Lets talk

Health Informatics on FHIR Review

Health Informatics on FHIR Review

Table of Contents

Have you ever wondered how modern healthcare systems manage to seamlessly share and access medical information between different organizations and technologies? “Health Informatics on FHIR: How HL7’s API is Transforming Healthcare” provides an in-depth look into how HL7’s FHIR (Fast Healthcare Interoperability Resources) standard is making this possible.

Health Informatics on FHIR: How HL7s API is Transforming Healthcare Hardcover – 11 February 2022

This image is property of Amazon.com.

Find your new Health Informatics on FHIR: How HL7s API is Transforming Healthcare Hardcover – 11 February 2022 on this page.

Overview of the Book

Before diving into the nitty-gritty, let’s start with an overview of what this book is all about. Written by experts in health informatics, this book not only discusses the technical aspect but also investigates the broader impact FHIR is having on the healthcare sector. It’s a journey through the intersection of healthcare and technology aimed at professionals, students, and anyone interested in the subject.

Health Informatics on FHIR: How HL7's API is Transforming Healthcare Hardcover – 11 February 2022

Health Informatics on FHIR: How HL7's API is Transforming Healthcare Hardcover – 11 February 2022

AED352.74
AED221   Usually ships within 3 to 4 days

Author Background

The authors of this book are well-versed in both the healthcare and technology fields. Their experience and expertise shine through each chapter, making complicated concepts more digestible.

What is FHIR?

To appreciate the impact of this book, we need to understand what FHIR is. FHIR stands for Fast Healthcare Interoperability Resources. It’s a standard describing data formats and elements (known as “resources”) and an API for exchanging electronic health records. Let’s break down its core components:

Component Description
Resources Individual units of data like patients, medications, or allergies.
RESTful API Provides different methods (GET, POST, PUT, DELETE) to interact with these resources.
Data Formats Uses standardized formats like JSON and XML to represent resources for easy exchange and readability.

Health Informatics on FHIR: How HL7s API is Transforming Healthcare Hardcover – 11 February 2022

This image is property of Amazon.com.

Learn more about the Health Informatics on FHIR: How HL7s API is Transforming Healthcare Hardcover – 11 February 2022 here.

Why FHIR?

Improved Interoperability

One of the major strengths of FHIR is its ability to interoperate across various platforms. This capability is crucial as it helps integrate systems that otherwise wouldn’t be able to “talk” to each other.

Flexible and Fast

FHIR is designed to be both flexible and fast, aiming to support a wide range of applications without sacrificing performance. The standard’s flexibility means it can adapt to different needs, whether that’s a large hospital or a small clinic.

Chapter Breakdown

This book is divided into several chapters that explore different aspects of FHIR. Below is a quick summary of what each chapter covers:

Chapter 1: Introduction to FHIR

This chapter serves as an entry point, explaining what FHIR is and why it was developed. It discusses its historical background and compares it to previous standards.

Chapter 2: Core Components

In this chapter, we get an in-depth explanation of FHIR’s core components. The authors introduce us to resources, APIs, and data formats, outlining how they work together.

Chapter 3: Implementing FHIR

Here, the book gets more technical, walking us through the process of implementing FHIR in a healthcare setting. There are practical examples that help understand the implementation steps.

Chapter 4: Use Cases

The authors provide several real-world use cases where FHIR has been effectively implemented. This section is particularly engaging as it shows the practical value of FHIR in solving specific problems.

Chapter 5: Challenges and Limitations

No technology is perfect, and FHIR is no exception. This chapter explores some of the challenges involved in adopting FHIR, such as integration issues and data security concerns.

Technical Insights

Resource Design

The design of FHIR resources is modular, intended to be simple yet comprehensive. This design allows flexibility in how data is managed and exchanged, making it easier to align with various healthcare systems.

API Functionality

The RESTful API in FHIR simplifies data exchange. With operations like GET for retrieving data, POST for creating new data, PUT for updating existing data, and DELETE for removing data, it follows common web standards making it accessible even to those who are not deeply technical.

Data Formats

Using JSON and XML as data formats, FHIR ensures that the data can be easily parsed and understood by different systems, thus facilitating smoother integration.

Impact on Healthcare

Patient Outcomes

By enabling quicker and more accurate data sharing, FHIR can significantly improve patient outcomes. For instance, doctors can have immediate access to a patient’s medical history even if they have been treated in different hospitals.

Reduction in Costs

Improved interoperability through FHIR can result in cost savings. Reducing duplicate tests and procedures, minimizing administrative work, and improving overall efficiency are some of the cost benefits discussed in the book.

Enhanced Research

The standardized data formats and APIs make it easier for researchers to access large datasets, accelerating medical research. This book provides examples of how FHIR is being used in the research field to develop new treatments and understand diseases better.

Practical Applications

Electronic Health Records (EHR)

One of the primary applications of FHIR is in Electronic Health Records. The book details how FHIR’s standardized resources can be used to create, read, update, and delete data within EHR systems.

Mobile Health Applications

Mobile health applications have seen rapid growth. The book discusses how FHIR APIs can be integrated into mobile apps to provide real-time health monitoring and data sharing between patients and healthcare providers.

Integration with Other Standards

FHIR doesn’t work in isolation. The book emphasizes how it integrates with other standards like HL7 v2 and CDA. This ensures that even legacy systems can benefit from FHIR’s advancements.

Pros and Cons

To make it easier to understand the advantages and disadvantages, here’s a quick table summarizing them:

Pros Cons
Improves interoperability Requires initial investment in training
Flexible and scalable Complexity can be high
Uses common data formats (JSON, XML) Security concerns
Faster implementation May not be fully adopted everywhere
Enhances patient care Integration issues with legacy systems

Advantages

Interoperability

As we’ve mentioned before, one of FHIR’s biggest strengths is its ability to facilitate communication among disparate systems. The book uses several examples to illustrate this point effectively.

Flexibility

Whether for a large hospital setting or a smaller clinic, the book shows how FHIR’s modularity allows it to be adapted to various needs.

Disadvantages

Learning Curve

Despite its advantages, FHIR is not without its challenges. For example, there is an initial investment required in terms of training and getting familiar with the new standard.

Security Concerns

Data security is a big issue in healthcare, and while FHIR aims to make data exchange seamless, it doesn’t entirely eliminate security risks. The book discusses some of these concerns and suggests ways to mitigate them.

Conclusion

“Health Informatics on FHIR: How HL7’s API is Transforming Healthcare” is a comprehensive guide for anyone looking to understand and implement FHIR in their healthcare systems. From its technical insights to practical applications and real-world use cases, the book provides a well-rounded view of the role FHIR is playing in transforming healthcare.

Whether you are a healthcare professional, a student, or just someone interested in the intersection of healthcare and technology, this book offers valuable insights and information that are both engaging and educational.

See the Health Informatics on FHIR: How HL7s API is Transforming Healthcare Hardcover – 11 February 2022 in detail.

Disclosure: As an Amazon Associate, I earn from qualifying purchases.

Want to keep up with our blog?

Get our most valuable tips right inside your inbox, once per month!

Related Posts

Writing a Dissertation For Dummies Review

University Essentials

Artificial Intelligence Textbook For Class IX Review

Artificial Intelligence

Succeeding with Your Master’s Dissertation Review

University Essentials

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.