Journal:Health informatics: Engaging modern healthcare units: A brief overview

Abstract

With a large amount of unstructured data finding its way into health systems, health informatics implementations are currently gaining traction, allowing healthcare units to leverage and make meaningful insights for doctors and decision makers using relevant information to scale operations and predict the future view of treatments via information systems communication. Now, around the world, massive amounts of data are being collected and analyzed for better patient diagnosis and treatment, improving public health systems and assisting government agencies in designing and implementing public health policies, while also instilling confidence in future generations who want to use better public health systems.

This article provides an overview of the |Health Level 7 FHIR architecture, including the workflow state, linkages, and various informatics approaches used in healthcare units. The article discusses future trends and directions in health informatics for successful application to provide public health safety. With the advancement of technology, healthcare units face new issues that must be addressed with appropriate adoption policies and standards.

Keywords: health informatics, public health, information systems, health policy, public health systems

Introduction

Machine learning is the fastest-growing topic in computer science today, and with it a health informatics implementation of ML is one of the more difficult problems to solve. [1, 2]

Emerging economies are increasing their investments in healthcare, which makes sense and encourages health professionals to adopt sound frameworks and regulatory standards, as well as health IT, to improve the quality and efficacy of care. [3] In this expanding field, new age occupations can be established. This new field has the potential to be a lucrative career path in the future. With a clear flow of information across many medical subsystems, adoption of electronic health record systems (EHRs) will improve the health care system going forward. [4]

Big data is frequently employed in the field of health informatics, as new data is constantly pouring into the system, requiring analysis and interpretation in order to make rational decisions. [5, 6] This big data has ushered in a new era for healthcare companies to improve decision-making through the comprehensive integration of data from a range of sources, allowing for much faster and more effective decision making. [7] As such, within and outside of the medical industry, computational health informatics has become an emerging field of study. [7–9]

In recent years, the healthcare industry has seen a rapid growth in medical and healthcare data, which can be used to improve facilities and public healthcare utilization and implementation using novel treatment and diagnosis methodologies. In turn, this more efficient use of healthcare data gives patients confidence in using the best public healthcare services available and aids governments in developing better healthcare policies. [10]

In today's increasingly complex social and economic environment, at hand is the vital issue of improving quality of offered healthcare services while lowering prices. This is largely what health informatics has attempted to solve. The major purpose of health informatics is to increase our understanding of medicine and medical practice by using real-world medical data. In the scope of healthcare, health informatics is practically a blend of information science and computer science. [15]

At the core of health informatics has historically been a collection of computerized systems for assisting patient analysis and diagnosis. More recent technologies have emerged that make it even easier for clinicians to make better healthcare decisions. [11, 12] As health informatics continues to evolve, it promises to improve public health activities through the advanced application of information and communication technologies (ICT). [13] ICTs have been shown to help healthcare systems increase productivity, which has resulted in significant cost savings in operations and service delivery. For administrative and healthcare objectives, ICTs have already proven to be quite effective. Additionally, new prospects for new medical equipment and systems are opening up as ICTs become smaller, quicker, wireless, and remotely controlled.

The internet and web have recently brought up new possibilities for increasing the response time of healthcare services, while also lowering costs. It is clear that we are in the early stages of a new era that will fundamentally alter the way healthcare services are provided. This will help us acquire the public's trust in using high-quality healthcare services. However, new e-Health services and technology must still be researched, developed, promoted, and disseminated with significant effort. With the COVID-19 pandemic presently sweeping the globe, increasing ICT use has demonstrated that healthcare can and will become more contactless in the future, with fresh means of treating patients and providing healthcare services emerging. This is a popular yet difficult research subject since it necessitates interdisciplinary competence. [14]

Additionally, as big data continues to increasingly find its way into healthcare, additional challenges exist in the effective use of big data within ICT frameworks. For example, big data in healthcare is intimidating not only because of its sheer magnitude, but also due to the variety of data types and the pace with which it must be managed. To gain people' trust and give quality healthcare services, all health service providers are now putting in extra effort to use the most up-to-date technologies to effectively use big data to provide quality health services and advanced treatments.

Various requirements drive innovation in this industry, such as finding appropriate accommodation with standardization and coordinating the acquisition and implementation of newer healthcare systems and services on a national/international level. With COVID-19 still threatening disruption in the healthcare sector, investments in this sector are gaining steam with new-age healthcare units in many nations, and growing economies such as India and China will continue to play a vital role in providing quality healthcare services to its citizens in the future. At the same time, those new-age healthcare units and systems will aid in dramatically lowering costs, making public healthcare systems more dependable, and instilling citizens' confidence in using inexpensive, high-quality healthcare.

Related work

"Big data" is a term used to describe a significant volume of data that is collected and stored yet has outgrown standard data management and analysis solutions. Solutions like Hadoop and Spark, according to Roger Fyre and Mark McKenney, have arisen to solve some of these big data concerns. [16] For example, researchers have used Hadoop to implement a variety of parallel processing algorithms to efficiently handle geographical data. [17, 18] Multistage map and reduce algorithms, which generate on-demand indexes and retain persistent indexes, are the end result of these techniques. [19]

Other techniques such as predictive analytics and data mining have also been employed. Much of the current work on predictive analytics, particularly in clinical contexts, is aimed at improving health and financial outcomes, which will aid in making better decisions. [20] Data mining, which is defined as the processing and modeling of huge amounts of medical/health data to identify previously unknown patterns or associations, is another important machine learning approach. [21, 22] Data mining has been used, for example, in the collection of data for diseases such as cancer and neurological disorders in order to improve disease prognosis. [23, 24] Cancer detection and diagnosis, as well as other health-related issues, have been made possible because to these breakthroughs. [25] Machine learning is also crucial in the testing and development of various models that take into account clinical and other important medical characteristics for decision making.

Deep learning is now also being used to solve more difficult problems in the arena of health informatics. [26, 27] For example, advances in medical imaging and its data management have made positive contributions to decision making. Today, medical imaging incorporates capabilities such as image segmentation, image registration, annotation, and database retrieval, holding greater promise for decision makers. As such, new deep learning and machine learning models can be employed with medical imaging for speedier decision making. [26] However, this means researchers in the fields of data science, machine learning, and deep learning remain in high demand for developing effective algorithms that adapt to changing data.

Holzinger et al. [28] examined many approaches to developing an explainable prediction model for the medical domain. Prediction explanations can be useful in a variety of situations, including teaching, learning, research, and even the courtroom. Similarly, the demand for interpretable and explainable models is growing in the medical field. However, these models must be able to re-enact the decision-making and knowledge-extraction processes. Ribeiro et al. (29) have emphasized this requirement, discussing how machine learning models are essentially black boxes. Understanding the reasons for predictions can help to build trust, better assess model performance, and construct better, more accurate, and correct models by providing insights into the model. They propose the LIME algorithm [29] for explaining predictions of any model. Similarly, though dealing with neural machine translation, the proposed model of Bahdanau et al. [30] can be used in a variety of other applications such as healthcare.

Introduction to HL7 FHIR architecture

In the last two decades, EHRs have been widely implemented in the United States to improve healthcare quality, increase patient happiness, and reduce healthcare costs. [31–33] As growing countries such as India, China, and Bangladesh experiment with innovative ways to establish EHR systems, they will significantly aid in the development of effective public health systems in those countries. In all cases, at the core of most effective EHRs is Health Level 7's (HL7's) FHIR architecture.

The basic idea behind HL7's FHIR was to create a set of resources and then create HTTP-based REST application programming interfaces (APIs) to access and use these resources. FHIR uses components called "resources" to access and perform operations on patient's health data at the granular level. This feature distinguishes FHIR from all other standards because it was not present in any earlier version of HL7 (v2, v3) or the HL7 clinical document architecture (CDA).

The fundamental building blocks of FHIR are the so-called resources, which are generic definitions of common health care categories (e.g., patient, observation, practitioner, device, condition). For data interchange and resource serialization, FHIR employs JavaScript object syntax and XML structures. FHIR not only supports RESTful resource exchange but also manages and documents an interoperability paradigm.

FHIR has grown in popularity and is being increasingly used by the healthcare industry since its inception. In 2018, six major technology companies—including Microsoft, IBM, Amazon, and Google—vowed to remove barriers to healthcare interoperability and signed a statement mentioning FHIR as an emerging standard for the interchange of health data. With the incorporation of Substitutable Medical Applications Reusable Technologies (SMART), a platform for interoperable applications [34], FHIR can be expected to attract even more attention to digital health tools in the future. As is, the use of FHIR for medical data transmission has the potential to deliver benefits in a wide range of disciplines, including mobile health apps, EHRs, precision medicine, wearable devices, big data analytics, and clinical decision support.

The primary goal of FHIR is to reduce implementation complexity while maintaining information integrity. Furthermore, this new standard integrates the benefits of existing HL7 standards (v2, v3, and CDA) and is projected to overcome their drawbacks. FHIR enables developers to create standardized browser applications that allow users to access clinical data from any healthcare system, regardless of the operating systems and devices used. Figure 1 represents the general architecture of FHIR. [35]

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. Some grammar and sentence placement was cleaned up for better readability.