How AR and VR Are Revolutionising the Healthcare Sector

24

September

2022

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Augmented Reality (AR) is a set of technologies that complements the physical (real) world with digital data and media and allows for an unprecedented human-virtual experience. This combination of technology and the physical aspect of the world allows its user to fully benefit from data, which is often said to be two-dimensional. Virtual Reality (VR) on the other hand replaces physical reality with a computer-generated environment in which the user is fully immersed thanks to hardware such as VR headsets (Porter and Heppelmann, 2017).

Since the beginning of the 21st century, the healthcare sector has undergone tremendous changes. Efforts to constantly improve the industry in terms of how patients can be cured, rising costs, and the expanding use of connected devices make medicine favourable to the implementation of AR and VR now more than ever. As a matter of fact, the use of AR and VR in the healthcare industry is expected to reach $5.1 billion by 2025 according to Goldman Sachs (2016).

Even though these technologies are still under development, they are already widely used in the healthcare sector. At George Washington University, advanced VR tools are utilised by neurosurgeons to explore patients’ brains prior to performing the medical procedure. By using these technologies, neurosurgeons can better prepare for operations and avoid accidents, thus improving their surgical efficiency (Li, 2022). In addition to VR, surgeons make use of AR through connected glasses to monitor the patient’s vital signs while remaining fully concentrated on the actual procedure.

VR can also be used for patient care and education. In an article about VR and AR in the healthcare industry, vStream (2018) discusses a program set up by the NHS that allows young patients to be taken through every step of an MRI scan thanks to a VR headset prior to the real procedure. This program aims to lessen the fear and anxiety of patients, thus allowing for a smoother execution of the medical procedure.

Although AR and VR are still under development, it is clear that they are capable of truly disrupting the healthcare industry and that current applications of these technologies are already drastically improving medical services and patient experience.

References:

GOLDMAN SACHS 2016. Virtual and augmented reality. Understanding the race for the next computing platform.

LI, D. 2022. How Virtual Reality Is Transforming Healthcare [Online]. Available: https://www.uschamber.com/technology/how-virtual-reality-is-transforming-healthcare#:~:text=VR%20has%20proven%20to%20be,and%20chronic%20pain%2C%20and%20more. [Accessed 21/09/2022].

PORTER, M. E. & HEPPELMANN, J. E. 2017. Why every organization needs an augmented reality strategy. HBR’S 10 MUST, 85.

VSTREAM. 2018. VR & AR FOR HEALTHCARE & MEDICINE [Online]. Available: https://vstream.ie/vr-ar-for-healthcare-medicine/ [Accessed 23/09/2022].

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AI and Robotics: The Future of Elderly Care?

19

September

2021

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As a result of declining birth rates and increasing life-expectancies, populations around the world are nowadays ageing at unprecedented rates. In Europe, the life expectancy at birth for both, men and women, has increased by 10 years within the past five decades, and is expected to rise even further in the future (European Commission, 2020). While the increasing longevity brought about by significant breakthroughs in healthcare has been a major achievement of our time, the changing demographics are also posing tremendous challenges on public health care systems in providing sufficient care for the growing share of older people. Already today, the demand for workers in elderly care is significantly exceeding the supply, a trend that will continue and accelerate even further.

Major developments in AI and robotic technology however provide an exciting opportunity to complement elderly care by counteracting the growing labor shortages within nursing facilities and home care and helping older people to preserve their independence for longer (Association for Advancing Automation, 2020).

Overview of service robots in elderly care

Broadly, one can distinguish between two categories of robots in elderly care. The first category includes rehabilitation robots which aim at physical assistance and would include devices such as smart wheelchairs and artificial limbs. The second category refers to assistive social robots which are seen as “social entities” that are meant to actively interact and communicate with the user. Within this category, there are two different kinds of social robots: service type robots and companion type robots (Broekens et al., 2009).

Service type robots

Service type robots have their main functionality in assisting older people in their everyday lives. One example is the robot “Pearl” . It reminds people of their daily activities, such as eating or going to the bathroom and supports them in their mobility, for instance by navigating them through the nursing facilities and accompanying them to appointments, social events or regular walks for exercise (Pollack et al., 2002).

Companion type robots

Apart from service type robots, robots are also being utilized as “companions” that can help counteracting loneliness and strengthening the psychological well-being amongst older people. One example is the robot “Pepper” which is utilized in several care homes in the UK. “Pepper” can learn about the interests of care home residents, allowing him to have conversations with them, play their favorite music and play games with them (The Guardian, 2020). Another type of companion robots includes therapeutic pet robots, such as the cat-shaped robot “iCat” or the seal-shaped robot “Paro”. Studies have shown, that “Paro” can indeed reduce loneliness, depression, agitation and blood pressure among older adults with dementia (Hung et al., 2019).

Reflection

Personally, I think these are exciting developments. While robots can by no means sufficiently compensate for the growing labour and capacity issues in elderly care, I do think that they can become an important pillar as an additional source of support. However, the emphasis here should clearly be on additional support. It is crucial to consider the ethics of using robots to care for the elderly and not to neglect the importance of interpersonal contact and quality of care for the sake of efficiency.

What are your thoughts on the utilization of AI and robotics in elderly care?

References

  • Broekens, J., Heerink, M., & Rosendal, H. (2009). Assistive social robots in elderly care: a review. Gerontechnology, 8(2), 94–103.
  • Hung, L., Liu, C., Woldum, E., Au-Yeung, A., Berndt, A., Wallsworth, C., Horne, N., Gregorio, M., Mann, J., & Chaudhury, H. (2019). The benefits of and barriers to using a social robot paro in care settings: a scoping review. Bmc Geriatrics, 19(1), 1–10. 

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My vision of the medical treatment industry in Germany in the year 2050 – Utopia or rather a dystopia?

9

October

2019

5/5 (2) The problems in the German medical treatment industry are multifarious (Müller, 2018). Besides having unnecessary and badly executed medical treatments, medicine is costly (Tautz, 2018) and people from the countryside are suffering from a decreasing number of medical care due to the rural exodus of many doctors (Kölsch, 2018). For example, Mecklenburg-Vorpommern has a need for doctoral replacement of around 25% because of the previously mentioned reasons (Korzilius, 2008). Nonetheless, not only the countryside is suffering from an undersupply of doctors as 52 000 doctors are expected to retire in Germany until 2020 (DAZ, 2010), but also hospitals are missing 80 000 caregivers currently (Heine, 2018). Furthermore, the absence of IT-networks or standards for data transfer (Banse, 2018) are fundamental reasons for inefficiencies and a poor allocation of resources in the Germany medical treatment industry (SVZ, 2017).

However, change drivers such as the technological development, digitization and new customer needs could potentially enable an enhanced medical treatment in the future (Gerst, 2015). First, the E-Health trend impacts the interaction between patient and service provider and simplifies the self-management of the patient via (mobile) health applications (Wicks, 2014). Secondly, technological developments such as the advancements in big data analysis, self-learning AI deep learning algorithms or the digitization in general allow an improvement in the analysis of patient data, better forecasts, prevention of upcoming illnesses and a rectified interconnectivity between the stakeholders in the medical treatment field (Ehneß, 2018). Additionally, the technological development also offers advancements on the hardware side. For example, hyperloop systems or drones could potentially allow a different medical treatment infrastructure (Rosser, 2018). Last but not least, biotechnical developments in genetic manipulation (Miller, 2018) or in reproduction of organs could facilitate a lifesaving opportunity for patients (Wallace, 2018).

In the following part I will elaborate on my vision for the medical treatment in Germany in the year 2050. In order to empower a vital discussion, I would be keen on knowing if you can identify with my vision of medical treatment in the year 2050. Ask yourself, if ethical aspects such as morality or freedom are considered.

1. Home (-station) treatment
The HomeStation is an interactive diagnostic and robotic system for home use. It can take over general medical tasks, replaces or supports nursing staff and thus guarantees 24/7 medical care. Part of that home treatment is the use of wearables, for example electronic medical tattoos or sensors, which are on the one hand able to measure data regarding blood sugar, respiratory rate etc. (Kraft, 2019) and are on the other hand able to transmit that data to the relevant device or doctor. The role of the doctor will be taken by a robot (Yasa, 2018) who will consult the patient based on 24/7 tracked data. In addition, the robot performs minor medical treatments such as blood sampling or vaccinations. Finally, a 3D-printers ensures an immediate supply of medication, prevents drug abuse and provides better drug treatment through networking with other systems (Soleil, 2019).

2. Stationary care
Stationary care includes supra regional hubs, local hospitals and hubs of expertise for special medical fields. These are connected via drones and an underground network of hyperloops to ensure a fast and efficient treatment of every patient, independent of the location of the patient. Treatment at the surgery will be performed by surgical robots (Crawford, 2016), which are more precise, faster and risk-free. Therefore, badly executed medical treatments can be avoided. Additionally, due to the development in biotech, new organs can be delivered on demand and personalized (Pollack, 2018). A further benefit of the advancements in biotech is the prenatal and postnatal repair of severe genetic defects through genetic manipulation (Sakuma and Yamamoto, 2018).

By using this vision as a guiding principle, the medical treatment industry improves in terms of interconnectivity, flexibility, resource allocation, quality, costs and equality of treatment. Nevertheless, the risks are ubiquitous as there are side and ethical effects of genetic manipulation, as well as a reduction of human individuality by using robots. Therefore, the question arises if humankind should detach itself from its natural state and “design” people by reproducing organs? I am really looking forward hearing your opinion on this very relevant topic.

References:
Banse, P. (2018) Digitalisierung der Medizin – Das deutsche Gesundheitswesen ist zu wenig vernetzt. Available at: https://www.deutschlandfunkkultur.de/digitalisierung-der-medizin-das-deutsche-gesundheitswesen.976.de.html?dram:article_id=413494 (Accessed: 5 October 2019).

Crawford, M. (2016) Top 6 Robotic Applications in Medicine – ASME. Available at: https://www.asme.org/topics-resources/content/top-6-robotic-applications-in-medicine (Accessed: 5 October 2019).

DAZ (2010) Neue Studie zum Ärztemangel: Knapp 52.000 Ärzte gehen bis 2020 in Ruhestand. Available at: https://www.deutsche-apotheker-zeitung.de/news/artikel/2010/09/03/knapp-52-000-aerzte-gehen-bis-2020-in-ruhestand (Accessed: 5 October 2019).

Ehneß, S. (2019) Wie sieht die Medizin der Zukunft aus? Available at: https://www.healthcare-computing.de/wie-sieht-die-medizin-der-zukunft-aus-a-833099/ (Accessed: 5 October 2019).

Gerst, T. (2015) Zukunft der Medizin: Trendstudie will den Weg weisen. Available at: https://www.aerzteblatt.de/archiv/171346/Zukunft-der-Medizin-Trendstudie-will-den-Weg-weisen (Accessed: 5 October 2019).

Heine, H. (2018) Personalmangel in Krankenhäusern: 35,7 Millionen Überstunden – Politik – Tagesspiegel. Available at: https://www.tagesspiegel.de/politik/personalmangel-in-krankenhaeusern-35-7-millionen-ueberstunden/22706004.html (Accessed: 5 October 2019).

Kölsch, T. (2018) Medizinischer Nachwuchs: Landflucht und Landarzt-Mangel. Available at: https://www.general-anzeiger-bonn.de/ratgeber/fit-und-gesund/landflucht-und-landarzt-mangel_aid-43810019 (Accessed: 5 October 2019).

Korzilius, H. (2008) Hausärztemangel in Deutschland: Die große Landflucht. Available at: https://www.aerzteblatt.de/archiv/59015/Hausaerztemangel-in-Deutschland-Die-grosse-Landflucht (Accessed: 5 October 2019).

Kraft, D. (2019) 12 innovations that will revolutionize the future of medicine, National Geographic magazine. Available at: https://www.nationalgeographic.com/magazine/2019/01/12-innovations-technology-revolutionize-future-medicine (Accessed: 5 October 2019).

Miller, J. (2018) The Future of Medicine. Available at: https://hms.harvard.edu/news/future-medicine (Accessed: 5 October 2019).

Müller, T. (2018) Gesundheitssystem Deutschland: Trotz hoher Gesundheitsausgaben – bei der Lebenserwartung hinken wir hinterher. Available at: https://www.aerztezeitung.de/medizin/krankheiten/herzkreislauf/article/976013/deutschland-hohe-gesundheitsausgaben-und-geringe-lebenserwartung.html (Accessed: 5 October 2019).

Rosser, J. C. et al. (2018) ‘Surgical and Medical Applications of Drones: A Comprehensive Review’, JSLS : Journal of the Society of Laparoendoscopic Surgeons. doi: 10.4293/JSLS.2018.00018.

Sakuma, T. and Yamamoto, T. (2018) ‘Genome editing for dissecting and curing human genetic diseases’, Journal of Human Genetics, 63(2), p. 105. doi: 10.1038/s10038-017-0380-0.

Soleil, V. (2019) 10 Possible Medical Treatments of the Future. Life Advancer. Available at: https://www.lifeadvancer.com/possible-future-medical-treatments/ (Accessed: 5 October 2019).

SVZ (2017) ‘Ländervergleich: Medizinische Versorgung: Gut ausgestattet, aber ineffizient. Available at: https://www.svz.de/deutschland-welt/politik/medizinische-versorgung-gut-ausgestattet-aber-ineffizient-id18296516.html (Accessed: 5 October 2019).

Szent-Ivanyi, T. (2014) Unnötige Todesfälle in deutschen Kliniken. Available at: https://www.fr.de/ratgeber/gesundheit/unnoetige-todesfaelle-deutschen-kliniken-11233271.html (Accessed: 5 October 2019).

Tautz, D. (2018) Gesundheitssystem: Hohe Kosten, trotzdem Mittelmaß. Available at: https://www.zeit.de/wissen/gesundheit/2018-03/gesundheitssystem-deutschland-bruttoinlandsprodukt-lebenserwartung (Accessed: 5 October 2019).

Wallace, L. (2018) Reproductive tech will let future humans inhabit the body they truly want, Clinical Endocrinology. doi: 10.1111/j.1365-2265.2009.03625.x.

Wicks, P. et al. (2014) ‘Innovations in e-health’, Quality of Life Research. doi: 10.1007/s11136-013-0458-x.

Yasa, D. (2018) Why robots could soon replace our doctors. Available at: https://www.dailytelegraph.com.au/lifestyle/health/body-soul-daily/why-robots-could-soon-replace-our-doctors/news-story/9c33db2f25e0fff6184603b38cdc641f (Accessed: 5 October 2019).

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The borderless future of health care

4

October

2019

No ratings yet. Telesurgery and robotics enable the healthcare industry to be at its best anywhere, anytime in the world. But is this realistic?

The healthcare industry is in great move, where hospitals and health clinics are not but the patient is key and at centre. The main technology that can currently provide borderless health care is telesurgery. In other words, when 5G and robotics are combined, surgery can be done from any place on earth on anybody anywhere on the planet, without any geographical limitations. The location of patients gets a subordinate role to access of top-quality health care.

Obviously, this has great impact on future surgery, now quality surgery is available from unlimited distances, even in developing countries where top quality health care is lacking. Moreover, these areas are very hard to reach for doctors, it would be a great time safer if they could work from ‘home’. Additionally, doctors from all over the world can now work together. For example, a patient needs a very tough and complicated brain surgery and only very few people know how to execute the operation. Moreover, performance increases and the most modern techniques can be used as telesurgery make use of robotics. Human imprecision, tremors and clumsiness can now be eliminated. (Choi et al. 2018)

5G is currently quickly expanding and rolled-out widely across the globe. According to Ericsson Mobility Report June 2019 (2019) up to 65% global population, or 1.9 billion people, could have access to 5G by 2024. 5G is the fifth generation of wireless mobile network technology. Speed, bandwidth and reaction time on this new network will improve drastically. Enabling fast and stable network. However, it is mostly adopted in crowded and wealthy regions, developing countries need to overcome some hurdles first. For example, energy sources need to be stable and powerful, coverage cannot be guaranteed with too little signal and users and the government needs to subsidize users and implementation. (Chiaraviglio et al., 2016)

In conclusion, telesurgery is a big step in the right direction for reaching SDG3 (“Health – United Nations Sustainable Development”, 2019), good health and well-being for everyone. Limitations are that the changes will only take place at a slow pace since it is a very robust, inflexible industry. It is not agile and not sensitive to disruptive technologies. Moreover, the benefit will really be visible once 5G is implemented more widely, not just in a few advanced countries.

 

References:

Chiaraviglio, L., Blefari-Melazzi, N., Liu, W., Gutierrez, J. A., Van De Beek, J., Birke, R. & Wu, J. (2016, November). 5G in rural and low-income areas: Are we ready? In 2016 ITU Kaleidoscope: ICTs    for a Sustainable World (ITU WT) (pp. 1-8). IEEE.

Choi, P. J., Oskouian, R. J. & Tubbs, R. S. (2018). Telesurgery: Past, Present, and Future, Cureus.

Ericsson Mobility Report June 2019. (2019). Retrieved 4 October 2019, from

https://www.ericsson.com/en/mobility-report/reports/june-2019

Health – United Nations Sustainable Development. (2019). Retrieved 4 October 2019, from         https://www.un.org/sustainabledevelopment/health/

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Will Artificial intelligence replace our doctors?

2

October

2018

5/5 (1) There is a worldwide shortage of doctors. More than half of the world population doesn’t have of has bad access to healthcare. The waiting lines are very long in a lot of places. AI could offer a solution here, giving more people access to health advice of good quality.

Although artificial intelligence (AI) is still in the early stages of testing and adoption in the healthcare space, many say it will have a huge impact in this field. Some even say it will gradually come to replace doctors.

Babylon Health, a company based in the United Kingdom, is testing an AI medical chatbot in Rwanda. It works like this: a patient enters information into the chatbot. The chatbot then aggregates the data and suggests solutions for the patient. It recommends the patient to see a doctor or to get a prescription rather than diagnosing him/her, although Babylon claims it could. Babylon also launched a site with the same idea, making it possible for people around the world to fill in their symptoms and get possible diagnosis.

Even though some are sceptical about the accuracy of the new ‘doctor’, the chatbot even passed mock medical exams with a higher score compared to a human doctor. Furthermore, in questions it had seen before, it had 98% accuracy, so once a machine learns something, it never forgets.

Although a lot of benefits are scientifically proven, some senior doctors are sceptical of the claims robots will replace humans, stating the human aspect of health will remain too important and can never fully be replaced by a robot.

 

What are your thoughts about this topic? Would you want to be seen by artificial intelligence instead of a human doctor? Do you think it could be possible that a machine can completely replace a doctor and is it ethical to replace doctors by artificial intelligence?

 

 

 

Babylon Health (2018). Babylon Health. [online] Available at: https://www.babylonhealth.com/news [Accessed 29 Sep. 2018].

Norman, A.(2018). Your future doctor may not be human. This is the rise of AI in medicine. [online] Futurism. Available at: https://futurism.com/ai-medicine-doctor [Accessed 30 Sep. 2018].

Vallancien, G.(2016). Tomorrow’s doctors will be replaced by machines, so their role will be that of advisor. [online] L’Atelier BNP Paribas. Available at: https://atelier.bnpparibas/en/health/article/tomorrow-s-doctors-replaced-machines-role-advisor [Accessed 29 Sep. 2018].

Wilson, C.(2018). Is an AI chatbot really better than a human doctor? [online] New Scientist. Available at: https://www.newscientist.com/article/2173056-is-an-ai-chatbot-really-better-than-a-human-doctor/ [Accessed 29 Sep. 2018].

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Taking Care of Virtual Patients

30

September

2018

No ratings yet. 1*l9qYtN7RcBOk9ontE6Nm4A

In engineering, the concept of ‘Digital Twins’ has gained attention during the last decade. A Digital Twin is a virtual representation of a physical object, which is continuously fed with data from embedded sensors and software. Hereby, the Digital Twin tightly connects the physical system with its computer model. Digital Twins are, for examples, widely used to continuously monitor and forecast the health of jet engines. This allows airlines to identify how and where potential problems could occur, whereby predictive maintenance is deployed to keep the system healthy.

In this blog, I will explain what the possibilities and corresponding benefits and risks are for this technology in the healthcare industry. The enhancement of computational power and molecular readout technologies has increased the potential of ‘virtual patients’ to continuously track health and lifestyle parameters. As of now, the digital models used in healthcare are quite partial (such as twin models of the heart) and basic. Yet, already signs of the effectiveness of these models can be observed as well as the benefits it could bring in the future.

First, the data-rich Digital Twins would allow for the creation of a more detailed picture of the patients which results in faster and more accurate identification of actual or potential disease states. Hereby, a shift to more preventive solutions could result in significant health improvement and hence reductions of health care costs. Second, the multidimensional properties of the digital twins could allow practitioners to more accurately compare a patient’s health with the health stats of similar patients. Since clustering can be based on more elements than, for example, age and gender, deviations from the ‘normal’ can be identified faster and more accurately.

Yet three main societal concerns are also worth noting. First of all, Digital Twins could raise inequality since developing a digital version of yourself could be very costly. Hence, the benefits of improved health and possible life extension could potentially only be accesses by wealthy people. Second, the Digital Twin could lead to self-fulfilling prophecy mechanisms where knowing that you could potentially become sick in the future will make you indeed feel sick and weak. Thirdly, it is of great importance to ensure data protection. Data leaks could quickly offset the potential benefits of Digital Twins, as for example, insurance companies could use the data to modify the insurance policies for individuals in their favor

The future will tell whether we will be able to effectively govern this emerging technology in the healthcare industry; thereby significant health and cost benefits can be obtained by actively managing the associated concerns.

 

 

 

Sources:

 

Bruynseels, K., Santoni de Sio, F., & van den Hoven, J. (2018). Digital twins in health care: Ethical implications of an emerging engineering paradigm. Frontiers in genetics, 9, 31.

 

Mussomeli, A. (2018). Expecting Digital Twins. Deloitte Insights. Retrieved from: https://www2.deloitte.com/insights/us/en/focus/signals-for-strategists/understanding-digital-twin-technology.html

 

Van Houten, H. (2018). The Rise of the Digital Twin: How Healthcare Can Benefit. Philips Research. Retrieved from:https://www.philips.com/a-w/research/blog/the-rise-of-the-digital-twin-how-healthcare-can-benefit.html

 

 

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How AI is saving lives

16

September

2018

5/5 (1) As the world becomes more connected, diseases are able to spread more rapidly. To prevent this from happening, the World Health Organization (WHO) has used various Epidemic Intelligence Tools over the last couple of decades. By monitoring various data sources, these tools try to detect future epidemics and monitor current outbreaks. These tools are becoming increasingly important to fight diseases and save lives. In 2017, the Epidemic Intelligence from Open Sources (EIOS) initiative was launched by WHO. The system was released on August 6th, 2018 and had already proven to be valuable.

Epidemic intelligence works in a continuous cycle of 8 steps. The first step is the collection of data from various sources. Examples of these sources are social media, national surveillance and laboratory networks. After the data is collected, it is processed to make sure that duplicate data is deleted and the data is linked to the correct diseases and locations. Thereafter, the system tries to detect patterns which suggest disease outbreaks. These possible outbreaks then need to be verified by lab confirmation, field verification or credible local sources. The situation gets analysed and the risk is determined. After making a visual representation of the current (and future) situation, action is taken if necessary. As new data is collected continuously, this cycle never stops.

The key feature from the new EIOS system is the large number of data sources. Over 8000 data sources are used to detect over half a million pieces information per month. This leads to 7000 signals, which are manually screened for relevance by the staff of WHO. After investigation, 100 events are investigated and 10 risk assessments are made each month.

During the first weeks that the EIOS system has been used, it has already detected several outbreaks which would have been detected in a later stage otherwise. For example, the cholera outbreak in Algeria and the leptospirosis outbreak in India have been detected in an early stage, enabling WHO to react quickly and save lives. These cases show demonstrate the potential to use data to improve global health.

Over the next period of time, WHO plans to further improve the EIOS system. One of the main priorities is to use speech-to-text technology to include radio sources to the system. This is especially valuable in poor regions, where radio still is one of the main methods of communication. Features like this could help WHO to further improve EIOS and save more lives.

 

 

This article is based on a presentation by Dr Oliver Morgan, Director Health Emergency and Risk Assessment – WHO Health Emergencies Program. Dr Oliver Morgan was one of the speakers at Intelligent Health 2018, an international congress on the use of artificial intelligent in health care. For more information about Intelligent Health, visit https://intelligenthealth.ai/

This presentation is not online accessible. However, a similar PowerPoint presentation can be found at: http://www.oie.int/eng/BIOTHREAT2017/Presentations/6.2_BARBOZA-presentation.pdf
Please note that this is not the version of that was used at Intelligent Health on September 13th 2018.

Article on cholera outbreak in Algeria: https://www.aljazeera.com/news/2018/08/algeria-person-dies-cholera-outbreak-180825173701802.html

Article on leptospirosis outbreak in India: https://www.news18.com/news/india/as-leptospirosis-threat-looms-after-floods-kerala-plots-30-day-micro-plan-1860783.html

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Robots Taking Over Surgical Procedures

25

September

2017

5/5 (2)  

 

 

Would you believe me if I said that in 5 years’ time the number of doctors in the world might be halved? Can you imagine being surgically operated by a robot? This sounds like something from a futuristic movie, however this in fact already happening.

Robotic assisted surgery (RAS) allows surgeons to perform a variety of complex procedures with more precision, flexibility and control than what was previously possible (Mayo Clinic, 2017). In 2000 Robotic surgery with the da Vinci Surgical System was approved by the Food and Drug Administration. Since this time, the use of robotic assistance in surgery has expanded exponentially (Kirkpatrick and LaGrange, 2017). This system includes a camera arm and mechanical arm each with surgical instruments attached to them. The surgeon sits behind a computer with a console which gives the surgeon a magnified 3D high-definition view.

So why would people rather be surgically operated by a robot than a professionally trained surgeon?

Robotic assisted surgery offers many advantages as it makes surgical procedures minimally invasive. Typical advantages include fewer complications, less pain and blood loss, quicker recovery, smaller scars and quicker procedures (Mayo Clinic, 2017).  Furthermore, the improved ergonomics and dexterity compared to traditional procedures allow for a shorter learning curve for surgeons (Kirkpatrick and LaGrange, 2017).

New robotic drill performs surgery 50 times faster than before. The University of Utah has created an automated machine which reduces a complicated cranial surgery from two hours to two-and-a-half minutes. (Yurieff, 2017)

Often times patients travel from afar in order to be treated or operated by certain specialized surgeons. With robotic surgery it may be possible to eventually complete surgical procedures from across the world. In this case patients in critical condition can stay at home, yet still receive the care from the best specialist surgeons in the world.

Aside from offering patient benefits, RAS offers many economic benefits for both the patient as well as the hospital. Due to less invasive procedures, patients are able to have shorter hospital days and are also able to return to their daily activities and work much faster. (Kirkpatrick and LaGrange, 2017)

So far robotic surgery sounds like a logical improvement, however there are also risks involved. As the current technology involves a robot as assistance, next to human error there is also the added risk of mechanical failure. There are possibilities that mechanical problems are experienced during the procedure, possibly causing the robotic arms not to respond as expected. Furthermore, the energy source from the robot machinery could potentially cause internal burn injuries from the cautery device (Kirkpatrick and LaGrange, 2017). There are possibilities that mechanical problems are experienced during the procedure, possibly causing the robotic arms not to respond as expected.

Overall, many professionals argue that the benefits of robot assisted surgery far outweigh the risks. As RAS is increasingly used in a widespread amount of surgeries around the world, techniques will be refined and developed.

Although robotic surgery offers advances in surgical practice, can you imagine the ethical issues around the topic? Think of equipment safety and reliability, information provision and patient confidentiality, just to mention a few.

In the future, will we ever have to go to hospitals? Currently robots are being used in the assistance of surgeries, however, their usage has increased exponentially in the past decade. The entire value chain of surgical procedures may become entirely robotized. Although this offers many surgical advances, it is vital to consider the risks associated with such robotization of a critical procedure.

 

 

References

Kirkpatrick, T. and LaGrange, C. (2017). Robotic Surgery: Risks vs. Rewards | AHRQ Patient Safety Network. [online] Patient Safety Network. Available at: https://psnet.ahrq.gov/webmm/case/368/robotic-surgery-risks-vs-rewards- [Accessed 25 Sep. 2017].

Mayo Clinic. (2017). Robotic surgery. [online] Available at: http://www.mayoclinic.org/tests-procedures/robotic-surgery/basics/definition/prc-20013988 [Accessed 18 Sep. 2017].

Yurieff, K. (2017). New robotic drill performs skull surgery 50 times faster. [online] CNNMoney. Available at: http://money.cnn.com/2017/05/01/technology/robotic-drill-surgery/index.html [Accessed 25 Sep. 2017].

 

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Big Data: Shaping the healthcare industry

4

October

2016

5/5 (2) For decades, the basis of nearly every medical discovery and research has been about the collection and analysis of data: who gets sick, why do they get sick and why. However, big data has enabled the potential for enormous breakthroughs in this sector.

A recent example of this is, is a study conducted by computer scientists of the Free University in Amsterdam in collaboration with the University Medical Centre Utrecht, which found a new predictor for intestine cancer. The computer scientists looked for distinctive patterns in 263.000 (anonymised) electronic patient records, of which 1292 received the diagnosis of intestine cancer. They compared both groups (cancer versus no cancer) by allowing software to search for the differences in the run-up to the diagnosis. The research has reconfirmed previously known predictors (iron deficiency, age, and constipation) and found one new one: metabolic syndrome, which is a metabolic disorder.

The technique used in this research will, in the near future allow a risk calculation at the desk of the doctor’s office, based on routine patient information of an individual with intestinal complaints. This can help in the decision whether to have the patient examined further or not.

The strength of this new predictive model is that the software uses the electronic patient records data without any bias. It is not programmed in a way to search for specific predictors, as is usually the case in medical research based on data analysis. This causes the model to be applicable on all kinds of data, and therefore it has enormous potential to enhance disease prediction in other diseases, making early detection and intervention possible.
Big data has been known to be beneficial in many industries, often allowing companies to achieve substantial competitive advantage. However, its potential and importance in the healthcare industry exceeds those of other industries since it can actually save lives.

References:

Kop, R., Hoogendoorn, M., Teije, A. T., Büchner, F. L., Slottje, P., Moons, L. M., & Numans, M. E. (2016). Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records. Computers in Biology and Medicine, 76, 30-38. doi:10.1016/j.compbiomed.2016.06.019

 

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