Common Challenges Healthcare Organizations Face in Integrating Data from Disparate Sources

It is truly so incredible the number of places that we are able to receive data in healthcare – from automatic collections like EHRs and wearable devices to self-reporting collections like surveys and questionnaires. However, the number of disparate places we receive data from can be tricky for providers to navigate. In order for your provider to give you the best care possible, it’s important that they have all of the necessary data and information, which is hard to do if they need to log in to several different portals to see the different places your data was collected. The obvious answer to this problem is to integrate all of this data into one unified system, but that opens up a whole new can of worms.

To help you navigate the best way to prepare yourself for potential problems when integrating your data, we reached out to our incredible Healthcare IT Today Community to ask — what challenges do healthcare organizations face in integrating data from disparate sources, such as EHRs, wearables, and patient-reported data, into a unified system? Below are their answers.

Greg Miller, Vice President of Business Development at Carta Healthcare
AI tools have proven to accelerate the abstraction of clinical data for analytics, especially to support quality and process improvement efforts, but without context-rich data, even the most advanced AI models fall short. Healthcare organizations face real challenges in harmonizing data from EHRs, disparate data sources, and across data formats, not just technically, but in preserving clinical nuance. To support meaningful analytics and decision-making, providers must prioritize accurate data capture and abstraction that reflects clinical intent, not just surface-level documentation.

Phill Tornroth, VP of Engineering at Elation Health
Integrating health data into a unified system is a challenge that’s existed for some time, but predictive systems are amplifying the need for tools and surfaces that do an effective job providing context and levels of understanding and confidence for the information. Disparate sources come with needing to consider the data differently, and most of the platforms we have today presume that all clinical information has passed through the hands of a provider and manually blessed into the record. It is critical to provide nuance and support for that data provenance for clinicians.

Kilee Yarosh, Sr Manager, Clinical Strategists at Omnicell
Healthcare organizations run into a lot of challenges when integrating data from sources like EHRs, wearables, and patient-reported outcomes. Different formats, uneven data quality, and lack of interoperability make it tough to create a clear picture of a patient’s health. Security and privacy concerns add another layer of complexity, and if the information isn’t easy to get to inside existing workflows, clinicians aren’t likely to use it.

As health systems grow, it’s becoming more important to connect the entire pharmacy care enterprise to support safe, effective medication management. Cloud-native platforms provide a connected backbone for technology systems across an enterprise, while also offering scalability as healthcare systems no longer need to rely on servers that require manual updates and downtime. Connecting devices through the cloud helps streamline medication dispensing workflows, reduce redundancies, and surface insights designed to help optimize inventory, improve efficiency, and ease pressures like drug shortages, rising costs, and workforce challenges.

Christopher Johnson, Co-CEO at TeleTracking
While systems like the EHR have become ubiquitous with healthcare data, accessing that data for interoperable integration remains a challenge. The problem isn’t the data itself, but what organizations do, or more accurately can’t do, with it. For all the dashboards and analytics available, the real challenge is having the right data for the task at hand. As an example, operations and patient flow challenges can’t be solved by clinical data alone. To be successful, health system leaders need both the right data and the expertise to transform it into coordinated, actionable insights that are integrated directly into daily workflows.

Rob Stuart, Founder and President at Claim.MD
Healthcare has no shortage of data, but the challenge is making it useful in real time. Claims and eligibility information, when standardized and integrated with clinical data, can reveal important patterns about utilization, costs, and outcomes. These insights are especially valuable because claims data captures the broadest view of the healthcare system, spanning providers, payers, and patient populations. Advanced analytics and AI can help surface trends earlier, whether it’s spotting inefficiencies in administrative processes or identifying patient groups that may need additional support. But the value of those insights ultimately depends on the quality of the underlying information. Clean, consistent data and the ability to connect disparate sources are what ensures analytics can be trusted and acted upon.

Todd Knain, Senior Vice President & Chief Information Officer at Noridian Healthcare Solutions
With all its promise, AI technology alone isn’t enough. The biggest challenge is unifying data from electronic health records, wearables, and patient-reported sources into a system that clinicians can actually trust. Something as simple as defining anemia can vary from one lab to another. Data entry can be automated, manual, or scanned, each with its own formats and accuracy risks. Wearable devices must be validated for clinical use, and patient-reported outcomes hinge on the patient’s health literacy and consistency. When you finally add issues of privacy, equity, and ownership, the promise of using this data for clinical or public health purposes becomes complicated quickly.

The future lies at the intersection of interoperability and intelligence. When healthcare systems can integrate diverse data streams and apply AI responsibly, they create real-time, reliable, and explainable insights that help clinicians, executives, and patients make better decisions.

Andrea Romero, Head of Revenue Cycle Services at TruBridge
Healthcare organizations face three primary obstacles when integrating data from EHRs, wearables, and patient-reported sources: interoperability, data privacy and security, and operational complexity.

Interoperability is a persistent challenge, as fragmented systems from hospitals, clinics, and insurance providers keep information siloed, leading to incomplete patient records and the risk of clinical errors. Transmitting sensitive patient data between systems exposes organizations to breaches, regulatory penalties, and a profound loss of patient trust. Finally, the operational load is significant, increasing administrative burdens for clinicians and IT teams who must build costly, one-off integrations that lack scalability.

To address these challenges, healthcare organizations should adopt a comprehensive approach, including the implementation of recognized standards and the use of technology to facilitate seamless data exchange.

Tim Brennan, Vice President, Health IT at e4 health
Data quality issues are one of the most common challenges associated with integration projects. These problems often arise when organizations consolidate IT systems, along with their Master Patient Index files (MPI) and patient data, into the core EHR. This typically happens during acquisitions or EHR transitions, when legacy applications and their data are migrated into a new platform. IT teams must validate legacy patient data before migration and test to safeguard data quality in the go-forward EHR. Close collaboration between Health Information Management (HIM) leaders and IT teams is critical for success. The goal is to maintain high data integrity and minimize patient record duplication. A best-practice benchmark is keeping duplicate rates below one percent in the new EHR.

Jake Hochberg, Chief Data Officer at Arcadia
Healthcare data is complex and often fragmented. Despite innovation in interoperability and technology, many healthcare organizations still face fundamental challenges in integrating disparate sources like EHRs, claims, SDoH, and patient-reported data. There’s plenty of promise in using AI to better analyze and interpret healthcare data. That’s why investing in tools that create clean, unified, longitudinal data sets is essential. With modern technology, we can analyze and run simulations at a faster rate than ever before. The hard part is the curation.

The healthcare organizations I work with have had a lot of success using large language models (LLMs) to convert unstructured data, such as clinician notes and PDFs, into structured information, further enhancing a data set’s completeness. Without the critical foundation of high-fidelity data, even the most advanced AI tool will deliver limited value.

An emerging AI-driven capability that supports the democratization of data analysis and interpretation is conversational analytics. Such tools enable anyone, regardless of technical ability, to interrogate data in plain language. These tools can also deliver value to those with technical ability. For example, chatbot-style analytics tools can help reduce the time analysts spend coding so they can spend more time interpreting a query. By reducing manual effort and improving efficiency, organizations can more quickly transform raw data into actionable insights.

Shobha Phansalkar, PhD, FAMIA, VP of Client Solutions and Innovation at Wolters Kluwer, Health Language
Healthcare organizations face several challenges in integrating data- the most fundamental being how that data is expressed using varying or inconsistent terminologies or coding standards, leading to a lack of semantic interoperability between systems. Regulations for the enforcement of standards such as FHIR have brought us farther along, but there is still an opportunity to better maintain and normalize standard terminologies to help make sense of the data for clinical or analytical use cases.

In addition, the need for a shared contextual understanding for which the data were created, and how they are fully captured and communicated to the downstream systems for correct interpretation, is also a challenge.

Kimberly Smith, Senior Clinical Solutions Executive at Net Health
Healthcare organizations, particularly those in critical care areas such as wound care, can lose efficiency and accuracy and experience worse patient outcomes when integrating data from multiple disparate sources. Clinicians need real-time, accurate recording and tracking of patient information, which requires unified solutions that seamlessly share and integrate data. Oftentimes facilitated by strong partner engagements, these unified technology ecosystems reduce documentation errors, support informed decision-making, and facilitate the coordination of care among various healthcare providers, leading to improved care delivery.

For example, wound care practices traditionally balance data from electronic health records, mobile wound imaging, and AI-powered documentation solutions. Without a comprehensive ecosystem, providers must manually compile this data, which increases their workload and compromises the holistic view necessary for patient care. Healthcare organizations can benefit from having data from different tools compiled within a single, integrated platform specifically tailored for complex specialties like wound care. This will enhance operations and allow them to access the essential data required for clinical decisions and improved patient outcomes.

Heather Trafton, President at Evergreen Nephrology
Health data today is abundant. From electronic health records (EHRs) and connected medical devices to personal fitness trackers and health experience platforms, patients generate and access a wealth of information about their health. The challenge is not data collection, but rather data dissemination.

Despite this abundance of health data, much of it remains siloed within individual providers, specialties, or health systems. This fragmentation prevents both patients and care teams from accessing a comprehensive view of an individual’s health status. Given that many health conditions are interrelated, a unified and holistic understanding of patient data is essential for effective, coordinated care.

Healthcare organizations must prioritize interoperability and a seamless exchange of data across platforms and stakeholders to overcome these barriers. This requires robust data security, scalable infrastructure, and the ability to integrate AI and machine learning. By embracing these principles, the healthcare industry can move beyond fragmented data systems toward a future where information flows freely, securely, and meaningfully empowering better outcomes for every patient.

Richard Kwong, Director at Connecting for Better Health
AI will make it increasingly easy and automated to integrate data from disparate sources; however, privacy, security, and compliance will need to be addressed at a similar pace. Regulations like HIPAA support a conservative approach to sharing, while the wave of wearables, EHRs, and other new patient-oriented technologies work best when having full access to complete data. To address this gap, organizations must take a human-design approach, creating workflows that enable advanced technology to be applied at the right time and the right place, which will ensure providers and patients both benefit from technology while also preserving important safety, privacy, and compliance measures. Organizations that continue to only take a conservative approach will risk being left out of the future of connected, technology-enabled care.

Sunil Konda, Chief Product Officer at SYNERGEN Health
Interoperability isn’t just a pipe problem; it’s a semantics and provenance problem. Even with FHIR, data from EHRs, remote monitors, and patient-reported tools arrive with uneven quality, inconsistent codes, and missing context. Wearables generate a high volume of data and velocity that can overwhelm clinical workflows if not prefiltered to identify clinically meaningful signals. We need governance guardrails for privacy and security, as well as to design ingestion with the clinician in mind, with summaries, thresholds, and alerts that reduce admin burden.

Such great insights here! Huge thank you to everyone who took the time out of their day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.

What challenges do you think healthcare organizations face in integrating data from disparate sources, such as EHRs, wearables, and patient-reported data, into a unified system? Let us know over on social media, we’d love to hear from all of you!