Google DeepMind and healthcare in an age of algorithms

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(New page: Hodson, Hal., Powles, Julia. (2017). Google DeepMind and healthcare in an age of algorithms. ''Health and Technology'', 7(4), 351-367. Find article online: Link goes here DOI: 10.1007/s...)
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DOI: 10.1007/s12553-017-0179-1 DOI: 10.1007/s12553-017-0179-1
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==Context== ==Context==
-The online presence via social media for online service brands has become an important factor in order for brands to promote themselves through advertisement and marketing. Many services that were once ‘offline’ have shifted their focus from one-way communication to customer-customer interaction via social media. (Mills & Plangger, p. 522). This shift creates a more meaningful relationship between customer and service, offering long-lasting relationships and personal connections between customer and service. The authors begin by explaining the various forms of social media and conclude their argument by offering a strategy of how to resource for online services.+This article discusses the relationship of DeepMind Technologies Limited and Royal Free London NHS Foundation Trust with their collaboration in the management of acute kidney injury. DeepMind which is apart of the Google conglomerate Alphabet Inc. is a company that specializes in artificial intelligence (AI). The purpose of this article was to analyze the case study of DeepMind-Royal Free by evaluating datasets on large private prospectors and identifying critical questions within healthcare. This case study was performed when Royal Free transferred the records of patients without consent in order for the development of a clinical alert app for kidney injury.
==Overview== ==Overview==
-The authors of this article discuss how brands can establish beneficial communication between their customers by improving or creating an online presence via social media (Mills & Plangger, p. 529). They argue how the communication of dialogue between brand service and customers develops a level of trust between brands and customers. With the integration of social media and the internet, there has been a migration of how these service brands must develop their interactive relationship with customers, which is now done through online services. The integration of social media has an outlonging reach as well, a third of 18-44 year-old social media users consciously follow brands and brand pages on social media networks (Mills & Plangger, p. 528). The young-adult market is a crucial market segment in order to retain customers for long-term sustainability, social media acts as the bridge for service brands to create a relationship with their targeted audience. To further their argument about the importance of an online presence for a brand, the authors provide a nine-step process of a strategic process for creating the relationship between brand and customer.+This article is split between three sections. The first two sections discuss how DeepMind was able to gather data for Royal Free by giving a chronological timeline of the case study. The first section discusses the contractual work performed between DeepMind and Royal Free and also explains the algorithm used to detect acute kidney injury. The authors point out how the algorithm has the probability of failure when attempting to detect acute kidney injury. The algorithm of detecting acute kidney injury provides a way to detect any potential harm in patients through the history of their medical data. DeepMind had developed a medical app as the function in detecting acute kidney injury. In the second section, the process of the patient data being given without consent is highlighted where the legality of the issue is discussed. In this section the authors highlight how data-driven tools should be grappled within the domain of public healthcare. The third section explores DeepMind’s ambitions in their reasoning of their relationship with Royal Free. The authors elaborate of how the transfer of data is a problematic issue that may be abused in the future. In this section there is elaborate discussion about the protection of data and how there is a responsibility for companies to keep privatized data in a secure fashion.
==Strengths and Weaknesses== ==Strengths and Weaknesses==
-The first strength of this article is the constant reiteration of why the concept of communication between brand and customer is important. The authors go into detail about how the connection between brand and customer establishes a relationship where the customer is likely to feel association towards the brand and therefore be more trustworthy of that brand. Another point the authors make is how the constant activity of social media allows for their viewers to have a constant channel of communication between their audience. The article explains how self-promotion of a brand through social media also simultaneously engages a relationship with their customer, thus causing a constant flux of activity between brand and user. Finally, the article outlines a strategy through steps as to how a brand should think when wanting to interact with their audience. It can be summed down to targeting a set audience, building a platform for that audience and managing the interaction of the platform between the targeted audience.+The main strength of this article is how it expresses how algorithmical searches of databases can be a concern, especially due to how automation and machine learning become more prevalent technologies that are utilized. The article does a brief but concise explanation as to how algorithmic data can be utilized in order to alert medical practitioners of acute kidney injury in their patients, expressing how machine learning can play a pivotal role in the realm of healthcare. The second strength is how the authors express their concern of subsidized data is an issue, although this is done extensively within the article. It is important to understand how there are limitations and challenges when large datasets are being utilized as there is the possibility of data that is not related towards the purpose of the data being breached.
-The main weakness this article has is it doesn’t establish how a service brand should go about creating an online persona for their service. There is constant reiteration of the importance of why service brands should utilize social media to promote themselves, although the authors don’t dictate how that exactly should be done. They provide a process of steps, although these steps are more about reasoning than implementation. These steps explain as to what the service brand should be doing to target their audience but don’t establish how a service brand should go about creating a proper ‘persona’ of themselves on social media. This persona being what the company represents and what they offer for their customers.+The main weakness of this article is how it is focused upon the privacy concerns of the relationship between DeepMind and Royal Free while not going into great detail about how the algorithms pertaining to detecting acute kidney injury are utilized. This lack of explanation comes across as a bias of the authors to try and explain why algorithms and the transfer of data between governments is an issue. Outside of acknowledging the relationship the case study between DeepMind and Royal Free the authors do not reference other practices between companies from a statistical sense. There is a narrow-minded focus on explaining why the transfer of data is an issue of concern.
==Assessment== ==Assessment==
-In conclusion, the article addresses the importance of why a service brand should go online, specifically through the integration of social media. The article describes different strategies of utilizing social media, and how different forms of social media can be utilized for different marketing purposes. After a brand establishes a channel of communication between their customer, it creates a level of trust that the customer has for that brand through the relationship that is built. The article analyzes the importance of an online presence and how social media should be used by brands to build their online presence.+In conclusion, this article provides a light to the concerns of practices that companies such as DeepMind perform when subsidizing data from other companies that have rich databases such as Royal Free. Although the algorithm that was developed can help detect acute kidney injuries in their patients, there are many privacy concerns created when data is being traded between companies. Machine learning is a tool that can help physicians in their prognosis of diseases such as acute kidney injury. While the authors focus on the morality of data privatization, health datasets can be utilized in order to detect problems that can be present in potential patients. Nonetheless, this article addresses legitimate concern to how subsets of data can be abused and how the transfer of data between companies for the usage of algorithmic machine learning can be a hindrance to the privacy of patients.
 +----
 +--[[User:Bl14hl|Bl14hl]] 22:11, 19 March 2019 (EDT)

Revision as of 22:11, 19 March 2019

Hodson, Hal., Powles, Julia. (2017). Google DeepMind and healthcare in an age of algorithms. Health and Technology, 7(4), 351-367.

Find article online:

DOI: 10.1007/s12553-017-0179-1

Contents

Context

This article discusses the relationship of DeepMind Technologies Limited and Royal Free London NHS Foundation Trust with their collaboration in the management of acute kidney injury. DeepMind which is apart of the Google conglomerate Alphabet Inc. is a company that specializes in artificial intelligence (AI). The purpose of this article was to analyze the case study of DeepMind-Royal Free by evaluating datasets on large private prospectors and identifying critical questions within healthcare. This case study was performed when Royal Free transferred the records of patients without consent in order for the development of a clinical alert app for kidney injury.

Overview

This article is split between three sections. The first two sections discuss how DeepMind was able to gather data for Royal Free by giving a chronological timeline of the case study. The first section discusses the contractual work performed between DeepMind and Royal Free and also explains the algorithm used to detect acute kidney injury. The authors point out how the algorithm has the probability of failure when attempting to detect acute kidney injury. The algorithm of detecting acute kidney injury provides a way to detect any potential harm in patients through the history of their medical data. DeepMind had developed a medical app as the function in detecting acute kidney injury. In the second section, the process of the patient data being given without consent is highlighted where the legality of the issue is discussed. In this section the authors highlight how data-driven tools should be grappled within the domain of public healthcare. The third section explores DeepMind’s ambitions in their reasoning of their relationship with Royal Free. The authors elaborate of how the transfer of data is a problematic issue that may be abused in the future. In this section there is elaborate discussion about the protection of data and how there is a responsibility for companies to keep privatized data in a secure fashion.

Strengths and Weaknesses

The main strength of this article is how it expresses how algorithmical searches of databases can be a concern, especially due to how automation and machine learning become more prevalent technologies that are utilized. The article does a brief but concise explanation as to how algorithmic data can be utilized in order to alert medical practitioners of acute kidney injury in their patients, expressing how machine learning can play a pivotal role in the realm of healthcare. The second strength is how the authors express their concern of subsidized data is an issue, although this is done extensively within the article. It is important to understand how there are limitations and challenges when large datasets are being utilized as there is the possibility of data that is not related towards the purpose of the data being breached.

The main weakness of this article is how it is focused upon the privacy concerns of the relationship between DeepMind and Royal Free while not going into great detail about how the algorithms pertaining to detecting acute kidney injury are utilized. This lack of explanation comes across as a bias of the authors to try and explain why algorithms and the transfer of data between governments is an issue. Outside of acknowledging the relationship the case study between DeepMind and Royal Free the authors do not reference other practices between companies from a statistical sense. There is a narrow-minded focus on explaining why the transfer of data is an issue of concern.

Assessment

In conclusion, this article provides a light to the concerns of practices that companies such as DeepMind perform when subsidizing data from other companies that have rich databases such as Royal Free. Although the algorithm that was developed can help detect acute kidney injuries in their patients, there are many privacy concerns created when data is being traded between companies. Machine learning is a tool that can help physicians in their prognosis of diseases such as acute kidney injury. While the authors focus on the morality of data privatization, health datasets can be utilized in order to detect problems that can be present in potential patients. Nonetheless, this article addresses legitimate concern to how subsets of data can be abused and how the transfer of data between companies for the usage of algorithmic machine learning can be a hindrance to the privacy of patients.


--Bl14hl 22:11, 19 March 2019 (EDT)

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