I am Nikos and I am an intern in the Global eHealth Unit of the School of Public Health of Imperial College London.
Together we are going to have a webinar on digital health. My objective on this slideshow it to give you a bird eye’s view on the process from identifying an idea until being actually able to deliver a digital health product or service.
The slides of this presentation are self explanatory as much as possible to help you search the content online when you will come back to review it. Personally I would strongly suggest you to do so.
So, off we go with 5 rounds of things around health and digital technologies. As I said there is going to be a lot of content, around 50 slides, so try to look at the general picture.
Before starting building, especially in healthcare you must have a problem. Many times in consumer markets people build technologies and products and then they try to find afterwards how these fit into people’s needs.
Personally I believe that healthcare is kind of a different case and I would really like to hear your views on this after the presentation.
Theoretically if someone wanted to start building something he should start by making a needs assessment and identifying problems that his technical skills would allow him to build effective solutions.
Giving you a random example, if you would like to work on big data it would be useful to have good understanding and skills on statistics and data analytics. Otherwise, depending on your role in a project, you would find problems on the initial stages of this process.
If you need to build something on the healthcare domain it would be useful to start by making a needs assessment on healthcare needs. You much understand that there are personal health and population health issues. Health is a multifactorial condition and solving problems around it can be a fuzzy process.
More specifically health is only about the things you see, the symptoms or the various indicators, but things on the biological or social environment that we can not monitor or we do not have the knowledge yet.
For example, social deprivation is a strong indicator of readmission to hospital and lack of adherence to a healthy life style
There are many types of medical conditions, diseases and disorders. For example, chronic or acute diseases, infectious and lifestyle ones, diseases bases on the human body system that they affect, and so on. In order to fight diseases clinical science has been developing several models. Some of them focus on population, some on a person’s behavioral aspects, some on biological pathways, etc.
Depending on the type of health problem different types of solutions are needed. There isn’t a silver bullet for all.
Especially for the biological causes of a disease you must take into consideration that some conditions are multifactorial. A person may have multiple problems and finding a specific or only one cause will not feasible. Treating only one problem may not heal or make his quality of life substantially better.
Another point to consider here is that multimorbidity also makes treatment of the patient very difficult because drugs and life-style recommendations are typically tested on patients without comorbidities.
Last but not least many times we monitor health with biomarkers that are not directly related to the cause of the problem. They are estimates. So we may monitor things that we can collect, that may not be directly related to the cause of the problem, and that may be confounded by many parameters that we can not control.
Improving health is not only about improving our analysis and predictive models but also our data collection methods. (you repeat this in the next slide)
Improving health is not only about improving our analysis and predictive models but also our data collection methods. If we do not know what to collect, how to monitor, what and how to analyze and how to provide proper feedback and support to a patient most probably we will build a technological product but not a health solution necessarily.
I like to consider engineering a tool for understanding, exploring and solving problems on healthcare. It may be a data collection, a data analysis, or a service delivery channel. Health is always the problem, and technology is always a solution.
We move now to digital health itself. Although you will find several definitions of its elements there isn’t a clear definition of the overall paradigm.
In this Venn diagram you can see most of its elements and their overlapping areas. Some of them, like robotics, system biology, augmented reality, etc are missing. We will see many of them later in the presentation. Try to get a glimpse of the overall picture and then return to these slides and try to note things you are interested in and Google them. The domain is very big and my aim is to give motivation to explore it. Most of these would need at least semester to be presented and explained.
I do not mention anything about the engineering paradigm as most of you are engineers. All my previous slide wanted to give you a glimpse why healthcare services and systems are regarded complicated and why healthcare is considered an expensive and difficult to handle domain.
Let’s say that you identified a specific problem that would be interested. I won’t say anything about the criteria on which you should choose a specific problem. You will find relevant information later in the powerpoint and I hope that you can make your own criteria yourselves.
In order to build a solution for healthcare you would want to address problems on some of its components. Some components that I hope sound familiar to you are prevention, diagnosis, rehabilitation, administration and integration.
As healthcare may have fatal results, every product or service is evaluated on a framework of criteria. Consequently a solution for healthcare must be safe, have a real effect on a problem, around patients’ needs, able to deliver on time, efficient and equitable. Quality in healthcare is not only protecting the system against pseudoscience but also maximizing the allocation and use of resources, and addressing the patients’ needs as best as possible.
The current paradigm of healthcare is this one.
Healthcare stations and professionals are the central point of collecting, analyzing and disseminating information and solutions for patients. In order to learn about your health condition, to make a test, have a treatment or buy a healthcare product you must go through these stations.
Your solutions could address problems around the functional components and business processes of these stations. Through technologies some of these process may become obsolete in the near future. If this happens so, it won’t be for the shake of technology but for the shake of patients and the whole healthcare system.
Through these data collection points we will gather several data. These data are not necessarily machine friendly. We may still have paper copies, as we still large accumulation of data in natural language.
Gathering data appropriately won’t be only about collecting them on time and in the context needed, but also about clearing the noise and being able to filter in the signal.
In the literature there are several beautiful frameworks for helping you understand how to properly design and implement an intervention in healthcare. This specific one was made for eLearning.
As you can see it consists of the integration of three domains. Pedagogy principles, Content (in our case clinical science theories) and technology standards.
I would suggest keeping in mind the principles of such diagrams so when you want to build something do not get reassured by your assumptions only. Try to find a friend or a group of people with complementary skills and expertise to you. Digital health is a multidisciplinary domain.
In order to identify problems you may need to track a user’s journey and see where these problems or system’s inconsistencies happen
Let’s say that we decided to build a digital health solution for patients. Through surveys, interviews, background review on the literature we tracked his daily routine and problems and we decided to start developing a solution.
Do we collect the relevant and right type of healthcare indicators of the problem? Are the professionals and patients willing to share the information we want to collect? Is our method reliable and timely to the context of activities and processes to monitor?
Considering the functionality of our solution, I guess you already know that building digital solutions is not only about solving equations but also delivering the answers to a human like language. There are various elements in human computer interaction theories that are going to be needed and these may also important factors for which one technological solution may fail.
Technology is made for humans not for their developers. It should address they literacy levels, skills, everyday habits.
Finally, implementing a new service to the current daily routine of professionals and patients is not self-evident. On the macro level there are legislation, regulation, financial and several other problems. You can see a glimpse of them in the Behavior Change Wheel.
On the personal level in order to motivate someone to use something new and change his regular routine will need voluntary and involuntary methods of persuasion.
As healthcare has high quality standards, the diffusion of innovation and the penetration to the market is much more complicated that we think.
Let’s say that we found a great idea on digital health and decided to devote ourselves to making it a sustainable product.
How long may this whole procedure take?
This is a very typical diagram of a product’s life cycle. We have already spoken about the first steps, what is very different in the healthcare domain is the testing and the diffusion of innovation process.
In order to test the safety, efficacy and effectiveness of a product we do clinical trials.
For the financing part, let’s say very roughly, that we will do cost effectiveness analysis. In most countries of the world healthcare is subsided by public insurance schemes. This means that you do pay for a service out of your pocket but the system pays it for you. As there the resources of the system are limited every service used but have the optimal ratio of cost to effectiveness.
In more simple words if you develop a very effective solution that it matters only to few people (except maybe rare diseases) or is very expensive, this solution most probably will fail in the market. The same applies to solutions providing marginal improvements in a overcrowded market of solutions. In order to test the safety, efficacy and effectiveness of a product we do clinical trials.
Here is a roadmap of a drug development process. It may take up much more than 10 years. It will start with many molecules, and it will end hopefully with only one. That is why you will find only big companies on the domain, and that is why drugs have a patent protection for 20 years.
This is the life cycle of medical device in the US. In this diagram you will see that depending of the medical device Class the process may take longer. In many cases it could take even around 10 years. Medical devices are also protected from patents regarding they design and use.
The medical class is determined by how invasive a device is to the human body, how much harm it can cause due to malfunction or misuse. This diagram may explain why there are mostly big companies for hospital medical machinery also.
Please take into account that different countries have different regulations. This means less or more years depending on the country you want to sell your product.
Let’s see now an approximation of the life cycle of a digital health intervention. The diagram is made by Steve Blank who is a Silicon Valley pioneer in technological entrepreneurship.
You can see that the lifecycle is much shorter and you may even skip complex procedures with the FDA.
The more the technology integrates into healthcare, and the more people’s safety, privacy and confidentiality may be threatened by the proper use of it, there are going to be even more regulations.
Let me show you an example how things may get complicated.
Airbnb is service that let’s common people rent their rooms to customers. It violates several regulation applied to the hotel accommodation industry. There were legal cases against them, still the company runs from 2007 without major disruptions in their function.
23andme is a major personal genomic service where you can send your saliva and have your DNA analyses. They could make you genealogy but also make predictions for your probability of having various diseases. Although they were founded in 2006, last year there were forced from FDA to stop delivering their disease prediction services. Apparently these services were not documented by clinical trials. As there is a strong danger of people having wrong diagnosis, making them to search for improper treatment, the company has to stopped delivering the most important of its services.
Business wise, you can imagine that this costed in prestige, time and money.
Healthcare is not like other consumer markets, and you should not regard this way. Retractions are not rare phenomena in healthcare.
Two more diagrams and we are off to examples. This is the famous Hype cycle. It depicts how technological innovation is adopted through time. Adoption needs maturity both for the product and the market.
This is a little bit different diagram. You can see that in order to sell an innovative product you will have to address to different types of customers, with different types of messages and most probably generation of solutions.
For example let’s take the wearables paradigm.
They started with devices that were used mostly for people that very keen on sports, fitness and wellness. All the other people were not interested as they could not find value in them. Their design may seem flashy but in many cases is not user friendly.
When wearables will get integrated to more common problems, for example trembling walking in eldery people or activity coach for people with heart problems, the more they will start getting adopted by common people. Together with their functionality their design and interface will also change. They will look much more like everyday clothing and accessories than they look now.
Before we go to the conclusions, let’s skim some examples of where digital health can apply its solution.
Here is big list of different types of digital health startups. Depending on the healthcare component and the problem they are solving they may have overlapping characteristics. I would suggest you all to visit Rock Health’s website, which is one of the biggest startup accelerators on the domain ,and browse trough these categories and companies.
Such ventures deliver solutions to the current healthcare stations. Try to imagine the processes and flow of information that could be properly automated and the system would gain from that.
Home care, with smart environments, and 1/3 of the developed countries citizens being elderly patients, is also a field where you could explore.
We mentioned already wearables. I would like to highlight that ubiquitous data collection, analysis and delivery from smart sensors in general is growing every year.
Here is list of devices that are digitized and in many cases they are integrated with mobile and online technologies. You should not regard these as separate solutions necessarily. In many real life scenario someone may use different types of these devices and he may need their information combined into some meaningful action.
A very important field that is mostly related to the life sciences domain is –omics one. By –omics I mean genomics, proteomics, metabolomics and microbiomics. The more we solve biological problems computationaly the more we will have new type of digital health services. Services that can predict diseases like cancer, that can make blood tests on your mobile phone or you home test kit, and so on.
Then we have augmented reality which can be useful in surgery but in medical education as well. Imagine the integration of this technology with technologies like google glass, or digital contact lenses.
We also have robotics. Robotics for surgery, for taking blood, for prosthetic human parts, or for nursing support.
Development in printing means that we can not only build cheaper outside human parts but also create new types of inside human organs also. In the future we may even start printing our own food. I remind you last years news on the first fully lab grown artificial burger.
In general there is the tendency to go as much more invasively as possible to a patient’s context. The image on the upper left corner shows a device that integrates all the different monitoring data from a critical care unit does. If you have seen related scenes from movies you must have notices that these monitors usually take a lot of space. Well, new devices are going to be different.
The device on the upper right corner is a smart pill. It tracks whether the patient took it and sends a signal to a sensor patch on the patient’s body.
The other two images depict a tatoo like electronic sensor and a smart contact lens.
Finally, besides the various open software projects we are having some new types of open hardware in healthcare. This one uses adruino and raspberry Pi in order to facilitate a data collection platform for various health monitoring devices.
I would suggest especially students to explore the web for such open projects and see how they could play and learn with them.
I will close with some comments and personal thoughts on this presentation.
I believe that due to the technological paradigm, digital health is also becoming more and more ubiquitous in data collection, in analysis and dissemination of services.
Population health indicators, together with personal health indicators, together with biological indicators are becoming more and more integrated. They do create a huge potential but they also bring forward a massive complexity which is not so straightforward to get solved.
One thing that I did not mention explicitly in this presentation is interoperability. You must consider that with so many different data collection points, data collection methods, data types and users inside the healthcare system interoperability is more than fundamental.
It may not sound such an exciting topic but it truly is an important prerequisite for making this emerging ecosystem of services and products integrate efficiently.
If you wonder how to build expertise on the field of digital health please consider proper needs assessment also. We design solutions for problems, not technologies as gadgets or accessories.
Two last sentences before I close.
On the web you will find dozens of guides of digital health startups. I trying to skip these as you can find them even with a simple google search.
Last, please do remember that ideas are only a part of the process. The whole process is a journey. As the picture highlights ideas are fun. Execution is hard work.
Thank you very much.
Introduction to engineering entrepreneurship in the public health and life sciences domains
Introduction to engineering entrepreneurship
in the public health and life sciences
Nikos Papachristou MSc, MHTA
Radboud University Nijmegen
Global eHealth Unit, School of Public Health, Imperial College London
June 26th, 2014
IEEE Educational Activities Division (EAD)
- Webinar objectives
- Topics of discussion:
- Digital health
- Intervention design
- Implementation science and Diffusion of innovation
- Only an introduction
Health (problem) + Digital (solution)
How do we build a product or a service?
Idea on a problem
How do we define “health”?
What you see is not what you need
source: health nexus sante
Health Models (1)
source: Population health model, Integrated behavioral Model
Digital health “definition”
“ The lexicon of Digital Health is extensive and includes all or elements of :
• mHealth (aka mobile health),
• Wireless Health,
• Health 2.0,
• Healthcare IT / Health IT (information technology),
• Big Data,
• Health Data,
• Cloud Computing,
• Quantified Self,
• Wearable Computing,
• Telehealth / Telemedicine,
• Precision and Personalized Medicine,
• plus Connected Health.”
Digital health landscape
and so on…
Examples of engineering innovation
Type of startups (indicative)
- Big data
- Diagnose or treat it
- Efficiency tools for administration
- Enhanced care delivery
- Enhancing the provider-patient relationship
- Find a doctor, book a doctor, rate a doctor
- Food and nutrition
- For children and their neurotic parents
- For the ladies
- For the older crowd
- Get in shape
- Got health questions? They have answers
- Mental health
- Modernizing the clinical trial
- Patient / caregiver empowerment tools
- Paying the bills / cost transparency
- Quantify yourself
- Sensing stuff
- The new emr
- Tools for providers