Find Examples Of Applications For Each Of The Following D ✓ Solved

Find Examples Of An Applications For Each Of The Following Decisi

Find examples of an applications for each of the following: decision support systems (DSS), business intelligence (BI), and analytics. Summarize the applications in 300 words. Include a list of resources you used. (10 points) 2. Identify the future of analytics in healthcare? What will it be used for and what are some expected outcomes. (300 words) 3. What role does and should IBM Watson play in healthcare? Are there significant findings that Watson assisted in obtaining? (300 words) How will artificial intelligence (AI) improve your business? What advice would you give managers and executives about using AI? (300 words) 2. What are the major drivers of AI? Are there any impediments your organization needs to be aware of? Are there any reasons organizations should avoid using AI? (300 words) 3. Identify a voice recognition software and explain some of the applications. What type of processing is used for voice recognition? What are some of the issues still with voice recognition? (800 words). The above assignments should be submitted in one-word document. Include an APA cover page and a reference page. When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.

Sample Paper For Above instruction

Find Examples Of An Applications For Each Of The Following Decisi

Applications of Decision Support Systems, Business Intelligence, and Analytics

Decision support systems (DSS), business intelligence (BI), and analytics are integral components of modern data-driven decision-making processes across various industries. DSS are computer-based systems designed to assist managerial decision-making by analyzing large volumes of data to support problem-solving and decision processes. An example of DSS application is in supply chain management, where systems analyze logistics data to optimize inventory levels and reduce operational costs (Power, 2002). In healthcare, DSS helps clinicians make better diagnostic and treatment decisions by integrating patient data with medical knowledge databases (Berner, 2007). Meanwhile, business intelligence involves the collection, integration, analysis, and presentation of business information to support strategic planning. Retail organizations often use BI tools like dashboards and data visualization to monitor sales trends, customer behavior, and inventory levels in real time (Chaudhuri & Dayal, 1997). Analytics, particularly predictive analytics, leverage statistical techniques and machine learning algorithms to forecast future events. For example, financial institutions apply analytics to detect fraudulent transactions or assess credit risks, enhancing security and efficiency (Shmueli & Bruce, 2016). The synergy of DSS, BI, and analytics enables companies to make proactive, informed decisions, improve operational efficiency, and achieve competitive advantage. As organizations increasingly appeal to big data, these tools have become vital for extracting actionable insights from complex datasets, thus transforming raw data into strategic opportunities.

Future of Analytics in Healthcare

The future of analytics in healthcare is promising, driven by advancements in artificial intelligence (AI), machine learning, and data interoperability. Analytics will become central to personalized medicine, enabling treatments tailored to individual genetic profiles, thus improving patient outcomes and reducing adverse effects (Raghupathi & Raghupathi, 2014). Predictive analytics will increasingly predict disease outbreaks, monitor patient health remotely, and assist in early diagnosis, which will substantially lower healthcare costs and improve quality of life (Obermeyer & Emanuel, 2016). Moreover, healthcare analytics will facilitate more efficient resource allocation, hospital operations, and workforce management. The integration of electronic health records (EHRs) with analytics will offer real-time insights, empowering clinicians with decision support tools that improve diagnostic accuracy and treatment planning (Chen et al., 2021). Outcomes of these developments are anticipated to include enhanced preventative care, reduced readmission rates, and lower healthcare expenditures. As data security and ethical considerations evolve, analytics will also play a pivotal role in ensuring privacy and compliance with regulatory standards. The convergence of IoT devices, wearable technology, and advanced analytics promises a future where healthcare is proactive, personalized, and accessible.

The Role of IBM Watson in Healthcare

IBM Watson has emerged as a transformative tool in healthcare, leveraging artificial intelligence (AI) to support clinical decisions, research, and patient care. Watson's ability to analyze vast datasets, including medical literature, electronic health records, and clinical trial data, allows it to assist clinicians in diagnosing complex cases and suggesting personalized treatment options (Levant, 2019). For instance, Watson has been effectively used in oncology to identify optimal cancer treatments based on patient-specific genetic information (Chen et al., 2019). Significant findings facilitated by Watson include improving the accuracy of cancer diagnoses and streamlining clinical workflows, thus leading to better patient outcomes. Healthcare organizations have reported reductions in diagnostic errors and faster treatment decisions after integrating Watson-powered solutions (Wang et al., 2020). Furthermore, Watson's ability to learn continuously and adapt makes it a promising partner in clinical research and drug discovery. However, challenges such as data privacy concerns, integration complexity, and the need for high-quality data remain hurdles. Overall, IBM Watson is poised to play an increasingly vital role by augmenting physician expertise, improving diagnostic accuracy, and supporting personalized medicine initiatives.

The Impact of AI on Business and Recommendations for Managers

Artificial intelligence (AI) can significantly improve business operations by automating routine tasks, providing predictive insights, and enhancing customer experience. For example, AI-driven chatbots streamline customer service, while predictive analytics forecast sales trends and optimize supply chains (Brynjolfsson & McAfee, 2017). AI also enables data-driven decision-making, leading to increased efficiency and competitive advantage. However, managers should consider the ethical implications, data security, and the need for appropriate talent and infrastructure investments to maximize AI benefits (Davenport & Ronanki, 2018). It is essential for leaders to develop clear strategic goals for AI implementation, maintain transparency with stakeholders, and foster a culture of continuous learning. Moreover, organizations should assess potential biases in AI models that may lead to unfair outcomes and establish governance frameworks to address ethical concerns. Sustained investment in employee training and collaboration between domain experts and data scientists is crucial for AI success. Overall, with careful planning and ethical considerations, AI can be a catalyst for innovation and growth in any business environment.

Major Drivers and Challenges of AI Adoption

The major drivers of AI adoption include technological advancements, increasing data availability, and the pursuit of operational efficiency and competitive advantage (Mayer-Schönberger & Cukier, 2013). The proliferation of big data analytics, cloud computing, and high-performance processing has made AI applications more feasible and cost-effective. Additionally, customer expectations for personalized experiences and automation push organizations toward AI integration. Despite these drivers, organizations face impediments such as high implementation costs, talent shortages, and data privacy concerns. Regulatory frameworks and ethical questions about AI decision-making further complicate deployment (Crawford & Calo, 2016). Some organizations may consider avoiding AI due to fears of job displacement, reduced transparency, or loss of control over automated systems. Moreover, reliance on AI systems can introduce vulnerabilities to cyberattacks and biases embedded in training data. Awareness and proactive management of these challenges are critical for sustainable AI integration, ensuring that AI delivers value without compromising ethical standards or operational security.

Voice Recognition Software and Its Applications

Voice recognition software has become an integral part of modern technology, enabling hands-free operation and accessibility. Applications of voice recognition include virtual assistants like Amazon Alexa, Google Assistant, and Apple's Siri, which facilitate tasks such as setting reminders, controlling smart home devices, and retrieving information through spoken commands (Kumar, 2020). In healthcare, voice recognition is used for transcribing doctor-patient interactions, improving documentation efficiency, and supporting telemedicine services. Automotive systems incorporate voice recognition for hands-free control of navigation and entertainment systems, enhancing safety and driver convenience. In customer service, voice bots handle inquiries, process transactions, and provide support, reducing operational costs (Liu et al., 2018). The core processing used in voice recognition includes acoustic modeling, language modeling, and pattern matching, often powered by deep learning algorithms that analyze audio signals to convert speech into text (Huang et al., 2014). Despite advancements, issues such as background noise interference, accents, speech disfluencies, and limited understanding of context continue to challenge the accuracy of voice recognition systems. Ongoing research aims to improve robustness, contextual understanding, and multilingual capabilities to make voice recognition systems more reliable and versatile.

References

  • Berner, E. S. (2007). Clinical decision support systems: State of the art. Academic Medicine, 82(12), 1246-1251.
  • Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. W. W. Norton & Company.
  • Chaudhuri, S., & Dayal, U. (1997). An overview of data warehousing and OLAP technology. ACM Sigmod Record, 26(1), 65-74.
  • Chen, M., et al. (2021). Data-driven healthcare: Challenges, opportunities, and future directions. IEEE Journal of Biomedical and Health Informatics, 25(8), 2934-2942.
  • Chen, J. H., et al. (2019). Artificial intelligence in oncology: Review and future prospects. Nature Reviews Cancer, 19, 678–690.
  • Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Harvard Business Review. https://hbr.org/2016/11/there-is-a-blind-spot-in-ai-research
  • Davenport, T., & Ronanki, R. (2018). Artificial intelligence for the real world. , 96(1), 108-116.
  • Huang, X., et al. (2014). Deep learning for speech recognition: A review. IEEE Transactions on Audio, Speech, and Language Processing, 22(12), 2312-2322.
  • Kumar, A. (2020). Voice recognition technology: Applications and future trends. International Journal of Speech Technology, 23, 503–510.
  • Levant, F. (2019). IBM Watson's role in modern healthcare. Healthcare Informatics, 36(4), 45-49.
  • Liu, B., et al. (2018). Applications of voice recognition systems: A systematic review. IEEE Access, 6, 57326-57339.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Eamon Dolan/Houghton Mifflin Harcourt.
  • Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216-1219.
  • Power, D. J. (2002). Decision support systems: Concepts and resources for managers. Greenwood Publishing Group.
  • Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2, 3.
  • Shmueli, G., & Bruce, P. (2016). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. Wiley.
  • Wang, Y., et al. (2020). IBM Watson’s role in clinical decision support: A review. Journal of Medical Systems, 44(8), 1-9.

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