How can Smart-Remote Diagnostics change the Healthcare Industry?

Posted by Holly Maunders on 30 May 2018

During the course, Squares are asked to create a whitepaper on how future digital trends will impact an industry. The top 3 from each cohort are published on this blog to share their exciting research with all of you!

Congratulations to Group 17 for this amazing paper on smart-remote diagnostics in the healthcare industry. Team members include Kantar's Anja Milosavljevic, Maciej Wojnarowski, Vanessa Iglesias Garcia, Ignacio Fernandez Gaspar, Virginia Garavaglia, Markus Eberl, Ana Monteiro, Lindsay Kunkle and Prasad Viswanath, from various locations, including: Poland, the UK, Spain, Germany, Brazil and the USA!

Introduction

In Ancient Egypt, women used to urinate on wheat and barley seeds to determine whether they were expecting. Hippocrates, commonly known as the "Father of Medicine," smelled patients sweat to assess their condition. Fortunately, medicine has advanced considerably since these times, but understanding what is going on inside our bodies remains a key factor in healthcare.
 
Medical errors account for $17.8 billion annually 1 and add up to some $312 billion of identified waste in healthcare spending in the U.S. alone 2 . A recent study by Google revealed that machine learning can significantly improve diagnosis accuracy and greatly reduce time, costs and waste 3As illustrated by a Stanford University team, new technology can not only improve process efficiency, but also change the way we access diagnostic tools and collect data 4 .
 
Technology can help the industry reduce costs, increase healthcare access and improve accuracy in diagnoses. Virtual assistants, connected wearable devices, and telemedicine are more likely to be accepted by the population as access to digital platforms expands. Soon a broad spectrum of diagnostic services will be available at our fingertips, health conditions will be monitored for symptoms and examined with nearly perfect accuracy.
 

How can remote diagnostics hep improve healthcare?

Remote diagnosis has already been successfully implemented in specific situations, such as long expeditions (i.e., space, submarine), conflict zones and more recently isolated populations. These experiences have paved the way for wider use of remote diagn
osis, especially in emerging countries.
 
Increased efficiency is needed to meet increasing patient 
Image 1-1
demand. Emerging markets are experiencing considerable population growth and mature markets are facing the strains of an ageing populations; meanwhile life expectancy is rising globally. Doctors are not able to see all the patients that require their care. Remote diagnosis will enable doctors to see more patients in the same amount of time. 

At the same time, greater healthcare reach is required to serve people in remote places or areas without modern medical facilities. Mobile technology offers a pre-existing infrastructure that provides easy and affordable means to reach people any time of day.
 

Remote diagnosis gathers and analyzes data, taking in a greater range of data—both an individual's past medical history, as well as correlated data from patients with similar conditions—than doctors usually have available to them. Utilizing AI supported technologies; remote diagnosis can guide doctors to make decisions faster and more accurately.

Our proposed technology will extend low-cost health care to the most remote areas and create structural efficiencies in determining the right care plans for patients. The advantages of remote diagnosis include:

  • Smart, practical, functional and fast
  • Empowers patients to self-monitor their conditions
  • Bridges the gap between remote areas and hospitals, between the level of care in the field and in large medical centres
  • Provides medical care to remote communities
  • Helps to compensate for the shortage of suitably skilled healthcare professionals, giving time back to doctors that would otherwise be spent deciphering test results
  • Guarantees confidentiality
  • Delivers data-supported, accurate diagnoses
  • Creates benchmarks – Health KPIs

How is technology creating the opportunity for remote diagnostics?

The latest developments of technology create a window for the adoption of remote diagnosis on a larger scale. Disruptive innovations enable on-going health data collection, as well as analysis and treatment recommendations.

Gathering health care data

The quantity of medical data has grown exponentially in recent years 5, and we are just beginning to identify the many ways we can seamlessly gather it.

The following are just some of the many early manifestations of technologies that collect data and will help to inform the future of remote diagnosis:

Image 2Wearables. Sensors tracking health data like blood pressure, temperature, heart rate and skin resistance are not new – however their miniaturization and connection to smartphone apps has generated a complete new segment of technology that could be worth $34 billion by 2020 according to CCS insight 6 .

Image 3Implantables. In 2017, the Food and Drug Administration (FDA) for the first time approved a digital pill - a medication embedded with a sensor that can tell doctors whether, and when, patients take their medicine. They can also release specified doses of medicines at the right time.

Image 4-2Monitoring and prevention devices in hospitals. Philips is investing in different solutions such as continuous mobile monitoring for emergency patients while they are being moved around hospitals for tests and systems to be used by cardiac patients that prevent and warn caregivers hours before a potential heart attack 7 .

Image 5-1Image recognition and diagnostics. The start-up Butterfly Network 8 has developed a handheld 3D ultrasound tool that produces real time images and sends the data to a cloud service that identifies the characteristics and automates diagnosis.

Analysing data

Once health data has been gathered, the next step is to make sense of it and identify the right treatment or course of action—while this was once a step regulated to doctors only, technology is quickly taking over. Indeed, we are already seeing AI begin to aid in diagnostics:

Image 6-1

  • Decision on next steps when initiating a case. Babylon Health 9 is piloting a program in London where people contacting an emergency line are encouraged to consult a chat-bot instead of a human being. AI is used to assess the urgency.
  • Combine data ready to be reviewed. Offering a diagnosis may involve gathering data from different sources and is often a time-consuming task for doctors. AI helps to synthesise the results and present the key data points to be reviewed faster by a patient's medical team.
  • Support during the analysis. Algorithms can be built to analyse images to identify sets of pixels that suggest the development of cancer. Machines can review millions of images per day to support doctors in their judgement and create with data to define standards and spot odd results.
  • Learning progressively. Algorithms improve as more images are fed into the database and doctors give feedback about the suggested diagnosis, so future diagnoses have the benefit of learning from all past diagnoses and results.

What does this mean for remote diagnostics?
 
Algorithms will be created to suggest when to perform tests so disease does not go untreated. Alerts, which will be triggered when metrics undergo an unusual change, will ensure urgent care is provided at the earliest of signs of an emergency advising the individual to contact a doctor or even by calling emergency help for the individual. Additionally, doctors will gain time and headspace to devote to the care of their patients rather than getting bogged down in repeat diagnoses. Understanding of diseases will become more robust and nuanced. 
 
Are we ready for remote diagnosis?
 
As these examples demonstrate, it is clear that the availability of technology is not the barrier for adoption. However, there is a need to consider concerns around data privacy, and in particular, in Europe the implications of GDPR. As with any adoption curve, there will be a minority of early adopters only followed by mass acceptance after a given period of time – the question is where on the curve we currently sit.
 
Data privacy will be a key area to reassure consumers on the safety of this technology. We have already seen mass leaks of private NHS health records in the UK, and this was not data continuously collected and being streamed to servers where there is even greater opportunity for it to be breached. For remote diagnosis to work, a fully encrypted and safe system needs to be set up to ensure consumer confidentiality is preserved. The fall out of hacke
d health information could be hugely damaging to individuals and the stability of society at large.
 
Whilst patients may be ready for remote diagnosis and AI in healthcare to varying degrees, there may also be a potential barrier posed by doctors who view AI and robotics as a threat to the future of their profession
. As a result, doctors of this mindset will be slow to instate remote diagnosis in their practice. 
 
Image 7
AI systems in healthcare are not yet developed sufficiently to 
replace the human element in diagnosis. It is complementary to the usual remit of doctors and in fact can be a powerful weapon in their arsenal. AI and machine learning are able to process huge amounts of data quickly, finding patterns in real time and discerning if they are significant or not. This level of analytics enables doctors to leverage continuous data collected by wearables.
  
Strategy for change
 
Certain populations will benefit from remote diagnostics more than others and therefore will be more open to adopting these tools. These areas can be used to test the tools, develop best practices and gather data around the advantages remote diagnostics provide. Populations that should be prioritized for early adoption include:
 
  • Remote or rural areas where the population is scarcely distributed
  • Countries with dysfunctional health systems or with limited medical resources
  • Healthcare systems that are overcrowded
  • Communities with too few doctors or healthcare professionals
These aspects need to be considered for a successful deployment:
 
Provide wearables to the population involved. These devices will monitor critical health measures needed for ongoing, non-intrusive remote diagnosis. Wearables should also empower the wearer. Not only will people receive personalised wellness recommendations from the device, they will also develop a better understanding for what is going on inside their own body based on the data the wearable collects. To ensure adoption, all data the wearables collect will be encrypted and secured—a sizeable, but necessary investment to protect patient privacy. Any data that is leveraged either on a personal care or a community health level will require consent from every individual involved. Depending on the market, wearables ought to be provided by an individual's private health insurance company or by the government.
 
Connect health benefits to medical insurance investment. There are significant cost savings in remote diagnostics. Critical health conditions are caught at first onset, eliminating the need for elaborate and costly medical procedures or medicines. Health insurance companies and governments will financially benefit from full adoption of remote diagnostics and can further incentivise adoption by transferring some of the financial benefit to the individual.
 
Create hubs where people can connect remotely with doctors. Hospitals and doctor offices are less crowded because only those who really need a doctor's in-person care come in. Patient´s medical story is stored and accessed by any doctor that the patient visits. Doctors' feedback or follow up may be done remotely too.
 
Re-train health professionals. Remote diagnosis and ongoing health tracking puts the patient at the centre of the healthcare process and re-frames how care is provided—the doctor is part of the team, not the centre. Implementing such systems requires training. The Social Health Activist programme in India, for example, has trained nearly one million community health workers.
 
Protect against the risk of misdiagnoses and their impact on public opinion. For the continued adoption of remote diagnostics public opinion will need to be favourable. In order to avoid misdiagnosis during the adoption phase, a human doctor should supervise diagnostic tools using AI.
 
Equalise access to Internet. More than half of the world's population is still without Internet access, ensuring that access continues to spread is a must for success of remote diagnosis. There are several Internet balloon networks that travel on the edge of space in order to extend Internet connectivity to people in rural and remote areas worldwide— going forward this will be something that governments will need to provide more readily for the health of their people. Mobile already is the most widespread communication infrastructure in the world.
 

Conclusions

Remote diagnosis can be applied to reduce healthcare costs, save time and, more important, save lives. In the U.S., costs of Professional Services (including doctors) account for 26% of healthcare expenses 10. Remote diagnosis will significantly reduce this expenditure and free-up resources for other investments to better public health (e.g., cures, preventative health initiatives, etc.).

With technology advancements like AI, machine learning and connected devices, to monitor and recognise health conditions, this field of telemedicine trends will continue to expand and evolve quickly. Remote diagnosis provides high value to customers and the optimises resources for health providers.

Healthcare companies that are seeking advantages over their competitors should be the pioneers to offer remote diagnosis. Governmental health agencies, in emerging markets especially, should investigate the potential use and feasibility for immediate implementation of remote diagnosis solutions.

Image 8

 

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Topics: Digital trends, Digital marketing, Whitepaper, Business case studies, Squared Network