Primary Reflection

Longevity Industry has made efforts in providing life-extensions through “Novel Financial System”, P4 Medicine, Geroscience and AgeTech. In this paper, I would indulge myself in performing research on AgeTech and hence collecting data to provide support to the elderly people in future. AgeTech indulges in providing suitable support with the implementation of information technology to elderly people (World Health Organization, 2017). I suggest that the implementation of AgeTech will be beneficial in the enhancement of neuroplasticity, psychological “well-being” and mental functioning. It will also be beneficial in increasing the social activity and independence levels of these elderly people on a large scale.

I suggest that Longevity is focussed in providing cure to the elderly people and thereby developing a simulation model in business for understanding the different roles and contributions involved in the development of such typical projects. The major goal of this project is to implement non-medical procedures for providing cure to the elderly people regarding the age-related problems developed amongst them in future. Based on this specified goal, efforts are made by my team members and me in developing strategies and thereby providing decisions by a suitable collection of data for AgeTech. The steps that are involved in the determination of contributions and strategies involved in making suitable research are as follows:

•       In the initial stage, I, with the help of my team members, have consulted different medical organisations and hospitals for getting a record of different elderly patients who are admitted under their governance.
•       In the second stage, my team members will diagnose the different types of problems that happen to most of the elderly people in the future and hence diagnose the problems efficiently. Strategies are developed based on the symptoms that are developed amongst them while ageing.
•       In the third stage, my team members and I put efforts for the development of clocks for deep ageing (Gajewski et al.2018). These clocks are developed with the implementation of artificial intelligence in information technology which proves to be beneficial in tracking records of different individuals and hence making suitable predictions through clinical trials for diagnosing different problems generated by elderly people.
•       In the final stage, my team members and I have been successful in diagnosing the different problems and hence implementing measures for providing counselling for increasing the mental stability of the elderly people—entertainment, development of mobile apps, and also providing education that will help in providing mental guidance.

The major contributions of my team members and I are based on the goals for providing non-medical guidance with the utilisation of information technology for increasing the mental conditions of the elderly people. My team members are involved in the collection of suitable data from different health organisations and hence implementing those problems for the generation of proper counselling methods for providing proper treatment to the elderly people.

Secondary Reflection

AgeTech basically involves the use of information technology with the help of artificial intelligence for diagnosing the different types of mental stress and problems that are developed amongst elderly people. Predictions are developed with the implementation of artificial intelligence for suitable tracking of the problems which are mostly affecting the elderly people on a large extent.

Figure: Deep ageing clocks

(Source: https://www.linkedin.com/pulse/longevity-industry-biggest-most-complex-human-history-colangelo)

I am personally involved in this project for collecting data from different hospitals and medical organisations and thereby providing data of different elderly people to my team members for developing clocks for deep ageing (Boas et al. 2016). After visiting the hospitals, I consulted the doctors for taking the case history of different elderly patients for my benefit. Then I consulted the elderly patients for understanding the behavioural traits of them and hence diagnosing the problems and stress that are faced by them while ageing. Based on the data that is collected from them, I recommended an IT analyst, who is a part of my team, to recommend strategies that will prove to be beneficial in the development of suitable methods for making predictions. With the help of IT analysts and software developers, the use of artificial intelligence is used for making suitable predictions through calculations. I provided them with the list of problems that are being diagnosed by me while consulting the doctors and elderly patients at hospitals and thereby providing that list to both of them for performing suitable calculations through different algorithms present in those problems. This proved to be beneficial in the development of “deep ageing clocks” which helps in making suitable tracking of different data about ageing and thereby making predictions through different clinical trials. Now, after the development of ageing clocks, suitable predictions are developed and thereby collecting those data for providing to a strategy expert for providing a suitable strategy for counselling to the elderly people. Education and entertainment are suggested to be the best methods that should be provided to elderly people for increasing their mental stability (Kerr 2018). I consulted the IT expert for the development of mobile apps which will be beneficial in the development of the mental condition of elderly people and thereby installing games and counselling programs in the smartphones with the help of these apps for taking actions against ageing for these elderly people.

Table 1: Simulation strategy table

The implementation of Gibb’s model is preferred by me for providing a suitable reflection of the different roles and responsibilities of team members who are directly involved in providing solutions for the complications developed through ageing (Biscop 2016). With the help of this model, a description is provided by me by making consultations with the doctors and elderly patients for understanding the range of problems raised with ageing. Hence, I share the feeling with software developers and IT analysts in making them understand the scenario and thereby indulging them in the development of “deep ageing clocks” for making suitable predictions through evaluation. I suggest that analysis is done by performing calculations through algorithms in artificial intelligence and thereby making predictions through tracking and clinical trials. After this, I consult a strategy expert for getting a conclusion through decisions that will help in the development of mental conditions of elderly people, which is suggested to be education and entertainment. Based on these two strategies, I suggest that an action plan developed for the generation of mobile apps will help in the introduction of these two strategies for the development of the mental condition of the elderly people in future.

Figure: AgeTech

(Source: https://www.linkedin.com/pulse/longevity-industry-biggest-most-complex-human-history-colangelo)

Future Intentions

I suggest that the initiatives that are taken by Longevity through the implementation of AgeTech for diagnosing and providing solutions through the increase in mental stability to the elderly people in future. I suggest that Longevity has been successful while making such attempts (World Health Organization, 2016). However, there are some drawbacks that are present in this initiative for getting a productive outcome through suitable predictions. The drawbacks areas are as follows:

•       Collection of data by performing face-to-face interviews of different elderly patients is a hectic process. Often, the doctors will be disappointed in visiting such patients and hence require legal permission for paying such visits to those patients.
•       Since the data that is generated through predictions by “Deep ageing clocks” are basically involved through the efforts that are conducted by me for performing such research; so it might generate wrong predictions for the wrong data that are provided by me for making predictions.
•       The apps that will be developed for providing entertainment and education indulge the use of smartphones for making better implementation. Often, elderly people did not prefer the use of smartphones, or they might not possess suitable knowledge regarding the usability of smartphones (Rao 2017). So, this will result in the failure of aforesaid objectives for the development of mobile apps for providing entertainment and education.

Based on the above drawbacks which are being mentioned by me while developing AgeTech for providing solutions against the problems of ageing amongst the elderly people. The recommendations are as follows:

•       Development of online forms for taking reports from medical counsellors regarding the behavioural traits of different elderly patients.
•       Collection of data will be online and will be implemented for making predictions with the help of clocks for deep ageing. The data that will be collected will be more accurate as compared to previous attempts.
•       Attempt to be made for making the smartphones user-friendly for elderly people and thereby making efforts for making them understand the usability features of smart phones (Bendiek 2016). After this step, efforts are made for making them understand about the usability feature of the mobile apps that will enable them to get entertainment and education for providing suitable counselling for improving their mental conditions.

Reference List

Journals

Bendiek, A., 2016. The global strategy for the EU’s foreign and security policy.

Biscop, S., 2016. The EU global strategy: Realpolitik with European characteristics. Might and Right in World Politics, pp.91-100.

Boas, I., Biermann, F. and Kanie, N., 2016. Cross-sectoral strategies in global sustainability governance: towards a nexus approach. International Environmental Agreements: Politics, Law and Economics, 16(3), pp.449-464.

Gajewski, J., Bijlmakers, L. and Brugha, R., 2018. Global surgery–informing national strategies for scaling up surgery in sub-Saharan Africa. International Journal of Health Policy and Management, 7(6), p.481.

Kerr, W.A., 2018. Eliminating the constraints on trade policy–The strategy that underpins US negotiations in the Trump administration. Estey Journal of International Law and Trade Policy, 19(1753-2019-085), pp.75-84.

Rao, P.N., 2017. Global leprosy strategy 2016–2020: Issues and concerns. Indian Journal of Dermatology, Venereology, and Leprology, 83(1), p.4.

World Health Organization, 2016. Global strategy on human resources for health: workforce 2030.

World Health Organization, 2017. Global strategy and action plan on ageing and health.

Appendices

Appendix 1

(Source: https://www.linkedin.com/pulse/longevity-industry-biggest-most-complex-human-history-colangelo)