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Intelligent Automation Learn How To Harness Artificial Intelligence To Boost Business & Make Our Wo Review

Intelligent Automation Learn How To Harness Artificial Intelligence To Boost Business & Make Our Wo Review

Table of Contents

Have we ever wondered how businesses seamlessly integrate artificial intelligence into their day-to-day operations? “Intelligent Automation: Learn How To Harness Artificial Intelligence To Boost Business & Make Our Work Smarter” might just be the brilliant guide we need. This engaging book, published on October 10, 2020, is a substantial read and promises to be an indispensable tool for anyone looking to leverage AI in their business endeavors. Let’s take a closer look.

See the Intelligent Automation Learn How To Harness Artificial Intelligence To Boost Business Make Our Wo Paperback – Big Book, 10 October 2020 in detail.

Content Quality and Structure

Unpacking the Contents

The book opens with a robust introduction, guiding us gently into the complex world of AI, much like finding our footing before diving into the deep end of the pool. It’s immediately clear that the authors have a knack for making a challenging subject palatable and even enjoyable. The chapters are aptly divided and follow a logical progression, which ensures that our learning journey is smooth.

Depth of Information

Delving deeper, each chapter feels adequately researched. The authors provide real-world examples and case studies, making concepts not only easier to grasp but also relatable. We are continually impressed by how the book balances technical information with actionable advice.

Writing Style

The friendly tone of the book reminds us of a conversation with a knowledgeable friend rather than a dry lecture from a professor. Complex terms are broken down and explained thoroughly, ensuring that we never feel left behind. The authors’ enthusiasm for the subject matter is contagious.

Intelligent Automation Learn How To Harness Artificial Intelligence To Boost Business & Make Our Wo Paperback – Big Book, 10 October 2020

Intelligent Automation Learn How To Harness Artificial Intelligence To Boost Business & Make Our Wo Paperback – Big Book, 10 October 2020

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Practical Applications

Making AI Understandable

Artificial intelligence can be a daunting topic, but this book does an excellent job of demystifying it. Descriptions and explanations of algorithms, machine learning, and data analytics are presented in a way that is both engaging and non-intimidating. If there were ever a Rosetta Stone for AI, this would be it.

Real-World Examples

Through various case studies, we gain insights into how AI has revolutionized different industries. For instance, we see how AI has optimized supply chains, enhanced customer experiences, and driven innovation in tech companies. These stories not only illustrate points but also inspire us to see the potential AI holds.

Actionable Steps

At the end of each chapter, the book provides actionable steps to implement the discussed principles. This “how-to” approach transforms theoretical knowledge into practical skills. Additionally, there is a systematic approach that we can follow, making the integration of AI in our businesses seem very achievable.

Intelligent Automation Learn How To Harness Artificial Intelligence To Boost Business Make Our Wo Paperback – Big Book, 10 October 2020

Learn more about the Intelligent Automation Learn How To Harness Artificial Intelligence To Boost Business Make Our Wo Paperback – Big Book, 10 October 2020 here.

User-Friendliness

Easy Navigation

The layout of the book is clean and well-organized. Sections and subsections are clearly marked, making it easy for us to find specific information quickly. This is particularly useful when revisiting certain topics or trying to clarify particular points.

Visual Aids

While the book avoids overly technical jargons, it does employ useful visual aids such as charts, graphs, and tables to further clarify points. For example, one of the tables succinctly breaks down the differences between various machine learning algorithms and their applications.

Algorithm Type Best Used For Key Benefits
Supervised Learning Predictive analytics, regression, etc. High accuracy, direct feedback
Unsupervised Learning Customer segmentation, anomaly detection Identifies hidden patterns, useful for complex datasets
Reinforcement Learning Robotics, game playing Learns from trial and error, improves over time

Appendix and Resources

The appendix is a treasure trove of additional resources for those who want to dig even deeper. From recommended readings to useful websites, the book points us toward valuable information sources that further enrich our understanding.

Building a Strategy

Framework for Implementation

One of the book’s standout features is the detailed framework it offers for implementing AI. This isn’t a mere run-through of high-level concepts; it’s a concrete guide that breaks down the process into manageable steps. From initial assessment to pilot testing and full-scale deployment, every phase is meticulously detailed.

Risk Management

Change invariably comes with risks, and the book does an excellent job of highlighting potential pitfalls. More importantly, it provides strategies to mitigate these risks, ensuring that our foray into AI doesn’t turn into a costly misadventure.

Continuous Improvement

AI is an evolving field, and the book emphasizes the importance of staying updated. It encourages us to foster a culture of continuous learning and adaptation within our organizations, ensuring that we always stay ahead of the curve.

Intelligent Automation Learn How To Harness Artificial Intelligence To Boost Business Make Our Wo Paperback – Big Book, 10 October 2020

Community and Collaboration

Building a Culture of Innovation

The book stresses the importance of creating a culture that nurtures innovation. By fostering an environment that encourages experimentation and values new ideas, our organizations can not only adapt to AI but thrive in an AI-driven world.

Collaborative Efforts

AI isn’t a one-person job, and the book promotes a collaborative approach. From forming cross-functional teams to involving external experts, it’s clear that teamwork is critical. This collaborative mindset is constantly reinforced throughout the book.

Ethical Considerations

In an era where data privacy and ethical AI are hot topics, the book doesn’t shy away from these discussions. It encourages us to adopt responsible AI practices, ensuring that our AI initiatives align with ethical standards and contribute positively to society.

Verdict

Strengths

“Intelligent Automation: Learn How To Harness Artificial Intelligence To Boost Business & Make Our Work Smarter” excels in making a complex subject approachable without sacrificing depth. It’s well-organized, filled with real-world applications, and provides actionable steps for implementation. Importantly, it doesn’t just tell us what AI can do but shows us how to embark on an AI journey.

Areas for Improvement

If we had to nitpick, the book could benefit from a few more visual aids and infographics. While it currently provides a substantial amount of visual data, additional graphical elements could further enhance comprehension. Also, a more expansive discussion on AI tools and software would be welcome, although the provided resources do guide us towards further reading.

Final Thoughts

Overall, this book is a must-have for any business professional looking to leverage AI. It’s not just about understanding AI; it’s about strategically implementing it to achieve tangible business results. This friendly yet authoritative guide equips us with the knowledge and confidence needed to navigate the fascinating world of AI.

We believe that “Intelligent Automation: Learn How To Harness Artificial Intelligence To Boost Business & Make Our Work Smarter” is more than just a book—it’s a compass guiding us through the modern landscape of AI-driven business innovation. Whether we are seasoned professionals or newcomers to the world of AI, this book offers valuable insights and practical advice that can make a transformative impact on our businesses.

Click to view the Intelligent Automation Learn How To Harness Artificial Intelligence To Boost Business Make Our Wo Paperback – Big Book, 10 October 2020.

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Emmanuel Bazile All Rights Reserved

About Me

With 25 years of experience in healthcare IT implementation, Emmanuel began his career at the University of Pittsburgh Medical Center, working as an assistant manager for a billing system implementation. Over the years, he has explored various aspects of the healthcare IT domain, successfully implementing several laboratory information systems and electronic medical record (EMR) systems, such as Cerner Millennium and Epic EMR.

In 2005, Emmanuel shifted his focus to public health, working on bio-surveillance implementation for the Centers for Disease Control and Prevention (CDC). He contributed to the BioSense Data Provisioning Project and performed extensive analysis of HL7 messages in hospitals and healthcare facilities. Additionally, Emmanuel requirements analysis for the CDC BioSense Analysis, Visualization and Reporting (AVR) project and played a key role in publishing the Situational Awareness updates to the BioSense System Requirements Specification (SRS).

Over the past 11 years, Emmanuel has worked in the Middle East, implementing the Epic EMR system at Cleveland Clinic Abu Dhabi. As a multidisciplinary team member, he has taken on various roles, including SCRUM Master, Project Manager, Integration Engineer, and Platform Engineer. Concurrently working as an adjunct university faculty member, teaching graduate-level courses in Systems Life Cycle and undergraduate courses in Health Information Systems

From a technological standpoint, Emmanuel has designed, installed, and implemented complete hospital integration systems using Rhapsody Integration Engine, MS SQL Server, and Public Health Information Networks Messaging System (PHINMS). He has also developed over 10,000 interfaces some of which coded in Java and JavaScript.

In 2019, Emmanuel expanded his skill set and entered the field of digital marketing, quickly becoming a proficient Digital Marketing Strategist. He has since helped numerous clients develop robust digital marketing strategies for their businesses. His expertise encompasses Social Media Marketing, On-page and Off-page SEO, Google Ads, and Google Analytics. Additionally, he and a team have managed clients’ website development projects, ensuring that each site is optimized for SEO, further enhancing their online presence and performance.

Alongside their digital marketing expertise, Emmanuel has delved into the world of Affiliate Marketing, where Emmanuel and his team successfully managed and executed campaigns for a variety of clients. By identifying the right products and services to promote, Emmanuel and his team helped clients generate passive income streams and increase their overall revenue.

Their approach to Affiliate Marketing involves creating valuable content that educates and engages the target audience, while strategically incorporating affiliate links. Emmanuel and his team have experience working with multiple affiliate networks and platforms, ensuring optimal tracking and reporting of performance metrics. By staying up to date with the latest trends and best practices, Emmanuel and his team have been able to optimize affiliate campaigns for maximum results, fostering long-term partnerships and sustainable growth for their clients.

As an accomplished professional, Emmanuel holds dual Bachelor of Arts degrees in Linguistics and English, a Master of Science in Health Information Systems from the University of Pittsburgh, and a Ph.D. in Information Systems from Nova Southeastern University.

My Teaching History

Professor Bazile is a dedicated technology instructor and Adjunct Faculty professor, who began his teaching career in April 2000 at the Business Career Institute in Las Vegas, Nevada.

In 2001, he expanded his expertise by training nurses in the use of Electronic Medical Records (EMR) systems. His experience in both technology and healthcare led to his appointment as an Adjunct Faculty professor at the University of Phoenix in May 2008, where he has taught several graduate-level information technology and healthcare information systems courses.

Dr. Bazile has also developed an HL7 course, which he has taught at various healthcare facilities, drawing from his own book, “HL7: Introductory and Advanced Concepts,” currently available on Amazon. With a passion for teaching and a commitment to ensuring students get the most out of each course he teaches, Dr. Bazile is a valuable asset to both his students and the institutions he serves.

My Teaching Philosophy

My teaching philosophy as an Information Systems professor in healthcare is built on the concept that education should equip students to be confident and capable problem solvers who are prepared to traverse the complicated and ever-changing landscape of Healthcare IT.

In order to accomplish this, I prioritize the creation of a dynamic and engaging learning environment that encourages students to engage with course material and with one another. This involves employing a range of teaching approaches, such as lectures, seminars, and hands-on activities, to ensure that students learn in the manner that best matches their learning style.

I believe the reason we have Information Systems as a discipline is to allow students to apply technology to solve real world problems. If that is the case, both undergraduate and graduate students have to be challenged to incorporate their core academic courses with their matriculated subjects. As such, it is important that students enter their Junior and Senior years with a strong command of the core courses such as Programming, databases, networks, hardware and software, as they serve as the foundation upon which real-world solutions will be built.

I also believe in the importance of incorporating real-world examples and case studies into my courses, as this helps to connect abstract concepts to practical applications. Additionally, I encourage students to apply what they are learning to their own personal and professional goals, as this helps to make the material more meaningful and relevant to their lives.

I strive to foster a positive and supportive learning environment where all students feel comfortable asking questions and participating in class discussions. I believe that this is key to fostering a sense of community and ensuring that all students have the opportunity to succeed.

I have also taught online courses. I have found in an asynchronous learning environment it can be difficult to apply the Peer Teaching or Experiential Learning Pedagogical Approaches. However, I have found the Discovery Learning approach to works quite well. Along with a boost to students’ self-confidence, Discovery Learning in an online environment allows students to synthesize information, expand on existing concepts on their own, while experiencing a positive outcome through trial and error.

Ultimately, my mission as an educator, and a Healthcare IT Information Systems professor is to provide students with the knowledge, skills, and confidence they need to thrive and succeed in their careers and to be technological leaders. By creating a positive and supportive learning environment, incorporating real-world examples and case studies, and encouraging students to apply what they are learning to their own objectives; my hope is to inspire and empower all students to achieve their full potential.

Population Size:

A total of 310 responses were originally received. Any response containing missing data due to unclicked radio buttons or unchecked checkboxes were first reviewed, and, if justified, were omitted from analysis. For surveys with missing data, a total of 18 responses were removed. In order to address any issues with response-set, the data was downloaded into Microsoft Access and queries ran to identify responses that contained the same values for each question. A total of 16 responses were found to be qualified for removal. Another 18 were identified as outliers and removed leaving a total of 258 responses for the study analysis.

In order to assess multivariate outliers, the Mahalanobis distances were calculated and plotted against their corresponding Chi-Square distribution percentiles (Schmidt & Hunter, 2003). The resulting scatterplot is similar to a univariate normal Q-Q plot, where deviations from a straight line show evidence of non-normality. The data showed indications of moderate deviations from multivariate normality, as indicated by the concavity of the data points. There were no additional multivariate outliers or missing values in the data after the removal of 52 responses.

Descriptive Statistics

Frequencies and percentages were conducted for the demographics indicators, while means and standard deviations were calculated for the continuous indicators. For gender, there were 151 females (59%) and 107 males (41%) in the sample. For ethnicity, most participants were Caucasian (119, 46%), followed by African American (56, 22%). The two most populous education levels were Bachelor’s (90, 35%) and Master’s (62, 22%). The biggest proportion of the sample by age group was the 35-44 age group (101, 39%) followed by the 45-54 age group (59, 23%).

Analysis:

Confirmatory Factor Analysis and Composite Reliability

A CFA was conducted along with a reliability analysis to assess construct validity. Examination of modification indices and factor loadings indicated that CSE1, CSE5, CSE7, PC5, ATE1, ATE6, ATE8, PP5, and PP6 were all causing significant problems with the model parameters. The results of the last iteration of the CFA performed in R showed significantly improved fit, although still poor overall (χ2(545) = 2125.61, p < .001, CFI = 0.82, TLI = 0.81, RMSEA = 0.11). The high degrees of freedom indicate that a very large number of parameters are being estimated in this model.

Composite Reliability

For the full model, each construct had excellent reliability. The ATE latent construct had a composite reliability value of 0.89. The ORC construct had a composite reliability value of 0.94. The CSE latent construct had a composite reliability value of 0.85 and PC had a composite reliability value of 0.95. For PP and RES, the composite reliability scores were 0.80 and 0.96 respectively. These values indicate that the loadings for each construct were all directionally similar, and that the items in each construct show a high degree of consistency.

Cronbach’s Alpha

Cronbach’s alpha values were calculated for the items in each construct. The alphas for PC (α = 0.90), AXY (α = 0.94), and RES (α = 0.94) indicated excellent reliability. The alphas for CSE (α = 0.80), ATE (α = 0.88), and PP (α = 0.83) all showed good reliability. These values confirm the results of the composite reliability tests, and reiterate the high degree of reliability within each latent construct.

Partial Least Squares – Structural Equation Modeling

A partial least squares- structural equation modeling (PLS-SEM) was conducted to determine how well the data fit the proposed model, and discern whether significant relationships existed between the independent and dependent constructs. The full model showed AVE values of 0.53 for ATE, 0.69 for AXY, 0.44 for CSE, .72 for PC, .35 for PP, and 0.81 for RES. The high values for AXY, PC, and RES indicate that the amount of variance accounted for in the manifest variables is sufficiently high. The values for ATE, CSE, and PP indicate that some of the variance in the manifest variables is left unexplained.

Structural Model

Once the measurement model had been tested for model specification, the structural model was tested to determine if ATE, AXY, CSE, PC, and PP had a significant effect on RES. A path weighted model was calculated using 10,000 bootstrap samples in R. The results showed a pseudo R-squared value of 0.78. This indicates that approximately 78% of the variance in RES is explainable by the collective effects of CSE, PC, ATE, PP, and AXY.

Further examination of the effects indicated that AXY had a highly significant effect on RES (B = 0.87, p < .001). This indicates that a standard deviation increase in AXY increases the expected value of RES by 0.87 standard deviations. CSE did not have a significant effect on RES (B = 0.02, p = .423). Additionally, CSE (B = 0.02, p = .423), PC (B = 0.05, p = .334), ATE (B = 0.00, p = .983), and PP (B = 0.03, p = .407) did not have significant effects on RES. Table 11 outlines the results of the path estimates.

Correlation Analyses

Both Pearson and Spearman correlations were calculated on the composite scores. The results of the Pearson correlations indicated that CSE was significantly correlated AXY (r = 0.22, p < .001) and RES (r = 0.21, p < .001). The results also indicated that PC was significantly correlated with ATE (r = -0.79, p < .001), AXY (r = 0.18, p < .001), and RES (r = 0.20, p < .001). ATE was significantly correlated with AXY (r = -0.19, p < .001) and RES (r = -0.19, p < .001). AXY was significantly correlated with RES (r = 0.85, p < .001).

ANCOVA Analyses

An analysis of covariance (ANCOVA) was conducted to determine if a significant relationship existed between the AXY, PP, CSE, PC, ATE scores and RES controlling for Gender, Age, Ethnicity, Education, and Specialty. The overall model was found to be significant (F(63,194) = 53.39, p < .001), with an R2 value of .95, indicating that 95% of the variance in RES was explained by the collective effect of the independent variables and covariates.

Since the overall model was found to be significant, the model’s covariates were assessed. The AXY (F(10,194) = 262.20, p < .001), ATE (F(7,194) = 2.20, p = .036), Years computers (F(1,194) = 5.71, p = .018), and PC (F(12,194) = 2.00, p = .026) scores were found to be significant, indicating that a significant amount of variance in RES is explained by AXY, ATE, and PC.

A path diagram depicting the results of the structural model.

Results

This research investigated Computer Self-Efficacy (CSE), Perceived Complexity (PC), Attitudes toward EMR Systems (ATE), Peer Pressure (PP), and Anxiety (AXY) to determine whether these constructs as individuals, or as a group, or coupled together with some other factors could significantly explain resistance to EMR systems. Quantitative examination of self-reported survey results was performed to understand the strength and significance of the relationships, while these relationships were investigated to test the strength of model fit.

the regression paths of the structural model were examined to test the hypotheses. Significance was determined using an alpha level of .05. The model had an overall R2 value of 0.78. This indicates that approximately 78% of the variability in RES can be accounted for by CSE, PC, ATE, PP, and AXY. Since the overall model was significant, the individual coefficients can be interpreted. Some of the hypotheses were supported by the results of this study, and some were rejected. The construction of a data model of the relationships in this study could not meet thresholds that would be evidence of a good fit of the relationships identified in the study.

The fifth hypotheses tested the influence of AXY on resistance to EMR systems. AXY was expressed to be significantly related to resistance (r=.87, p<.001). This finding supports the hypothesis that anxiety with the EMR system will lead to medical care professionals rejecting use of the system. Unlike the findings of the first four hypotheses, the findings of the current study support previous research. Angst and Agarwal (2009) indicated that AXY is a factor which is significantly related to the problem of EMR system resistance. Based on the empirical findings of previous research, the present research and conceptual propositions and conclusions in previously written scholarly articles, there is a great deal of support for the finding that AXY is significantly influenced by EMR resistance.

The findings of this research do not support all findings by previous researchers, and there are multiple relationships which had been established as being significant that were identified as being insignificant in the current research. Generally, because of the inconsistency of previous findings and the current study there may be elements related to the sample examined or other contextual factors which may contribute to the inconsistency that exists. Ultimately, it is suggested that there be further research done on the problem of resistance to EMR system use.

Ultimately the findings support a new take on the problem of EMR system resistance that may contribute to the ways in which scholars investigate the problem of EMR resistance in general. This may also help with the way practitioners approach EMR systems, and articulate value of the systems to medical professionals investing record-keeping systems in the workplace.