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Health Informatics on FHIR: How HL7’s New API Review

Health Informatics on FHIR: How HL7’s New API Review

Table of Contents

Have you ever wondered how the landscape of healthcare is evolving with the latest technology? Let’s dive into “Health Informatics on FHIR: How HL7’s New API is Transforming Healthcare,” a splendid book that sheds light on this very transformation.

Health Informatics on FHIR: How HL7s New API is Transforming Healthcare Hardcover – 6 August 2018

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What is FHIR?

FHIR stands for Fast Healthcare Interoperability Resources, a standard developed by HL7. FHIR is all about modernizing how healthcare information is exchanged between different systems. It emphasizes simplicity and ease of implementation without sacrificing security and accuracy.

Author’s Expertise

The author of this book clearly has deep insights and expertise in Health Informatics. Their background provides a solid foundation for understanding the intricate workings of FHIR and how it can be effectively implemented in the healthcare industry.

Health Informatics on FHIR: How HL7's New API is Transforming Healthcare Hardcover – 6 August 2018

Health Informatics on FHIR: How HL7's New API is Transforming Healthcare Hardcover – 6 August 2018

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Key Features of the Book

This book spans several key areas that make it a compelling read.

Easy-to-Understand Language

While the subject matter is quite technical, the book is written in an accessible language. It’s as if the author is having a casual conversation with us. No need for a background in health informatics to understand what’s going on.

Comprehensive Coverage

This book isn’t just about the basics. It delves deep into the nitty-gritty of FHIR, providing a comprehensive understanding of its applications and benefits. From data exchange protocols to security measures, it’s all covered here.

Real-World Examples

What makes this book stand out are the real-world examples. These practical scenarios help us understand the impact and implementation of FHIR in actual healthcare settings. These examples drive home the point that FHIR isn’t just theoretical but is actively being used to transform healthcare.

Table of Contents Breakdown

For a more structured understanding, let’s break down some of the main chapters and sections:

Chapter Key Topics Description
Introduction Basics of FHIR What FHIR is and why it’s important.
Chapter 1 Evolution of Health Informatics Historical overview and how FHIR fits in.
Chapter 2 FHIR Components Detailed look at resources, references, and APIs.
Chapter 3 Implementation Practical steps for adopting FHIR.
Chapter 4 Case Studies Real-world applications and success stories.
Conclusion Future of FHIR Predictions and future trends in health informatics.

Introduction

The introduction sets the stage by explaining what FHIR is and why it matters. It highlights the pressing need for better data interoperability in healthcare and how FHIR proposes to solve these issues.

Chapter 1: Evolution of Health Informatics

This chapter provides a historical context, tracing the development of health informatics over the years. Understanding where we came from helps us appreciate why FHIR is a game-changer in the current landscape.

Chapter 2: FHIR Components

Here’s where we get into the meat of the matter. The book breaks down the different components of FHIR, including resources, references, and APIs. Each of these is explained in a straightforward manner, making it easier for us to understand their function and importance.

Chapter 3: Implementation

This chapter provides a step-by-step guide on how to implement FHIR in various healthcare systems. It’s practical advice, enriched with tips and best practices, making the daunting task of implementation seem manageable.

Chapter 4: Case Studies

Seeing theory put into practice is always enlightening. This chapter includes several case studies that demonstrate how FHIR has been successfully adopted in different healthcare settings. These examples provide invaluable insights and prove that FHIR isn’t just a fad, but a functional solution being used today.

Conclusion: Future of FHIR

The book wraps up with a forward-looking perspective. What will the future hold for FHIR? How will it evolve? These and more questions are pondered upon in the conclusion, leaving us with much to think about.

Health Informatics on FHIR: How HL7s New API is Transforming Healthcare Hardcover – 6 August 2018

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Find your new Health Informatics on FHIR: How HL7s New API is Transforming Healthcare Hardcover – 6 August 2018 on this page.

Overall Impressions

Engaging Writing Style

One of the standout features of this book is its engaging writing style. We never felt bogged down by jargon or overly complex explanations. The author does a fantastic job of breaking down complex concepts into digestible parts.

Clear Explanations

Whether it’s a deep dive into FHIR components or an exploration of how FHIR can be implemented in healthcare systems, the explanations are clear and concise. This makes the book an excellent resource for both beginners and seasoned professionals.

Practical Advice

The book is rich with practical advice and best practices, which makes it an invaluable tool for anyone looking to implement FHIR in their organization. These nuggets of wisdom come from real-world experience, making them all the more relevant and useful.

A Word on Accessibility

Who Should Read This Book?

This book is suitable for a wide audience. Whether we are healthcare professionals, data scientists, or IT experts, we will find valuable information here. It’s also a great read for policy-makers and educators who are looking to understand the future of health informatics.

Prerequisites

No prior knowledge of FHIR or health informatics is needed to read this book. The author does an excellent job of bringing everyone up to speed, making complex subjects accessible to all.

Impact on Healthcare

Enhanced Data Interoperability

One of the most significant benefits of FHIR is enhanced data interoperability. By adopting FHIR, healthcare systems can easily exchange information, leading to better patient outcomes and more efficient care.

Improved Patient Care

Better data exchange means that healthcare providers have access to all the information they need, leading to more informed decision-making. This ultimately results in improved patient care, which is the end goal of any healthcare system.

Cost-Effectiveness

Implementing FHIR can also be cost-effective in the long run. By streamlining data exchange processes, healthcare organizations can save on costs associated with data management and storage.

Challenges and Limitations

Initial Implementation Costs

While FHIR has many benefits, the initial costs of implementation can be a hurdle for some organizations. However, the long-term benefits often outweigh these initial expenses.

Learning Curve

Like any new technology, there is a learning curve associated with FHIR. The book does a great job of breaking down these complexities, but organizations should be prepared for an adjustment period.

Security Concerns

Any system dealing with healthcare data must prioritize security. The book discusses various security measures and best practices, but implementing these effectively is crucial to protect sensitive patient information.

Future Prospects

Continued Evolution

FHIR is not a static standard; it is continually evolving. The book discusses how FHIR is likely to change and adapt in the future, providing insights into where health informatics is headed.

Wider Adoption

As more organizations see the benefits of FHIR, wider adoption is inevitable. This will lead to a more interconnected healthcare system, benefiting everyone involved.

Innovative Applications

The future holds endless possibilities for innovative applications of FHIR. From integrating with AI to creating smarter healthcare apps, the potential uses are limitless.

Final Thoughts

“Health Informatics on FHIR: How HL7’s New API is Transforming Healthcare” is a must-read for anyone interested in the future of healthcare. Its clear and engaging writing style, comprehensive coverage, and practical advice make it an invaluable resource. Whether we’re looking to understand the basics of FHIR or implement it in our organization, this book offers the insights and guidance we need.

Reading this book not only gives us a deeper understanding of FHIR but also inspires us to be part of the transformative changes happening in healthcare. As we navigate through the complexities of health informatics, this book serves as a trustworthy companion, guiding us every step of the way. We wholeheartedly recommend it to anyone looking to stay abreast of the latest developments in healthcare technology.

Find your new Health Informatics on FHIR: How HL7s New API is Transforming Healthcare Hardcover – 6 August 2018 on this page.

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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.