By Chisom J. Mefor
In today’s world, data has become one of the most valuable assets in modern industries, driving innovation, efficiency, and informed decision-making—especially in healthcare. As Olajide Adebola, a leading voice in health data governance in Nigeria, once remarked, ‘Anyone who controls health data controls the world.
This control can translate to the ability to improve population health, monitor disease trends, and provide tailored patient care based on reliable data. It empowers healthcare providers to make smarter, faster, and more precise decisions that enhance patient outcomes.
Jeff White of Gravy Analytics aptly captured the critical role of data in decision-making. He notes in a Forbes article, ‘Relying on low-quality data to make critical business decisions can result in costly mistakes that far outweigh the expense of using high-quality data.’
This statement applies directly to healthcare, where fragmented, inaccessible, or inconsistent data can lead to inefficiencies, errors, and delayed decisions, especially in the absence of interoperability.
While platforms like DHIS2 have significantly improved health data management and analysis, data quality challenges persist, limiting the full potential of these systems in driving better health outcomes.
Digital health is revolutionizing healthcare through modern technologies, helping us bridge gaps that once seemed insurmountable.
However, the success of digital health still depends on interoperability—the ability of these diverse software and systems to exchange data seamlessly. Without interoperability, data remains fragmented and siloed, undermining the potential of digital health and preventing the true care transformation often spoken about.
So, what happens when interoperability is overlooked—or worse, ignored? The consequences are far-reaching, affecting patient outcomes, healthcare efficiency, and economic sustainability.
Fragmented data systems can lead to medical errors, such as incorrect diagnoses or duplicated prescriptions, compromising patient safety. Inefficiencies in accessing patient history delay treatment, increasing the burden on already overstretched healthcare facilities.
Economically, redundant tests, unnecessary treatments, and administrative bottlenecks drive up costs for both patients and providers.
A small yet telling example comes from my experience when our parish youth council tasked the youth secretariat with moving members’ biodata from stacks of paper forms into a digital format. Despite repeated announcements, the response rate on the forms was disappointingly low, prompting us to ask members to send their information via WhatsApp instead. The result? A disorganized mix of data formats across fields that required extensive cleaning before it could be used—mirroring the same data fragmentation challenges seen in healthcare systems.
Interestingly, when it came to the ‘date of birth’ section, the data was so inconsistent: some wrote ‘1st January, 1999,’ others ‘January 1st, 1999,’ and some used formats like ‘1999/1/1,’ ’01-01-1999,’ ‘Jan 1, 1999,’ or even ‘Nil.’ This lack of uniformity highlights how fragmented data—whether due to human error or incompatible systems—can create significant challenges. If this is the case for something as simple as church biodata, imagine the complications when it comes to health data, where precision and consistency are critical at the point of care.
Data fragmentation poses challenges across all levels of healthcare, from small-scale projects to very large, complex systems. For instance, a 2001 U.S. Senate hearing revealed that pharmacies made approximately 150 million calls annually to physicians to clarify prescriptions—a figure that has grown over time.
These communications often arose from missing dosage information, formulary concerns, prior authorization issues, or questions about drug interactions or allergies.
Nearly two decades later, the IQVIA Institute reported that in 2020, the United States dispensed 6.3 billion drug prescriptions, further highlighting the scale of healthcare data challenges.
Even in a well-resourced environment like the U.S., these examples underscore the critical role of effective interoperability in streamlining communication, reducing errors, and improving patient safety.
The need for interoperability is even more pressing at primary healthcare level, particularly for routine immunisation (RI). According to the Journal of the American Medical Informatics Association, the absence of standardised data formats and nationally recognised identifiers impedes the exchange of immunisation data across health information systems.
In low- and middle-income countries (LMICs), this lack of interoperability is compounded by challenges such as illiteracy, which prevents mothers from fully understanding which vaccines their children have received. As a result, healthcare providers may administer unnecessary or redundant vaccines, leading to increased costs, potential vaccine-related side effects, and uncertainty about vaccination statuses across entire populations.
Some people may argue that industries like banking have successfully achieved seamless interoperability, so why not healthcare?
Firstly, it’s important to understand that banking systems are profit-driven, prioritizing efficiency and innovation to maximize returns. In contrast, healthcare operates on a public good model, often underfunded and focused primarily on patient care and health outcomes rather than operational profits.
Secondly, healthcare faces unique complexities, such as the diversity of data types. Financial data is primarily numerical and transactional, whereas healthcare data is contextual and multifaceted, encompassing clinical notes, imaging files, laboratory results, genetic information, and much more.
A “patient” may recover from an incorrect transaction in banking, but an incorrect diagnosis or an overlooked allergy in healthcare can lead to life-threatening consequences. The stakes are higher for us, and the consequences of errors or failing to maintain interoperability in healthcare are far-reaching.
To build a truly connected healthcare ecosystem, it is critical for organizational leadership to prioritize interoperability from the get-go. By adopting open, widely recognised data standards and protocols and building strong partnerships with organizations like the Digital Health Interoperability Network (DHIN) that can support interoperability efforts, we can lay the foundation for a future where health data flows freely and securely.
Only then will we be able to realize the full potential of digital health in improving outcomes, reducing costs, and enhancing patient care.
In conclusion, the path to a more efficient, safer, and cost-effective healthcare system begins with embracing interoperability. It’s not just about technology—it’s about saving lives, improving outcomes, and ensuring that healthcare resources are used wisely and effectively.
By committing to seamless data exchange and prioritizing collaboration, we can build a healthcare ecosystem that truly serves the needs of patients, providers, and communities. The time to act is now, and the responsibility lies with all of us to make it happen.
Chisom J. Mefor, a digital health professional based in Nigeria, working to bridge gaps in healthcare systems through multi-stakeholder collaboration.
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