Issues and Challenges in Using Electronic Health Records for Smart Hospitals

Rao, Krishna Prasad N.; Manvi, Sunilkumar S.

doi:10.1007/978-981-19-2161-2_8

Cited by 1 publication

(2 citation statements)

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“…Predictive analytics in healthcare can significantly influence life-saving efforts [ 5 ]. This is mentioned by 88.5% of the participants.…”

Section: Discussionmentioning

confidence: 99%

“…The evolution of EHRs is a subject of immense interest within the healthcare industry. As we look toward 2030 and beyond, EHR systems are anticipated to transition from static records to dynamic, interactive engagement systems [ 5 ]. EHRs currently exhibit a highly structured format, but future iterations are expected to adopt a more flexible, responsive, and adaptive hierarchical model.…”

Section: Introductionmentioning

confidence: 99%

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Clinicians’ Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond

Alanazi

2023

Cureus

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Introduction: The healthcare industry has made significant progress in information technology, which has improved healthcare procedures and brought about advancements in clinical care services. This includes gathering crucial clinical data and implementing intelligent health information management. Artificial Intelligence (AI) has the potential to bolster further existing health information systems, notably electronic health records (EHRs). With AI, EHRs can offer more customized and adaptable roles for patients. This study aims to delve into the current and potential uses of AI and examine the obstacles that come with it. Method: In this study, we employed a qualitative methodology and purposive sampling to select participants. We sought out clinicians who were eager to share their professional insights. Our research involved conducting three focus group interviews, each lasting an hour. The moderator began each session by introducing the study's goals and assuring participants of confidentiality to foster a collaborative environment. The facilitator asked open-ended questions about EHR, including its applications, challenges, and AI-assisted features. Results: The research conducted by 26 participants has identified five crucial areas of using AI in healthcare delivery. These areas include predictive analysis, clinical decision support systems, data visualization, natural language processing (NLP), patient monitoring, mobile technology, and future and emerging trends. However, the hype surrounding AI and the fact that the technology is still in its early stages pose significant challenges. Technical limitations related to language processing and context-specific reasoning must be addressed. Furthermore, medico-legal challenges arise when AI supports or autonomously delivers healthcare services. Governments must develop strategies to ensure AI's responsible and transparent application in healthcare delivery. Conclusion: AI technology has the potential to revolutionize healthcare through its integration with EHRs and other existing technologies. However, several challenges must be addressed before this potential can be fully realized. The development and testing of complex EHR systems that utilize AI must be approached with care to ensure their accuracy and trustworthiness in decision-making about patient treatment. Additionally, there is a need to navigate medico-legal obligations and ensure that benefits are equitably distributed.

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“…Predictive analytics in healthcare can significantly influence life-saving efforts [ 5 ]. This is mentioned by 88.5% of the participants.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%