Journal:Transforming healthcare analytics with FHIR: A framework for standardizing and analyzing clinical data

Full article title	Transforming healthcare analytics with FHIR: A framework for standardizing and analyzing clinical data
Journal	Healthcare
Author(s)	Ayaz, Muhammad; Pasha, Muhammad F.; Alahmadi, Tahani J.; Abdullah, Nik N.B.; Alkahtani, Hend K.
Author affiliation(s)	Monash University, Princess Nourah bint Abdulrahman University
Primary contact	Email: muhammad dot ayaz at monash dot edu
Year published	2023
Volume and issue	11(12)
Article #	1729
DOI	10.3390/healthcare11121729
ISSN	2227-9032
Distribution license	Creative Commons Attribution 4.0 International
Website	https://www.mdpi.com/2227-9032/11/12/1729
Download	https://www.mdpi.com/2227-9032/11/12/1729/pdf?version=1686738253 (PDF)

Abstract

In this study, we discuss our contribution to building a data analytic framework that supports clinical statistics and analysis by leveraging a scalable standards-based data model named Fast Healthcare Interoperability Resources (FHIR). We developed an intelligent algorithm that is used to facilitate the clinical data analytics process on FHIR-based data. We designed several workflows for patient clinical data used in two hospital information systems (HIS), namely patient registration systems and laboratory information systems (LIS). These workflows exploit various FHIR application programming interfaces (API) to facilitate patient-centered and cohort-based interactive analyses. We developed a FHIR database implementation that utilizes FHIR APIs and a range of operations to facilitate descriptive data analytics (DDA) and patient cohort selection. A prototype user interface for DDA was developed with support for visualizing healthcare data analysis results in various forms. Healthcare professionals and researchers would use the developed framework to perform analytics on clinical data used in healthcare settings. Our experimental results demonstrate the proposed framework’s ability to generate various analytics from clinical data represented in the FHIR resources.

Keywords: data analytics, data analysis, FHIR, EMR, EHR

Background

To provide a comprehensive idea to readers about the applications of data analytics in the healthcare industry, this section introduces the data analytics concept employed in the healthcare sector. We also discuss the data analytics concept in the clinical data represented in the latest healthcare data standard, Fast Healthcare Interoperability Resources (FHIR).

Healthcare data analytics

Healthcare data analytics is the process of analyzing and interpreting large sets of healthcare data to gain insights and improve healthcare outcomes. It involves using a range of analytical techniques and tools to process data from various sources, such as electronic health records (EHRs), electronic medical records (EMRs), medical devices, claims data, patient-generated data, etc. The rapid advancements in hardware and software technologies in recent years have ushered in a new era of data collection and processing, resulting in remarkable progress in the field of healthcare data analytics. In the realm of healthcare organizations, clinical data serve a dual purpose. Firstly, it is utilized for the delivery of healthcare services to patients. Secondly, it is used for secondary purposes such as research, analysis, quality improvement, and more. In particular, the secondary use of clinical data has emerged as a critical component of healthcare data analytics. This has resulted in a paradigm shift in recent healthcare settings, where the secondary use of healthcare data is deemed just as important as its primary use.

EHR systems are leveraged to facilitate the secondary use of healthcare data, for activities such as quality improvement, safety measurement, payments, provider certification, marketing, and research. [1] Moreover, the secondary use of healthcare data has the potential to significantly enhance the healthcare experiences of individuals. It can facilitate the learning of diseases and their effective treatments, deepen people’s knowledge and understanding of the effectiveness and efficiency of healthcare systems, and aid in supporting public health initiatives. [1] However, the secondary use of healthcare data also raises complex ethical, social, and technical issues; for example, questions regarding data ownership and access privileges continue to challenge the field. [2]

The healthcare industry has witnessed a remarkable surge in the volume of healthcare data in recent times, primarily driven by the widespread adoption of EHR systems worldwide. [3] In addition, there has been an unprecedented growth in other types of healthcare data, such as genome sequencing and other biological structures. [4] The analysis of this clinical data is commonly referred to as analytics or healthcare data analytics, which falls under the category of secondary use of clinical data. While the term "data analytics" is extensively used in and outside of healthcare [3], our focus in this study is on its application in the healthcare industry.

Analytics has been deployed across various domains, including healthcare. However, experts from different fields offer diverse definitions of analytics. Nonetheless, the ultimate objective of analytics, as perceived by all experts, remains consistent. Data analytics experts characterize analytics as “the comprehensive exploitation of data, statistical and quantitative analysis, explanatory and predictive models, fact-based management to drive decisions, actions, and much more.” [5] Similarly, IBM defines analytics as “the methodical use of data and associated business insights developed through applied analytical disciplines (e.g., statistical, predictive, contextual, quantitative, cognitive, and other models) to drive evidence-based decision making for planning, management, measurement, and learning. Analytics can be descriptive, predictive, or prescriptive.” [6]

Moreover, the two eminent healthcare data analytics experts, Adams and Klein, outline three distinct levels and applications of analytics in the healthcare domain [7]. Each level is associated with increasing functionality and value:

1. Descriptive: This level refers to standard reporting types that depict current situations and problems.
2. Predictive: This level refers to simulation and modeling techniques that forecast trends and anticipate the outcomes of implemented actions.
3. Prescriptive: This level concerns financial, clinical optimization, and other outcomes.

All three levels of healthcare data analytics are of paramount importance. However, predictive analytics has gained more attention in the current healthcare landscape [3], as medical experts seek to predict various clinical-related variables in healthcare data to enhance healthcare delivery services and optimize health and financial outcomes.

With the advent of digital medical records, hospitals and other healthcare organizations are accumulating vast amounts of data at an unprecedented rate. The clinical data captured by these organizations take multifarious forms, ranging from structured data (such as laboratory results and images) to unstructured data (such as textual notes comprising clinical narratives, reports, and various other documents). For example, the well-known US healthcare company Kaiser-Permanente has a current data store for over nine million members that surpasses a staggering 30 petabytes of data. [8] Another notable example is the American Society for Clinical Oncology (ASCO), which is developing its Cancer Learning Intelligence Network for Quality (CancerLinQ). [9] The clinical data accumulated by CancerLinQ serve myriad healthcare data analytics purposes, providing clinicians and researchers with an extensive platform for EHR data collection, data mining, and visualization, as well as the application of clinical decision support, among others.

The ultimate goal of healthcare data analytics is to use data to make informed decisions and identify patterns and trends that can help improve patient outcomes, optimize operational efficiency, and reduce costs. By analyzing data, healthcare providers can identify areas for improvement, predict health outcomes, and personalize care for individual patients.

Some common applications of healthcare data analytics include population health management, clinical decision support, disease surveillance and monitoring, and quality improvement initiatives. The field of healthcare data analytics is constantly evolving as new technologies and approaches emerge, and it is a critical area of focus for healthcare organizations looking to improve their performance and deliver better care to patients.

To summarize, data analytics has become a pivotal aspect of current healthcare settings, a core requirement for both the industry and its experts. [4] Moreover, the future of healthcare holds tremendous promise when it comes to data analytics. With the burgeoning volume of clinical and research data, coupled with the methods employed to analyze and put it to use, there is tremendous potential for improving healthcare delivery, personal health, and biomedical research. However, there is also a continuing need to improve the quality of clinical data and conduct research aimed at demonstrating how best to apply data analytics to address healthcare challenges.

Healthcare data analytics using the FHIR data standard

FHIR is the latest healthcare data standard that is gaining popularity in the healthcare sector. [10] FHIR provides a standardized way to represent and exchange healthcare information electronically. [11] This avant-garde standard has captured the imagination of healthcare providers due to its unparalleled ability to reduce the costs of interoperability and its potential to catalyze a new ecosystem of third-party applications. [12] FHIR’s revolutionary interoperability capabilities have surpassed the antiquated data standards of yore, such as Health Level 7 (HL7; v2, v3, CDA).

In a recent survey conducted by Australian and New Zealand healthcare executives, the adoption of FHIR was found to increase interoperability from a measly 11% to a staggering 66%. [13] Consequently, its adaptable nature for data exchange is increasing at a rapid pace within the healthcare industry as it garners favor among stakeholders for data exchange. The survey further revealed that 55% of healthcare providers are willing to make the shift to a FHIR-based interoperability platform. Additionally, it is estimated that FHIR will be widespread in the world healthcare industry by 2024. [14] This showed the popularity of FHIR-based interoperability in the healthcare industry and healthcare providers’ interest in its adaptability.

However, the healthcare industry’s needs go beyond mere clinical data exchange. Clinical data need to be processed for other purposes, such as data analysis, data analytics, research, and so forth. Thus, the clinical data represented in the FHIR standard need to fulfill these requirements. FHIR’s adoption is expected to increase data availability for analytics and solve the data exchange and analytics problems faced by the healthcare industry. [13] Nevertheless, the adoption of FHIR in the analytics domain remains relatively low, as the standard is still young. [15] Moreover, the tools supporting FHIR data analytics are still relatively immature. [16] However, the healthcare providers argue that they are not only interested in sharing clinical data across healthcare organizations to improve data interoperability but are more excited to process clinical data for other purposes, such as data analysis and research, to provide real-time medical services to patients. Therefore, the tools provided these services are essential in the healthcare industry.

On the flip side, the cutting-edge FHIR standard for patient clinical information presents plenty of new opportunities for visualizing, analyzing, and automating various types of healthcare data. With each passing day, fresh use cases for FHIR data analytics are building in the healthcare industry, such as real-time alerts for patient satisfaction, identifying patterns in patients’ medical records across datasets, real-time visibility into patient readmission rates, cost savings while upholding top-notch care quality, and countless more. [17,18,19,20] However, analyzing and implementing these use cases can prove challenging owing to the young stage and practicality of FHIR.

To facilitate data processing and exchange, FHIR employs REST APIs. Nonetheless, for the domain of FHIR data analytics, the FHIR APIs must possess a dynamic nature regarding data queries and processing. As data analytics are based on diverse types of data housed in varied FHIR resources, the FHIR APIs must query this data in various ways to enable effective data analysis. Additionally, FHIR has accelerated the swift delivery of a massive volume of new healthcare applications that can integrate with EHR or EMR data via the FHIR APIs. However, most of these applications are limited to perusing data relevant to a single patient. [15] One contributing factor, among many others, could be that the FHIR APIs are not optimally suited to queries that aggregate and categorize data across a vast clinical dataset.

A related and parallel trend within the realm of health information systems involves investing in higher-quality structured data via the coding of clinical records at the point of care. With the implementation of EMRs, healthcare providers are now able to incorporate a multitude of concepts into medical records using advanced terminologies, including ICD-10, LOINC, and SNOMED CT. [21,22] This affords the opportunity for more detailed analysis by enabling access to specific clinical concepts as well as the ability to query the ontology based on additional attributes and relationships to other clinical concepts.

While this technique is highly effective when analyzing clinical data based on specific codes or terminologies, it proves to be less fruitful in general concept analysis. Therefore, other scenarios, including modifications to FHIR APIs, must be considered to enable various ways of analyzing medical data for deep clinical data analysis. However, this technique is extremely challenging and requires an individual with extensive skill and experience to change the core implementation mechanisms of FHIR APIs.

Currently, the level of expertise required to make the best use of FHIR and other clinical terminology within a data analysis workflow is relatively rare in the healthcare domain. [23,24,25] The applications of data analytics and analysis in healthcare settings using the FHIR data standard are also a relatively new concept and have scarcely been applied. However, due to the rapid adoption of FHIR for medical data exchange, data analytics and analysis are now a core demand of the healthcare industry to process patient medical data in various ways and provide real-time medication to improve healthcare delivery. In summary, the standardization of healthcare data plays a crucial role in clinical and translational data analysis systems, especially when large-scale data are involved. Moreover, healthcare applications for clinical statistics and analysis can significantly enhance healthcare by connecting clinical data with analytic tools, thereby engaging practitioners or clinicians in the process of medical data analysis. [26,27]

In response to the pressing need to address the complex and multifaceted challenges of data analytics in the healthcare industry, this research study puts forth a cutting-edge and innovative FHIR standard-based data analytics framework. This platform is designed to tackle the healthcare industry’s data analytics issues and provide them with a scalable, standards-based data model. At present, this pioneering framework is tailored to work with workflows specifically designed for patient clinical data originating from two distinct hospital information systems: patient registration systems and laboratory information systems (LIS). Other possible data analysis workflows and customized research scenarios on the patient data from other HIS could be performed on FHIR-based data but are not currently directly supported by our framework without any modification.

The developed framework utilizes a FHIR database as its dataset, with FHIR RESTful APIs that query different types of FHIR resources from the database algorithmically. The mapping algorithm and analytic engine then process the retrieved data and generate various data analytics from patient clinical data, presenting the results to end-users via a user-friendly interface.

In short, this research study provides a state-of-the-art solution for healthcare data analytics, offering healthcare professionals an innovative platform to conduct data analysis on clinical data using FHIR. With the FHIR Data Analytics Framework, healthcare professionals can now extract meaningful insights from patient data and leverage these insights to enhance patient care delivery, promote better health outcomes, and drive healthcare industry advancements forward.

This research work has three main contributions: First, the entire framework and workflow design follow the FHIR data standard, which could be reused for any other clinical data domains and could provide support for any clinical data that follow the FHIR standard. Second, the data analysis workflow and tools incorporate the experience of clinical researchers and statisticians, which could provide a starting point for FHIR researchers in this cutting-edge standard. Third, the intelligent mapping algorithm is artfully designed to facilitate the sublime process of data analytics or data analysis within the realm of FHIR-based data. The mapping algorithm could be reused for any other clinical data that follow the FHIR specification and need to process the FHIR-based data for other purposes, such as research, developing an artificial intelligence (AI) model or machine learning (ML) model, etc.

The FHIR Data Analytics Framework comprises six layers: the FHIR database, the FHIR query engine layer, the mapping algorithm/agent layer, the FHIR-compliant database layer, the analytics engine layer, and the user interface. The rest of this manuscript is structured accordingly. The next section provides a comprehensive literature review, followed by a discussion of the five major materials used in this study. Then, the framework’s architecture is described in detail, followed by the implementation details, an explanation of the experiment setup, and the results. We close by describing the limitations of this approach, as well as a discussion, future plans, and finally a conclusion.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, though grammar and word usage was substantially updated for improved readability. In some cases important information was missing from the references, and that information was added.