This study critically investigated the potential impact of AI adoption in health insurance’s claims processing performance. The future study should attempt to focus on expanding it to investigating
48
the AI effects on to downstream industries (service providers and the insured). This will allow for more generalizations. Future studies can also try to look at the empirical evidence of the effects of AI in their industries.
49 REFERENCES
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Journal of Logic, Language and Information, 9(4), pp. 391–395. DOI:
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• Banerjee, A., Bandyopadhyay, T., and Acharya, P. (2013) “Data Analytics: Hyped Up Aspirations or True Potential?,” 38(4), pp. 1–11. DOI: 10.1177/0256090920130401.
• Bennett, C. C., and Hauser, K. (2013) “Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach,” Artificial Intelligence in Medicine, 57(1), pp. 9–19. DOI: 10.1016/j.artmed.2012.12.003.
• Brundage, M. (2015) “Taking superintelligence seriously Superintelligence : Paths, Dangers, Strategies by Nick Bostrom,” Futures. Elsevier Ltd, 72, pp. 32–35. DOI:
10.1016/j.futures.2015.07.009.
• Buchanan, B. G., and Smith, R. Q. (2015) “Fundamentals of expert system,” Springer Series in Materials Science, 206, pp. 31–39. DOI: 10.1007/978-3-662-44497-9_3.
• Business Report (2017) Medical aid fraud rife globally: prominent health insurance
company, Herald, 13 June 2017. Available at: https://www.herald.co.zw/medical-aid-fraud- rife-globally-cimas/ (accessed 18 September 2019)
• Chronicle report (2015) prominent health insurance company loses $1.2m to fraudulent claims, Chronicle, 18 May 2015. Available at: https://www.chronicle.co.zw/cimas-loses- 12m-to-fraudulent-claims/ (accessed 18 September 2019)
• Corchado, J. M. and Lees, B. (1998) “Integration AI Models,” Workshop on Knowledge Discovery and Data Mining, (c), pp. 1–3.
• Cortis, D. et al. (2019) “InsurTech,” pp. 71–84. doi: 10.1007/978-3-030-02330-0.
• Dickson, A., Adu-Agyem, J. and Emad Kamil, H. (2018) “Theoretical and conceptual framework: ” International Journal of Scientific Research, 7(1), pp. 438–441.
• Erb, B. (2016) “Artificial Intelligence & Theory of Mind Artificial Intelligence & Theory of Mind Benjamin Erb Seminar Cognition and Emotion Summer Term 2016 Dept. Applied Emotional and Motivational Psychology,” Artificial Intelligence & Theory of Mind, (September). DOI: 10.13140/RG.2.2.27105.71526.
• Fauconnier, G. (2015) “Emergence and Development of Embodied Cognition (EDEC2001),”
Emergence and Development of Embodied Cognition (EDEC2001).
• Hehner, S., Körs, B. and Marting, M. (2017) “Smart claims management with self-learning software,” (September).
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247–302.
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MACHINE LEARNING ? WHAT ARE NEURAL NETWORKS ?” pp. 1–12.
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52 GLOSSARY OF TERMS
Artificial Intelligence this is a computer system that is made up of hardware and software with capability to think, learn and act in same similar way as human beings.
Expert Systems This is a piece of software that uses databases of expert knowledge to offer advice or make decisions in such areas as medical diagnosis. Computer programs that are capable of emulating humans in a number of ways that might include repetitive tasks
Health Insurance a firm or entity that is created to pay for medical, surgical and sometimes dental expenses incurred by the insured. The insured pays some form of subscriptions in order to be covered.
Machine Learning – this is an application of artificial intelligence that provides computer with the ability to learn and improve by using algorithms and other AI components like data mining.
53 APPENDICES
COVER LETTER FOR ENTRY INTO ORGANISATION Stand No. 5461 Cranbrook Park
Ruwa
28 November 2019 The HR Director Dear Sir/Madam
RE: REQUEST FOR ACCESS TO CARRY OUT ACADEMIC RESEARCH IN YOUR ORGANISATION
I am seeking the authority to access your organization in order to facilitate my academic dissertation research work.
I am an MBA student with the University of Zimbabwe and currently doing a dissertation on “A critical investigation into the potential impact of Artificial Intelligence adoption in the health insurance claims processing function – The case of medical insurance firms in Harare.”
I intend to study the current claim processing system and find out the potential effects the Artificial Intelligence technology would have on the whole claims management process, positive & negative effects this would have within the organization, downstream partners (your service provider) as well as the insured (your clients).
The purpose of this research is strictly for academics and I assure confidentiality and the highest privacy & anonymity upholding to the respondents and the organization’s data that I will collect.
Please find enclosed the supporting letter from the University of Zimbabwe’s Graduate School of Management.
I greatly appreciate your assistance.
Yours faithfully
Give Kasongo (UZ-MBA Strategic Leadership Student)
54 QUESTIONNAIRE
Dear sir/madam
My name is Give Kasongo, a Master’s in Business Administration (MBA) student with the University of Zimbabwe’s Graduate School of Management. To complete my program, I have to carry out a study and my research title is shown above.
I do hereby assure the respondents that the research is purely for academic purpose and that the data collected will be treated as confidential and anonymous. Thus, participants should not write their name or any personal information on the questionnaire.
May you kindly complete the questionnaire as honestly as possible by either putting a tick or an X in the space provided.
For any future clarification regarding the questions do not hesitate to contact me on 0772 220 151 or [email protected].
If you prefer to use online forms, follow the link below:
https://forms.office.com/Pages/DesignPage.aspx?fragment=FormId%3DNxFNtmu2r0e82XTgOsYQ r0UQIYHfsHNHqQDS4RzoktRUM1I4UTZVQUVHV1dKVTVOQTc1Q000MTgzVi4u%26Token
%3D31b2fe07098c4177931eaea768b92329
Give Kasongo - Student No. R1712201
55 A: DEMOGRAPHICS
General information about the respondent and organization
Please indicate your selection by either a tick or a star in the appropriate box.
1. Please indicate your gender
Male [ ] Female [ ]
2. Indicate your age range
Less than 25 years 25 – 30 years 31 – 35 years 36 – 40 years Above 40 years
3. What is your current position in the organization?
4. Depart ment
5. What is your length of service in this organization?
Less than 1 year Between 1-3 years Between 3-5 years Above 5 years
SECTION B: General appreciation and awareness of AI technologies
For the questions to follow, may you rank your opinion on a Lickert scale of 1-5 as guided below:
Strongly disagree Disagree Neutral Agree Strongly agree
1 2 3 4 5
B: Appreciation and awareness of AI technologies 1 2 3 4 5 6. I am aware of AI & its related technologies.
7. I do understand the importance of AI & its associated technologies.
8. I have heard about AI & its associated technologies 9. The company is using some of the AI technologies
10. The ICT workforce is aware of the positive impact AI can cause to the company
11. AI technologies makes employees work smarter SECTION C: The sector’s Level of Preparedness
Non-Managerial Junior Management Middle Management Senior Management
Admin/claims Processing Human Resources ICT HO
56
C: Level of AI preparedness 1 2 3 4 5 12. The company has the right ICT infrastructure for the AI
adoption.
13. The company has a basic ICT infrastructure to get the AI projects going.
14. The organization’s data is ready for AI adoption.
15. The data needs to be tweaked a little to be ready for AI adoption.
16. The organisation understands the quality of data needed for AI to work
17. Staff in all departments is ready to implement & support AI technology
SECTION D: The Industry’s AI skills & talent pool
D: The Industry’s AI skills & talent Management 1 2 3 4 5 18. The company has internal resources to drive its AI agenda.
19. Plans are underway to recruit data analysis and AI experts 20. The company has invested in AI talent development.
21. Developing & retaining AI talent is a challenge
22. The company is investing in re-skilling its own workforce.
23. The industry a good AI skill & talent base
SECTION E: AI & Business Strategy Alignment
E: AI & Business Strategy alignment 1 2 3 4 5 24. Tying AI to the company’s overall strategy is essential.
25. Failure to adopt the AI technology is a strategic risk.
26. Do you think AI is an opportunity
27. Leadership believes AI strategies that are aligned to overall business strategy influence high organizational performance.
28. AI strategy integrates into corporate strategy.
29. The company treats AI as a strategic initiative.
SECTION F: Problems & Barriers to AI adoption
F: Possible Barriers to AI adoption 1 2 3 4 5 30. Employees feel threatened by AI Technology
31. Our organization perceives AI as both competitive risk and opportunity.
32. The organization perceive AI as an opportunity more than it is a risk
33. AI adoption will result in some job losses 34. Lack of leadership support
35. The computing power is not adequate for AI adoption 36. Insurance data is sensitive thus security concerns are
prohibiting its adoption