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MIS741 - Ethics of Digital Transformation Assignment - Moral Dilemma Analysis Report, Deakin University, Australia

Unit Learning Outcome (ULO) -

• Justify resolutions to ethical dilemmas faced by IS professionals resulting from competing personal, organisational and client interests using ethical theories and frameworks.
• Present convincing resolutions to ethical dilemmas in written form, and self-evaluations in written and oral form.

Task - Produce a written Moral Dilemma Analysis report using the Ethical Decision-Making Framework introduced in topic: Justify the ethical considerations that IS professionals must address when designing artificial intelligence solutions to automate health insurer decisions to offer someone a new health insurance policy.

Answer - ETHICAL DECISION-MAKING

Introduction

The report will discuss in detail about the ethical considerations at the time of designing artificial intelligence to help health insurer decision-making. Artificial intelligence or AI's influence has helped in expanding which goes way beyond many instances since company focused AI answers can increase the operational level of effectiveness, can help a better way to sell and provide services and many more (Agrawal, 2018).

Discussion

Artificial Intelligence-definition

The concept of AI, conventionally based on artificial creating human-focused intelligence that can reason, perceive or procedure of natural kind of language. AI is, in addition, can be defined as a narrow level of AI or general level of AI. A narrow concept of AI, that people communicate with the current time, is developed to work particular works as a part of the domain. General AI can be hypothetical and is not domain focused specific, however, can understand and work on tasks at different places (Balsari et al., 2018).

AI is considered as one of the present trends which has emerged from broad level of digitization associated with economy and the community. The smart level of AI based technologies have gained the attention of lot of consumer goods industries, insurance policies like health insurance and e-business. Services like Siri or Alexa which is an automated voice introduced by companies like Apple and Amazon, are two important instances that shape the opinion of people at large. Services like automated image identification structure and automobiles that comes with self-driving options are making an important mark also. Many private companies have identified the overall potential of AI long back and the significance of different kind of new technologies (Bloom et al., 2018). Cognitive structures as well as self-learning option can be part of complete value chain or can be found towards the end of deployment covering factors like predictions and tools crucial for pricing for shopping and management of inventory and many more. Applications of AI can also assist the organizations in order to help in optimizing varied services and low expenses, accelerated procedures as well as form better or ideal decisions.

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Artificial intelligence in health insurance policy

A similar kind of growth is part of healthcare industry, however while exploring different possibilities that AI provides in the area of medical care as well as management which is present in initial phases. The maximum development till date has been formed with AI usage of situations around number of providers (Capone et al., 2016). Medical units care more and more utilising initial detection structured as helped or assisted by automated identification or right kind of algorithms based on the patterns which are part of information of patients. Less popular are the options that the usage of smart level of technology which empower many health insurers. Starting usage situations can be found for many AI supported structures that can increase the care. For example, in the growth of personalized offers for the patients that suffer from may chronic diseases or to recognize clinical directions that are failing to agree to varied guidelines. Still AI has the capacity of more. Cognitive structure can assist case executives to effectively screen situations, test them with high level of precision and take informed level of decisions. Hospitals also thinks that management is another phase that comes with many gains. For instance, a place like Germany elaborates the level of all possible benefits. The expense at national level of many inpatient treatment can comes to seventy-three billion euro and forms up to thirty and forty percent of health insurance and the total budget, but between eight to ten percent of all possible claims as perceived as wrong (Cruz et al., 2018). Certainly, recognizing and altering varied kinds of wrong claims would save every stakeholder along with health insurers that takes a lot of time, efforts as well as money.

Benefits of Artificial intelligence in health insurer decisions

The concept of AI's perspective spans all kind of insurance phases. Constant insights that emerge with the help of infinite mix up of structured as well as unstructured data which can elevate a company's capacity to gain clarity over changing dynamics of the market, competition-based functions and consumer needs and wants with help of unprecedented granularity. Following are some of the benefits:

1. Expansion of revenue: the learning when done real-time comes with many adaptive capacities of AI can provide a ready-made platform for number of insurers to further explore new line of products along with geographies as well as different consumer segments, and rapid recognition new forms of avenues for revenue focused expansions (Dhar, 2016). Companies can easily scale in an exponential manner by raising the scale of ability in order to manage different phases like data search, management of data and automating procedures, offering many virtual bits of help across the different business procedure.

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2. Excellence on the advisory level: there is a number of Robo-advisors that are able to hold the future to dramatic transformation with the dynamics focused on insurance advisory by not just removing the disadvantage of many human advisors however also help them to grow different skill sets. To improve the performance of the job, human advisors with utilising virtual help to deal with day to day work and pay attention to a highly constructive, creative level of communicative issues (Gupta, 2017). This way to assist them to show round the clock presence to many external elements of the world.

3. Improved level of operational effectiveness: AI related systems provide many options for the insurance sector to improve the effectiveness on an operational level. This consists of the following elements:

a. Decreased turnaround time: AI focused requirement analysis structure permits insurers to not just help in improving the possibility of lead to quote focused conversion however can also decrease the TAT also called turnaround time.

b. Low expenses: AI answers helps in enabling companies to decrease the manpower needs and therefore gain from important saving in many overhead expenses, specifically those related to day-to-day jobs (Joudaki et al., 2015).

c. Improved level of productivity: with the help of AI structure working on day to day functions, workforces must pay attention to many skills works, constructing expertise and can evolve with AI answers. Increasingly, insurers can anticipate an immense surge of the operational level of effectiveness with AI focused answers while focusing on many non-tangible gains focusing on elements like fast access to data, eliminating any kind of subjectivity as answers or actions from the workforce and in the end decreasing the requirement for system changes with the ever-changing culture.

4. Maximum consumer experience: with the help of NLP, speech identification and virtual help (Robo), insurers can help in embracing many innovative methods of changing the consumer experience. With virtual helps present at many touch elements, insurer can take the consumer service to different heights by providing contextual and customized goods and answers, achieving first time ideal answers for complaints and also provide nudges after every interval and work as a reminder to finish important work in order to maintain the wellness financially (Liang et al., 2017).

5. Competitive gain: insurers with AI abilities can place themselves to deal with market issues which are better than their competition which is constantly changing insurance business. Leveraging machine focused learning along with the capacity to read data which is unstructured by nature, an insurer can grow appreciation in real time of different prospects attitude and demographic steps, identify imperceptible transformation in many market forces that can supervise changes and predict optimal answers.

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Status quo: management of claims manually

The mature level of healthcare industry and broader variety of any private insurers along with many statutory warnings. Countries like Germany provide an ideal kind of context for evaluating growths that can impact many health insurers. A mid-size insurer with more than millions of members get more than seven lakhs claims for cost-based refunds from different health centre or hospitals each year. Insurer have the right to verify whether such claims are right which is a work that constantly can tie down many or huge number of workforces. The experience across many health insurers shows that one in ten kind of claims are wrong as well as the number of claims must be summoned by the health insurance agents (Mamoshinab et al., 2018).

The procedure is very ponderous and as a policy which covers many seventy percent of claims which are not normal which means a potentially wrong depending on health insurance agents which comes with particular kind of book covering all kind of rules. The staff working in administration department then evaluate different claims in details and depending on the claim data and any present patient information, the workforce then focus on different experience to determine whether or not to interfere.

Aims can succeed for just like ten percent of all abnormal kind of claims. Therefore, just ten percent of abnormal situations can be intervened in a successful manner. This way, it becomes more crucial to importantly recognize different claims for which different kind of intervention is more likely to be part of varied pay off. This objective is specifically critical since the number of wrong claims as challenged by hospital is also one rise and this is an outcome related to high number of cases of inpatient mixed with a tight personnel level of ability at insurers (Meskó et al., 2018). Varied claims audits can further absorb valued workforce, resources along with time that can put it for better usage some where else, not only at health insurers however also for varied providers.

In addition, incorrect claims can amount that must not be paid however slip via cracks in process of audit consist of added financial prospective waiting which can be unlocked. Currently, health insurance agents can, in a right case, decrease the amount of money mainly submitted as a part of different claims by just three percent and important savings from which the insurer as well as wrong society gain. But, this stage of success which is part of premises on the right recognition of different claims for which such interferences can gain a lot of success (Pesapane et al., 2018).

Requirements for setting up an AI system for management of claims

Constructing an AI structure is clearly a complicated kind of undertaking. The insurers must consider on the usage of AI structure in management of claims must thus ensure that they have all the factors which is actually a solid framework to achieve success and following is the detailed discussion:

• Digitized level of claims that are original by nature. Invoices that are awaited must come from hospitals which digitized kind and this way AI structure can extract needed information in a seamless manner without any added directions by the health insurer (Russelland Norvig, 2016).
• A set up further claim the process of management and structured process must be places for review claims and determine on intervene decisions.
• Digitized document of outcomes: to track the results of many claims-based management functions is important to give an initial information for the AI structure.

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Readiness of stakeholders

Employees: insurers are willing to execute AI answers will require careful management of threats to their employees by redeveloping work, management-based practices and performance focused objectives. From an opinion of insurer, operations, contact unit teams and advisory will go through an important kind of transition that can be aligned with AI executions. Workforces in different departments will require to construct expertise and test optional work profiles.

Consumers: companies will require to test the mindset of the consumer in the direction and readiness towards AI. Insurers can be done via asking people some question that help them in placing the mindset of consumers like focusing on important questions related to can people gain confidence of machine in making a long run decision financially and trusted AI system with conventional systems and procedures that will be utilised to serve ideally (Varian, 2018).

Future endeavours: There are so many dollars that are invested in the pilot and can further commercialize technologies that can drive a lot of AI abilities and insurers are starting to understand the number of advantages mentioned here. But people can see the concept of AI as curable sales, management of risks conundrums, insurers must further follow a sequence of steps which are self-diagnostic by nature right before it embarks the journey (Vayena et al., 2018).

Readiness accessibility: this further can be performed with socializing different experience of AI answers and also soliciting answers via studies and discussions. Also, the decision maker of AI must spend right and quality time with their managerial teams and functional leaders to show on the current potential impacts of new AI products along with application on varied operational models, goods and operational flow of work. In addition, there are a number of decision-makers who should also assess the online technology and related landscape and the level of historical data present in term of quality, unstructured and structured information internally and externally, and methods and scalable means and helping AI-based applications (Vogenberg and Santilli, 2018).

Health insurers must also experience and study focused on the cultural level risks faced by the sectors and health insurer must start by growing proof of principles models that can securely be evaluated and can be adapted in an environment which is risk-free. Because AI devices can excel at day-to-day tasks and related algorithms usual learning over a period of time, insurers must pay attention the early time efforts on the procedures and assess that can be understood widely and adding value with a modicum. With the help of information and knowledge collected from the phase of assessment, insurers must then recognize the usage situations which work as a proof of varied concept, focusing on functions that need agility and constant innovation (Wahl et al., 2018). The second step will recognize the right kind of technology-focused partner and AI answers to change the recognized use case from reality to concept.

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Change management: since AI focused abilities can potentially help in displacing human being, health insurers require an efficient HR strategy. Complete interaction and retain impacted workforce, and pay attention to building new sets of skills as well as training, can go a long way in reducing resistance and motivating acceptance (West, 2015). This also means that health insurers should pay attention to the efficient management of change to make sure that influenced workforces can gain clarity over the techniques that are deployed to assist them to perform in a better manner and this, in turn, will increase the employee satisfaction along with retaining capacities.

Conclusion

Health insurers and the workforce should collectively have a belief system in many potential amplifying productivities which is possible via human augmented AI answers. The overall success of AI answers focuses on constant learning and can turn the AI model. Therefore, the concept of learning or unlearn can play an exclusive role at this phase (Zayas et al., 2016). More decisions will help in making machines and the data they evaluate, the better equipped to undertake rising complicated work and offer right and appropriate steps.

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