Top 6 Interoperability Challenges in Digital Health

Interoperability in digital health allows various healthcare systems to work together seamlessly.

HealthTech applications can exchange data, facilitate collaboration, and improve patient care through interoperability.

For instance, in 2023, about 70% of non-federal hospitals in the US engaged in all domains of interoperable exchange (send, find, receive, and integrate).

This provides professionals, such as doctors and nurses, with access to all the necessary information to provide accurate diagnoses and treatment.

Additionally, you must’ve seen new and advanced technologies, such as healthcare apps and wearables, that add to the pool of health data. Their data streams further increase the importance of interoperability in healthcare.

The diverse healthcare data sources make interoperability in digital health difficult. Even today, organizations in the health and well-being sector struggle to optimize processes and enhance the patient experience.

In this article, let’s look at the data interoperability challenges in healthcare and discover effective solutions.

1. Legacy Systems and Vendor Lock-In

Healthcare institutions and businesses, such as hospitals and pharmacy stores, operate on their traditional processes. Each organization’s workflows and principles around the collection and usage of health data and HealthTech applications are unique and customized.

For instance, clinics have their electronic medical record (EMR) formats. These EMRs rarely change or evolve. While this approach provides simplicity and keeps clinical workflows lean, it lacks interoperability in healthcare.

When a doctor or institution refers a patient elsewhere, the patient must transfer their details through physical means (printouts of the EMRs), which is tedious and slow.

Moreover, the legacy EMR may lack the capability to capture extra information for customers with nuanced requirements. These issues can affect the efficacy of the treatment.

Further, this can be challenging for the healthcare professionals and organizations themselves.

Suppose you are using a legacy tool to manage the health data of your customers. Down the line, when scaling up, you must stay dependent on the software vendor. It will be challenging to convince them to add certain features tailored to your personalized needs.

2. Lack of Data Standardization

Different healthcare systems use different representations of the same medical concept. For example, some HealthTech applications record “hypertension,” while others may use “HTN” or “High Blood Pressure.”

The same problem becomes sharper in lab, diagnostics, and genomic workflows. A lab result may arrive as a structured code, CSV export, PDF report, or scanned document, depending on the instrument, LIS, or reporting system that produced it.

Genomic data adds another layer of complexity. Results often need to move from sequencing labs to referring physicians, EHRs, prescribing systems, and downstream clinical tools. If those results arrive as free-text PDFs or scanned reports, the data becomes harder for software to search, compare, validate, or reuse.

Standards such as LOINC, HL7 FHIR, SNOMED CT, HL7 FHIR Genomics Reporting, VCF, and HGVS help keep lab and genomic data structured as it moves between systems. Without that structure, diagnostic and genomic information can turn into documents that staff need to read, interpret, and re-enter manually.

Unstructured data such as clinician notes, pharmacist recommendations, scanned reports, and newly added records creates the same problem downstream. This is also where pharmacogenetics breaks down in practice: a genetic result that should change what gets prescribed only helps if the prescribing workflow can read it as structured data at the point of care, not as a PDF buried in a chart.

In pharmacogenetics platform case study, this showed up in provider communication workflows, where PGx information, denial notifications, API-backed alternatives, and research-backed explanations had to reach providers without exposing sensitive information before authentication.

The result is inconsistent diagnoses, redundant tests, billing errors, extra administrative work, and unreliable healthcare analytics.

The fix starts with structured data capture at entry. HealthTech products should enforce required fields, use standardized medical terminology, validate inputs early, and make data usable across downstream systems.

SoluteLabs helps healthcare teams design systems around standards such as FHIR, SNOMED CT, LOINC, VCF, and HL7 FHIR Genomics Reporting so data stays consistent, searchable, and interoperable across workflows.

3. Data Silos and Fragmentation

Data silos are especially visible in lab and diagnostics workflows.

Labs, pathology teams, radiology groups, and genomic testing providers often work across separate LIS, LIMS, reporting tools, instrument exports, and hospital EHRs. Results may move as PDFs, CSV files, scanned reports, or manual uploads instead of structured, queryable data.

This creates problems beyond slow data transfer. Ordering physicians may receive the final report without the discrete data behind it. Lab teams may generate reports, but the data stays locked inside local files or disconnected systems. Analytics, validation, trend detection, and downstream clinical workflows become harder because the result was never designed to move cleanly across systems.

Roche faced this problem in a lab reporting workflow that depended on a legacy Excel macro. SoluteLabs rebuilt it into a [cloud-native, offline-first lab reporting platform](/case-studies/roche) with instrument data ingestion, validation, offline capture, sync, and configurable report generation.

Fragmentation becomes harder to manage as healthcare organizations add more departments, tools, and digital workflows. Patients repeat the same details across systems. Clinicians move data manually. Operations teams struggle to maintain a complete view of the patient or workflow.

The root cause is often architectural. Many HealthTech systems start as monoliths, where the user interface, business logic, and database are tightly coupled. As the product grows, every new workflow becomes harder to modify, integrate, or scale.

Healthcare organizations can reduce this by shifting toward modular architectures, API-first systems, and shared data models. These designs support cleaner integrations, reusable services, automated data flows, and a more reliable source of truth across departments.

4. Security, Privacy, and Regulatory Compliance

Healthcare data, by definition, is deeply personal and sensitive. In many cases, it also includes the financial details of an individual and their family members. Therefore, when stringing HealthTech applications to improve interoperability in digital health, privacy is critical.

Evolving healthcare and digital security regulations, such as HIPAA, GDPR, and SOC 2 certification, add to the complexity. These guidelines and frameworks do overlap on certain directives but often differ in execution, which impacts HealthTech infrastructure.

Furthermore, existing digital health applications and databases exist in different environments, which bring unique security and privacy challenges. For instance, cloud-native infrastructures, such as Kubernetes, can introduce vulnerabilities, demanding constant oversight.

Moreover, healthcare professionals and institutions might encounter misconfigurations in software delivery, integration, and implementation. Some examples include poor CI/CD pipeline setups, improper access control, and insecure cloud drives.

It is crucial to embed security into healthcare software development processes to enforce early controls, reduce potential vulnerabilities, and align code deployment with regulatory requirements.

During HealthTech application development, use prevalent compliance frameworks, such as CIS and HIPAA, as automated gating criteria. This approach ensures safe interoperability in health data, thereby protecting patients’ privacy and data, and preventing legal conflicts and reputational damage.

You should also use Security Information and Event Management (SIEM) solutions to collect, analyze, and report on healthcare security data in real time. These solutions are pivotal for spotting potential compliance issues early, enabling agility.

Beyond solving compliance challenges, earning user confidence requires healthcare apps to be transparent, reliable, and patient-first. Explore our guide on building trust in HealthTech apps to understand how trust drives long-term adoption.

Adhering to security and privacy regulations improves interoperability in healthcare by securing data exchange, boosting software development rate, and increasing digital readiness.

5. Outdated Processes and Workforce Resistance

The technology adopted by your healthcare organization is as efficient as the employees and professionals using it. And it can be difficult to encourage personnel in the medical or digital health industry to change how they perform their daily tasks.

When departments are set in traditional workflows that involve paper forms and manual updates, it can be challenging to get them to change their minds. Healthcare staff find it difficult to break long-standing habits, especially when heavy workloads overwhelm them.

Additionally, some older professionals in the medical industry might fear new technology, leading to a lack of interoperability in healthcare. This occurs due to two reasons. First, they may struggle to trust the novel tools. Second, they might want to avoid the operational disruption to routine workflows.

Another cause of workforce resistance when adopting innovative HealthTech applications is skill gaps. Clinicians and other staff members could lack the knowledge or confidence in adopting and operating modern healthcare software solutions independently.

Teams should approach such scenarios carefully.

If you force the latest tool or a modified workflow, it may affect operational productivity. Low productivity in the healthcare industry often delays patient care and increases general health risks.

Furthermore, healthcare professionals and employees might resist the change with more force, leading to long-term disruptions while delivering patient care.

You can solve this challenge by considering the needs of the staff members before digitally transforming your healthcare data workflows. Ask doctors, nurses, pharmacists, etc., about how they would like to improve the existing processes.

6. Resource Constraints

Departments in healthcare and medical institutions often lack enough personnel to perform daily operations effectively. You may have heard how common it is for healthcare professionals across the board to work overtime.

This occurs due to fixed headcounts, making adaptation to interoperability demands slow, difficult, and sometimes impossible.

Moreover, when integrating digital health data systems to build a unified solution, agility is essential. Speed ensures that teams complete development and testing quickly, leading to faster implementation of the HealthTech application.

This not only delays data interoperability in healthcare but also incurs additional costs for organizations. When teams pause projects, overhead costs increase because the half-finished HealthTech application has yet to generate returns.

Businesses and institutions in the healthcare sector may struggle even without budget constraints. Digital transformation requirements to facilitate health data interoperability are often dynamic. Building the right team that can tackle the current problem can take time.

A better alternative is to outsource healthcare interoperability projects to dedicated firms like SoluteLabs. Our agile teams work with your in-house teams seamlessly to unite your databases and enhance interoperability.

SoluteLabs: Your Trusted Health Data Interoperability Expert!

Interoperability in healthcare is essential for delivering coordinated, efficient, and patient-centered medical care.

When digital health databases exchange information seamlessly, it improves clinical decision-making, reduces redundancies, and enables innovation across the healthcare ecosystem.

However, there are several challenges along the way. Legacy systems, change resistance, compliance concerns, and resource constraints prevent organizations from improving their digital healthcare workflows.

SoluteLabs offers comprehensive digital transformation services to healthcare organizations and businesses to enhance data interoperability.

Whether it is building dedicated HealthTech applications from scratch, modernizing an existing tool you use, or integrating your overall tech stack while adhering to global compliance regulations, we’ve got you covered.

Contact us today to learn how we can help your healthcare organization.

Finally, provide personalized training and support to ensure everyone, including non-technical healthcare professionals, gets up to speed. Getting everyone on the same page is critical for enhancing interoperability in health data over choosing the most advanced or recommended software.