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 varied representations of medical terminologies when storing patient data. For example, some HealthTech applications record “hypertension”, while others may use “HTN” or “High Blood Pressure.”
Healthcare professionals, such as doctors and nurses, can easily understand these terms, but digital healthcare systems cannot, unless explicitly coded. Consequently, data remains in silos, affecting healthcare interoperability.
Other forms of unstructured data can include clinician notes, pharmacist’s recommendations, and new records. The lack of standardization in all these forms of healthcare data complicates downstream use of data.
This leads to various problems, such as inconsistent or incorrect diagnoses, redundant tests, billing errors, increased administrative workload, and unreliable healthcare analytics. The root cause is the lack of interoperability in health data.
An effective way to solve this challenge going forward is by ensuring structured patient data capture at entry.
The key is to develop and use healthcare technologies that enforce required fields and use a standardized medical lexicon. You can transform HealthTech technologies, such as testing tools, applications, and wearables, to capture structured data at entry.
SoluteLabs’ digital transformation services will deliver those outcomes for your healthcare organization. We analyze your workflow to integrate universal standards, such as FHIR, SNOMED CT, and LOINC, to ensure consistent, codified data across systems.
This will lead to clinical clarity, enabling doctors and other healthcare professionals to provide accurate diagnoses and treatments. Moreover, it will improve operational efficiency across the organization through automated data validation, minimizing or preventing input mistakes.
3. Data Silos and Fragmentation
Most departments within a medical or healthcare organization operate independently. Testing labs, for instance, seldom communicate with billing departments, reducing interoperability in health data.
Combine that with an increasing number of departments and healthcare solutions, and you can quickly estimate how disjointed the general digital health infrastructure is. This is challenging for both patients and organizations.
Patients feel overwhelmed by providing details about themselves in stressful situations, and institutions believe they don’t have enough health data to offer an accurate diagnosis.
Sometimes, healthcare staff must move data manually. This slows down care delivery, increases error rates, and adds to clinician burnout. It also raises operational costs. These fragmented data workflows create interoperability challenges that limit health systems' ability to detect trends and respond quickly.
The root cause of this fragmentation is the monolithic architecture of many HealthTech systems. Monoliths are easier to build initially. All components, the user interface, business logic, and database run as a single, tightly coupled unit.
As systems grow more complex, modifying or scaling monoliths becomes harder. Often, teams end up building new monolithic applications to meet specific needs. This adds more silos and worsens interoperability issues across health data systems.
Healthcare organizations can address this by shifting to modular or serverless architectures. These designs improve scalability, support reuse, and enable a single source of truth. They also reduce reliance on local IT and lower administrative overhead through vendor-managed platforms.
You can look forward to complete patient records, automated data flows, analytics-ready data, and collaborative governance among departments and healthcare professionals.
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.
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.
