Analytics & Decision Support

Artificial Intelligence (AI) is increasingly becoming a cornerstone in the evolution of project management. Its capabilities extend beyond simple automation, fostering improved decision-making processes and enhancing collaborative efforts. Among the various AI tools available, chatbots stand out as particularly transformative for project management. This study delves into the role of AI-powered chatbots in project stakeholder engagement, a critical aspect of successful project management. Chatbots, powered by sophisticated AI algorithms, can provide continuous support and interaction with project stakeholders. This is particularly vital in managing complex projects where continuous communication and prompt responses can significantly influence project success. The study examines how these AI-driven chatbots facilitate stakeholder engagement, focusing on key benefits such as improved communication, increased stakeholder satisfaction, and better overall project outcomes. Through a detailed analysis, we have identified that chatbots enhance communication by offering stakeholders immediate, personalized responses, thereby reducing response times and improving the efficiency of information exchange. This immediacy and personalization contribute to heightened stakeholder satisfaction, as stakeholders feel their concerns and queries are addressed promptly and effectively. Furthermore, our findings suggest that these improvements in stakeholder engagement directly correlate with enhanced project outcomes, including better adherence to timelines, improved project quality, and increased likelihood of meeting project objectives. However, the deployment of chatbots in project management is not without its challenges. One significant hurdle is the need for advanced natural language processing (NLP) capabilities. Effective chatbots must understand and process complex human language nuances to interact effectively with stakeholders. Another challenge observed is the potential for chatbots to become disruptive or annoying. This can occur when chatbots fail to provide relevant or accurate information, or when their interaction style does not align with stakeholder expectations. In conclusion, AI-powered chatbots hold substantial promise for revolutionizing stakeholder engagement in project management. While they present remarkable benefits in improving communication, stakeholder satisfaction, and project outcomes, there are challenges that need to be addressed. These include enhancing NLP capabilities and fine-tuning the interaction style of chatbots to suit diverse stakeholder groups. With these improvements, AI-powered chatbots could significantly contribute to the success of various projects, marking a new era in project management where AI plays a pivotal role in stakeholder engagement and overall project success.

Artificial Intelligence (AI) is increasingly becoming a cornerstone in the evolution of project management. Its capabilities extend beyond simple automation, fostering improved decision-making processes and enhancing collaborative efforts. Among the various AI tools available, chatbots stand out as particularly transformative for project management. This study delves into the role of AI-powered chatbots in project stakeholder engagement, a critical aspect of successful project management. Chatbots, powered by sophisticated AI algorithms, can provide continuous support and interaction with project stakeholders. This is particularly vital in managing complex projects where continuous communication and prompt responses can significantly influence project success. The study examines how these AI-driven chatbots facilitate stakeholder engagement, focusing on key benefits such as improved communication, increased stakeholder satisfaction, and better overall project outcomes. Through a detailed analysis, we have identified that chatbots enhance communication by offering stakeholders immediate, personalized responses, thereby reducing response times and improving the efficiency of information exchange. This immediacy and personalization contribute to heightened stakeholder satisfaction, as stakeholders feel their concerns and queries are addressed promptly and effectively. Furthermore, our findings suggest that these improvements in stakeholder engagement directly correlate with enhanced project outcomes, including better adherence to timelines, improved project quality, and increased likelihood of meeting project objectives. However, the deployment of chatbots in project management is not without its challenges. One significant hurdle is the need for advanced natural language processing (NLP) capabilities. Effective chatbots must understand and process complex human language nuances to interact effectively with stakeholders. Another challenge observed is the potential for chatbots to become disruptive or annoying. This can occur when chatbots fail to provide relevant or accurate information, or when their interaction style does not align with stakeholder expectations. In conclusion, AI-powered chatbots hold substantial promise for revolutionizing stakeholder engagement in project management. While they present remarkable benefits in improving communication, stakeholder satisfaction, and project outcomes, there are challenges that need to be addressed. These include enhancing NLP capabilities and fine-tuning the interaction style of chatbots to suit diverse stakeholder groups. With these improvements, AI-powered chatbots could significantly contribute to the success of various projects, marking a new era in project management where AI plays a pivotal role in stakeholder engagement and overall project success.

Interoperability has become a crucial catalyst for transformative developments within the healthcare sector. It facilitates the flawless flow of data across various healthcare platforms, thereby revolutionizing patient care, optimizing clinical operations, and enhancing the scope of medical research. This article examines the vital role of interoperability in improving data sharing, equipping healthcare providers with detailed patient insights, and enhancing patient involvement. It investigates the benefits, such as better patient outcomes, increased clinical productivity, and support for medical research. Nevertheless, there are significant hurdles-such as the need for standardization, ensuring data security, and achieving technical integration-that need addressing to realize broad implementation. Looking ahead, this paper anticipates a future where interoperability, in conjunction with the Internet of Medical Things (IoMT), precision medicine, and artificial intelligence (AI), will forge a more efficient, accurate, and patient-focused healthcare system. As interoperability continues to mold the future of healthcare, the sector is poised to fulfill its aim of creating a healthier and more contented global community.

Large and complex datasets in the quickly changing field of healthcare defy conventional research methods due to their sheer number, minute details, and fast-paced nature. Techniques that can efficiently handle and analyze large datasets—including clinical, personal computer, and medical usage data—are desperately needed. Conventional statistical models face difficulties because, in spite of their vastness, these datasets are either incomplete or restricted to particular segments of the population. Although machine learning approaches have demonstrated their ability to overcome these obstacles, they are not impervious to the biases that are frequently present in observational studies. For these models to be reliable and accurate in research applications, they must be rigorously validated using industry-standard testing techniques like lasso or ridge regression.

In the US healthcare industry, the use of Artificial Intelligence (AI) in Project Portfolio Management (PPM) has become a vital factor in efficiency, strategic decisionmaking, and value optimization. By highlighting the important role of AI technologiesmachine learning, natural language processing, genetic algorithms, and beyond-in expediting healthcare PPM, this research offers a fresh viewpoint. We offer an extensive analysis outlining AI's capacity to manage the healthcare system's increasing complexity and budgetary limitations, arguing in favor of an AI-enhanced approach to project selection, management, and outcome alignment with organizational goals. Our study highlights the role of AI in facilitating data-driven decision-making, operational effectiveness, and risk management. These factors allow for better patient care and increase organizational performance. However, it also recognizes the difficulties that AI integration presents, such as worries about algorithmic transparency, data privacy, and ethical ramifications. As a future step, the report suggests creating advanced AI tools with ethical foundations that are specifically designed to meet the needs of the healthcare industry. This study lays forth a future in which PPM and AI are combined to create a more strategic, effective, and patient-focused field for healthcare project management.

Background: The integration of informatics projects into healthcare systems is essential for enhancing patient care, optimizing workflows, and achieving superior outcomes through technological innovation. Despite the potential benefits, the implementation of these projects often encounters significant challenges that must be addressed to ensure success. Methods: This systematic review was conducted over six months and involved a comprehensive synthesis of literature from the past decade. Articles were selected from Scopus, PubMed, and Web of Science using keywords such as "informatics projects," "healthcare," "challenges," and "solutions." A total of 25 articles were identified and analyzed to explore the key challenges and solutions in healthcare informatics. Results: The review identified four primary challenges in implementing informatics projects in healthcare: interoperability issues, data security concerns, user resistance, and financial constraints. The analysis revealed that strategies such as collaborative interoperability efforts, enhanced cybersecurity measures, user-centered design approaches, and innovative financing mechanisms are effective in addressing these challenges. Conclusion: By offering actionable insights, this review serves as a valuable resource for healthcare professionals, researchers, and policymakers. It facilitates informed decision-making and paves the way for the effective utilization of informatics projects to enhance healthcare delivery and outcomes.

The “Tongue Dive System” is a tongue operated Assistive Technology (AT) developed for people with severe disability to control their environment. Tongue Drive consists of an array of Hall Effect magnetic sensors mounted on a mouthpiece to measure the magnetic field generated by a small permanent magnet secured on the tongue. The sensor signals are transmitted across a wireless link and processed to control the powered wheelchair. In past a lot of Assistive Technologies have been designed but each one of them had certain demerits. The tongue is considered an excellent appendage in severely disabled people for operating an Assistive device. This paper presents an efficient, low cost solution to all the issues encountered in previous AT’s. Detailed analysis of various design
processes has also been discussed. Complete system proposed in this paper has been designed around PIC microcontroller and a RF module. The design has been tested and result achieved confirms the design approach illustrated.

Congenital talipes equinovarus (CTEV), also known as clubfoot, is a complex congenital deformity of the foot that, left untreated, can limit a person’s mobility by making it difficult and painful to walk. Worldwide, 80% of children born with clubfoot are in low- and middle-income countries. The management of clubfoot has a long history. Non-operative management did not become popular, as an increasing number of orthopaedists started leaning towards surgical treatment. The late Dr. Ignacio Ponseti developed a method of clubfoot correction that successfully realigns  clubfoot in infants without extensive and major surgery. The aim of the study was to assess the functional outcome of CTEV management by the Ponseti technique, to study the severity of CTEV
deformity using the Pirani score, and to evaluate the cost-effectiveness of the technique. A total of 356 cases with 402 feet with CTEV were treated by the Ponseti method. The average age of the children and the number of casts applied before full correction were 4.03 months and 6.91,
respectively. There was a good functional outcome in 95.45% of cases (score > 30) at the last follow up. The management of CTEV by the Ponseti technique provides a good functional and cosmetic outcome. In a developing country like India, this technique is a safe, easy, economical method of clubfoot management.