Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review

Background Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care. Objective This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care. Methods A systematic scoping review was conducted according to the 5-stage framework by Arksey and O’Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science for studies that were published from inception until June 21, 2021. Articles that were not specific to AI, not research studies, and not written in English were omitted. Results Of the 3666 articles retrieved, 26 (0.71%) were eligible and included in this review. The mean age of the participants ranged from 30 to 72.6 years, the proportion of men ranged from 0% to 73.4%, and the sample sizes for primary studies ranged from 11 to 2780. The perceptions and needs of various populations in the use of AI were identified for general, primary, and community health care; chronic diseases self-management and self-diagnosis; mental health; and diagnostic procedures. The use of AI was perceived to be positive because of its availability, ease of use, and potential to improve efficiency and reduce the cost of health care service delivery. However, concerns were raised regarding the lack of trust in data privacy, patient safety, technological maturity, and the possibility of full automation. Suggestions for improving the adoption of AI in health care were highlighted: enhancing personalization and customizability; enhancing empathy and personification of AI-enabled chatbots and avatars; enhancing user experience, design, and interconnectedness with other devices; and educating the public on AI capabilities. Several corresponding mitigation strategies were also identified in this study. Conclusions The perceptions and needs of AI in its use in health care are crucial in improving its adoption by various stakeholders. Future studies and implementations should consider the points highlighted in this study to enhance the acceptability and adoption of AI in health care. This would facilitate an increase in the effectiveness and efficiency of health care service delivery to improve patient outcomes and satisfaction.

Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review (Preprint)

BACKGROUND Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care. OBJECTIVE This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care. METHODS A systematic scoping review was conducted according to the 5-stage framework by Arksey and O’Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science for studies that were published from inception until June 21, 2021. Articles that were not specific to AI, not research studies, and not written in English were omitted. RESULTS Of the 3666 articles retrieved, 26 (0.71%) were eligible and included in this review. The mean age of the participants ranged from 30 to 72.6 years, the proportion of men ranged from 0% to 73.4%, and the sample sizes for primary studies ranged from 11 to 2780. The perceptions and needs of various populations in the use of AI were identified for general, primary, and community health care; chronic diseases self-management and self-diagnosis; mental health; and diagnostic procedures. The use of AI was perceived to be positive because of its availability, ease of use, and potential to improve efficiency and reduce the cost of health care service delivery. However, concerns were raised regarding the lack of trust in data privacy, patient safety, technological maturity, and the possibility of full automation. Suggestions for improving the adoption of AI in health care were highlighted: enhancing personalization and customizability; enhancing empathy and personification of AI-enabled chatbots and avatars; enhancing user experience, design, and interconnectedness with other devices; and educating the public on AI capabilities. Several corresponding mitigation strategies were also identified in this study. CONCLUSIONS The perceptions and needs of AI in its use in health care are crucial in improving its adoption by various stakeholders. Future studies and implementations should consider the points highlighted in this study to enhance the acceptability and adoption of AI in health care. This would facilitate an increase in the effectiveness and efficiency of health care service delivery to improve patient outcomes and satisfaction.

Improving service engagement in healthcare through internet of things based healthcare systems

Purpose The purpose of the study was to empirically validate the linkages between IoT adoption and how it overarched influenced the patient care service engagement. This contributes to the body of knowledge and helps hospital managers to understand the relationship and relevance of IoT adoption; otherwise healthcare sector are late movers towards technology adoption. This gives a nuanced framework towards establishing empirically validated framework which will motivate healthcare services providers to be motivated to adopt and implement IoT enabled care delivery. The physician patient interaction and alignment during decision making will foster positive word of mouth, superior care service and reduce extra overheads for healthcare providers without compromise or rather with increment in service delivery proposition. Design/methodology/approach The study theoretically and empirically describes that with the adoption of internet of things (IoT) devices in health care, better services can be provided to patients by using partial least square – structure equation modelling-based robust technique and explains the better understanding of the health-care process with the help of information pervasiveness, physician-patient orientation and improved patient and physician involvement in the decision-making process. Findings This study shows that wearable IoT device adoption in health-care service delivery opens new opportunities and disrupts the conventional and traditional way of health-care service delivery by empowering the patient to take part in decision-making and enhancing their engagement in health-care service delivery. Research limitations/implications The study might influence by generalizability. Perception-based cross-examination knowledge from the patient’s perspective. It is likely that patients who use these devices will grow accustomed to using them and become more capable of using them. Thus, time-series tests have not been used to catch enhanced skills. New patients’ experiences will be altered over time. Regardless, non-response bias and traditional process bias received excessive interest. Practical implications The study aims at unravelling how the adoption of IoT enabled practices and usage of IoT devices bolsters the available data points in the context of healthcare especially with respect to patient care delivery. The study conceptualizes and empirically validates how the usage of IoT interface enabled technology enables better patient treatment and caregiver participation. The study puts forth a nuanced understanding regarding how pervasively available ubiquitous care information fosters shared decision making. This study further emphasizes that importance of ensuring a reliable computing environment devoid of privacy and security risks. The study attempts at Emphasizing empirically how the enhanced information pervasiveness catapults the patient-provider interactions, through health data exchange. Highlighting the importance of search feature in cloud storage and recovery mechanisms. The study not only fulfills the overarching linkage between enhanced service engagement with IoT adoption, it provides a mental map and ready to refer framework for hospital and healthcare experts to refer to, which prescribes thar care providers must build new methods aimed at empowerment of patients to participate and take more inclusive role. This unique confluence between patients and physicians will unravel the sync; helping not only avoid costly decision errors, but also improve patient care delivery environment. Patients should be permitted to participate in decision-making,inspire patients to be participatory. Originality/value The study efforts to empirically investigate and discover the link between how wearable sensor-based IoT enhances health-care service engagement is underway. Using primary data this linkage validation allows the community and readers at large to gain a nuanced understanding of how superior interaction is enabled by a digital-health-care process with the help of IoT-enabled information pervasiveness, physician-patient orientation and empowered involvement.

Influence of Organizational Change on Public Health Maternal Health Care Service Delivery in Kitui County (A Survey of Level 4 Hospitals in Kitui County)

The County Governments in Kenya are faced with poor service delivery especially in the provision of maternal health care services. Maternal health care services in public hospitals are not meeting up to the quality standard as outlined by the Ministry of Health in Kenya. The paper sought to determine the influence of leadership and universal health coverage on public health maternal health care in Kitui County. This study was guided by Transformational Leadership Theory and Theory X & Y. The study focused on 11 public hospitals providing maternity services in Kitui County. The target population of the study was 203 health officers that include 26 doctors, 10 specialists, 41 clinical officers and 126 nurses across the 13 level 4 hospitals providing maternity services in Kitui County. Data was collected by use of structured closed ended questionnaire. Data analysis was conducted using SPSS Version 25.0 Software. Pearson Correlation showed that leadership and universal health coverage have a positive correlation with public health maternal health care service delivery. Model summary results indicated that leadership and universal health coverage explain 52.1 percent of public health maternal health care service delivery. Coefficient regression revealed that coefficient of leadership has appositive and significant influence (β=.203, p=.001<0.05) on and public health maternal health care service delivery. It was also found that coefficient of Universal Health Coverage and public health maternal health care service delivery have a positive and significant relationship (β=.662, p=.000<0.05). The study concludes that leadership is one of the key health systems factors affecting the performance of maternal health services at facility level. Conclusion can be made further that universal health coverage improves public health maternal health care service delivery. The study recommends for the need of maternal health care providers to review their leadership guidelines and styles with aim of enhancing quality of leadership in the management of hospitals. Though universal health coverage is on trial, the study recommends for the need to adequately support the implementation of universal health coverage.