How Are Platform Ecosystems Transforming The Traditional Value Chain?

27

September

2017

No ratings yet.

According to a survey conducted by Accenture (2017), 27% of the executives questioned indicated that digital ecosystems are transforming their value chain. Companies are looking beyond the borders of their business to fulfill core business functions. By partnering with third parties and their platforms, they are designing future value chains that will revolutionize their business as they enter the digital economy.

In the digital economy where platforms and their ecosystems are central, gaining a competitive advantage is not only about improving your own business and its strategy. The digital partnerships you choose become increasingly important as they can provide access to a powerful new digital ecosystem (Accenture, 2017).

Take for example the partnership between Whole Foods and Instacart. The Instacart platform allows U.S. citizens to buy groceries from local grocery stores online. A personal shopper will pick up your groceries in his/her own car and deliver it to you on the same day. Whole Foods, a U.S. based grocery store, partnered up with Instacart to allow customers to place an online order via the Instacart platform and. Customer can choose to (1) pick up their groceries in store, or choose for (2) personal delivery (Bold, 2017).

By partnering up with Instacart, Whole Foods gained a competitive advantage over other local grocery stores by providing quick store pickup and same-day home delivery. In addition to this, Whole Foods gained access to a powerful digital ecosystem due to Instacart’s large user database. In the digital economy, physical and intangible assets will be of less importance as information and interactions are crucial for increasing the value of a platform. The value of a platform will increase if more people use it, creating network effects (Van Alstyne, Parker & Choudary, 2016).

As digital ecosystems become central in the digital economy, companies must choose the right digital partners that will create value for their customers. The strength of these partnerships will decide whether companies can maximize their success in the digital economy (Accenture, 2017).

 

Instacart (1)

 

References:

Accenture. (2017). Technology Vision 2017 (pp. 34-44). Retrieved from https://www.accenture.com/t20170530T164033Z__w__/us-en/_acnmedia/Accenture/next-gen-4/tech-vision-2017/pdf/Accenture-TV17-Full.pdfla=en?la=en

Bold, C. (2017). I Had My Groceries Delivered by Instacart, and Here’s How It Went.. Kitchn. Retrieved 27 September 2017, from http://www.thekitchn.com/i-had-my-groceries-delivered-by-instacart-and-heres-how-it-went-214795

Van Alstyne, M. W., Parker, G. G., & Choudary, S. P. (2016). Pipelines, platforms, and the new rules of strategy. Harvard Business Review, 94(4), 54-62.

Please rate this

Technology Of The Week – AI In Medical Imaging

22

September

2017

No ratings yet.

In recent years, the number of artificial intelligence startups has rapidly increased to a total of 106 (CB Insights Research, 2017), indicating that the role of AI will gain importance in healthcare . AI has especially made a lot of progress in the medical imaging industry. The number of image scan on human body parts has skyrocketed, while the number of radiologists has not significantly increased in the last years (Steve O’Hear, 2017).

Artificial intelligence aims to solve this problem by processing the imaging data in the cloud, using deep learning algorithms which can identify a patient’s potential medical issues, aiding the doctor in patient diagnosis (Fornell, 2017). AI system is able to perform these human tasks by ‘studying’ an excessive database of patient cases. In this way, its deep learning algorithms allow the system to recognize body part anomalies. Study has proven that if an AI system has been given sufficient amounts of data, it can outperform human diagnosis (Fornell, 2017). However, the use of AI does not imply the end of physicians. It simply means that the workflow of physicians changes as they are needed to verify the diagnosis and to devise corresponding treatment plans. This results in increased face-to-face time with patients, and helping more patients in less time. AI will enable cost cuttings up to 50% while improving performance outcomes by 30 to 40% (Frost & Sullivan, 2017).

To analyze how AI is disrupting the medical imaging industry, Porter’s Five Forces model can be applied. Barriers to entry are lower, as the industry is accessible to all who have expertise in AI software. Due to dependency on medical imaging hardware, the bargaining power of buyers, in this case hospitals and clinics, remains unchanged. Bargaining power of suppliers however does increase as AI systems require millions of images to learn before its performance reaches acceptable levels. This data, is usually owned by traditional suppliers such as Philips. Threat of substitutes diminish as medical imaging techniques are becoming more accessible, making the development of other techniques less interesting. As core IT businesses, such as IBM, are applying their AI technologies in healthcare, traditional players will experience increasing industry rivalry.

By 2021, Accenture (2017) expects the AI health market to be worth $6.6 billion which means that its yearly compound growth rate equals 40%. These market predictions demonstrate the continuous investment in AI healthcare technology, which can eventually be integrated in everyday life. If done so, healthcare becomes accessible to anyone, anywhere and anytime. An example of accessible healthcare is the portable 3D-ultrasound device Butterfly Network Inc. is developing. The device creates 3D images of human body parts and sends this data in real-time to the cloud, where it is analyzed and potential anomalies are identified. This device will make medical imaging more accessible, as it allows anyone, anywhere and anytime to create medical images without being dependent on hospital equipment.

In conclusion, the future of AI healthcare is a promising one, but research and development is still required to optimize the technology for everyday use. As Bill Gates assured us “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.”

-Group 16

Please check out our video: https://youtu.be/NnBcmE0mZ2o

 

References

Accenture. (2017). Artificial Intelligence in Healthcare | Accenture. [online] Available at: https://www.accenture.com/us-en/insight-artificial-intelligence-healthcare [Accessed 20 Sep. 2017].

CB Insights Research. (2017). From Virtual Nurses To Drug Discovery: 106 Artificial Intelligence Startups In Healthcare. [online] Available at: https://www.cbinsights.com/research/artificial-intelligence-startups-healthcare/ [Accessed 20 Sep. 2017].

Fornell, D. (2017). How Artificial Intelligence Will Change Medical Imaging. [online] Imaging Technology News. Available at: https://www.itnonline.com/article/how-artificial-intelligence-will-change-medical-imaging [Accessed 19 Sep. 2017].

Frost & Sullivan. (2017). From $600 M to $6 Billion, Artificial Intelligence Systems Poised for Dramatic Market Expansion in Healthcare. [online] Available at: https://ww2.frost.com/news/press-releases/600-m-6-billion-artificial-intelligence-systems-poised-dramatic-market-expansion-healthcare [Accessed 20 Sep. 2017].

O’Hear, S. (2017). AIDoc Medical raises $7M to bring AI to medical imaging analysis. [online] TechCrunch. Available at: https://techcrunch.com/2017/04/26/aidoc-medical/ [Accessed 20 Sep. 2017]

 

Please rate this

The Changing Face of Healthcare

17

September

2017

No ratings yet.

At Auckland City Hospital, New Zeeland’s largest public hospital, a diabetic man died after his medications where mixed up with those of another patient. In Singapore, a one-year old baby was given adult doses of steroid, receiving up to four times of the prescribes dosage (“Baby given adult dose of steroids”, 2017). These are just a few examples of errors in healthcare in which provision of accurate information was lacking.

The capabilities of digital technologies, such as data integration and real-time tracking, have tremendous implications for transforming the healthcare industry. However, the digital technologies also bring challenges with them.

According to the U.S. Food & Drug Administration, 50% of patients do not take their medication as prescribed, and 20-30% of new prescriptions are never filled at the pharmacy (“Why You Need to Take Your Medications as Prescribed or Instructed”, 2016). Telemedicine enables remote diagnosis and treatment of patients using telecommunication technology (Sood et al., 2007). Telemedicine applications can, for example, notify patients to take their medications. It can also enable physicians to remotely monitor their patients, significantly reducing costs and improving the overall quality of care provided. Another way digital technologies are being used in healthcare is through electronic health records (EHRs) , which contain and share medical information from all providers involved in a patient’s care (“Definition and Benefits of Electronic Medical Records (EMR) | Providers & Professionals | HealthIT.gov”, 2017). They provide many benefits, including accurate and up-to-date information, reduced medical errors, and lowered administrative costs. By analyzing the data stored in EHRs medical knowledge can be improved. This is where Artificial Intelligence (AI) comes in. AI will assist physicians by using this data to provide outstanding medical solutions (Das, 2016).

Now, let’s take a look at the other end of the spectrum. Would you be concerned if your entire health record is stored in a supposedly secure database? Are there any illnesses you do not want a third party to know about? These are just a few questions to think about. One of the challenges regarding telemedicine, EHRs and AI is privacy issues. Therefore, it is crucial that policy makers reevaluate regulations to ensure patients that their data is stored in a secured manner, and that it will only be accessed by parties for medical reasons. Another challenge facing AI is the lack of computing power. For AI to succeed, the next generation’s computing infrastructure must be able to process a large amount of calculations at a rapid rate, which implicates a lot of processing power (Marr, 2017).

 

References:

Baby given adult dose of steroids. (2017). AsiaOne. Retrieved 16 September 2017, from http://www.asiaone.com/health/baby-given-adult-dose-steroids

Das, R. (2016). Forbes Welcome. Forbes.com. Retrieved 17 September 2017, from https://www.forbes.com/sites/reenitadas/2016/03/30/top-5-technologies-disrupting-healthcare-by-2020/#492ecdaa6826

Definition and Benefits of Electronic Medical Records (EMR) | Providers & Professionals | HealthIT.gov. (2017). Healthit.gov. Retrieved 17 September 2017, from https://www.healthit.gov/providers-professionals/electronic-medical-records-emr

http://www.providersedge.com/docs/km_articles/Innovation_for_Value_Creation.pdf

Marr, B. (2017). Forbes Welcome. Forbes.com. Retrieved 17 September 2017, from https://www.forbes.com/sites/bernardmarr/2017/07/13/the-biggest-challenges-facing-artificial-intelligence-ai-in-business-and-society/#616a6ebf2aec

Sood, S., Mbarika, V., Jugoo, S., Dookhy, R., Doarn, C. R., Prakash, N., & Merrell, R. C. (2007). What is telemedicine? A collection of 104 peer-reviewed perspectives and theoretical underpinnings. Telemedicine and e-Health, 13(5), 573-590.

Why You Need to Take Your Medications as Prescribed or Instructed. (2016). Fda.gov. Retrieved 17 September 2017, from https://www.fda.gov/drugs/resourcesforyou/specialfeatures/ucm485545.htm

 

Please rate this