, Opportunities and Market Forecasts 2015 – 2020 report by Mind Commerce Publishing, value of Internet of Things in Healthcare will reach $117 billion by 2020. With CAGR of 15.1%, Healthcare will be the fastest growing segment in the whole IoT market.
See also: IoT in Financial Services and Banking
 

Telemedicine consult: Dr. Juan Manuel Romero, a cardiologist in Sonora, Mexico, engages in a pre-op consultation with Alma Guadalupe Xoletxilva, ...
SeaaDr. Juan Manuel Romero, a cardiologist in Sonora, Mexico, engages in a pre-op consultation with Alma Guadalupe Xoletxilva. Wikimedia Commons.

Internet of Things: Definition

Internet of Things (IoT), sometimes called also Internet of Everything, is a concept of enabling Internet-based connection between computing devices embedded into everyday objects. Internet of Things is already technically feasible and applied in multiple scenarios. With wider adoption, Internet of Things brings revolutionary changes to existing processes in most, if not all industries..

Video: Healthcare – The Internet of Things and Big Data
https://www.youtube.com/watch?v=qO9unY31820
Video: Internet of Everything | True Stories of the Connected: Rural Healthcare in Northern Canada

Internet of Things in Healthcare and Pharma

Internet of Things applications are impacting all three stages of health care: prevention, diagnosis and treatment.
Wearable sensors and quantified self software embedded in smart-watches are good example of using Internet of Things in Healthcare for disease prevention and healthy lifestyle promotion. Going forward, wearable or digestible connected sensors are part of diagnosis useful especially in remote areas and in chronic conditions.
As for the treatment itself, connected packages and medication dispensers such as CleverCap, MedMinder or Philips Lifeline are used to improve patients adherence to the treatment. A step forward from adding connectivity to packages is putting it directly on medication. Proteus Digital Health, a company backed by Novartis and Oracle has already received FDA market clearance in the United States and a CE mark in Europe for its wearable and Ingestible Sensor devices.
https://www.youtube.com/watch?v=-hhOtjdkU34
Video: Proteus Digital Health – Your Health, Powered By You

Internet of Things in Clinical Environment: MBANs

MBAN stands for Medical Body Area Network and is a concept of low power network of body sensors worn directly or in close proximity to the patient. MBAN is connecting to the hub via LAN of health care facility (ie. hospital). MBAN allows constant monitoring of patient’s health parameters while in the facility, even while moving. Outside of the facility, MBAN may also serve patient – for example by connecting blood sugar level sensor with insulin injection pump.
https://www.youtube.com/watch?v=vloCv3J-Wo8

Video: Medical Body Area Networks (MBANS) should expand patient monitoring

Internet of Things and Big Data

With wider adoption of connected wearable sensors and MBANs the amount of available relevant real world evidence becomes so huge, that medical research may, at least partially, shift its paradigm from experimental to statistical approach. Instead of setting up costly trials and recruiting patients with specific conditions, investigators will be able to perform analysis of existing data gathered from already diagnosed and treated population.

Video: Data analytics: Changing the practice of medicine

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Digital Health MedTech Pharma Marketing

Wearables in healthcare to be promoted by NHS England. What’s next?

NHS England has announced its plans to provide free WiFi in hospitals to push for adoption of wearable technology. It hopes that wearables can be used in clinical settings. At K-message we looked at wearable technology and quantified self impact on pharma in early 2014. Although NHS’s announcement brings wearables in healthcare one step closer, there is still a lot to do.

According to the research quoted by NHS over a fifth of patients with diabetes will have experienced a largely avoidable hypoglycemic episode whilst in hospital. Wearable technology may help doctors to detect deterioration early and act without delay. NHS England hopes, that connected sensors will be used to monitor health of people with long term conditions such as diabetes, heart failure, liver disease or asthma. The information gathered by wearable technology will be uploaded directly into patients’ records through the digital health services platform.
Nowadays, NHS maintains highly successful application (50 million hits per month) NHS Choices, that allows UK citizens to search and register for GP visit, book prescription and register for other services. NHS Choices provides also preliminary symptom checker, medical knowledge database and health related news promoting healthier lifestyle.
From the pharmaceutical marketing perspective embracing wearables in healthcare clear opportunity. Wearable (but also ingestible and implantable) devices and sensors will provide us with valuable real world evidence.With the mass adoption of such sensors assessment of treatment efficiency and drug safety will improve on unimaginable scale.  Use of wearables in healthcare may also greatly impact treatment adherence (making patient to take medication as and when prescribed). In general, it moves medicine from population-based to individual data based, truly personalized healthcare.

wearable sensors nature
The types of physiological data points and the wearable sensors under development or on the market to monitor them. – Nature.com



Technology providers seeing vast business opportunity are joining those efforts. Then, they go back seeing how hard it is to operate in strictly regulated market.We all may remember how Google’s Larry Page and Sergey Brin were discussing their work on sensor contact lenses for diabetes (with Novartis/Alcon) and longevity medicine project of Calico. Their learning was in Sergey Brin’s words:

“Generally, health is just so heavily regulated. It’s just a painful business to be in. It’s just not necessarily how I want to spend my time. Even though we do have some health projects, and we’ll be doing that to a certain extent. But I think the regulatory burden in the U.S. is so high that think it would dissuade a lot of entrepreneurs.”

 
For pharmaceutical companies and healthcare providers this regulatory burden is given for their core business. For them, raise of wearable devices and mobile health applications raises some issues. Our smartphones, fitbits, jawbones and misfit shine wearables are not meant to be medical devices. Data acquired by wearables may not be accurate as they do not hae to be. They are not designed to the clinical and healthcare standard – there are no backup systems if the battery goes down, if user switches the phone in silent mode etc. Today wearable technology for pharma is still not good enough to bet patients safety on it. FDA’s guidance allows wearables in healthcare only as general wellness, low risk devices.
 
The solution may come from the alliance of pharma and technology. Novartis with Google and Qualcomm, UCB with MC10, GSK with Medidata have chosen this direction. Another may be innovative startups companies willing to align with regulatory and compliance burden for a benefit of entering profitable niche leftover by more established players.
K-message believes that finally wearables and more generally, the internet of things will change and shape the future of healthcare and pharmaceutical industry. If you know or, even better, make a product or service intended to be a part of this future we will be happy to cover your story.

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Digital Health MedTech Pharma Marketing

How Quantified Self, mHealth and Wearable Technology are Changing Pharma Marketing

Quantified self, mHealth and wearable technology. While you could hear about those trends in the past, the tipping point has been reached at the CES 2014. What was supposed to be the future is our present much faster than industry expected. Enterprise market is again far behind consumers. Health care industry tethered by regulations just cannot catch-up quickly enough.

At K-message however, we can take a look at the forefront of the consumer technology and assess its possible impact on the industry, and our focal point – pharmaceutical marketing. But first, let us define what we are talking about.

What is quantified self? Who wears technology? What is mHealth?

Quantified self (QS)

Quantified self (QS) is a trend of personal data collection via technology. The idea is to acquire data on person’s state, actions and performance using wearable technology and/or mobile applications.

Track Yourself Map of Quantified self apps by Rachelle DiGregorio

Wearable technology is a description of any electronics that one can wear. It may be something with a sensor for quantified self purposes, but it can be also a T-shirt with LEDs intended just to look nice. From quantified self perspective, wearable technology is a trend that enables the whole movement by devices that can capture personal data.

mHealth is a general term for usage of mobile devices (mobile phones, smartphones, tablet computers etc.) in connection to medicine or health care. mHealth includes providing information to the patients or HCPs, but also collecting patients data.

Is Quantified Self big and mature enough to have an impact on health care industry?

The topic is huge. On the Quantified Self Guide – a website that collects different Quantified Self applications, there are 505 different tools listed at the time of writing this article. Of those 65 are tagged with medicine and 124 with fitness category tag. Wearable technology was main topic of CES 2014. If you look around in the office of any healthcare corporation (or, even better, on the jogging path) in the developed world,  you will notice wearable sensors in form of bracelets, chest bands or small items in the shoes used by increasingly high population. Users of the smartphones install “measuring” applications on their devices.

Gary Wolf: The quantified self


What are the numbers? Runtastic, a mobile app dedicated to track running performance has recorded 60 million downloads worldwide and 25 million registered users on Runtastic.com. Similarly targeted device and app Nike+ platform claims 18 million users. Fitbit.com, the website that allows to see the results of tracking with Fitbit range of devices according to Quantcast has around 2 million users from the U.S. only. Quantified Self is definitely mass market now and it will not fade away. Instead it seems it will get more devices and applications as the tech industry embraces it.

Quantified self: dangers versus benefits

From the pharma marketer perspective quantified self may be even more disruptive than the raise of social media (which, by the way is still not accommodated properly). As it gives more knowledge to the user it takes away control from the HCPs. Fitness trackers are obviously beneficial as they encourage the best prevention against disease – exercise and movement. On the other hand the trend brings some risks with it.

PBS: The Quantified Self: Data Gone Wild?

Interpretation of the data gathered by the device or application, even if supported with some mHealth resource filled with scientifically proven knowledge may lead to wrong decisions. Innocent life-logging app that counts calories intake may lead to starvation, or at least to non-balanced diet for some users who want to lose weight too quickly (not mentioning here eating disorders). A non-calibrated blood pressure and pulse tracker may put people with cardiovascular issues at risk (I cannot breath but the reader says I can still run…). The device alone can affect users health by allergy (that happened with Fitbit Force recently), heat, permanent exposure to radiation. There was also at least one occurrence when using activity tracking device, and competing for better score was connected with a tragic death of one too motivated biker.
In pharmaceutical industry there is a lot of pressure put on the patient data privacy. Quantified self puts those data in open, sharing the very personal information on the activity publicly, sometimes without informing user about it. This was a real case when Fitbit.com allowed public to see users who were logging their 30 minutes very active sexual encounters.
What is fascinating in Quantified Self movement is how the application can change focus from empowering by giving the knowledge to the patient to enslaving by enforcing control over users behavior. In one of the quantified self business use cases, not related to health care, a QS device called Hitachi Business Microscope worn by office workers was mapping their communication patterns within organization, pinpointing unnecessary meetings, organization social graph and communication issues.

If we take it to the field of pharma marketing, QS may be seen as a great tool to improve patient compliance or to provide personalized healthcare, but also as a menace of higher insurance rates for any misbehavior – be it sitting too long on the couch or having one drink too much.

Yuri van Geest – The quantified self: within 20 years no doctors needed

Quantified self and EHR, EMR and PHR solutions

One of the promising features of the quantified self is possibility to include the data acquired by sensors directly into electronic health records systems (EHR). Electronic Health Record is not exactly what industry widely embraces as EMR – electronic medical record. Although the data gathered, stored and processed in EHR are more or less the same, the source is different. EMR can include only data provided by medical institutions and healthcare professionals.
EHR is open to any source of data. It includes what can be gathered from EMR, but also accepts patient input, quantified self devices and applications feeds and other sources. A specific range of EHR, that includes only data provided and managed by the user (in this case – patient) is Personal Health Record (PHR). The most renowned solutions of this kind are Google Health (decommissioned) and Microsoft Health Vault, but there are also other providers.  

Microsoft HealthVault – an example of PHR
Microsoft HealthVault – an example of PHR

This brings new opportunities as we get really Big Data in EHR, but also some risks. Data in EHR and PHR cannot be really trusted, as they come from not validated sources and can be contradictory.  The sampling (how often you take a data point) is not standardized and quality of the input is questionable. Nowadays, adoption of EHR and PHR is very limited, as is their functionality and usability. However with the growth of the quantified self we can expect rising importance of such hubs for the medical information.

Quantified self and regulatory compliance: HIPAA and HITECH

Quantified self movement adoption is nowadays limited to developed nations, and the biggest market for those solutions is in the United States of America. There are two regulatory bodies in the US that overlook quantified self devices and applications. For non-medical use the main authority is FTC. Privacy and access to the health related data is regulated by HIPAA and HITECH regulations.

HIPAA Compliance Checklist

  • Have you formally designated a person or position as your organization’s privacy and security officer?
  • Do you have documented privacy and information security policies and procedures?
  • Have they been reviewed and updated, where appropriate, in the last six months?
  • Have the privacy and information security policies and procedures been communicated to all personnel, and made available for them to review at any time?
  • Do you provide regular training and ongoing awareness communications for information security and privacy for all your workers?
  • Have you done a formal information security risk assessment in the last 12 months?
  • Do you regularly make backups of business information, and have documented disaster recovery and business continuity plans?
  • Do you require all types of sensitive information, including personal information and health information, to be encrypted when it is sent through public networks and when it is stored on mobile computers and mobile storage devices?
  • Do you require information, in all forms, to be disposed of using secure methods?
  • Do you have a documented breach response and notification plan, and a team to support the plan?

If you answered no to any of these questions you have gaps in your security fence.
If you answered no to more than three you don’t have a security fence.

Quantified self and regulatory compliance: FDA guidance on medical mobile applications

For medical mobile applications relevant authority is the FDA. The Agency considers mobile phone as a medical device as soon as it meets one of the following:

  • It works expressly for medical purposes and offers medical or health-related apps
  • It acts as an effective accessory or component to aid medical health

While assessing medical mobile applications the FDA applies the same risk-based approach as for other medical devices.  The guidance document  provides examples of how the FDA might regulate certain moderate-risk (Class II) and high-risk (Class III) mobile medical apps. The guidance also provides examples of mobile apps that are not medical devices, mobile apps that the FDA intends to exercise enforcement discretion and mobile medical apps that the FDA will regulate in Appendix A, Appendix B and Appendix C.
For many mobile apps that meet the regulatory definition of a “device” but pose minimal risk to patients and consumers, the FDA will exercise enforcement discretion and will not expect manufacturers to submit premarket review applications or to register and list their apps with the FDA. This includes mobile medical apps that:

  • Help patients/users self-manage their disease or condition without providing specific treatment suggestions;
  • Provide patients with simple tools to organize and track their health information;
  • Provide easy access to information related to health conditions or treatments;
  • Help patients document, show or communicate potential medical conditions to healthcare providers;
  • Automate simple tasks for healthcare providers; or
  • Enable patients or providers to interact with Personal Health Records (PHR) or Electronic Health Record (EHR) systems.

Note, that PHR and EHR systems are not covered by the guidance on medical mobile applications.
Here you can see the list of examples of mobile medical applications the FDA has cleared or approved.

This is the list of examples of mobile medical applications for which the FDA will exercise enforcement discretion. Those applications may meet the definition of medical devices but they pose a lower risk to the public in the Agency’s view.

Quantified self: issues to resolve

The quantified self will gain more importance in the healthcare industry. However, there are still some issues to be addressed before embedding them in the marketing strategy. Before building your own application or choosing one from the market consider them carefully.

Tracking versus privacy

Regardless of HITECH privacy considerations, the pharmaceutical company has to be extremely careful about patient data privacy. It has to be absolutely clear to the patient and the organization, that all data are owned by the user of the application and not the company. You need to have users’ direct consent if you are going to aggregate, store, process, or use the data in any way.

Data reliability

Your quantified self-application or device will gather data in connection to patients’ health. Therefore it is important to achieve possibly high level of accuracy and ensure the integrity of this data. It should be also clearly stated what are limitations of the sensors and technology used.

Fallback in case of failure

You need to make sure that in case of failure or loss of the device, patients will still be able to be treated or diagnosed with a fallback solution.

Interoperability

If your application allows data exchange with EMR, make sure it uses open standards, so that it can be used regardless of the health care provider chosen by the patient. This applies also to interoperability with PHR solutions.

Clear guidance for the interpretation of data

Make sure that the data gathered and provided to the patient are not subject to misinterpretation. There should be a clear explanation of the result provided, and if not possible the instruction should point the patient to the HCP who will be able to interpret the data. Even the best result on the application should not encourage patients to be not compliant with the treatment ordered by his doctor.

Co-operation with HCPs

If every patient comes to his GP with gigabytes of life-logging data, there is no time for a proper diagnosis. Valuable information will be hidden in the noise like a needle in a haystack. Not to mention different UIs of the applications and general annoyance with non-standard requests coming out of the blue. To avoid this you need to provide HCPs with clear instructions on what to look for and make sure they will know it at the first glance. Think about a separate dashboard for the physician or even better – distribute the app through the trained HCPs.

Example of quantified self in pharma marketing: Eli Lilly’s Talking Progress.

Talking Progress - Eli Lilly & Company Quantified Self mHealth App Screenshots
Talking Progress – Eli Lilly & Company Quantified Self mHealth App Screenshots

Talking Progress (this name applies for UK & Ireland markets) is an application available for iOS and Android, that was presented by Claire Perrin on the recent Social Media in the Pharmaceutical Industry conference in London.
Talking Progress is dedicated for adults suffering from depression. Using this app patient can record his/her mood to produce progress charts which can track the recovery and help inform discussions with the doctor. It is extremely important, as one of the symptoms of depression is lack of focus and gaps in the memory.


The app also contains useful hints and tips about lifestyle changes as well as information on causes of depression and treatments.
Talking Progress Features:

  • Educational information about depression
  • Mood Diary
  • Note pages
  • Healthy living advice
  • Medicine reminder alarm

Together with an app Lilly provides a booklet for the patient and small information desk stand for the HCPs. Embedding quantified self elements (diary and note pages) with mHealth features (educational information, lifestyle advice and compliance reminder) makes this app a perfect companion for patients suffering depression. Providing HCPs with the information pack (they are supposed to “prescribe” an app) guarantees that the data gathered via the app will be used and understood by the doctor.

 Quantified self in pharma marketing – an opportunity for everyone

It looks like the quantified self movement will stay with us for longer. Lilly’s example described above shows that properly used it may be beneficial for all – patients, HCPs and pharmaceutical industry. We can without doubt add payers to the list. Correctly applied quantified self is great way for prevention via changing lifestyle habits, increasing disease awareness and improving patient’s adherence to the prescribed treatment. Will we use this opportunity? Quantified self may save lives and money. It seems that even regulatory bodies are up to date with the trend, so the only thing missing is pharma marketers involvement. Do you plan to include quantified self and mHealth elements in your brand strategy?