Effectiveness of Artificial Intelligence Mobile App-Guided Prevention and Treatment Protocols on Cancer Patients and Their Impact on Healthcare Workers' Competence

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Shokr This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8368116/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Mar, 2026 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted 14 You are reading this latest preprint version Abstract Background The integration of artificial intelligence into healthcare has garnered significant attention for its ability to revolutionize healthcare delivery, improve prevention and treatment protocols for cancer patients, extend healthcare coverage, enhance the competence of healthcare workers, and transform patient outcomes. This study was conducted to evaluate the impact of artificial intelligence mobile app-guided prevention and treatment protocols on cancer patients and their impact on healthcare workers competence. Methods A quasi-experimental design was used to collect data from a purposive sample of 60 cancer patients and 60 healthcare providers using Google Forms. Five tools were utilized for data collection. Results Patients reported improved usability of the mobile app, with most finding it welcoming, understandable, and helpful, though some experienced minor confusion or irrelevant responses. Healthcare workers’ perceived benefits and efficiency in the use of the mobile app by health workers increased significantly after the intervention at p < 0.01. Health awareness among nurses was improved by the intervention from 0% to 33.3% who stated that this had a positive effect. Symptoms attributed to chemotherapy, including nausea, fatigue, alopecia, loss of appetite, and anxiety, were successfully enhanced by the mobile app-guided prevention and treatment protocols. The AI-based application has emerged as a promising supportive intervention in oncology care due to improving patient service, compliance with treatment regimens, and personnel performance. Conclusion The mobile app based AI significantly improved patients' care by increasing adherence to protocols of prevention and treatment, thereby improving symptoms related to chemotherapy. It also enhances the efficiency of health workers to support better patient outcomes and is an effective tool for the optimization of healthcare delivery and quality. Healthcare coverage Artificial intelligence Cancer patients Healthcare Workers Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The health care organization has seen significant progress-inspired advances from quick technological progress, including the use of Artificial Intelligence (AI) and computer technologies. It has helped to progress the quality of health services and increase the efficiency of health professionals [ 1 ]. Integrating AI into the health care system has an excellent ability to identify patterns to cross human benefits in many aspects of health care. AI provides accuracy, reduced costs, time savings while diminishing human errors, and improving patient education, and influencing patient-healthcare workers trust [ 2 ]. The World Health Organization (WHO) constituted in its 2022 report, that cancer is one of the most essential causes of death and accounts for approximately 16% of deaths around the world [ 3 ]. Cancer still is a formidable challenge in healthcare, needing innovative approaches in betterment of prevention, treatment, and care of patients. In the last few years, integration of technologies of artificial intelligence, especially through mobile applications, has shown a lot of promise for revolutionizing cancer care delivery [ 4 ]. These AI-driven mobile apps provide personalized prevention and treatment protocols, thus fulfilling the needs of an individual patient [ 5 ]. By integrating AI technology into the delivery of cancer care, fundamental opportunities are provided to healthcare professionals for improving their competencies and practices. The integration provides new opportunities to create the skills, improve the quality of patient care, and support the making of informed timely decisions [ 6 ]. Such apps bring developed healthcare team efficiency through instant access ways to clinical guidelines, automation of routine tasks, and facilitating accuracy in data interpretation. In this way, digital support enables nurses and doctors to devote more time to direct patient care, while the patients themselves will benefit from quicker access to services, better communication, and a more personalized experience of care [ 7 ]. In the study conducted by Tursynbek et al., 2024 [ 8 ] to explore patients' views on artificial intelligence and its application in health care, the researchers interviewed 13 patients using semi-structured interviews. They reported that the integration of artificial intelligence into health care was perceived to be mostly positive by patients. Similarly, it was observed that patients preferred artificial intelligence to be used as an assisting tool under human supervision. Moreover, research conducted by Pan et al. (2022) [ 9 ] has also discussed the application of AI-empowered digital health technologies in caring for cancer patients. Their results confirmed that the application of AI-based digital health technologies in cancer patients has been associated with a positive improvement in motivations in patient-reported outcomes, fatigue, and pain levels. Additionally, there has been improvement in the quality of life and physical function. Also, Samadbeik et al. [ 7 ] have conducted a research on the use of Mobile App-guided health information and services to support cancer patients in different aspects of the disease process. They have reported that artificial intelligence mobile apps provided support for pain management and enhanced the quality of life and holistic wellbeing of patients. Moreover, they reported evidence of an improved quality of life and lifestyle, reduced chemotherapy-related side effects, improved mental health, and improved pain management. Despite the many studies conducted on the utilization of artificial intelligence in cancer care, few have specifically addressed the usage of AI-guided prevention and treatment protocols through mobile phone applications. The studies have mostly focused on diagnostic and predictive tools, with limited attention given to mobile-based solutions that support patient appointment scheduling, awareness, and health education. This study attempt to bridge that gap by investigate the comprehensive role of AI mobile apps in enhancing both patient care and healthcare worker competence. Hence, the aim of the current study to evaluate the impact of artificial intelligence mobile App-guided prevention and treatment protocols on cancer patients and their impact on healthcare workers competence. Significance of the Study: The application of AI in healthcare systems is expected to transform the delivery of services, leading to enhanced health worker efficiency, comprehensive healthcare coverage, and significant impacts on cancer patients. Disease burdens, including cancer, have substantial economic implications, costing approximately 500 billion US dollars annually and hindering economic productivity. In addition, the global healthcare system faces challenges such as limited resources, uneven access to care, and the need for effective management of the health sector. However, AI apps provide promising solutions as they can bridge health-related intervals, providing personalized care plans for cancer patients, which can improve health outcomes and promote equity. Recent data indicates that nurses are facing increasing challenges globally. The World Health Organization reported a global shortage of 5.9 million nurses in 2018, estimated to increase by 10 million by 2030 (World Health Organization, 2020) [ 3 ]. This highlights the importance of studying attention to increasing nursing efficiency through AI applications, reducing potential stress on health professionals, and improving patient care quality. Purpose of the Study: The purpose of this study is to investigate the impact of artificial intelligence mobile app-guided prevention and treatment protocols on cancer patients and their impact on healthcare workers competence. Research Hypotheses: H1: The utilization of artificial intelligence mobile app-guided prevention and treatment protocols has a positive impact on the clinical outcomes and overall care experience of cancer patients. H2: The application of mobile app-guided prevention and treatment protocols in healthcare significantly improves the competence, efficiency, and decision-making abilities of healthcare workers. Methods Study Design A quasi-experimental design (pretest and posttest) was used to achieve the aim of the study. Setting: The research was conducted in a single oncology institution in Shebin Elkom, located in the capital of Menoufia Governorate, Egypt. The oncology institution provides care to more than 4000 patients each month. This institution is affiliated with the Ministry of Higher Education and represents the first point of contact for patients within the healthcare system when they have a health concern or require cancer-related investigation and treatment. This institution provides primary prevention services, including cancer prevention programs, diagnostic investigations, and therapeutic treatments such as chemotherapy and radiotherapy, in addition to outpatient services. Sample The study's target population included Group (1): A purposive sampling technique was utilized to include all accessible healthcare workers (n = 60) who met the following inclusion criteria: aged between 20 and 50 years, use of a smartphone, prior exposure to artificial intelligence (AI), experience with digital training, proficiency in the English language, awareness of the study’s objectives, and willingness to participate. The sample included physicians, nurses, and ancillary healthcare providers. Group (2): A convenience sampling technique was employed to select all available oncology patients who fulfilled the following inclusion criteria and agreed to participate in the study: patients receiving care at the Oncology Institution, and being an adult between 18 and 70 years of age. The sample consisted of 60 patients. Research instruments: The data for this study were collected using five pretested and validated questionnaires. I. Demographic Data Structured Sheet : It collects detailed information about the patients demographic characteristics. It includes their age, gender, home address, number of years with the disease, use of a smartphone, and Mobile Apps. Additionally, the sociodemographic data of healthcare workers includes their age, level of education, work shifts, and years worked in their current position. II. Chatbot Usability Questionnaire : The Chatbot Usability Questionnaire (CUQ) is a standardized tool designed to evaluate the usability and user satisfaction of chatbot systems. The questionnaire used in this study was adopted from a previously published and validated instrument developed by Holmes and Murgatroyd (2020) [ 10 ]. The CUQ has been subsequently used in previous studies, including Nguyen et al. (2024), supporting its applicability and relevance in evaluating chatbot-based health applications [ 11 ]. To calculate the CUQ score, first assign each question a score from 1 to 5 based on your agreement (1 = Strongly disagree, 5 = Strongly agree). Sum all odd-numbered (positive) questions and subtract 8, then sum all even-numbered (negative) questions and subtract that total from 40. Add the two results to get a score out of 64, then divide by 64 and multiply by 100 to obtain the CUQ score as a percentage. The internal consistency of the tool was measured using Cronbach’s Alpha, resulting in a score of 0.89, an acceptable level of reliability for research purposes. III. Perceived benefits of healthcare mobile app chatbot tool : This tool consists of 8 multiple-choice questions designed to assess the benefits of healthcare chatbot to patients. The tool was developed by Palanica et al. (2019) [ 12 ]. Each item reflects a specific benefit, including improved self-management of health, enhanced quality and personalization of care, reduced travel time and unnecessary visits, greater disclosure of information, increased privacy, and better access to timely care. Responses are rated on a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), with total scores ranging from 8 to 40. Higher scores indicate greater perceived benefits. The questionnaire was translated into Arabic, and both its validity and reliability were assessed. The internal consistency of the tool, measured using Cronbach’s Alpha, was 0.84, indicating an acceptable level of reliability for research purposes. IV. Health awareness efficiency tool of the medical team : This tool includes a pre-and post-application evaluation consisting of 10 questions. Each question is rated using a 5-point Likert scale, where (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree). The tool is developed by the researcher based on research conducted by Pan et al. (2022) [ 9 ], to measure how increasing patient awareness can influence the medical team’s performance and communication. Higher total scores indicate a stronger perceived impact of health awareness on team efficiency. The total score ranges from 10 to 50, with higher scores indicating a greater perceived impact of health awareness on team efficiency. The scoring system is categorized as follows: a score from 10 to 26 indicates less impact, 27 to 38 reflects a moderate impact, and 39 to 50 signifies a positive impact on the efficiency of the medical team as perceived by the respondents. The questionnaire was translated into Arabic, and both its validity and reliability were assessed. The internal consistency of the tool, measured using Cronbach’s Alpha, was 0.81, indicating an acceptable level of reliability for research purposes. V. Effectiveness of the application in controlling the side effects of chemotherapy tool : This tool includes 8 questions, with answers in a binary format: (1 = No, 2 = Yes). The aim is to assess whether patients believe the application helps them better manage or reduce the impact of chemotherapy-related side effects. A higher total score reflects greater effectiveness of the application in addressing these side effects from the patient’s point of view. Validity of the Instruments: The validity of the instruments was evaluated through content validity by a panel of three experts specializing in Community Health Nursing. They reviewed each component of the tool in terms of relevance, clarity, coherence, and simplicity. Based on their feedback, necessary modifications were made. The experts confirmed that the tool was appropriate and effective for its intended purpose. Pilot study: A pilot study was conducted on 10% of the study sample to assess the feasibility of the study, as well as the clarity and objectivity of the tools. The pilot study was excluded from the total study sample size. Field work and data collection After obtaining approval from the Research and Ethics Committee, all participants were invited to complete a pretest survey via a secure online link using Google Forms. This initial survey was designed to assess baseline knowledge and competence regarding cancer care protocols before introducing the intervention. The study was organized into three consecutive phases to ensure systematic development, implementation, and evaluation of the AI-based mobile application. The first phase focused on designing and developing the mobile application powered by artificial intelligence. The chatbot was created using the Dialogflow platform and tailored specifically to meet the needs of cancer patients. Its core functions included scheduling and booking medical appointments, providing automated reminders for upcoming visits, and delivering personalized guidance on treatment preparation, medication adherence, nutrition, and self-care during therapy. The educational content integrated into the chatbot was based on internationally recognized oncology guidelines, including those published by the World Health Organization (WHO) and the American Cancer Society (ACS). To ensure clarity, accuracy, and cultural appropriateness, the content was carefully reviewed by a panel of oncology nursing experts and physicians. Before the official rollout, the application underwent two weeks of pilot testing to confirm system stability, user-friendliness, and readiness for use by patients. In the second phase, the application was introduced to the target group of cancer patients to facilitate appointment booking and communication with healthcare teams. Patients were able to book and confirm their medical appointments through the chatbot, receive reminders about visit dates, and access educational materials related to treatment phases, side effect management, and self-monitoring practices. Simultaneously, healthcare workers were oriented on how to utilize the system to track bookings, monitor patient adherence, and provide additional support when necessary. This integration streamlined coordination between patients and care providers, reducing missed visits and enhancing the continuity of care. The third phase involved conducting a posttest survey using the same tool administered at baseline. This final assessment measured the improvement in patients’ understanding of care protocols, adherence to scheduled visits, and overall satisfaction with the intervention. Additionally, it evaluated the impact of the AI-guided system on healthcare workers’ competence in managing appointments and providing patient-centered education. The findings from the pretest and posttest were subsequently analyzed to determine the overall effectiveness of the AI-based mobile application in improving cancer care outcomes and supporting healthcare staff performance. Statistical analysis Data were entered and analyzed using the Statistical Package for the Social Sciences (SPSS) version 22. Descriptive statistics, including means, standard deviations, and frequencies, were used to summarize participant characteristics and baseline data. Paired t-tests were conducted to compare pretest and posttest scores for both patients and healthcare workers, while Chi-square tests were used for categorical variables. A significance level of p < 0.05 was considered statistically significant. Results Table 1a clarifies the distribution of demographic data of the studied patients. This table reveals that the mean age of the studied patients was 25.23 ± 8.59. 85.0% of the studied patients were females, and 58.3% had secondary education. Most of them (96.7%) have had the disease for more than 5 years, and 68.3% of them use smartphones. Table 1b clarifies the distribution of demographic data of the studied Health Care Workers. This table reveals that the mean age of the studied Health Care Workers was 37.81 ± 5.89. Half of the studied Health Care Workers (50.0%) are nurses. 48.3% were females, and 58.3% have a Bachelor’s Degree. 41.7% worked for 6 to 10 years. Half of them have worked rotating work shifts. Table 2 shows the percentage distribution of Chatbot Usability Questionnaires about the positive effects of the chatbot on providing care for patients from the patient's point of view. About two-thirds of the studied patients (65.0%, 66.7%, 66.7%) agreed that the chatbot was welcoming during the initial setup, understood them well, and coped well with any errors or mistakes, respectively. 58.3% of the patients agreed that the chatbot responses were useful, appropriate, and informative. Table 3 clarifies the percentage dissemination of Chatbot Usability Questionnaires about the negative effects of the chatbot on providing care for patients from the patient's point of view (number = 60 patients). More than half of the studied patients (53.3%) agreed that the chatbot would be easy to get confused when using it, that the chatbot responses were irrelevant, and that the chatbot seemed unable to handle any errors. 70.0% of them agreed that the chatbot failed to recognize many of their inputs. Table 4 revealed the total score and mean percentage of chatbot usability in providing care for patients. It clarifies that the mean score of positive effects of chatbot usability on providing care for patients was 30.33 ± 4.20, and negative effects were 25.47 ± 3.69. Also, the total mean score of Chatbot Usability was 36.86 ± 6.26. Table 5 demonstrates the means and standard deviations of Perceived Benefits of Health Care Chatbots to patients (N = 60 Health Care Workers). It reveals an increase in the Perceived Benefits of Health Care Chatbots to patients. There was a highly statistically significant difference between pre and post-intervention at the 1% level of significance of the perceived benefits of health care chatbots to patients. Table 6 and Fig. 1 show the total mean score of Perceived Benefits of Health Care Chatbots to patients. It clarifies that the total score of Perceived Benefits of Health Care Chatbots to patients on pre-intervention was 27.74 ± 3.69 and 34.35 ± 4.20 on post-intervention. There was a highly statistically significant difference between pre and post-intervention at the 1% level of significance. Table 7 demonstrates the means and standard deviations to assess the impact of health awareness on the efficiency and medical staff using the chatbot on pre and post-application (60 nurses). There was improved health awareness on the efficiency and medical staff using the chatbot on post-application than pre-intervention. There was a highly statistically significant difference between pre and post-intervention at the 1% level of significance. Table 8 and Fig. 2 show the level of health awareness on the efficiency and medical staff using the chatbot on pre and post-application (60 nurses). It clarifies that 56.7% of the studied nurses had a moderate impact on pre-intervention, while 33.3% of them had a positive impact. There was a highly statistically significant difference between pre and post-intervention at the 1% level of significance. Figure 3 illustrates the level of health awareness among medical staff before and after the application of the chatbot intervention. The results show a notable improvement post-intervention, with the proportion of participants reporting “less impact” decreasing markedly from 43.3% to 10%. The “moderate impact” category remained constant at 56.7%, suggesting that some participants shifted from low to moderate levels of awareness. Most importantly, the “positive impact” category emerged at 33.3% after the intervention, compared to 0% before, indicating that the chatbot contributed substantially to enhancing health awareness and improving staff efficiency. Figure 4 demonstrates the distribution of the effectiveness of the application in controlling the side effects of chemotherapy of the studied patients on pre and post-application. The figure demonstrates a clear improvement in managing chemotherapy side effects after using the healthcare chatbot application. Before the intervention, high levels of symptoms were reported, including nausea and vomiting (70%), fatigue (80%), hair loss (90%), loss of appetite (60%), constipation (50%), diarrhea (40%), mouth sores (55%), peripheral neuropathy (45%), sleep disturbances (65%), and depression or anxiety (50%). After the intervention, all symptoms showed a noticeable reduction: nausea and vomiting declined to 35%, fatigue to 45%, hair loss to 80%, loss of appetite to 30%, constipation to 25%, diarrhea to 20%, mouth sores to 28%, peripheral neuropathy to 22%, sleep disturbances to 30%, and depression or anxiety to 28%. Discussion AI has revolutionized healthcare service delivery, especially in chronic disease management, through its integration into mobile health applications. AI-powered mobile apps offer personalized preventive and treatment approaches, empowering patients in self-management, therapy adherence, and timely advice receipt. In cancer, digital interventions have the potential to enhance treatment outcomes, self-management, and alleviate healthcare system burdens. AI-based tools may enhance healthcare workers' competence by providing decision support, real-time patient data, and evidence-based recommendations. This study explores the effectiveness of AI mobile applications in improving patient outcomes and their impact on the skills and confidence of healthcare providers. In this study, the mean age of patients was 25.23 ± 8.59 years, with 63.3% in the age group of 30 to less than 40 years. The majority were female (85%), reflecting gendered health-seeking behavior and mobile health app usage trends. Regarding education, 58.3% reported secondary education and 33.3% university level or higher, indicating sufficient literacy for AI-powered mobile app use. The patient demographics, predominantly middle-aged females with moderate to high educational levels, reflect trends in mobile health adoption and gendered health-seeking behaviors, consistent with studies by Melhem, (2023) [ 13 ] and Moon, & Walsh. (2025), [ 14 ] which report higher engagement among women and literate populations. The high proportion of patients living with chronic illness for over five years further supports receptiveness to AI-guided interventions, echoing findings that longer disease duration correlates with greater digital health engagement Ezeigwe et al., 2025 [ 15 ]. The demographic characteristics of the healthcare workers in this study align closely with findings reported by Khan Rony et al. (2024) [ 16 ], who reported that mid-career healthcare professionals demonstrate the highest readiness and adaptability to digital innovations compared with younger or pre-retirement staff. The high percentage of workers holding a Bachelor’s degree corresponds with findings from Ventura et al. (2023) [ 17 ], who noted that higher educational attainment significantly enhances digital competence and facilitates acceptance of AI-based applications. The results indicated that the chatbot was generally usable and well-received by patients, reflecting positive experiences in managing their care. Patients reported that the chatbot was helpful, responsive, and supportive, facilitating engagement and adherence to treatment protocols. Although some negative aspects were noted, overall usability was perceived as beneficial. These findings are consistent with prior studies demonstrating that AI chatbots enhance patient engagement, adherence, and access to personalized health information (Aggarwal et al., 2023) [ 18 ]. Similarly, healthcare workers reported improved perceptions of AI chatbot benefits, particularly regarding clinical communication and patient support, corroborating findings that structured exposure and training increase confidence and competence in digital health technologies (Wang et al., 2023) [ 19 ]. Notably, this study revealed that AI chatbot usage improved the management of chemotherapy-related side effects, including fatigue, nausea, hair loss, and sleep disturbances. Similarly, a recent pilot study using a text messaging–integrated, chatbot‑interfaced system for gastrointestinal cancer patients undergoing chemotherapy reported that patients found the system user‑friendly and valuable for self‑management; the intervention was associated with improved patient activation and better symptom control Gomaa, et al. (2023) [ 20 ]. Overall, the findings reinforce the value of AI-powered mobile applications and chatbots in enhancing patient outcomes and healthcare worker performance. They further suggest that integrating AI-based interventions into both patient care and professional training programs can strengthen digital health literacy, promote patient-centered care, and reduce healthcare system burdens, consistent with the broader literature on AI in healthcare (Aggarwal et al., 2023) [ 21 ]. Future studies should explore long-term impacts, scalability, and integration into routine clinical workflows to maximize these benefits. The results of the current study reveal that the use of an AI-based chatbot significantly improved patient care by enhancing adherence to prevention and treatment protocols, reducing chemotherapy-related side effects, and increasing healthcare workers’ efficiency. Similarly, the study conducted by Greer et al. (2019) [ 22 ] demonstrated that chatbots provide emotional support, improve patient engagement, and facilitate symptom monitoring among cancer survivors. Moreover, recent research by Gomaa et al. (2023) [ 19 ], reported that chatbot-integrated interventions enhanced self-management, symptom control, and patient activation in oncology settings. These findings collectively support the role of AI-driven chatbots as effective tools for improving both patient outcomes and healthcare staff performance [ 23 , 24 , 25 , 26 ]. Limitations of the study This study used a purposive sample, which may limit the generalizability of the findings to broader cancer patient populations. Data collection relied on self-reported measures from patients and healthcare providers, which could introduce response bias. Additionally, the quasi-experimental design did not include a control group, limiting the ability to establish causality between AI application use and observed outcomes. Finally, the study focused on short-term impacts, and the long-term effects of AI-guided interventions on patient outcomes and healthcare provider competence were not assessed. Recommendations Healthcare organizations should prioritize the implementation of AI-based mobile applications as part of routine cancer care to enhance service accessibility, patient safety, and adherence to treatment protocols. In addition, continuous training programs should be provided for healthcare workers to ensure effective use of AI tools and maximize their potential in patient care. Patient engagement strategies should also be developed to increase comfort and proficiency in using AI applications for self-management and remote care. Furthermore, future research should explore integrating AI applications with other healthcare technologies to further optimize patient outcomes and workflow efficiency. Conclusion The integration of artificial intelligence (AI) mobile applications into cancer care has a significant positive impact on both patients and healthcare providers. AI-guided prevention and treatment protocols improve patients’ access to healthcare services, enhance adherence to medical instructions, and contribute to safer, more efficient care. For healthcare providers, AI tools help reduce patient overcrowding, streamline workflow, and enhance professional competence. Overall, AI applications serve as a valuable tool in optimizing cancer care delivery and improving patient outcomes. Declarations Ethics approval and consent to participate Permission to conduct the study was obtained from the Research and Ethics Committee of the Faculty of Nursing, Menoufia University (Approval No. 976). Previous to data collection, informed consent was obtained from all participants. Completion of the questionnaire was considered as an indication of voluntary participation. All procedures followed in the study adhered to the ethical standards of the institutional research committee and were consistent with the principles of the Helsinki Declaration (2013). All methods were carried out in harmony with relevant guidelines and regulations. Consent for publication Not Applicable. Competing interests The authors declare no competing interests. Clinical trial number Not applicable. Funding This study did not receive any specific funding from public, commercial, or nonprofit organizations. Author Contribution The author exclusively conducted, designed, and analyzed the study, as well as wrote and revised the manuscript. The author was the only one who finished every step of the research process, including data collection, analysis, and writing. Acknowledgments The author would like to thank the participants for their cooperation. Data Availability The dataset used for this study is not publicly available due to the possibility of compromising individual privacy but is available from the corresponding author on reasonable request. Declaration of Competing Interest The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Hensher M, Canny B, Zimitat C, Campbell J, Palmer A. Health care, overconsumption and uneconomic growth: A conceptual framework. Social Sci Med Volume. 2020;266:113420. https://doi.org/10.1016/j.socscimed.2020.113420 . 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Shehadeh HA, Jebril IH, Jaradat GM, Ibrahim D, Sihwail R, Hamad A, Alia H. Intelligent diagnostic prediction and classification system for parkinson’s disease by incorporating sperm swarm optimization (SSO) and density-based feature selection methods. Int J Adv Soft Compu Appl. 2023;15(1):114–32. 10.15849/IJASCA.230320.08 . Abualruz H, Hayajneh F. Effectiveness of a Theory-Based resiliency intervention for nurses. J Contin Educ Nurs. 2023;54(12):581–8. 10.3928/00220124-20231013-03 . - PubMed. Tables Table (1a) Distribution of demographic data of studied patients (n=60). Items No % Age (Mean ± SD) 25.23 ± 8.59 Age group Under 30 years old 18 30.0 From 30:Under than 40 years old 38 63.3 over 40 years old 4 6.7 Sex Male 9 15.0 Female 51 85.0 Educational level Basic Education 5 8.3 Secondary Education 35 58.3 University Education or above 20 33.3 Number of years have the disease More than 5 years 58 96.7 Less than 5 years old 2 3.3 Tools used to have the Mobile Apps Personal Computer 15 25.0 The smartphone 41 68.3 laptop 4 6.7 Table (1b) Distribution of demographic data of studied Health Care Worker (n=60). Items No % Age (Mean ± SD) 37.81 ±5.89 Age group ≤ 20 35 58.3 21 – 40 10 16.7 41 – 50 15 25.0 Health Care Worker Nurses 30 50.0 Physicians 20 33.3 Others health care workers 10 16.7 Sex Male 31 51.7 Female 29 48.3 Educational level Bachelor’s Degree 35 58.3 Master’s Degree 10 16.7 Technical Institute 15 25.0 Years Worked Less than 5 years 20 33.3 From 6 to 10 years 25 41.7 More than 10 years 15 25.0 Work Shifts Morning/Afternoon 20 33.3 Nights Only 10 16.7 Rotating (12h or more) 30 50.0 Table (2) Percentages distribution of Chatbot Usability Questionnaires about positive effects of the chatbot on providing care of Patient from the patient's point of view number = 60 patients). Positive effect Strongly disagree Disagree Neutral Agree Strongly agree No % No % No % No % The chatbot’s personality was realistic and engaging 0 0.0 13 21.7 25 41.7 22 36.7 0 0.0 The chatbot was welcoming during initial setup 0 0.0 0 0.0 1 1.7 39 65.0 20 33.3 The chatbot explained its scope and purpose well 0 0.0 19 31.7 0 0.0 24 40.0 17 28.3 The chatbot was easy to navigate 0 0.0 26 43.3 2 3.3 22 36.7 10 16.7 The chatbot understood me well 0 0.0 4 6.7 4 6.7 40 66.7 12 20.0 The chatbot responses were useful, appropriate and informative 0 0.0 0 0.0 2 3.3 35 58.3 23 38.3 The chatbot coped well with any errors or mistakes 0 0.0 4 6.7 4 6.7 40 66.7 12 20.0 The chatbot was very easy to us 0 0.0 21 35.0 1 1.7 19 31.7 19 31.7 Table (3) Percentages distribution of Chatbot Usability Questionnaires about negative effects of the chatbot on providing care of Patient from the patient's point of view number = 60 patients). Negative effect Strongly disagree Disagree Neutral Agree Strongly agree No % No % No % No % The chatbot seemed too robotic 1 1.7 19 31.7 16 26.7 24 40.0 0 0.0 The chatbot seemed very unfriendly 7 11.7 13 21.7 13 21.7 27 45.0 0 0.0 The chatbot gave no indication as to its purpose 8 13.3 29 48.3 0 0.0 23 38.3 0 0.0 It would be easy to get confused when using the chatbot 3 5.0 19 31.7 6 10.0 32 53.3 0 0.0 The chatbot failed to recognize a lot of my inputs 0 0.0 10 16.7 3 5.0 42 70.0 5 8.3 Chatbot responses were irrelevant 6 10.0 13 21.7 9 15.0 32 53.3 0 0.0 The chatbot seemed unable to handle any errors 5 8.3 10 16.7 3 5.0 32 53.3 10 16.7 The chatbot was very complex 5 8.3 15 25.0 3 5.0 27 45.0 10 16.7 Table (4): Total score and Mean percentage of Chatbot Usability on providing care of Patient (N = 60 Health care Workers). Items Min Max Mean SD Positive effects 22.0 37.0 30.33 4.20 Negative effects 18.0 32.0 25.47 3.69 Total score of Chatbot Usability 22.0 46.0 36.86 6.26 Mean percentage of Chatbot Usability 34.38 71.88 57.60 9.78 Table (5): Means and standard deviations of Perceived Benefits of Health care chatbots to patients (N = 60 Health care Workers) Items Pre intervention Post intervention Independent t test P value Help patients better manage their own health 2.78±0.76 3.88±0.94 4.641 HS 0.000 Improve quality of patient care 3.72±0.94 4.05±0.96 8.600 HS 0.000 Help provide more personalized treatment 3.61±1.09 4.88±1.06 9.442 HS 0.000 Reduce travel time to health care provider 3.63±1.12 4.91±1.07 9.835 HS 0.000 Prevent unnecessary visits to health care providers 2.86±1.02 3.92±1.15 4.252 HS 0.000 Patients may disclose more information to chatbots compared with health care providers 3.71±1.07 4.01±0.99 3.586 S 0.015 Increase patient privacy 2.61±0.95 4.75±0.97 12.01 HS 0.000 Improve access and timeliness to care 3.82±1.016 3.95±0.85 .756 NS 0.451 Note : HS : M eans highly statistical significance S: M eans statistical significance. NS: Means no statistical significance Table (6): Total mean score of Perceived Benefits of Health care chatbots to patients Items Pre intervention Post intervention Independent t test P value Total score of Perceived Benefits of Health care chatbots to patients 27.74±3.69 34.35±4.20 8.732 HS .000 Note: HS : M eans highly statistical significance Table (7) : The means and standard deviations to assess the impact of health awareness on the efficiency and medical staff using the chatbot on pre and post application (60 nurses) Items Pre intervention Post intervention Independent t test P value The health awareness provided by the application contributes to Increasing the efficiency of the service provided to patients, not overburdening them, and bearing the expenses: 2.86±0.85 3.58± 1.22 -3.71 HS .000 Maintaining the safety and safety of the environment in which health care is provided 2.65±0.95 3.31±0.89 -3.95 HS .000 Health awareness through the application contributes to how to use available resources to provide the best service at the lowest cost and improve development economy. 2.18±0.89 3.32±1.36 -5.39 HS .000 Health awareness of hospital management through application contributes to taking into account the expected obstacles and determining how to deal with them or avoided 2.62±0.76 3.45±1.015 -5.08 HS .000 Health awareness through the application contributes to improving the quality of effective nursing interventions and raising the health economy 2.68±0.72 3.53±1.15 -4.82 HS .000 Reducing the consumption of resources used daily within the hospital and thus kills the cost of health consumables spent 2.83±0.56 3.45±0.85 -4.69 HS .000 Improve the application of practical content to patients effectively 2.53±0.67 3.80±1.25 -6.92 HS .000 Health awareness can mobilize and employ various resources Extracting distinguished service by using the human workforce to perform specific roles 2.73±0.89 3.20±1.09 -2.56 S .012 Health awareness through the application contributes to improving current medical problems and how to deal with obstacles that hinder growth economical 2.78±0.69 3.09±0.94 -1.98 S .049 Reduce the use of paper effort 2.60±0.81 3.71±0.95 -1.93 S .043 Total 26.48±3.41 33.48±5.89 -7.96 HS .000 Note : HS : M eans highly statistical significance S: M eans statistical significance. NS: Means no statistical significance Table (8) : The level of health awareness on the efficiency and medical staff using the chatbot on pre and post application (60 nurses) Level Pre intervention Post intervention No % No % Less impact 26 43.3 6 10.0 Moderate impact 34 56.7 34 56.7 Positive impact 0 0.0 20 33.3 X 2 32.50 HS p-value .000 Additional Declarations No competing interests reported. Supplementary Files Highlights.docx Cite Share Download PDF Status: Published Journal Publication published 31 Mar, 2026 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted Editorial decision: Revision requested 08 Jan, 2026 Reviews received at journal 01 Jan, 2026 Reviews received at journal 26 Dec, 2025 Reviewers agreed at journal 25 Dec, 2025 Reviews received at journal 25 Dec, 2025 Reviewers agreed at journal 25 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviewers invited by journal 23 Dec, 2025 Editor assigned by journal 23 Dec, 2025 Editor invited by journal 22 Dec, 2025 Submission checks completed at journal 19 Dec, 2025 First submitted to journal 19 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":37252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTotal mean score of Perceived Benefits of Health care chatbots to patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8368116/v1/ece74ec1b41306b2c8405758.png"},{"id":99216970,"identity":"105c70f8-a43f-4a3a-a6fc-0c6c25247e25","added_by":"auto","created_at":"2025-12-30 09:09:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89256,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe level of health awareness on the efficiency and medical staff using the chatbot on pre and post application (60 nurses)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8368116/v1/fae161bf9d9be45614150593.png"},{"id":99216966,"identity":"660aab54-5872-470e-b8b9-09c07897b987","added_by":"auto","created_at":"2025-12-30 09:09:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLevel of health awareness among medical staff before and after the chatbot intervention\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8368116/v1/3c1db271485f34e010d653af.png"},{"id":99216981,"identity":"d5c40138-24fb-42b6-9db7-80dbdb008acc","added_by":"auto","created_at":"2025-12-30 09:09:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84638,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of the Effectiveness of the application in controlling the side effects of chemotherapy of the studied patients on pre and post application.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8368116/v1/d047c24283b204f66e4d3c87.png"},{"id":106343583,"identity":"3349ff25-1632-492e-94a8-a9b3fbe07a0a","added_by":"auto","created_at":"2026-04-07 16:06:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2278023,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8368116/v1/a0b5c688-d786-4a37-85cc-caf6b7beb9f9.pdf"},{"id":99216978,"identity":"dba6ae54-ac7a-4395-b568-28a4ad8218fa","added_by":"auto","created_at":"2025-12-30 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It has helped to progress the quality of health services and increase the efficiency of health professionals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Integrating AI into the health care system has an excellent ability to identify patterns to cross human benefits in many aspects of health care. AI provides accuracy, reduced costs, time savings while diminishing human errors, and improving patient education, and influencing patient-healthcare workers trust [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe World Health Organization (WHO) constituted in its 2022 report, that cancer is one of the most essential causes of death and accounts for approximately 16% of deaths around the world [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Cancer still is a formidable challenge in healthcare, needing innovative approaches in betterment of prevention, treatment, and care of patients. In the last few years, integration of technologies of artificial intelligence, especially through mobile applications, has shown a lot of promise for revolutionizing cancer care delivery [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These AI-driven mobile apps provide personalized prevention and treatment protocols, thus fulfilling the needs of an individual patient [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy integrating AI technology into the delivery of cancer care, fundamental opportunities are provided to healthcare professionals for improving their competencies and practices. The integration provides new opportunities to create the skills, improve the quality of patient care, and support the making of informed timely decisions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Such apps bring developed healthcare team efficiency through instant access ways to clinical guidelines, automation of routine tasks, and facilitating accuracy in data interpretation. In this way, digital support enables nurses and doctors to devote more time to direct patient care, while the patients themselves will benefit from quicker access to services, better communication, and a more personalized experience of care [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the study conducted by Tursynbek et al., 2024 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] to explore patients' views on artificial intelligence and its application in health care, the researchers interviewed 13 patients using semi-structured interviews. They reported that the integration of artificial intelligence into health care was perceived to be mostly positive by patients. Similarly, it was observed that patients preferred artificial intelligence to be used as an assisting tool under human supervision. Moreover, research conducted by Pan et al. (2022) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] has also discussed the application of AI-empowered digital health technologies in caring for cancer patients. Their results confirmed that the application of AI-based digital health technologies in cancer patients has been associated with a positive improvement in motivations in patient-reported outcomes, fatigue, and pain levels. Additionally, there has been improvement in the quality of life and physical function. Also, Samadbeik et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] have conducted a research on the use of Mobile App-guided health information and services to support cancer patients in different aspects of the disease process. They have reported that artificial intelligence mobile apps provided support for pain management and enhanced the quality of life and holistic wellbeing of patients. Moreover, they reported evidence of an improved quality of life and lifestyle, reduced chemotherapy-related side effects, improved mental health, and improved pain management.\u003c/p\u003e \u003cp\u003eDespite the many studies conducted on the utilization of artificial intelligence in cancer care, few have specifically addressed the usage of AI-guided prevention and treatment protocols through mobile phone applications. The studies have mostly focused on diagnostic and predictive tools, with limited attention given to mobile-based solutions that support patient appointment scheduling, awareness, and health education. This study attempt to bridge that gap by investigate the comprehensive role of AI mobile apps in enhancing both patient care and healthcare worker competence. Hence, the aim of the current study to evaluate the impact of artificial intelligence mobile App-guided prevention and treatment protocols on cancer patients and their impact on healthcare workers competence.\u003c/p\u003e\n\u003ch3\u003eSignificance of the Study:\u003c/h3\u003e\n\u003cp\u003eThe application of AI in healthcare systems is expected to transform the delivery of services, leading to enhanced health worker efficiency, comprehensive healthcare coverage, and significant impacts on cancer patients. Disease burdens, including cancer, have substantial economic implications, costing approximately 500\u0026nbsp;billion US dollars annually and hindering economic productivity. In addition, the global healthcare system faces challenges such as limited resources, uneven access to care, and the need for effective management of the health sector. However, AI apps provide promising solutions as they can bridge health-related intervals, providing personalized care plans for cancer patients, which can improve health outcomes and promote equity. Recent data indicates that nurses are facing increasing challenges globally. The World Health Organization reported a global shortage of 5.9\u0026nbsp;million nurses in 2018, estimated to increase by 10\u0026nbsp;million by 2030 (World Health Organization, 2020) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This highlights the importance of studying attention to increasing nursing efficiency through AI applications, reducing potential stress on health professionals, and improving patient care quality.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePurpose of the Study:\u003c/h2\u003e \u003cp\u003eThe purpose of this study is to investigate the impact of artificial intelligence mobile app-guided prevention and treatment protocols on cancer patients and their impact on healthcare workers competence.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Hypotheses:\u003c/h3\u003e\n\u003cp\u003eH1: The utilization of artificial intelligence mobile app-guided prevention and treatment protocols has a positive impact on the clinical outcomes and overall care experience of cancer patients.\u003c/p\u003e \u003cp\u003eH2: The application of mobile app-guided prevention and treatment protocols in healthcare significantly improves the competence, efficiency, and decision-making abilities of healthcare workers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cstrong\u003eStudy Design\u003c/strong\u003e \u003cp\u003eA quasi-experimental design (pretest and posttest) was used to achieve the aim of the study.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eSetting:\u003c/h3\u003e\n\u003cp\u003eThe research was conducted in a single oncology institution in Shebin Elkom, located in the capital of Menoufia Governorate, Egypt. The oncology institution provides care to more than 4000 patients each month. This institution is affiliated with the Ministry of Higher Education and represents the first point of contact for patients within the healthcare system when they have a health concern or require cancer-related investigation and treatment. This institution provides primary prevention services, including cancer prevention programs, diagnostic investigations, and therapeutic treatments such as chemotherapy and radiotherapy, in addition to outpatient services.\u003c/p\u003e\n\u003ch3\u003eSample\u003c/h3\u003e\n\u003cp\u003eThe study's target population included Group (1): A purposive sampling technique was utilized to include all accessible healthcare workers (n\u0026thinsp;=\u0026thinsp;60) who met the following inclusion criteria: aged between 20 and 50 years, use of a smartphone, prior exposure to artificial intelligence (AI), experience with digital training, proficiency in the English language, awareness of the study\u0026rsquo;s objectives, and willingness to participate. The sample included physicians, nurses, and ancillary healthcare providers.\u003c/p\u003e \u003cp\u003eGroup (2): A convenience sampling technique was employed to select all available oncology patients who fulfilled the following inclusion criteria and agreed to participate in the study: patients receiving care at the Oncology Institution, and being an adult between 18 and 70 years of age. The sample consisted of 60 patients.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eResearch instruments:\u003c/h2\u003e \u003cp\u003eThe data for this study were collected using five pretested and validated questionnaires.\u003c/p\u003e \u003cp\u003e \u003cb\u003eI. Demographic Data Structured Sheet\u003c/b\u003e: It collects detailed information about the patients demographic characteristics. It includes their age, gender, home address, number of years with the disease, use of a smartphone, and Mobile Apps. Additionally, the sociodemographic data of healthcare workers includes their age, level of education, work shifts, and years worked in their current position.\u003c/p\u003e\u003cp\u003e \u003cb\u003eII. Chatbot Usability Questionnaire\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe Chatbot Usability Questionnaire (CUQ) is a standardized tool designed to evaluate the usability and user satisfaction of chatbot systems. The questionnaire used in this study was adopted from a previously published and validated instrument developed by Holmes and Murgatroyd (2020) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The CUQ has been subsequently used in previous studies, including Nguyen et al. (2024), supporting its applicability and relevance in evaluating chatbot-based health applications [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To calculate the CUQ score, first assign each question a score from 1 to 5 based on your agreement (1\u0026thinsp;=\u0026thinsp;Strongly disagree, 5\u0026thinsp;=\u0026thinsp;Strongly agree). Sum all odd-numbered (positive) questions and subtract 8, then sum all even-numbered (negative) questions and subtract that total from 40. Add the two results to get a score out of 64, then divide by 64 and multiply by 100 to obtain the CUQ score as a percentage. The internal consistency of the tool was measured using Cronbach\u0026rsquo;s Alpha, resulting in a score of 0.89, an acceptable level of reliability for research purposes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIII. Perceived benefits of healthcare mobile app chatbot tool\u003c/b\u003e: This tool consists of 8 multiple-choice questions designed to assess the benefits of healthcare chatbot to patients. The tool was developed by Palanica et al. (2019) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Each item reflects a specific benefit, including improved self-management of health, enhanced quality and personalization of care, reduced travel time and unnecessary visits, greater disclosure of information, increased privacy, and better access to timely care. Responses are rated on a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), with total scores ranging from 8 to 40. Higher scores indicate greater perceived benefits. The questionnaire was translated into Arabic, and both its validity and reliability were assessed. The internal consistency of the tool, measured using Cronbach\u0026rsquo;s Alpha, was 0.84, indicating an acceptable level of reliability for research purposes.\u003c/p\u003e\u003cp\u003e \u003cb\u003eIV. Health awareness efficiency tool of the medical team\u003c/b\u003e: This tool includes a pre-and post-application evaluation consisting of 10 questions. Each question is rated using a 5-point Likert scale, where (1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 2\u0026thinsp;=\u0026thinsp;Disagree, 3\u0026thinsp;=\u0026thinsp;Neutral, 4\u0026thinsp;=\u0026thinsp;Agree, and 5\u0026thinsp;=\u0026thinsp;Strongly Agree). The tool is developed by the researcher based on research conducted by Pan et al. (2022) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], to measure how increasing patient awareness can influence the medical team\u0026rsquo;s performance and communication. Higher total scores indicate a stronger perceived impact of health awareness on team efficiency. The total score ranges from 10 to 50, with higher scores indicating a greater perceived impact of health awareness on team efficiency. The scoring system is categorized as follows: a score from 10 to 26 indicates less impact, 27 to 38 reflects a moderate impact, and 39 to 50 signifies a positive impact on the efficiency of the medical team as perceived by the respondents. The questionnaire was translated into Arabic, and both its validity and reliability were assessed. The internal consistency of the tool, measured using Cronbach\u0026rsquo;s Alpha, was 0.81, indicating an acceptable level of reliability for research purposes.\u003c/p\u003e\u003cp\u003e \u003cb\u003eV. Effectiveness of the application in controlling the side effects of chemotherapy tool\u003c/b\u003e: This tool includes 8 questions, with answers in a binary format: (1\u0026thinsp;=\u0026thinsp;No, 2\u0026thinsp;=\u0026thinsp;Yes). The aim is to assess whether patients believe the application helps them better manage or reduce the impact of chemotherapy-related side effects. A higher total score reflects greater effectiveness of the application in addressing these side effects from the patient\u0026rsquo;s point of view.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidity of the Instruments:\u003c/h3\u003e\n\u003cp\u003eThe validity of the instruments was evaluated through content validity by a panel of three experts specializing in Community Health Nursing. They reviewed each component of the tool in terms of relevance, clarity, coherence, and simplicity. Based on their feedback, necessary modifications were made. The experts confirmed that the tool was appropriate and effective for its intended purpose.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePilot study:\u003c/h2\u003e \u003cp\u003eA pilot study was conducted on 10% of the study sample to assess the feasibility of the study, as well as the clarity and objectivity of the tools. The pilot study was excluded from the total study sample size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eField work and data collection\u003c/h2\u003e \u003cp\u003eAfter obtaining approval from the Research and Ethics Committee, all participants were invited to complete a pretest survey via a secure online link using Google Forms. This initial survey was designed to assess baseline knowledge and competence regarding cancer care protocols before introducing the intervention. The study was organized into three consecutive phases to ensure systematic development, implementation, and evaluation of the AI-based mobile application.\u003c/p\u003e \u003cp\u003eThe first phase focused on designing and developing the mobile application powered by artificial intelligence. The chatbot was created using the Dialogflow platform and tailored specifically to meet the needs of cancer patients. Its core functions included scheduling and booking medical appointments, providing automated reminders for upcoming visits, and delivering personalized guidance on treatment preparation, medication adherence, nutrition, and self-care during therapy. The educational content integrated into the chatbot was based on internationally recognized oncology guidelines, including those published by the World Health Organization (WHO) and the American Cancer Society (ACS). To ensure clarity, accuracy, and cultural appropriateness, the content was carefully reviewed by a panel of oncology nursing experts and physicians. Before the official rollout, the application underwent two weeks of pilot testing to confirm system stability, user-friendliness, and readiness for use by patients.\u003c/p\u003e \u003cp\u003eIn the second phase, the application was introduced to the target group of cancer patients to facilitate appointment booking and communication with healthcare teams. Patients were able to book and confirm their medical appointments through the chatbot, receive reminders about visit dates, and access educational materials related to treatment phases, side effect management, and self-monitoring practices. Simultaneously, healthcare workers were oriented on how to utilize the system to track bookings, monitor patient adherence, and provide additional support when necessary. This integration streamlined coordination between patients and care providers, reducing missed visits and enhancing the continuity of care.\u003c/p\u003e \u003cp\u003eThe third phase involved conducting a posttest survey using the same tool administered at baseline. This final assessment measured the improvement in patients\u0026rsquo; understanding of care protocols, adherence to scheduled visits, and overall satisfaction with the intervention. Additionally, it evaluated the impact of the AI-guided system on healthcare workers\u0026rsquo; competence in managing appointments and providing patient-centered education. The findings from the pretest and posttest were subsequently analyzed to determine the overall effectiveness of the AI-based mobile application in improving cancer care outcomes and supporting healthcare staff performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were entered and analyzed using the Statistical Package for the Social Sciences (SPSS) version 22. Descriptive statistics, including means, standard deviations, and frequencies, were used to summarize participant characteristics and baseline data. Paired t-tests were conducted to compare pretest and posttest scores for both patients and healthcare workers, while Chi-square tests were used for categorical variables. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;1a clarifies the distribution of demographic data of the studied patients. This table reveals that the mean age of the studied patients was 25.23\u0026thinsp;\u0026plusmn;\u0026thinsp;8.59. 85.0% of the studied patients were females, and 58.3% had secondary education. Most of them (96.7%) have had the disease for more than 5 years, and 68.3% of them use smartphones.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;1b clarifies the distribution of demographic data of the studied Health Care Workers. This table reveals that the mean age of the studied Health Care Workers was 37.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.89. Half of the studied Health Care Workers (50.0%) are nurses. 48.3% were females, and 58.3% have a Bachelor\u0026rsquo;s Degree. 41.7% worked for 6 to 10 years. Half of them have worked rotating work shifts.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;2 shows the percentage distribution of Chatbot Usability Questionnaires about the positive effects of the chatbot on providing care for patients from the patient's point of view. About two-thirds of the studied patients (65.0%, 66.7%, 66.7%) agreed that the chatbot was welcoming during the initial setup, understood them well, and coped well with any errors or mistakes, respectively. 58.3% of the patients agreed that the chatbot responses were useful, appropriate, and informative.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;3 clarifies the percentage dissemination of Chatbot Usability Questionnaires about the negative effects of the chatbot on providing care for patients from the patient's point of view (number\u0026thinsp;=\u0026thinsp;60 patients). More than half of the studied patients (53.3%) agreed that the chatbot would be easy to get confused when using it, that the chatbot responses were irrelevant, and that the chatbot seemed unable to handle any errors. 70.0% of them agreed that the chatbot failed to recognize many of their inputs.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;4 revealed the total score and mean percentage of chatbot usability in providing care for patients. It clarifies that the mean score of positive effects of chatbot usability on providing care for patients was 30.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20, and negative effects were 25.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69. Also, the total mean score of Chatbot Usability was 36.86\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;5 demonstrates the means and standard deviations of Perceived Benefits of Health Care Chatbots to patients (N\u0026thinsp;=\u0026thinsp;60 Health Care Workers). It reveals an increase in the Perceived Benefits of Health Care Chatbots to patients. There was a highly statistically significant difference between pre and post-intervention at the 1% level of significance of the perceived benefits of health care chatbots to patients.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;6 and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show the total mean score of Perceived Benefits of Health Care Chatbots to patients. It clarifies that the total score of Perceived Benefits of Health Care Chatbots to patients on pre-intervention was 27.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69 and 34.35\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20 on post-intervention. There was a highly statistically significant difference between pre and post-intervention at the 1% level of significance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;7 demonstrates the means and standard deviations to assess the impact of health awareness on the efficiency and medical staff using the chatbot on pre and post-application (60 nurses). There was improved health awareness on the efficiency and medical staff using the chatbot on post-application than pre-intervention. There was a highly statistically significant difference between pre and post-intervention at the 1% level of significance.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;8 and Fig.\u0026nbsp;2 show the level of health awareness on the efficiency and medical staff using the chatbot on pre and post-application (60 nurses). It clarifies that 56.7% of the studied nurses had a moderate impact on pre-intervention, while 33.3% of them had a positive impact. There was a highly statistically significant difference between pre and post-intervention at the 1% level of significance.\u003c/p\u003e \u003cp\u003eFigure 3 illustrates the level of health awareness among medical staff before and after the application of the chatbot intervention. The results show a notable improvement post-intervention, with the proportion of participants reporting \u0026ldquo;less impact\u0026rdquo; decreasing markedly from 43.3% to 10%. The \u0026ldquo;moderate impact\u0026rdquo; category remained constant at 56.7%, suggesting that some participants shifted from low to moderate levels of awareness. Most importantly, the \u0026ldquo;positive impact\u0026rdquo; category emerged at 33.3% after the intervention, compared to 0% before, indicating that the chatbot contributed substantially to enhancing health awareness and improving staff efficiency.\u003c/p\u003e \u003cp\u003eFigure 4 demonstrates the distribution of the effectiveness of the application in controlling the side effects of chemotherapy of the studied patients on pre and post-application. The figure demonstrates a clear improvement in managing chemotherapy side effects after using the healthcare chatbot application. Before the intervention, high levels of symptoms were reported, including nausea and vomiting (70%), fatigue (80%), hair loss (90%), loss of appetite (60%), constipation (50%), diarrhea (40%), mouth sores (55%), peripheral neuropathy (45%), sleep disturbances (65%), and depression or anxiety (50%). After the intervention, all symptoms showed a noticeable reduction: nausea and vomiting declined to 35%, fatigue to 45%, hair loss to 80%, loss of appetite to 30%, constipation to 25%, diarrhea to 20%, mouth sores to 28%, peripheral neuropathy to 22%, sleep disturbances to 30%, and depression or anxiety to 28%.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAI has revolutionized healthcare service delivery, especially in chronic disease management, through its integration into mobile health applications. AI-powered mobile apps offer personalized preventive and treatment approaches, empowering patients in self-management, therapy adherence, and timely advice receipt. In cancer, digital interventions have the potential to enhance treatment outcomes, self-management, and alleviate healthcare system burdens. AI-based tools may enhance healthcare workers' competence by providing decision support, real-time patient data, and evidence-based recommendations. This study explores the effectiveness of AI mobile applications in improving patient outcomes and their impact on the skills and confidence of healthcare providers.\u003c/p\u003e \u003cp\u003eIn this study, the mean age of patients was 25.23\u0026thinsp;\u0026plusmn;\u0026thinsp;8.59 years, with 63.3% in the age group of 30 to less than 40 years. The majority were female (85%), reflecting gendered health-seeking behavior and mobile health app usage trends. Regarding education, 58.3% reported secondary education and 33.3% university level or higher, indicating sufficient literacy for AI-powered mobile app use. The patient demographics, predominantly middle-aged females with moderate to high educational levels, reflect trends in mobile health adoption and gendered health-seeking behaviors, consistent with studies by Melhem, (2023) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and Moon, \u0026amp; Walsh. (2025), [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] which report higher engagement among women and literate populations. The high proportion of patients living with chronic illness for over five years further supports receptiveness to AI-guided interventions, echoing findings that longer disease duration correlates with greater digital health engagement Ezeigwe et al., 2025 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe demographic characteristics of the healthcare workers in this study align closely with findings reported by Khan Rony et al. (2024) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], who reported that mid-career healthcare professionals demonstrate the highest readiness and adaptability to digital innovations compared with younger or pre-retirement staff. The high percentage of workers holding a Bachelor\u0026rsquo;s degree corresponds with findings from Ventura et al. (2023) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], who noted that higher educational attainment significantly enhances digital competence and facilitates acceptance of AI-based applications.\u003c/p\u003e \u003cp\u003eThe results indicated that the chatbot was generally usable and well-received by patients, reflecting positive experiences in managing their care. Patients reported that the chatbot was helpful, responsive, and supportive, facilitating engagement and adherence to treatment protocols. Although some negative aspects were noted, overall usability was perceived as beneficial. These findings are consistent with prior studies demonstrating that AI chatbots enhance patient engagement, adherence, and access to personalized health information (Aggarwal et al., 2023) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Similarly, healthcare workers reported improved perceptions of AI chatbot benefits, particularly regarding clinical communication and patient support, corroborating findings that structured exposure and training increase confidence and competence in digital health technologies (Wang et al., 2023) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, this study revealed that AI chatbot usage improved the management of chemotherapy-related side effects, including fatigue, nausea, hair loss, and sleep disturbances. Similarly, a recent pilot study using a text messaging\u0026ndash;integrated, chatbot‑interfaced system for gastrointestinal cancer patients undergoing chemotherapy reported that patients found the system user‑friendly and valuable for self‑management; the intervention was associated with improved patient activation and better symptom control Gomaa, et al. (2023) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, the findings reinforce the value of AI-powered mobile applications and chatbots in enhancing patient outcomes and healthcare worker performance. They further suggest that integrating AI-based interventions into both patient care and professional training programs can strengthen digital health literacy, promote patient-centered care, and reduce healthcare system burdens, consistent with the broader literature on AI in healthcare (Aggarwal et al., 2023) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Future studies should explore long-term impacts, scalability, and integration into routine clinical workflows to maximize these benefits.\u003c/p\u003e \u003cp\u003eThe results of the current study reveal that the use of an AI-based chatbot significantly improved patient care by enhancing adherence to prevention and treatment protocols, reducing chemotherapy-related side effects, and increasing healthcare workers\u0026rsquo; efficiency. Similarly, the study conducted by Greer et al. (2019) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] demonstrated that chatbots provide emotional support, improve patient engagement, and facilitate symptom monitoring among cancer survivors. Moreover, recent research by Gomaa et al. (2023) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], reported that chatbot-integrated interventions enhanced self-management, symptom control, and patient activation in oncology settings. These findings collectively support the role of AI-driven chatbots as effective tools for improving both patient outcomes and healthcare staff performance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of the study\u003c/h2\u003e \u003cp\u003eThis study used a purposive sample, which may limit the generalizability of the findings to broader cancer patient populations. Data collection relied on self-reported measures from patients and healthcare providers, which could introduce response bias. Additionally, the quasi-experimental design did not include a control group, limiting the ability to establish causality between AI application use and observed outcomes. Finally, the study focused on short-term impacts, and the long-term effects of AI-guided interventions on patient outcomes and healthcare provider competence were not assessed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations\u003c/h2\u003e \u003cp\u003eHealthcare organizations should prioritize the implementation of AI-based mobile applications as part of routine cancer care to enhance service accessibility, patient safety, and adherence to treatment protocols. In addition, continuous training programs should be provided for healthcare workers to ensure effective use of AI tools and maximize their potential in patient care. Patient engagement strategies should also be developed to increase comfort and proficiency in using AI applications for self-management and remote care. Furthermore, future research should explore integrating AI applications with other healthcare technologies to further optimize patient outcomes and workflow efficiency.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe integration of artificial intelligence (AI) mobile applications into cancer care has a significant positive impact on both patients and healthcare providers. AI-guided prevention and treatment protocols improve patients\u0026rsquo; access to healthcare services, enhance adherence to medical instructions, and contribute to safer, more efficient care. For healthcare providers, AI tools help reduce patient overcrowding, streamline workflow, and enhance professional competence. Overall, AI applications serve as a valuable tool in optimizing cancer care delivery and improving patient outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003ePermission to conduct the study was obtained from the Research and Ethics Committee of the Faculty of Nursing, Menoufia University (Approval No. 976). Previous to data collection, informed consent was obtained from all participants. Completion of the questionnaire was considered as an indication of voluntary participation. All procedures followed in the study adhered to the ethical standards of the institutional research committee and were consistent with the principles of the Helsinki Declaration (2013). All methods were carried out in harmony with relevant guidelines and regulations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study did not receive any specific funding from public, commercial, or nonprofit organizations.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author exclusively conducted, designed, and analyzed the study, as well as wrote and revised the manuscript. The author was the only one who finished every step of the research process, including data collection, analysis, and writing.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe author would like to thank the participants for their cooperation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset used for this study is not publicly available due to the possibility of compromising individual privacy but is available from the corresponding author on reasonable request.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHensher M, Canny B, Zimitat C, Campbell J, Palmer A. 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J Contin Educ Nurs. 2023;54(12):581\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3928/00220124-20231013-03\u003c/span\u003e\u003cspan address=\"10.3928/00220124-20231013-03\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. - PubMed.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable (1a) Distribution of demographic data of studied patients (n=60).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"607\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 422px;\"\u003e\n \u003cp\u003eItems\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e25.23 \u0026plusmn; 8.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eUnder 30 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eFrom 30:Under than 40 years old\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e63.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eover 40 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e85.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eEducational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eBasic Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eSecondary Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eUniversity Education or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Number of years have the disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eMore than 5 years \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e96.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eLess than 5 years old \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTools used to have the Mobile Apps\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePersonal Computer \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003eThe smartphone \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e68.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elaptop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable (1b) Distribution of demographic data of studied\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHealth Care Worker\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(n=60).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"607\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 409px;\"\u003e\n \u003cp\u003eItems\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e37.81 \u0026plusmn;5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026le; 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e21 \u0026ndash; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e41 \u0026ndash; 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Care Worker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eNurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003ePhysicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eOthers health care workers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e51.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003eEducational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s\u0026nbsp;Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eMaster\u0026rsquo;s Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eTechnical\u0026nbsp;Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears Worked\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eLess than 5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eFrom\u0026nbsp;6\u0026nbsp;to\u0026nbsp;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eMore\u0026nbsp;than\u0026nbsp;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Shifts\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMorning/Afternoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eNights Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRotating\u0026nbsp;(12h\u0026nbsp;or more)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable (2) Percentages distribution of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eChatbot Usability Questionnaires\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eabout positive effects of the chatbot on providing care of Patient from the patient\u0026apos;s point of view number = 60 patients).\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"730\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive effect\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrongly disagree\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisagree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutral\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgree\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrongly agree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 276px;\"\u003e\n \u003col\u003e\n \u003cli\u003eThe chatbot\u0026rsquo;s personality was realistic and engaging\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 276px;\"\u003e\n \u003col start=\"2\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot was welcoming during initial setup\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e65.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 276px;\"\u003e\n \u003col start=\"3\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot explained its scope and purpose well\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 276px;\"\u003e\n \u003col start=\"4\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot was easy to navigate\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 276px;\"\u003e\n \u003col start=\"5\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot understood me well\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 276px;\"\u003e\n \u003col start=\"6\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot responses were useful, appropriate and informative\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 276px;\"\u003e\n \u003col start=\"7\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot coped well with any errors or mistakes\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 276px;\"\u003e\n \u003col start=\"8\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot was very easy to us\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable (3) Percentages distribution of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eChatbot Usability Questionnaires\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eabout negative effects of the chatbot on providing care of Patient from the patient\u0026apos;s point of view number = 60 patients).\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"730\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 259px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative effect\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrongly disagree\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisagree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutral\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgree\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrongly agree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 259px;\"\u003e\n \u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot seemed too robotic\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 259px;\"\u003e\n \u003col start=\"2\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot seemed very unfriendly\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 259px;\"\u003e\n \u003col start=\"3\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot gave no indication as to its purpose\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 259px;\"\u003e\n \u003col start=\"4\" type=\"1\"\u003e\n \u003cli\u003eIt would be easy to get confused when using the chatbot\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e53.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 259px;\"\u003e\n \u003col start=\"5\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot failed to recognize a lot of my inputs\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 259px;\"\u003e\n \u003col start=\"6\" type=\"1\"\u003e\n \u003cli\u003eChatbot responses were irrelevant\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e53.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 259px;\"\u003e\n \u003col start=\"7\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot seemed unable to handle any errors\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e53.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 259px;\"\u003e\n \u003col start=\"8\" type=\"1\"\u003e\n \u003cli\u003eThe chatbot was very complex\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 259px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable (4): \u0026nbsp;Total score and Mean percentage of Chatbot Usability on providing care of Patient (N = 60 Health care Workers).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 342px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 342px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive effects\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e37.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e30.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 342px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative effects\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e25.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 342px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal score of Chatbot Usability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e46.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e36.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 342px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean percentage of Chatbot Usability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e34.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e71.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e57.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e9.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable (5): Means and standard deviations of Perceived Benefits of Health care chatbots to patients\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(N = 60 Health care Workers)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"681\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eItems\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003ePre intervention \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePost intervention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eIndependent t test\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;P value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003col\u003e\n \u003cli\u003e\u003cstrong\u003eHelp patients better manage their own health\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2.78\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e3.88\u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4.641\u003csup\u003eHS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eImprove quality of patient care\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3.72\u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4.05\u0026plusmn;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e8.600\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eHelp provide more personalized treatment\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3.61\u0026plusmn;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4.88\u0026plusmn;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.442\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003e\u003cstrong\u003eReduce travel time to health care provider\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3.63\u0026plusmn;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4.91\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.835\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003col start=\"5\"\u003e\n \u003cli\u003e\u003cstrong\u003ePrevent unnecessary visits to health care providers\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2.86\u0026plusmn;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e3.92\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4.252\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003col start=\"6\"\u003e\n \u003cli\u003e\u003cstrong\u003ePatients may disclose more information to chatbots compared with health care providers\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3.71\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4.01\u0026plusmn;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e3.586\u003csup\u003e\u0026nbsp;S\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003col start=\"7\"\u003e\n \u003cli\u003e\u003cstrong\u003eIncrease patient privacy\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2.61\u0026plusmn;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4.75\u0026plusmn;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e12.01\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003col start=\"8\"\u003e\n \u003cli\u003e\u003cstrong\u003eImprove access and timeliness to care\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3.82\u0026plusmn;1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e3.95\u0026plusmn;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.756\u003csup\u003e\u0026nbsp;NS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote :\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHS\u003cstrong\u003e: M\u003c/strong\u003eeans\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ehighly\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003estatistical significance \u0026nbsp; \u0026nbsp; \u0026nbsp;S:\u003cstrong\u003e\u0026nbsp;M\u003c/strong\u003eeans\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003estatistical significance. \u0026nbsp; \u0026nbsp; \u0026nbsp;NS: Means no\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003estatistical significance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (6):\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTotal mean score of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePerceived Benefits of Health care chatbots to patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"673\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre intervention \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost intervention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent t test\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;P value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal score of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePerceived Benefits of Health care chatbots to patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e27.74\u0026plusmn;3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e34.35\u0026plusmn;4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8.732\u003csup\u003eHS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u003cem\u003eHS\u003cstrong\u003e: M\u003c/strong\u003eeans\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ehighly\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003estatistical significance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(7)\u003c/strong\u003e\u003cstrong\u003e: The means and standard deviations to assess the impact of health awareness on the efficiency and medical staff using the chatbot on pre and post application (60 nurses)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"694\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre intervention \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost intervention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent t test\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;P value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eThe health awareness provided by the application contributes to Increasing the efficiency of the service provided to patients, not overburdening them, and bearing the expenses:\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.86\u0026plusmn;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.58\u0026plusmn; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-3.71\u003csup\u003eHS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eMaintaining the safety and safety of the environment in which health care is provided\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.65\u0026plusmn;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.31\u0026plusmn;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-3.95\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHealth awareness through the application contributes to how to use available resources to provide the best service at the lowest cost and improve development economy.\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.18\u0026plusmn;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.32\u0026plusmn;1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-5.39\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHealth awareness of hospital management through application contributes to taking into account the expected obstacles and determining how to deal with them or avoided\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.62\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.45\u0026plusmn;1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-5.08\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHealth awareness through the application contributes to improving the quality of effective nursing interventions and raising the health economy\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.68\u0026plusmn;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.53\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-4.82\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eReducing the consumption of resources used daily within the hospital and thus kills the cost of health consumables spent\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.83\u0026plusmn;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.45\u0026plusmn;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-4.69\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eImprove the application of practical content to patients effectively\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.53\u0026plusmn;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.80\u0026plusmn;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-6.92\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHealth awareness can mobilize and employ various resources Extracting distinguished service by using the human workforce to perform specific roles\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.73\u0026plusmn;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.20\u0026plusmn;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-2.56\u003csup\u003e\u0026nbsp;S\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHealth awareness through the application contributes to improving current medical problems and how to deal with obstacles that hinder growth economical\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.78\u0026plusmn;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.09\u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-1.98\u003csup\u003e\u0026nbsp;S\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eReduce the use of paper effort \u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.60\u0026plusmn;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.71\u0026plusmn;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-1.93\u003csup\u003e\u0026nbsp;S\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e26.48\u0026plusmn;3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e33.48\u0026plusmn;5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-7.96\u003csup\u003e\u0026nbsp;HS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote :\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHS\u003cstrong\u003e: M\u003c/strong\u003eeans\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ehighly\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003estatistical significance \u0026nbsp; \u0026nbsp; \u0026nbsp;S:\u003cstrong\u003e\u0026nbsp;M\u003c/strong\u003eeans\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003estatistical significance. \u0026nbsp; \u0026nbsp; \u0026nbsp;NS: Means no\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003estatistical significance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(8)\u003c/strong\u003e\u003cstrong\u003e: The level of health awareness on the efficiency and medical staff using the chatbot on pre and post application (60 nurses)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre intervention \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost intervention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eLess impact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eModerate impact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e56.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e56.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003ePositive impact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 397px;\"\u003e\n \u003cp\u003e32.50\u003csup\u003eHS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 397px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Healthcare coverage, Artificial intelligence, Cancer patients, Healthcare Workers","lastPublishedDoi":"10.21203/rs.3.rs-8368116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8368116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe integration of artificial intelligence into healthcare has garnered significant attention for its ability to revolutionize healthcare delivery, improve prevention and treatment protocols for cancer patients, extend healthcare coverage, enhance the competence of healthcare workers, and transform patient outcomes. This study was conducted to evaluate the impact of artificial intelligence mobile app-guided prevention and treatment protocols on cancer patients and their impact on healthcare workers competence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA quasi-experimental design was used to collect data from a purposive sample of 60 cancer patients and 60 healthcare providers using Google Forms. Five tools were utilized for data collection.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients reported improved usability of the mobile app, with most finding it welcoming, understandable, and helpful, though some experienced minor confusion or irrelevant responses. Healthcare workers\u0026rsquo; perceived benefits and efficiency in the use of the mobile app by health workers increased significantly after the intervention at p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Health awareness among nurses was improved by the intervention from 0% to 33.3% who stated that this had a positive effect. Symptoms attributed to chemotherapy, including nausea, fatigue, alopecia, loss of appetite, and anxiety, were successfully enhanced by the mobile app-guided prevention and treatment protocols. The AI-based application has emerged as a promising supportive intervention in oncology care due to improving patient service, compliance with treatment regimens, and personnel performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe mobile app based AI significantly improved patients' care by increasing adherence to protocols of prevention and treatment, thereby improving symptoms related to chemotherapy. It also enhances the efficiency of health workers to support better patient outcomes and is an effective tool for the optimization of healthcare delivery and quality.\u003c/p\u003e","manuscriptTitle":"Effectiveness of Artificial Intelligence Mobile App-Guided Prevention and Treatment Protocols on Cancer Patients and Their Impact on Healthcare Workers' Competence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 09:08:09","doi":"10.21203/rs.3.rs-8368116/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-08T08:31:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-01T14:19:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-26T10:21:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330312253906346873561039974468447803998","date":"2025-12-25T14:28:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T13:23:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38602289846187659606897224605381315619","date":"2025-12-25T11:30:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314462822293762768220689397489845540181","date":"2025-12-24T04:42:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212759260037775975940968283607266911889","date":"2025-12-24T03:18:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320500206286267716744915589384076127806","date":"2025-12-23T10:38:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-23T10:33:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-23T10:31:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-22T08:57:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-19T17:58:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2025-12-19T17:52:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5d9902c-d5ac-4cd9-8f1f-c9aff494f7dc","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:02:53+00:00","versionOfRecord":{"articleIdentity":"rs-8368116","link":"https://doi.org/10.1186/s12911-026-03432-1","journal":{"identity":"bmc-medical-informatics-and-decision-making","isVorOnly":false,"title":"BMC Medical Informatics and Decision Making"},"publishedOn":"2026-03-31 15:59:17","publishedOnDateReadable":"March 31st, 2026"},"versionCreatedAt":"2025-12-30 09:08:09","video":"","vorDoi":"10.1186/s12911-026-03432-1","vorDoiUrl":"https://doi.org/10.1186/s12911-026-03432-1","workflowStages":[]},"version":"v1","identity":"rs-8368116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8368116","identity":"rs-8368116","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}