Preprint
Article
Optimizing Healthcare Delivery: Investigating Key Areas for AI Integration and Impact in Clinical Settings
Altmetrics
Downloads
224
Views
168
Comments
0
This version is not peer-reviewed
Submitted:
28 February 2024
Posted:
29 February 2024
You are already at the latest version
Alerts
Abstract
This study investigates the pivotal areas where Artificial Intelligence (AI) integration in healthcare warrants further exploration, leveraging insights from Boston Specialists, a forefront healthcare provider in AI adoption. Employing a qualitative case study approach, enriched by in-depth interviews and comprehensive literature reviews, we identify three main domains for potential AI expansion: scheduling efficiency, clinical documentation, and AI-driven medical research. Our results underscore the substantial enhancements AI brings to operational workflows and patient care, notably through improved scheduling systems, accuracy in medical documentation, and the facilitation of data-driven research methodologies. These findings suggest a critical need for healthcare institutions to further invest in AI technologies, focusing on these key areas to harness AI's full potential in personalizing care, optimizing efficiency, and advancing medical knowledge. The study concludes with a call for continued investigation into AI's integration within healthcare settings, emphasizing the importance of a supportive organizational infrastructure, ethical considerations, and data security in realizing AI's transformative impact on healthcare delivery and patient outcomes.
Keywords:
Subject: Computer Science and Mathematics - Artificial Intelligence and Machine Learning
1. Introduction
The integration of Artificial Intelligence (AI) in healthcare represents a frontier in medical science and operational efficiency, promising transformative changes across patient care, administrative processes, and medical research. Despite its potential, the adoption of AI in healthcare has navigated a complex landscape of technological optimism and skepticism, raising significant discussions around its feasibility, ethics, and impact on the healthcare profession [1]. The current state of research exhibits a spectrum of perspectives, from the heralding of AI as a panacea for healthcare challenges to cautionary tales of its limitations and unintended consequences.
This study aims to delve into the practical application of AI within a healthcare setting, focusing on Boston Specialists, a leading clinical and research center. By examining the center’s integration of AI technologies, such as AI-driven scheduling and medical documentation systems, this research seeks to shed light on the tangible benefits and challenges of AI in healthcare [2]. Notably, while AI promises increased efficiency and enhanced patient care, it also poses ethical dilemmas and data security concerns that require careful navigation [3].
Central to our investigation is the exploration of how AI can support not just operational efficiency but also contribute to advanced patient care and innovative medical research. The study synthesizes insights from Boston Specialists with broader literature on AI in healthcare, aiming to provide a nuanced understanding of AI’s role and potential within the sector [4]. Through this approach, we contribute to the ongoing dialogue on AI in healthcare, offering evidence-based perspectives that navigate between the extremes of AI optimism and skepticism.
This work’s main aim is to critically assess the integration of AI in healthcare settings, with a focus on operational and clinical outcomes. By doing so, we highlight the principal conclusions that AI, when strategically implemented, can significantly enhance healthcare delivery and research, albeit with careful consideration of its ethical and security implications. This study not only contributes to the academic discourse on AI in healthcare but also provides practical insights for healthcare providers contemplating the integration of AI technologies into their operations and services.
2. Materials and Methods
2.1. Case Study
This study employed a case study method to investigate the adoption and effects of Artificial Intelligence (AI) in healthcare settings with emphasis on Boston Specialists. We conducted an in-depth interview with an employee from Boston Specialists that facilitated our understanding of existing applications of AI technologies as well as potential avenues for AI to further woven into the fabric of everyday medical practices [5].
We gained valuable insights from this discussion about how AI technologies are being used, showing both the promise of AI to improve healthcare delivery and specific focus areas for innovation and development [6]. The objective was not just to understand the immediate pros and cons of AI integration through a single hospital, but also analyze further implications for the whole health sector; identifying critical spaces whereby artificial intelligence would push forward patient care delivery and operational efficiency.
2.2. Additional Data Collection
Beyond the primary insights gained from our case study of Boston Specialists, our methodology in undertaking a case study of Boston Specialists involved using various sources for data acquisition to deepen the analysis [7]. Among these were comprehensive analyses of other academic journals, industry reports and media articles which helped us understand the merging of AI into healthcare better. This extensive literature review aimed to contextualize insights derived from Boston Specialists into perspective within the broader discussion on artificial intelligence in medicine by comparing trends, challenges and successes that have been witnessed across the entire healthcare industry.
To ensure a well-rounded examination of the topic, we used government reports and regulations, health policy documents as well as ethical recommendations concerning the use of AI in healthcare. This allowed us to evaluate how AI adoption in medical settings is guided by the regulatory environment and ethical concerns. Through a variety of data sources, we aimed to create a comprehensive picture of AI within healthcare, including identifying best practices, regulatory barriers and moral dilemmas faced by firms like Boston Specialists.
2.4. AI-Assistance
To aid our research, we utilized GPT-4, an NLP model developed by OpenAI. We employed GPT-4 to assist in sourcing and categorizing additional research articles. This enabled us to effectively collect relevant research articles for further manual analysis and triangulation of existing data. Additionally, during the writing process, GPT-4 was employed to paraphrase and refine our final article, suggesting alternative wording to enhance clarity and overall quality.
2.4.1. Ethical Considerations
We acknowledge the implications of using AI-generated content. To mitigate the risks, we verified all source articles for validity. All AI-revised content was generated using a custom GPT model to prevent inaccuracies. However, we still meticulously reviewed all content to maintain accuracy and uphold academic integrity.
2.3. Data Triangulation
In the vastly changing AI in the healthcare sector, ensuring the validity of our findings is paramount. To achieve this, we employed a strategy of data triangulation, where we cross checked our initial data with information from a variety of independent sources. The analysis of data from peer-reviewed journal articles, reputable databases, recent news publications and official company websites helped us in achieving this objective [8]. By incorporating these diverse sources, some elements of reliability and bias were excluded by which we justified our results on how AI impacts healthcare. This method helps us to present a more accurate picture with greater details about the role played by artificial intelligence in reshaping medical service provision.
3. Case Study Overview
3.1. Introduction to Boston Specialists
Founded in 2015 by Dr. John Leung, MD, AGAF, FACAAI, Boston Specialists has emerged as one of the premier independent clinical and research centers in New England. Treating between 250 to 300 patients weekly, the center has spearheaded numerous clinical research projects [9]. In recent years, Boston Specialists has seen significant expansion, not only in terms of its team size, including providers and employees, but also through its proactive adoption of new technologies.
3.2. Active AI Adoption
Actively, Boston Specialists is working on the development of a proprietary AI-powered platform designed to automate the creation of patient notes. This innovative approach aims to address one of the most time-consuming aspects of medical practice. During the patents visit, a physical audio recorder will transcribe the conversation in real time, and from these conversations, generate summary/medical notes. The physician will prompt the AI based of the need and chief complaint, and the system will synthesize this information into a structured summary (ie. HPI). The goal is to create a tool that not only improves efficiency but also enhances the accuracy of medical records, thereby supporting better patient outcomes.
The development journey has not been without its challenges and implications, notably in achieving the desired speed and comprehensiveness of note generation. Initial trials have revealed limitations in the platform’s ability to process information quickly and capture the full scope of patient consultations. Moreover, data security and compliance with Health Insurance Portability and Accountability Act (HIPAA) regulations have emerged as critical concerns, necessitating stringent measures to protect patient information and ensure the platform adheres to legal standards. The accuracy of the generated summaries also poses a significant challenge, as the system must correctly interpret and condense complex medical dialogues into precise, actionable notes.
3.3. Future Implementations
Boston Specialists highlighted two areas that stand out for their potential to significantly impact patient care and operational efficiency: the development of AI-driven surveys for research and the automation of scheduling processes.
Recognizing the crucial role of accurate and comprehensive patient data in clinical research, Boston Specialists is exploring the implementation of AI-driven surveys and questionnaires. This system aims to standardize the collection of patient symptom scores (PSS) and other relevant data, allowing for more precise and reliable research outputs. By leveraging AI to design and analyze these surveys, the center can ensure that data is collected in a consistent manner, thereby enhancing the quality of their research studies. This approach not only promises to streamline the research process but also opens new avenues for personalized patient care, as the insights gained from these surveys can inform more tailored treatment plans.
Another significant area of future implementation is the automation of scheduling processes. The current manual scheduling system at Boston Specialists has become increasingly cumbersome and time-consuming, particularly with the annual increase in staff and patients. By adopting AI to automate these processes, the center aims to optimize staff allocation and improve appointment scheduling efficiency. This technology can dynamically adjust schedules based on management needs, staff availability, and other critical factors, reducing administrative overhead, enhancing the overall patient experience, and staff satisfaction.
3.4. Summary
Boston Specialists illustrates a significant journey in the healthcare industry’s adoption of Artificial Intelligence (AI), setting a precedent for how medical practices can harness technology to improve operational efficiencies, patient care, and clinical research. This is showcased though initiatives such as the development of AI-powered note-taking software and plans for AI-driven surveys and scheduling automation. These efforts not only aim to address current challenges such as documentation inefficiency and data collection but also pave the way for future innovations that could transform patient engagement and healthcare delivery [10]. As such, Boston Specialists’ approach offers valuable insights into the potential of AI to enhance the healthcare sector, highlighting the importance of overcoming technical and regulatory challenges to realize the full benefits of this technology.
4. AI Pathways Proposition
4.1. Scheduling
Integrating an AI-scheduling system into healthcare operations can revolutionize the approach to managing weekly schedules, significantly reducing the reliance on manual planning [11]. By automating this process, the administrative load on support staff is alleviated, enabling the system to adapt seamlessly to last-minute alterations without necessitating a complete overhaul of the scheduling cycle [12].
Our proposition is a platform that simplifies the process for employees to submit their availability and specific requests. Management can then categorize staff into various roles, such as providers, scribes, and physician assistants. Based on these classifications and the operational needs, a trained algorithm undertakes the task of scheduling and planning on a weekly or bi-weekly basis. While basic scheduling platforms exist, our solution introduces several enhancements beyond mere accommodation of last-minute changes: it includes prioritization features for certain staff members and the capability for making informed decisions based on logical assessments, pending managerial approval.
4.1.1. Prioritization
Utilizing a trained model can allow for the AI to plan the schedule based on seniority and human unpredictability. The system can be calibrated to assign preferential slots to more tenured employees, such as prioritizing long-standing staff over interns. Moreover, it can assesses each staff member’s punctuality and absence record, enabling the schedule to anticipate and compensate for potential no-shows. For instance, on days when a surge in patient volume is anticipated (data drawn from an EMR like Epic), the schedule will favor assigning shifts to more dependable employees over those with a history of tardiness or absences [13].
4.1.2. Executive Decisions
While the AI system is equipped to make scheduling, decisions based on historical data and predefined criteria, final oversight should always remain human [14]. The AI may suggest schedule configurations that adhere to specific rules, such as always accommodating certain employees’ unavailability for specific shifts due to personal commitments. Conversely, it can assign shifts if certain criteria are deemed adjustable. Nonetheless, these automated suggestions are subject to human verification and approval, ensuring that the AI acts as a support tool rather than the ultimate decision-maker [15].
4.1.3. Possible Concerns
- Incorrect Schedules: Despite the best efforts of AI technologies, there’s always a risk of generating incorrect schedules due to misinterpretation of data or unforeseen variables.
- Accuracy and Fairness: Ensuring the fairness and accuracy of the scheduling process is paramount. Concerns may arise regarding the impartiality of AI in assigning shifts, especially when it involves prioritizing certain employees over others [16].
4.2. AI-Powered Documentation System
Our second proposition focuses on automatic note taking for medical documentation. Aimed at creating a more efficient and streamlined process for transcribing and organizing patent information, a system like this has the potential to both enhance efficiency and accuracy in patient care documentation [18].
Unlike the automated scheduling platform, AI-powered clinical note taking systems have experienced many recent advancements. Nuance Communications, Inc., a Microsoft Company, has recently announced their Dragon Ambient eXperience [18,19]. This platform combines conversational and ambient AI with OpenAI’s GPT-4, offering an automated solution that seamlessly integrates into healthcare workflows. The system is designed to capture patient-clinician interaction through ambient listening devices placed in the consultation room or via telehealth platforms. By analyzing and understanding the nuances of these conversations, DAX™ generates detailed, structured clinical summaries that are then seamlessly integrated into electronic medical records (EMRs).
Our proposition is to further enhance existing platforms like DAX™ to provide further support to clinicians and help patients reap benefits. After analyzing needs from practices such as Boston Specialists and other practitioners we came up with 4 main areas: contextual awareness of the system, predictive analysis, generative chat, and EHR integration [20].
4.2.1. Contextual Awareness
Integrating contextual awareness clinical documentation systems aims to understand the complexity and subtleties of patient-clinician interactions, enabling the AI to not only transcribe words but also interpret the clinical context, emotional tones, and non-verbal cues present during consultations [21]. By doing so, the system can generate more accurate and meaningful clinical notes that reflect the depth of each patient encounters. This poses several key insights that could significantly impact patient care.
- Personalized Care Plans: By understanding the nuances of patient conversations, AI can help identify individual patient needs and preferences that might not be explicitly stated. This can lead to more personalized care plans that consider not just the medical condition but the patient’s emotional and psychological state, lifestyle, and personal circumstances [22].
- Early Detection of Risks: Contextual awareness can enable the AI to pick up on subtle cues that may indicate early signs of distress, non-adherence to medication, or emerging health issues that a clinician might miss in a standard consultation. This early detection can prompt timely interventions, potentially preventing complications or worsening conditions [23].
- Improved Patient Engagement: Insights generated by understanding the context of patient interactions can improve communication strategies, making them more aligned with the patient’s level of comprehension and concerns. This can enhance patient engagement, satisfaction, and trust in their healthcare providers, leading to better health outcomes.
- Support for Mental Health: Contextual awareness in clinical documentation can reveal underlying mental health issues that might not be the primary focus of the consultation [24]. By capturing these nuances, AI can alert clinicians to potential mental health concerns, facilitating a holistic approach to patient care that addresses both physical and mental health needs.
4.2.2. Predictive Analysis
In conjunction with the integration of contextual awareness, predictive analysis systems can lead to a significant leap forward in personalized care and efficiency. Using verified data and handbooks, alongside leveraging historical data and machine learning algorithms predictive analysis systems can have the ability to forecast future health trends, potential complications, and patient needs before they become apparent [25]. For healthcare providers, this means being able to proactively adjust care plans, allocate resources more effectively, and intervene at earlier stages, potentially improving patient outcomes. Predictive analysis can identify patterns and risks that might not be visible through traditional analysis, such as spikes in specific conditions within a population or foreseeing the need for particular medical specialties at certain times [26].
4.2.3. Generative Chat
The integration of generative chat capabilities into AI-driven clinical documentation systems introduces a paradigm shift in how healthcare providers interact with electronic health records (EHRs) and patient data [27]. Generative chat, leveraging advanced AI models, can synthesize vast amounts of medical information into coherent, conversational responses, enabling more natural and intuitive access to patient histories, treatment plans, and medical literature. This technology not only streamlines the process of retrieving and understanding complex medical data but also enriches the decision-making process with AI-generated insights and suggestions, tailored to the specific context of each patient encounter.
- Benefits for Clinicians: Generative chat offers the dual benefits of efficiency and accuracy. It can automatically generate comprehensive and coherent summaries of patient encounters, medical histories, and treatment plans. This reduces the time spent on paperwork, allowing clinicians to focus more on patient care. Moreover, generative chat can provide clinicians with quick access to patient-specific insights and recommendations, enhancing decision-making and personalized care strategies.
- Benefits for Patients: Generative chat can significantly improve the patients’ understanding and management of their health conditions. By translating complex medical analyses into clear and concise language, patients can better comprehend their diagnoses, treatment options, and follow-up care instructions. This clearer understanding can lead to increased patient engagement, adherence to treatment plans, and overall satisfaction with the healthcare experience. Moreover, the ability for AI to answer basic questions about diagnoses or treatment plans adds another layer of support. Patients receive instant, reliable information, reducing their reliance on direct interactions with healthcare providers for every query. This not only speeds up the information delivery process but also empowers patients to take an active role in managing their health, leading to more informed decision-making in partnership with their healthcare team [28].
4.2.4. EHR Integration
EHR integration enables real-time access to a patient’s complete medical history, laboratory results, and diagnostic images, allowing AI systems to analyze this data for more informed clinical decision-making. For instance, AI can identify patterns or anomalies that may not be immediately apparent to clinicians, suggesting potential diagnoses or flagging patients at risk for certain conditions. This level of insight supports a more proactive and preventive approach to healthcare, potentially improving patient outcomes and reducing the need for costly interventions.
Moreover, EHR integration streamlines the workflow for healthcare professionals. With AI-driven insights and documentation assistance directly accessible within the EHR interface, clinicians can reduce the time spent on administrative tasks, focusing more on patient interaction and care delivery. This efficiency not only enhances the quality of care but also contributes to reducing clinician burnout, a significant concern in the healthcare industry.
4.3. AI-Driven Medical Research
Our third and final proposition is the introduction of AI into medical research. Integration of AI presents a pivotal shift towards more efficient, precise and innovative healthcare solutions. After our interview with Boston Specialists, it’s evident that AI has the potential to significantly enhance medical research methodologies and outcomes [29].
While still in it’s nascent stages, AI-driven tools can drastically enhance the identification of trends, patterns, and correlations that might be invisible to the human eye. This can lead to faster diagnoses, more effective treatment plans, and a deeper understanding of various medical conditions.
4.3.1. Enhancing Data Collection and Analysis
The development of AI-driven questionnaires and surveys illustrates a direct application of AI in improving the quality and efficiency of data collection in medical research. An AI application could ensure that data is collected in a standardized manner, minimizing variability and enhancing the reliability of research findings. Such tools can automate the tedious aspects of data collection, allowing researchers to focus on analysis and interpretation [30].
4.3.2. Impact on Medical Research Speed
The integration of AI into medical research paves the way for a new era of innovation in healthcare. With AI’s ability to process vast amounts of data rapidly and accurately, researchers can uncover new insights into diseases, patient outcomes, and treatment effectiveness. This could significantly shorten the time it takes to bring new treatments to the market and personalize healthcare solutions to meet individual patient needs.
5. Discussion
5.1. Contributions
Our research delves into the nuanced application of Artificial Intelligence (AI) within Boston Specialists, providing a detailed case study that enriches the broader discourse on AI in healthcare. This study makes several key contributions to the existing body of knowledge. Firstly, it illustrates the tangible benefits of AI adoption in healthcare operations, particularly through AI-driven scheduling and note-taking systems. This practical application underscores the potential of AI to streamline administrative processes, thereby enhancing operational efficiency and allowing healthcare providers to allocate more time to patient care.
Secondly, our examination of Boston Specialists’ initiatives offers insights into the strategic deployment of AI within healthcare settings. We highlight how a supportive organizational culture, and an agile structure are pivotal in successfully integrating AI technologies, thus providing a blueprint for other healthcare entities considering similar technological advancements.
Thirdly, our investigation into the challenges and implications of AI integration, such as data security and compliance with healthcare regulations, contributes to a critical understanding of the complexities involved in deploying AI within sensitive and highly regulated environments. Lastly, the study extends into the realm of AI-driven medical research, showcasing how AI can revolutionize data collection and analysis, paving the way for advancements in patient care and treatment outcomes. These contributions not only enhance our understanding of AI’s current role in healthcare but also stimulate further discussion on its future applications and impacts.
5.2. Implications
The findings of the study underscore the transformative potential of AI in enhancing patient care through improved operational efficiencies and more accurate medical documentation. This suggests a shift towards more patient-centric care models facilitated by technology [31]. For healthcare administrators, the findings emphasize the importance of fostering an organizational environment conducive to technological innovation, where AI can be seamlessly integrated into existing workflows without disrupting the core functions of healthcare delivery.
Additionally, the exploration of ethical considerations and data privacy concerns associated with AI adoption in healthcare presents crucial insights for policymakers and regulatory bodies. These insights can inform the development of guidelines and standards that ensure the responsible use of AI in healthcare, balancing innovation with patient safety and privacy. For the broader healthcare industry, our study illustrates the significant potential of AI to impact medical research and patient outcomes positively, advocating for increased investment in AI technologies and research to unlock these benefits fully.
5.3. Future Research and Limitations
This study, while comprehensive, is not without its limitations. Focusing predominantly on Boston Specialists, the research offers an in-depth look at AI integration in a specific healthcare setting, which may not fully represent the diverse range of healthcare environments and their unique challenges with AI adoption [32]. Future research should aim to broaden the scope by including multiple healthcare providers from various contexts to capture a wider spectrum of experiences and insights into AI integration in healthcare.
Furthermore, the potential of AI in enhancing medical research, as discussed in our study, warrants a more detailed investigation. Future studies could explore specific AI applications in clinical research, their effectiveness, and the ethical considerations surrounding their use. Additionally, as AI technology continues to evolve and new regulatory frameworks emerge, ongoing research will be essential to navigate the changing landscape of AI in healthcare [33]. Exploring these areas will not only address the limitations of the current study but also enrich our understanding of AI’s role in advancing healthcare in the 21st century.
5. Conclusions
Our study contributes to a growing body of research that recognizes the transformative potential of AI in healthcare. While the road ahead is fraught with challenges, the promise of AI to enhance every facet of healthcare, from patient care to operational efficiency and medical research, is undeniable. Future research should continue to explore these themes, focusing on the practical implementation of AI tools in diverse healthcare settings and the long-term impacts on the healthcare industry.
Author Contributions
Conceptualization, N.S.; Methodology, N.S.; Validation, H.C.; Formal analysis, N.S.; Investigation, H.C.; Resources, N.S. and H.C..; Writing—original draft preparation, N.S.; Writing—review and editing, H.C.; Supervision, N.S. and H.C.; Project Administration, N.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not Applicable.
Informed Consent Statement
Not Applicable.
Data Availability Statement
Not Applicable.
Acknowledgments
We acknowledge the use of ChatGPT GPT4, a NLP (Natural Language Processing) model developed by OpenAI, for assisting in the creation of content for this study. GPT4 was utilized to source and categorize additional cited research articles, as well as aid in paraphrasing and refining our final article.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Artificial Intelligence in Health Care: Benefits and Challenges of Technologies to Augment Patient Care. (2020). Available online: https://www.gao.gov/products/gao-21-7sp.
- Transforming Healthcare with AI: The Impact on the Workforce and Organizations. Available online: https://www.mckinsey.com/industries/healthcare/our-insights/transforming-healthcare-with-ai.
- Petersson, L.; Larsson, I.; Nygren, J.; Nilsen, P.; Neher, M.; Reed, J.; Tyskbo, D.; Svedberg, P. Challenges to Implementing Artificial Intelligence in Healthcare: A Qualitative Interview Study with Healthcare Leaders in Sweden. BMC Health Services Research 2022, 22. [Google Scholar] [CrossRef] [PubMed]
- Pr Murphy, M. What Are the Benefits and Drawbacks of Case Study Research? | Social Theory Applied Available online: https://socialtheoryapplied.com/2014/05/24/benefits-drawbacks-case-study-research/.iya, A. (2021). Case Study Methodology of Qualitative Research: Key Attributes and Navigating the Conundrums in Its Application. Sociological Bulletin, 70(1), 94–110. [CrossRef]
- Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. [CrossRef]
- Education, I.; Education, I. The Benefits of AI in Healthcare. Available online: https://www.ibm.com/blog/the-benefits-of-ai-in-healthcare/.
- Mayo-Wilson, E.; Li, T.; Fusco, N.; Dickersin, K. Practical Guidance for Using Multiple Data Sources in Systematic Reviews and Meta-analyses (with Examples from the MUDS Study). Research Synthesis Methods 2017, 9, 2–12. [Google Scholar] [CrossRef]
- Noble, H.; Heale, R. Triangulation in Research, with Examples. Evidence-Based Nursing 2019, 22, 67–68. [Google Scholar] [CrossRef] [PubMed]
- Boston Specialists. Available online: https://www.bostonspecialists.org/.
- Poalelungi, D.G.; Mușat, C.L.; Fulga, A.; Neagu, M.; Neagu, A.I.; Piraianu, A.I.; Fulga, I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. Journal of Personalized Medicine 2023, 13, 1214. [Google Scholar] [CrossRef] [PubMed]
- Bonifacio, R. AI in Employee Scheduling: The Future of Work for HR - Shiftbase. Shiftbase 2024. [Google Scholar]
- Kletzander, L.; Musliu, N. Solving the General Employee Scheduling Problem. Computers & Operations Research 2020, 113, 104794. [Google Scholar] [CrossRef]
- Knight, D.; Aakre, C.A.; Anstine, C.V.; Dabrh, A.M.A.; Biazar, P.; Mitri, G.; Valery, J.; Brigham, T.; Niazi, S.K.; Perlman, A.; et al. Artificial Intelligence for Patient Scheduling in the Real-World Health Care Setting: A Metanarrative Review. Health Policy and Technology 2023, 12, 100824. [Google Scholar] [CrossRef]
- McKendrick, J. AI Isn’t Ready to Make Unsupervised Decisions. Available online: https://hbr.org/2022/09/ai-isnt-ready-to-make-unsupervised-decisions.
- Akkara, J.D.; Kuriakose, A. Commentary: Is Human Supervision Needed for Artificial Intelligence? Indian Journal of Ophthalmology 2022, 70, 1138. [Google Scholar] [CrossRef]
- Ntoutsi, E.; Fafalios, P.; Gadiraju, U.; Iosifidis, V.; Nejdl, W.; Vidal, M.-E.; Ruggieri, S.; Turini, F.; Papadopoulos, S.; Krasanakis, E.; et al. Bias in Data-driven Artificial Intelligence Systems—An Introductory Survey. WIREs Data Mining and Knowledge Discovery 2020, 10. [Google Scholar] [CrossRef]
- Adarsh Kumar and ANJALI GOND. NATURAL LANGUAGE PROCESSING: HEALTHCARE ACHIEVING BENEFITS VIA NLP. ScienceOpen Preprints. 2023. [CrossRef]
- Parmar, A.; Parmar, A. Microsoft’s Healthcare AI Efforts Holds Huge Promise for Patients. MedCity News 2023. [Google Scholar]
- Hale, J. New AI Tools At Atrium Health Help Burnt Out Physicians - WCCB Charlotte’s CW. Available online: https://www.wccbcharlotte.com/2023/10/10/new-ai-tools-at-atrium-health-help-burnt-out-physicians/.
- Chamola, V.; Goyal, A.; Sharma, P.; Hassija, V.; Bình, H.T.T.; Saxena, V. Artificial Intelligence-Assisted Blockchain-Based Framework for Smart and Secure EMR Management. Neural Computing and Applications 2022, 35, 22959–22969. [Google Scholar] [CrossRef]
- Jiang, N.; Liu, X.; Liu, H.; Lim, E.T.K.; Tan, C.; Gu, J. Beyond AI-Powered Context-Aware Services: The Role of Human–AI Collaboration. Industrial Management and Data Systems 2022, 123, 2771–2802. [Google Scholar] [CrossRef]
- Johnson, K.B.; Wei, W.; Weeraratne, D.; Frisse, M.E.; Misulis, K.E.; Rhee, K.; Zhao, J.; Snowdon, J.L. Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and Translational Science 2020, 14, 86–93. [Google Scholar] [CrossRef]
- Badidi, E. EDGE AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions. Future Internet 2023, 15, 370. [Google Scholar] [CrossRef]
- Chen, I.Y.; Szolovits, P.; Ghassemi, M. Can AI Help Reduce Disparities in General Medical and Mental Health Care? AMA Journal of Ethics 2019, 21, E167–179. [Google Scholar] [CrossRef] [PubMed]
- Agarwal, R. Predictive Analysis in Health Care System Using AI. In Advanced technologies and societal change; 2021; pp. 117–131. [CrossRef]
- Miller, D.D.; Brown, E.W. Artificial Intelligence in Medical Practice: The Question to the Answer? The American Journal of Medicine 2018, 131, 129–133. [Google Scholar] [CrossRef] [PubMed]
- Doctors Receptive to AI Collaboration in Simulated Clinical Case without Introducing Bias. Available online: https://hai.stanford.edu/news/doctors-receptive-ai-collaboration-simulated-clinical-case-without-introducing-bias.
- How AI Will Improve Patient Care (Benefits & Use Cases) | UpTech. Available online: https://www.uptech.team/blog/how-ai-will-improve-patient-care.
- Lillehaug, S.-I.; Lajoie, S.P. AI in Medical Education—Another Grand Challenge for Medical Informatics. Artificial Intelligence in Medicine 1998, 12, 197–225. [Google Scholar] [CrossRef] [PubMed]
- Norori, N.; Hu, Q.; Aellen, F.M.; Faraci, F.D.; Tzovara, A. Addressing Bias in Big Data and AI for Health Care: A Call for Open Science. Patterns 2021, 2, 100347. [Google Scholar] [CrossRef] [PubMed]
- Bjerring, J.C.; Busch, J. Artificial Intelligence and Patient-Centered Decision-Making. Philosophy & Technology 2020, 34, 349–371. [Google Scholar] [CrossRef]
- Balagurunathan, Y., Mitchell, R., & Naqa, I. E. (2021). Requirements and reliability of AI in the medical context. Physica Medica, 83, 72–78. [CrossRef]
- Haug, C.; Drazen, J.M. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. The New England Journal of Medicine 2023, 388, 1201–1208. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
MDPI Initiatives
Important Links
© 2024 MDPI (Basel, Switzerland) unless otherwise stated