7055SOH Leading in Complex Health Systems Assignment Sample 2023
Background
A chatbot is a software application that is able to simulate a natural human conversation like a live human agent with the consumers on the behalf of the service providers. Consumers are able to communicate with chatbots through voice or chat interfaces in the same way one communicates with a real person (Sharma et al. 2017). This software application has been used to conduct a one-liner chat conversation via text-to-speech or text.
In this post-pandemic situation, the healthcare industry has witnessed different kinds of emergency situations regarding the deterioration of the patients’ health (Bao et al. 2020). In these kinds of situations, sometimes, the healthcare professionals face several difficulties in answering every single question on a regular basis to the family members of the patients. Innovative software such as a chatbot would be extremely beneficial in this case as it would be able to answer the many queries of the customers 24/7. Therefore, keeping an eye on these circumstances, it can be considered that the implementation of chatbots in the healthcare industry will be beneficial for both the healthcare professionals and the family members of the patients as well.
Context of the proposed innovation
Figure 1: Increased number of emergency cases worldwide
(Source: Park et al. 2020)
Several studies regarding the healthcare industry have displayed that in the post-covid-19 pandemic situation, the number of emergency cases has increased in an immense order. In accordance with the above figure, it can be stated that the number of emergency cases in several countries has increased at the rate of 5.96% in comparison to the pre-pandemic situation. As the cases of emergency have been raised at a sudden and immense rate, the majority of the time the healthcare professionals have faced issues in providing the details regarding the patient’s condition in a regular manner. It has become difficult for healthcare professionals to deliver every detail of the patients such as providing informational support, collecting patients’ data, providing medical assistance remotely in times of emergency and many more.
Based on this situational demand, it can be stated that the chatbot will be beneficial for both the parties, consumers and the service provider (Ni et al. 2017). In the time of any emergency, the chatbot can be able to provide information about the patient, which will help the doctors to take immediate action. On the other hand, the chatbot will be able to share the information of the patients regarding health and medical conditions and requirements, which will decrease the anxiety of the family members of the patients (Espinoza et al. 2020). Therefore, based on these beneficial aspects of the chatbot, it can be argued that the implementation of chatbots in the healthcare industry can improve the overall functioning of healthcare industries.
Proposed innovation
In order to collect information about the patients’ emergency and post situation of medication, the members of the patients’ families can take the help of an implemented chatbot service. As per the view of Ghosh et al. (2018), it can be stated that the healthcare industry professionals can implement a chatbot system that can be able to provide several information about the patients. On the other hand, these chatbot systems need to be built in such a way that they can be capable of providing any kind of situational advice to the members of the patients’ families.
In addition, an effective and advanced IoT needs to be implemented within the chatbot system, which will be able to protect the security and the confidentiality of the patient’s credentials (Kidwai & Nadesh, 2020). On the other hand, in order to build an effective and useful chatbot, technological engineers need to implement the most advanced Artificial Intelligence (AI) technology to provide a vast range of options in order to continue along with one-liner conversation with the consumers (Przegalinska et al. 2019). Therefore, in the time of implementation of chatbot in the IoT (Internet of Things) devices of the healthcare industry, several pieces of information regarding every patient and their health condition needs to be installed in the program. Only then, the chatbot will be able to answer any kind of questions asked by the family members of the patients.
In addition, the security system, which will be installed in the chatbot, needs to be strong enough to restrict any third party authentication (Ayanouz et al. 2020). The security needs to be strong in order to protect the credentials of the patients and the healthcare organisation from several kinds of cybercrimes. In order to build strong protection against cybercrime, the chatbot makers can implement such a programme in the chatbot that will be able to generate unique 4 or 5 digit codes for the individual patient’s name (Illescas-Manzano et al. 2021). Using this code, the customer will be able to pass the authentication phase and will be able to access the information about a particular patient.
Evidence-based benefits and barriers
Several studies have discovered that the implementation of the chatbot in the healthcare industry can encounter several issues not only regarding technology but of other aspects as well. As per the statement of Radziwill & Benton (2017), it can be stated that, in order to implement a chatbot successfully in the technological support system of a healthcare system, several barriers can occur during the process regarding user language, the randomness of being a human, limited attention span and many more. These identified barriers have been discussed in the below section:
Users’ way of texting
Several studies have confirmed that different persons have different styles of communication, therefore, have different styles of texting as well. In order to reply to the texts of the consumers, the chatbot needs to understand the message that has been sent by the user. However, in the majority of cases, it has been encountered that the chatbot has faced difficulties in understanding the intention of the chat conductors (Lalwani et al. 2018). It has happened because the chatbot was unable to match the texting language of the person with the implemented NLP (natural language processing) used during chatbot implementation. Therefore, it can be a serious issue because the chatbot will be useless if it will be unable to understand and achieve the intention of the communication conductor.
Limitations of NLP
It has been encountered that in the current state of technological advancement, the present state of natural language processing (NLP) is not so much improved. This scenario has been considered as a barrier in the path of implementation of such an advanced NLP within the chatbot system (Lalwani et al. 2018). The vocabulary of the chatbot has included the synonyms and extraction of entities but the mixed local language vocabulary and the slang are added in this system. Therefore, based on the requirement of the enhancement of chatbot’s vocabulary, the accessible range of language has started improving in order to provide an effective experience.
Recognizing the intention of the users
Recognizing the user’s intention can be a big issue for the auto text generated chatbot as different human beings have a unique and different style of texting or conducting communication. Therefore, it can be difficult for the chatbot to match the communicator’s text with the previously programmed vocabulary or language base of the chatbot. This phenomenon can lead to the wrong response of the chatbot, which can offend the user, and thus the efficiency and accuracy level of this advanced technological system will be lowered.
Besides the above discussion, this system has possessed several benefits as well which have been discussed in the below section:
Increase the customer engagement
It has been seen that implementation of the chatbot system is capable of increasing customer engagement as it provides 24/7 availability. Therefore, the patients’ family members can access the information at any time (Maniou & Veglis, 2020). In addition, as the chatbot is able to deliver instant responses, the customer satisfaction level increased accordingly.
Balance automation with a human touch
In this chatbot system, the algorithm of complex AI technology has been implanted with a human touch. The chatbot has the ability to respond to the customers’ text almost like a human being (Villegas-Ch et al. 2020). This facility has been considered as an advantage of the implementation of the chatbot system as it has been capable of increasing the customer engagement level.
Benefits
On the basis of the above discussion, it has been said that the implementation of such an advanced technological system as a chatbot in the healthcare industry will have several benefits. In the healthcare industry, for several useful reasons, a chatbot can be implemented which are stated in the below section:
Gaining patients’ feedback
The healthcare industry always has an intention to provide exceptional services to consumers. In order to achieve this primary goal, nothing can be more beneficial than the implementation of a chatbot system (Fadhil, 2018). This system has the ability to generate one-to-one communication that helps the patient’s family to get necessary information of the patient’s health condition in an individual manner and the family members will know about the patients’ feedback as well.
Appointment scheduling
The Healthcare Industry needs to deal with various kinds of patients on a regular basis in a huge number. Therefore, fixing appointments over the phone for individual patients has appeared like a difficult task and time consuming as well (Bao et al. 2020). Therefore, the implementation of a chatbot system with a beneficial aspect regarding appointment scheduling can be helpful for the professionals and the consumers at the same time.
Daily medication reminders
In the majority of the cases of emergency medication, doctors often prescribe different medicines to handle the various serious health conditions of the patients. Therefore, sometimes, it has become difficult for the person who is taking care of the patient at home, to remember all the medications and their timings as well. Based on this case scenario, the implementation of the chatbot system will enable the person to set reminders for the individual medication (Pereira & Díaz, 2019). This feature of chatbot will help the caretaker to remember the schedule of medication of the patients, thus the potentiality of the missing dose will be reduced accordingly.
Implementation plan
In order to serve the consumers and the patients’ family members with the benefits of a chatbot, an effective implementation plan needs to be developed by keeping eye on the below-mentioned aspects:
Cost of implementation
This aspect is important and has been related to producing an innovative and effective technological system for achieving growth in the ongoing process of the implementation of chatbots in the healthcare industry. As per the view of Telang et al. (2018), it can be stated that the implementation cost of the chatbot system in the healthcare industry includes two aspects such as maintenance cost and training cost.
Maintenance cost
In order to make the system run in an effective and successful manner without any technical faults or issues, regular maintenance of the system is important and necessary as well (Adamopoulou & Moussiades, 2020). Therefore, in order to build and implement such a technologically advanced system in the IoT system of Healthcare organisations, the engineers need to keep the maintenance cost of this advanced AI system.
Training cost
It is important to train the organisational professionals in order to provide knowledge to the consumers regarding the beneficial aspects of the newly implemented technologically advanced system ( Adam et al. 2021). Therefore, before making this chatbot available for the patients’ family members, the professionals need to be trained and have the proper knowledge about the work process of this advanced technological system. Based on this necessity, the organisation should prepare a change management to place the right individuals at the right place.
Implementation planning process
In order to build an effective and efficient chatbot, in the initial parts, a personality needs to be built up. However, planning is prone to trial and errors, and in many cases, the management may face issues for implementing chatbot system. Nevertheless, as per the view of Adam et al. (2021), proper leadership style such as democratic leadership style needs to be followed in order to get back up from the setback and get in the spotlight again. Democratic leaders are necessary in this plan to keep motivating everyone and not succumb to a few issues. Situational leadership theory is a good way to act by judging the entire situation. Companies such as Google and Microsoft seem to follow this leadership theory and have considerable success in advancing their technology.
Tools such as balanced scorecard and PDCA (Plan-Do-Check-Act) can be used to implement the chatbot system. For example, while implementing the chatbot, it might have technical issues related to its functioning. Patients or parents may not get the answer they are looking for from the chatbot, which would make them dissatisfied. Hence, tools such as PDCA can be used test the chatbots for various iterations and make continuous improvement. This tool can not only help to examine what the chatbot is lacking but also help in reprimand the internal system of chatbots. Thus, it can be argued that such tools would be greatly effective in the process of implementation and innovation of chatbots.
Apart from this, it can be stated that one must need to follow the below-mentioned steps for developing and implementing the chatbot system within the healthcare industry:
Determine the role of the chatbot and set goals
In order to achieve the benefits of the advanced technological system namely chatbot, the engineers need to possess a clear idea about why this system requires to be implemented in the healthcare system. In several studies, different beneficial aspects have been disclosed and various benefits of using chatbots within hospitals have been mentioned in this study previously (Abdellatif et al. 2020). Based on these facts and aspects, the goal of the chatbot will be to provide information about the patients’ family members during the occurrence of any kind of emergency.
Evaluate and pick a channel
The main purpose of the implementation of this chatbot system in the healthcare industry is to provide different information about the patients’ conditions in times of emergency medication. In order to do that, the online text-based chatbots will be beneficial for the patients’ family members as the work process of this kind of chatbot is less complicated and can be easily handled by the customers as well.
Create the conversational architecture
In order to respond to the text messages of the conversion conductors, the chatbot needs to have different types of user interfaces rather than traditional graphical UI as this system needs to respond in a frequent manner. The conversational UI is open-ended and enables the system to respond to any questions asked in any language by the human users (Abdellatif et al. 2020). Therefore, in order to achieve the facilities of this system, the engineers need to include the conversation based UI while building the programme of this chatbot.
Design dialogue flows and integration
The technique called random prompting needs to be implemented in this system of the chatbot because this technique will make the whole experience of chanting less robotic. On the other hand, this technique will enable the bot to use different wording variations for essentially saying the same thing by using different synonyms (Abdellatif et al. 2020). On the contrary, the integration will be done on the basis of the automation applications used in this system.
Barriers for the planning
In order to propose the planning of implementation of chatbots in the healthcare industry, several limitations and barriers have been encountered which have been mentioned in the below section:
It has been identified that, as chatbots have complex algorithms and advanced AI technology in them, the security issue has come forefront in the initial stages. The rate of having cyber-attack and security breaches of the chatbot is 4.6% in accordance with the increasing rate of cybercrime worldwide.
Figure 2: Types of security concerns
(Source: Ischen et al. 2019)
The threats of the security breach have included several aspects such as repudiation, spoofing, information disclosure, the elevation of privileges, and many more (Ischen et al. 2019). Therefore, the potentiality of having issues regarding the security of the chatbot system will be considered as a serious issue while developing the program for this technologically advanced auto-text generated system.
Implementation of technology to auto-generate individual numeric codes for individual patients
The implementation of auto-generating numerical code for individual patients can be difficult for technical engineers. In order to save and provide information to the patients’ family members individually, the chatbot needs to identify and match the family members data with individual patients’ details (Telang et al. 2018). Every time, in the initial states of text, providing every primary detail to the chatbot will be difficult for the caretakers of the patients. Therefore, if the chatbot can generate a unique and individual 4 or 5 digit numerical code for every patient, it will be easier to collect data regarding any specific patients (Maniou & Veglis, 2020). However, implementation of such an advanced technology of code generating can be difficult for the engineers as the technology related to this system is yet to be advanced enough for being able to do that.
Budget
| Particulars | Amount in $ |
| IT services | 25k |
| Implementation cost | 50k |
| Training cost | 30k |
| Maintenance cost | 45k |
Table 1: Budget planning
(Source: developed by the learner)
Measuring project success
The success rate of the project will be measured by the below-mentioned aspects:
Customer satisfaction
The main aim of this project is to satisfy the consumers by delivering information on patients’ health conditions in the time of emergency medication. Tools such as PDCA can be effective in determining what customer really wants and how the functions of chatbot can be improved. It can be argued that through continuous improvement and application of democratic leadership to not give up, the implementation of this chatbot system will be able to increase the consumer satisfaction rate as well.
Time
The success of chatbots depends on how much time the chatbot takes to answer the customer queries in specific. A tool such as balance scorecard needs to be used to keep track of the execution activities and control the chatbot functions. It has been found in several studies that a proper chatbot is able to decrease the time consumption level during scheduling an appointment with the doctors. On the other hand, if any member of the family wants to check the health statement of the patient, it can be easily done by the chatbot in no time, as it will be able to provide any kind of information about the specific patient.
Recommendations
Based on the identified issues in the implementation of the chatbot in the healthcare industry, several recommendations have been suggested in the below section:
- In order to develop the work process of this chatbot system and the information within it, this system needs to be fed with regular and new information.
- The technology regarding this chatbot system needs to be advanced as soon as possible to achieve the advancement of the UI technology used in it.
- The security of AI technology and IoT service needs to be strong enough to restrict any third party intrusion.
- In order to restrict the repudiation issue, digital signatures can be used in this system.
- Appropriate authentication needs to be involved in this system for handling the issue related to denial of service.
References
Abdellatif, A., Costa, D., Badran, K., Abdalkareem, R., & Shihab, E. (2020, June). Challenges in chatbot development: A study of stack overflow posts. In Proceedings of the 17th International Conference on Mining Software Repositories (pp. 174-185). Retrieved on 1st December 2021 and from: https://dl.acm.org/doi/pdf/10.1145/3379597.3387472?casa_token=jT7RO-eVe7IAAAAA:F5JRz3xbv-MrSQcoNn345NnVy8ifmIj4DH2aStiHgr7F_uwIO9tuq4sOcEGWJFTk9GMBWregkDg
Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427-445. Retrieved on 1st December 2021 and from: https://link.springer.com/article/10.1007/s12525-020-00414-7
Adamopoulou, E., & Moussiades, L. (2020, June). An overview of chatbot technology. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 373-383). Springer, Cham. Retrieved on 1st December 2021 and from: https://link.springer.com/chapter/10.1007/978-3-030-49186-4_31
Ayanouz, S., Abdelhakim, B. A., & Benhmed, M. (2020, March). A smart chatbot architecture based NLP and machine learning for health care assistance. In Proceedings of the 3rd International Conference on Networking, Information Systems & Security (pp. 1-6). Retrieved on 1st December 2021 and from: https://dl.acm.org/doi/pdf/10.1145/3386723.3387897?casa_token=G6F9tJtXM9kAAAAA:DVmrHWo4dCXU4e90EmFBUAe_05_zJf-6P8Or4K6jXkZEKcCilq0nNo0hyLeA6ZpxYf3pUURnliQ
Bao, Q., Ni, L., & Liu, J. (2020, February). HHH: an online medical chatbot system based on knowledge graph and hierarchical bi-directional attention. In Proceedings of the Australasian Computer Science Week Multiconference (pp. 1-10). Retrieved on 1st December 2021 and from: https://dl.acm.org/doi/pdf/10.1145/3373017.3373049?casa_token=YztZAyXl6PcAAAAA:h-ASxgAcDzmrDtnr91RWQc-NqW0Ziyk1Yy2VAXADCrvWNJLqHct7J5DNJJ22aGN8C4e9J-vLvRU
Cameron, G., Cameron, D., Megaw, G., Bond, R., Mulvenna, M., O’Neill, S., … & McTear, M. (2017, July). Towards a chatbot for digital counselling. In Proceedings of the 31st International BCS Human Computer Interaction Conference (HCI 2017) 31 (pp. 1-7). Retrieved on 1st December 2021 and from: https://www.scienceopen.com/document_file/cf655a90-1b6b-4a66-ab9a-9f391a983bb5/ScienceOpen/001_Cameron.pdf
Espinoza, J., Crown, K., & Kulkarni, O. (2020). A guide to chatbots for COVID-19 screening at pediatric health care facilities. JMIR public health and surveillance, 6(2), e18808. Retrieved on 1st December 2021 and from: https://publichealth.jmir.org/2020/2/e18808/?utm_source=TrendMD&utm_medium=cpc&utm_campaign=JMIR_TrendMD_0
Fadhil, A. (2018). Beyond patient monitoring: Conversational agents role in telemedicine & healthcare support for home-living elderly individuals. arXiv preprint arXiv:1803.06000. Retrieved on 1st December 2021 and from: https://arxiv.org/pdf/1803.06000
Ghosh, S., Bhatia, S., & Bhatia, A. (2018). Quro: facilitating user symptom check using a personalised chatbot-oriented dialogue system. Stud Health Technol Inform, 252, 51-56. Retrieved on 1st December 2021 and from: https://ebooks.iospress.nl/pdf/doi/10.3233/978-1-61499-890-7-51
Illescas-Manzano, M. D., Vicente López, N., Afonso González, N., & Cristofol Rodríguez, C. (2021). Implementation of Chatbot in Online Commerce, and Open Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 125. Retrieved on 1st December 2021 and from: https://www.mdpi.com/2199-8531/7/2/125/pdf
Ischen, C., Araujo, T., Voorveld, H., van Noort, G., & Smit, E. (2019, November). Privacy concerns in chatbot interactions. In International Workshop on Chatbot Research and Design (pp. 34-48). Springer, Cham. Retrieved on 1st December 2021 and from: https://www.researchgate.net/profile/Fabio-Catania-3/publication/338672348_The_Conversational_Agent_Emoty_Perceived_by_People_with_Neurodevelopmental_Disorders_Is_It_a_Human_or_a_Machine/links/6048f26d299bf1f5d83d6e78/The-Conversational-Agent-Emoty-Perceived-by-People-with-Neurodevelopmental-Disorders-Is-It-a-Human-or-a-Machine.pdf#page=45
Kidwai, B., & Nadesh, R. K. (2020). Design and development of diagnostic Chabot for supporting primary health care systems. Procedia Computer Science, 167, 75-84. Retrieved on 1st December 2021 and from: https://www.sciencedirect.com/science/article/pii/S1877050920306499/pdf?md5=1e7399b8f8cfbb698b7edfb031d71f5f&pid=1-s2.0-S1877050920306499-main.pdf
Lalwani, T., Bhalotia, S., Pal, A., Rathod, V., & Bisen, S. (2018). Implementation of a Chatbot System using AI and NLP. International Journal of Innovative Research in Computer Science & Technology (IJIRCST) Volume-6, Issue-3. Retrieved on 1st December 2021 and from: https://www.researchgate.net/profile/Shashank-Bhalotia/publication/327456963_Implementation_of_a_Chat_Bot_System_using_AI_and_NLP/links/6013da55a6fdcc071b9d54b8/Implementation-of-a-Chat-Bot-System-using-AI-and-NLP.pdf
Maniou, T. A., & Veglis, A. (2020). Employing a chatbot for news dissemination during crisis: Design, implementation and evaluation. Future Internet, 12(7), 109. Retrieved on 1st December 2021 and from: https://www.mdpi.com/1999-5903/12/7/109/pdf
Ni, L., Lu, C., Liu, N., & Liu, J. (2017, November). Mandy: Towards a smart primary care chatbot application. In International symposium on knowledge and systems sciences (pp. 38-52). Springer, Singapore. Retrieved on 1st December 2021 and from: https://www.cs.auckland.ac.nz/~jliu036/KSS2017.pdf
Park, C., Sugand, K., Nathwani, D., Bhattacharya, R., & Sarraf, K. M. (2020). Impact of the COVID-19 pandemic on orthopedic trauma workload in a London level 1 trauma center: the “golden month” The COVid Emergency Related Trauma and orthopaedics (COVERT) Collaborative. Acta orthopaedica, 91(5), 556-561. Retrieved on 1st December 2021 and from: https://www.tandfonline.com/doi/pdf/10.1080/17453674.2020.1783621
Pereira, J., & Díaz, Ó. (2019). Using health chatbots for behavior change: a mapping study. Journal of medical systems, 43(5), 1-13. Retrieved on 1st December 2021 and from: https://www.rachaelmental.org/static/mediaHeal/documentos/Using_health_chatbots.pdf
Przegalinska, A., Ciechanowski, L., Stroz, A., Gloor, P., & Mazurek, G. (2019). In bot we trust: A new methodology of chatbot performance measures. Business Horizons, 62(6), 785-797. Retrieved on 1st December 2021 and from: https://www.academia.edu/download/60691006/1-s2.0-S000768131930117X-main20190924-129154-1x6yb13.pdf
Radziwill, N. M., & Benton, M. C. (2017). Evaluating quality of chatbots and intelligent conversational agents. arXiv preprint arXiv:1704.04579. Retrieved on 1st December 2021 and from: https://arxiv.org/pdf/1704.04579
Sharma, V., Goyal, M., & Malik, D. (2017). An intelligent behaviour shown by chatbot system. International Journal of New Technology and Research, 3(4), 263312. Retrieved on 1st December 2021 and from: https://media.neliti.com/media/publications/263312-an-intelligent-behaviour-shown-by-chatbo-7020467d.pdf
Telang, P. R., Kalia, A. K., Vukovic, M., Pandita, R., & Singh, M. P. (2018). A conceptual framework for engineering chatbots. IEEE Internet Computing, 22(6), 54-59. Retrieved on 1st December 2021 and from: https://www.csc2.ncsu.edu/faculty/mpsingh/papers/columns/IC-22-06-Chatbots-NWI-18.pdf
Villegas-Ch, W., Arias-Navarrete, A., & Palacios-Pacheco, X. (2020). Proposal of an Architecture for the Integration of a Chatbot with Artificial Intelligence in a Smart Campus for the Improvement of Learning. Sustainability, 12(4), 1500. Retrieved on 1st December 2021 and from: https://www.mdpi.com/2071-1050/12/4/1500/pdf
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