Source of the photo:exeedqm.com
In today’s rapidly changing healthcare landscape, digital technologies are rapidly transforming how we deliver and experience medical care. From remote patient monitoring to AI-powered diagnostics, innovation is brimming with potential. Yet, amidst this promise lies a critical requirement: interoperability.
In this article, we argue that interoperability is a prerequisite for the digital innovations envisioned for future medicine. We focus on four areas where interoperable data and IT systems are particularly important: (1) artificial intelligence and big data; (2) medical communication; (3) research; and (4) international cooperation. We also discuss how interoperability can facilitate digital transformation in these areas to improve the health and well-being of patients worldwide. Drawing on the work of Moritz Lehne et al. (2019), we can gain valuable insights into why interoperability is essential and how it can transform patient care, healthcare systems, and medical research. Let’s dive in
What is Interoperability and Why Does it Matter?
Imagine a patient visiting a specialist after seeing their primary care physician. Ideally, the specialist should have immediate access to the patient’s medical history, medications, and allergies. But often, this information is siloed in different electronic health record (EHR) systems that don’t “talk” to each other. This lack of interoperability creates a fragmented view of a patient’s health, hindering effective care coordination and potentially leading to errors.
Interoperability can be broadly defined as “the ability of two or more systems or components to exchange information and to use the information that has been exchanged”. It’s the glue that binds the various digital components of healthcare together. Most definitions further distinguish between different components, layers or levels of interoperability. Although these components can slightly differ across definitions, they generally follow a distinction between lower-level technical components and higher-level organisational components.
In line with this conceptualisation, here’s a deeper dive into its different aspects:
Technical Interoperability
Technical interoperability ensures basic data exchange capabilities between systems (for example, moving data from a USB stick to a computer). This requires communication channels and protocols for data transmission. With today’s digital networks and communication protocols, achieving technical interoperability is usually relatively straightforward. However, moving data from A to B is not enough. To process the data and extract meaningful information, syntactic and semantic interoperability is needed.
Syntactic Interoperability
Syntactic interoperability specifies the format and structure of the data (for example, in an XML document). Syntactic interoperability relies on pre-defined standards that dictate how data should be formatted. These standards specify things like: Data types: Is a field a number, text, date, or something else?, Tags and delimiters: How is data separated and identified within a message?, Order of information: In what order should specific data points appear?
The structured exchange of health data is supported by international standards development organisations (SDOs). Common examples of standards used for syntactic interoperability in healthcare include: HL7 (Health Level Seven): A widely used standard for exchanging healthcare information electronically, LOINC (Logical Observation Identifiers Names and Codes): A standard for identifying medical laboratory tests and measurements and SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms): A standardised terminology for healthcare concepts, procedures, medications, etc.
Syntactic interoperability lays the foundation for more complex interoperability layers, such as semantic interoperability, which further ensures that the meaning of the data is also understood uniformly across systems.
Semantic Interoperability
Semantic interoperability is really the domain of medical terminologies, nomenclatures, and ontologies. They ensure that the meaning of medical concepts can be shared across systems, thus providing a digital “lingua franca”, a common language for medical terms that is, ideally, understandable to humans and machines worldwide.
In healthcare, the use of standardised coding systems like ICD (International Classification of Diseases) or SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms) ensures that medical terms have consistent meanings across different systems. Frameworks and standards like HL7 FHIR (Fast Healthcare Interoperability Resources) in healthcare also provide guidelines on how to structure and exchange data in a semantically consistent way. For example: FHIR defines resources and data formats for electronic health records, ensuring that different healthcare systems can exchange and interpret patient data accurately.
Syntactic and semantic interoperability work hand-in-hand. Syntactic interoperability ensures systems can read the data, while semantic interoperability unlocks its true meaning.
Organisational Interoperability
The highest layer, interoperability also involves organisations, legislations and policies. Exchanging data across health IT systems is not an end in itself but should, ultimately, help healthcare professionals to work more efficiently and improve patients’ health. This requires common business processes and workflows that enable a seamless provision of healthcare across institutions.
How Interoperability Can Improve Medicine
Source: https://www.nature.com/articles/s41746-019-0158-1
Artificial Intelligence and Big Data
Digital technologies such as artificial intelligence (AI) and large-scale analytics subsumed under the term “big data” are increasingly changing medicine and healthcare. These technologies rely on growing volumes of digital medical data. Therefore, to use AI algorithms and big data analytics to their full capacity and feed them with maximum input, processing information from different systems and across institutional boundaries is crucial. A comprehensive analysis of a patient’s health data could, for example, require information from general practitioners, hospitals, laboratories, mobile health apps, and wearable sensors. Unfortunately, today’s digital health infrastructure makes large-scale data processing across IT systems still unnecessarily difficult.
To avoid these pitfalls and provide AI algorithms and big data technologies with usable input, interoperability of health data is essential. The largest barrier to applying AI and big data to medicine is, arguably, not a lack of algorithms but a lack of suitable data for developing AI and big data applications.
Interoperability For Medical Communication
Digital medicine does not always require sophisticated analytics or complex AI algorithms. In many cases, simply making the right information available to the right person at the right time can significantly improve patient care. Often, important parts of medical information are lost as patients move through the healthcare system. For example, if a patient is rehospitalised, relevant information from previous visits to other hospitals may not be available (in Germany, medical information can sometimes not even be shared across different departments of the same hospital due to data protection regulations). This leads to inefficiencies in care and sometimes poses serious risks for patients (for example, if a lack of communication results in adverse drug interactions). Giving healthcare providers the necessary information about their patients can help to avoid such inefficiencies and improve the quality of care.
Promoting the use of interoperable electronic health records (EHRs) is particularly important in this context. Importantly, by making relevant health information easily accessible, interoperable health IT systems should also make lives easier for physicians and other healthcare professionals.
Interoperability For Research
Interoperability also advances medical research. This is particularly true for the field of real-world evidence: Using interoperable formats for real-world data (that is, data routinely collected in medical care or, increasingly, via mobile apps in patients’ everyday lives) opens up various opportunities for researchers. More generally, if health data are structured according to international standards, data are much easier to analyse, and efforts needed for data cleaning and pre-processing are reduced. This can speed up the research process and also make the development of analysis scripts more flexible: If researchers and data scientists know that data will conform to certain formats and semantics, analyses no longer need to be programmed with direct access to the data. In sum, interoperability can generate new medical insights, making it possible to analyse existing data sources more efficiently. This can advance translational medicine and help to move research discoveries swiftly from the laboratory to the point of care. On a larger scale, it can drive evidence-based practices in medicine and accelerate their implementation into public health policies.
Interoperability For International Cooperation
Interoperable interfaces and standard terminologies make health data exchangeable and comparable across systems, institutions and countries. This has obvious benefits for cross-institutional and international cooperation. Exchanging health data internationally is also essential for tackling global health issues more effectively. Arguably the most serious public health risk – or, for that matter, the most serious risk for humanity in general – is a global pandemic. If health data are interoperable so that information can be easily exchanged across borders and organizations, effective surveillance systems can be established that allow for an accurate tracking of global disease movements. Outbreaks can then be detected early and further spread prevented.
Importantly, interoperable health IT systems not only facilitate the exchange of data but also the exchange of algorithms, applications and technologies. The wide use of smartphones and mobile apps can further contribute to the dissemination of digital health technology. This can aid the “democratisation” of medicine, making health technologies globally accessible and improving healthcare in underprivileged regions of the world.
Key Takeaway: This article underscores the importance of prioritising interoperability initiatives and adopting interoperable solutions to unlock the full potential of digital medicine and transform healthcare delivery. Without interoperability, digital medicine solutions face significant challenges in achieving widespread adoption, seamless integration into existing healthcare workflows, and delivering comprehensive patient care.
At the African Digital Epidemiology Innovation Network(ADEIN), we play our role in fostering collaboration and interoperability within the digital health ecosystem across the African continent. Interoperability is fundamental to the success of digital medicine initiatives, and ADEIN’s efforts are aligned with this principle.
By bringing together various stakeholders, including researchers, healthcare professionals, policymakers, and technology innovators, ADEIN facilitates the exchange of ideas, data, and best practices in digital health. This collaborative approach helps to bridge the gaps between different health systems and technologies, ultimately leading to greater interoperability.
You can keep an eye on our page and find more information regarding our programs and events in this link: ADEIN. Feel free to contact us via the email address: publichealth@cchub.africa.
If you would like to learn more, here is a link to the interesting resource:
https://www.nature.com/articles/s41746-019-0158-1#Fig1
Meet the team:
Stephanie Okpere is a digital health expert and a strategy management professional with over 9 years of experience leading the development of public health technology innovations, and fostering collaborations among public health stakeholders. She has led the development, optimisation and adoption of country-level technology sol utions and has contributed to attracting project funding of up to $20M towards improving health and nutrition outcomes across countries, especially in Africa.
She holds a Master’s in public health from the University of Manchester, certification in Disruptive strategy from Harvard Business School, several qualifications in leadership and management and is a recipient of several academic awards. She is a resilient leader focused on pushing the frontiers of development and health tech innovation in Africa and beyond.
She currently convenes the African Digital Epidemiology and Innovation Network (ADEIN) and leads the Design for health practice at Cocreation Hub where she provides advisory support to health start-ups key players in the health ecosystem. She also supports innovative research and the development of unique solutions for the improvement of disease surveillance and predictability in Africa.
