Article
In the last couple of years, world politics seem to be on a path where nations are increasingly growing apart. At the age of connectivity, where the six degrees of separation1 theory looks more true than ever, this is quite a direction shift. Despite this multipolar scenario, there are various common global challenges to take on.
May 31, 2019
Two of the most prevalent are the increasing costs of healthcare and the demographic trend of an ageing population. To show how relevant these trends are, let us use these simple data points/projections: according to the United Nations2, the potential support ratio (PSR)3 is predicted to fall to 3.5 in 2050, from 7.0 in 2015, i.e., not only enlarging the elderly population, but also drastically4 reducing the caregivers population. Already today EU member states alone spend €4 billion every day providing healthcare.
The two challenges are closely related, as an ageing population is set to amplify the healthcare cost issue. As such, in order to maintain high quality standards and the affordability of the healthcare system, significant innovation will be needed. In this Thematic Insight, we explore how Digital Health may cause positive disruption. Specifically, we illustrate how sensors will play a crucial role that is expected to drive a paradigm shift of the current healthcare system.
"It takes the average American four years of doctors' visits to spend as much time with their physician as they spend with their phone in a single day. "Emmanuel Fombu, MD, MBA⁵
In order to get a sense of the roadmap ahead of us, let us first define our goal. Ideally, an effective healthcare system delivers a high quality service, is accessible and affordable, is patient-centric, and has a focus on prevention rather than just disease treatment. Given these characteristics, and the current healthcare systems’ architecture, it is clear that such an equation cannot be solved. However, a fundamental change in our assumptions, i.e. a paradigm shift, might solve the puzzle. That fundamental change may be the time compression that healthcare sensors can offer.
Consider that in the US, on average, each person visits their doctor 4 times per year6, thus totalling 1.3 billion visits per year. Essentially, a visit to your doctor is simply a bilateral exchange of information: at a specific point in time, your doctor observes a set of parameters and, based on those, gives you advice. In some cases, following your doctor’s advice, follow on visits are needed to monitor the reaction to therapy.
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Now, suppose that the parameters your doctor evaluates can be monitored by wearable sensors at a much higher frequency, and your doctor can access that data remotely. There are a number of advantages to this process: first benefit for both parties, is a richer dataset upon which the counselling is based; second, as the information is readily available, the physical displacement can be (in some cases) avoided; third, your doctor would be able to monitor the evolution of your organism reaction remotely. In other words, the use of sensors may be a game changer for healthcare systems.
As we did for the ideal healthcare system, let’s start by drawing the roadmap for sensor systems. Quoting Marschollek et al7,
"the ideal sensor-system for health related parameters would be deployed at one point in time and continuously measure and wirelessly report all health-related information thereafter. It would not constrain or affect its user in any way and it would need no maintenance."
This is a challenging goal, however, the gap between current technology and this goal is continuously narrowing.
The simplified framework in Figure 1, gives a simplified overview of the sensor space. Consider that at its origin, wearable sensor technologies focused largely on cardiovascular diseases, simply because the technology to record electric signals has been available since the 1960’s8. Today however, sensors span several disease areas and the potential applications greatly benefit from the rise of the Internet of Things (IoT). As in any other IoT related topic, the challenge will lie in extracting value from the large amount of data available On this matter, the “IT Future of Medicine”9 project, puts forward interesting proposals; for example, one possibility is to create a digital twin that contains information based on an individual’s –omics (genomics, proteomics, metabolomics) analyses, imaging techniques, and information from sensors of various forms. This digital twin profile would be updated continuously by sensors and intermittently otherwise, and would essentially be based on modelling every individual biology in order to generate the best prediction possible from a complex data set. Even though this makes a compelling case of a preventive healthcare system, we are still some steps away from this reality.
The use cases for sensor-systems span a wide spectrum. Depending on the type of sensor, the use cases can span from classic medical uses such as Constant Glucose Monitoring (biosensors), to surgical fluid management systems (pressure sensors) or electro-surgery (flow sensors); outside the pure medical spectrum, wearable sensors designed to measure oxygen levels in the muscles, i.e., performance optimization are a good example.
Currently, many of the existing digital health sensors that have been broadly adopted are perceived mainly as lifestyle products or lifestyle wearables. Flagship examples are FitBit® with its reported 25 million users10 by the end of 2017or the use of Microsoft Kinect in physiotherapy rehabilitation for stroke patients11.
Marschollek et al12 assert that sensor applications should focus on neuropsychiatric conditions based on a report13 of the World Economic Forum indicating that mental illnesses are expected to generate higher costs than cardiovascular diseases.
The main issues or risks stem from three main areas: data security, regulation, and technical development.
On the security issue, the common IoT data security issues also apply. This is further amplified by two factors: first, the nature of healthcare data is by definition sensitive (i.e., confidential); second, the use of mobile operating systems to collect and transmit information is prone to data exfiltration and malware. The health sensor industry will have to draw on the cybersecurity industry expertise in order to tackle data security.
On the regulation side, the integration of these devices into reimbursement schemes and all the regulation regarding data storage and usage will be critical for the mass adoption of the sensors.
Last but not least, technical advances can significantly enhance the value of sensors. For example, advances in energy harvesting have the potential to significantly increase the energy autonomy of sensors, thus reducing the maintenance burden and improving user experience.
As we have shown throughout this Thematic Insight, sensors are a cornerstone element for the success of Digital Health. The health sensor technology has significantly evolved since the early 1960’s, and the IoT revolution is the tipping point that may enable the transformation of healthcare systems. We are already at a stage where we can envision a future where implantable biosensors for blood glucose coupled to an insulin release system, can regulate the release of the drug fully autonomously
The driving forces behind the healthcare systems reform, i.e., the financial burden of healthcare and ageing societies, are long term global trends that will generate significant investment opportunities. As such, we see the exposure to the Digital Health topic as an essential part of a portfolio.
Fund management |
Credit Suisse Fund Management S.A. |
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Portfolio manager |
Credit Suisse Asset Management (Switzerland) Ltd, Zurich |
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Portfolio manager since |
December 14, 2017 |
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Fund domicile |
Luxembourg |
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Fund currency |
USD, EUR, CHF |
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Inception date |
December 14, 2017 |
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Management fee p.a. |
For unit class A, B, BH and CB: 1.60%; For unit class EB and EBH: 0.90% |
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TER (estimated) |
Unit class A, B and BH 1.90%, unit class EB,EBH, IB and IBH 1.20%, unit class UB and UBH 1.30% |
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Maximum Sales Charge |
5% for all unit classes except unit classes IB, IBH, EB and EBH (max. 3%) |
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Single Swinging Pricing (SSP)1 |
Yes |
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Benchmark |
MSCI World (NR) |
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Unit classes |
Unit class B, CB, IB, UB, EB in USD, unit class BH, IBH and UBH in EUR, unit class BH and UBH in CHF |
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ISIN |
USD unit class B: |
LU1683285164 |
USD unit class UB3: |
LU1683288424 |
USD unit class IB: |
LU1683285750 |
EUR unit class UBH3: |
LU1683288770 |
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EUR unit class IBH: |
LU1683285834 |
USD unit class EB2: |
LU1683287707 |
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EUR unit class BH: |
LU1683285321 |
CHF unit class BH: |
LU1683285248 |
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CHF unit class UBH3: |
LU1683288697 |
EUR unit class EBH2: |
LU1683287889 |
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CHF unit class EBH2: |
LU1796813662 |
EUR unit class A: |
LU1877633989 |
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Please note that not all share classes may be available in your country. |
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Source: Credit Suisse, February 06, 2019
1 SSP is a method used to calculate the net asset value (NAV) of a fund, which aims to protect existing investors from bearing indirect transaction costs triggered by in- and outgoing investors. The NAV is adjusted up in case of net inflows and down in case of net outflows on the respective valuation date. The adjustment in NAV might be subject to a net flow threshold. For further information, please consult the Sales Prospectus.
2 For Institutional clients only.
3 In Italy: For instituonal clients only.
1 In “Everything is Different” (1929) Frigyes Karinthy advances the idea that all living things and everything else in the world are six or fewer steps away from each other so that a chain of a “friend-of-a-friend” statements can be made to connect any two people in a maximum of six steps
2 World ageing population 1950-2050, pp.87. Available at: http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Report.pdf
3 Number of persons aged 15 to 64 years divided by the number of persons aged 65 years and above
4 The Future of Health Care: deep data, smart sensors, virtual patients and the Internet-of-Humans, pp.2
5 Source: “The Future of Healthcare: Humans and Machines Partnering for Better Outcomes”, Fombu E. (2018)
7 “Wearable Sensors in Healthcare and Sensor-Enhanced Health Information Systems: All Our Tomorrows?”, Marschollek, M. et al (2012), Healthcare Informatics Research
8 “New Method for Heart Studies”, Holter NJ (1961), Science; 134:1214-20
10 https://investor.fitbit.com
11 https://www.physio-pedia.com
12 “Wearable Sensors in Healthcare and Sensor-Enhanced Health Information Systems: All Our Tomorrows?”, Marschollek, M. et al (2012), Healthcare Informatics Research
13 “The Global Economic Burden of Non-Communicable Diseases”, Bloom et al (2011); World Economic Forum
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