Data Integration Challenges in Healthcare

Updated on June 9, 2025 by Jordan McGlone

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Top 7 Data Integration Challenges in the Healthcare Industry

Healthcare environments rely on data flowing smoothly across departments, devices, and platforms. But integrating that data isn’t always straightforward. Some of the most pressing challenges are listed below:

1. Clinical Context Gets Lost in Translation

Most integration engines focus on getting data into the system, but not necessarily in a way that preserves its clinical meaning. 

A diagnosis in one system might be linked to:

• A procedure
• A visit type
• A medication in another

This is where its context gets stripped out during integration; you’re left with isolated facts that tell only part of the story.

For example, a diagnosis might be recorded in the EHR, but without the associated procedure or visit type, the reason behind that diagnosis remains unclear.

2. Real-Time Data Integration Is Still Rare (But Critically Needed)

Many healthcare systems still rely on hourly or overnight data syncs, legacy habits from when databases couldn’t handle real-time performance. However, clinical care delivery is currently underway, and delayed data pose real risks.

If a patient is admitted through the ER and their recent lab results from a partner hospital aren’t available until the next morning, the care team is operating blind for hours.

The faster clinicians and care managers access updated vitals, labs, medication lists, or discharge summaries, the safer and more coordinated care becomes.

3. Workflow Mismatch Between Systems and End Users

Even when critical data is transmitted accurately and quickly, it often doesn’t appear in the right place, at the right time, or in the right format.

For example, let’s say you integrate lab data into your EHR. However, results are buried in a separate tab or batch report instead of appearing in the patient chart. Or worse, alerts get dumped into a general inbox with dozens of other notifications.

Clinicians require access to data quality that integrates seamlessly into their workflow. If they have to hunt for information, click through tabs, or re-enter it manually, adoption suffers, and frustration rises.

4. The Ethical Tension Around Automation and Data Sharing

With integrated data-feeding AI tools, predictive models, and automated workflows, the lines between medical data use and clinical judgment are becoming increasingly blurred.

For example, a predictive model can identify a high-risk patient and automatically generate a referral or trigger a case management workflow without requiring a clinician to review the data analysis.

Who’s responsible if that referral was unnecessary or worse, missed? Should a nurse be notified every time a score hits a threshold or only when certain conditions are met?

What you need are clear ethical guardrails around automated decision-making, including human-in-the-loop review, explainability of artificial intelligence outputs, and audit logs that track how a piece of data triggers an action.

5. Data Provenance Gets Murky Across Systems

As patient data moves between hospitals, HIEs, cloud platforms, third-party vendors, and payers, the origin of that data becomes less clear.

For example, a medication appears in the chart. Was it entered at the last hospital, through a pharmacy integration, or by the patient via an app? When did that entry occur, and has it been verified?

In the absence of provenance, clinical teams may question effective data integration, avoid relying on it, or even unnecessarily re-order tests. Legal and compliance teams as per industry standards also need audit trails to trace decisions back to their data source.

6. Integration Fatigue Across Teams and Vendors

Many healthcare providers in the healthcare sector are juggling dozens of integration projects at once: EHR upgrades, telehealth rollouts, remote monitoring pilots, payer APIs, app-based patient engagement tools, and more.

The result:

• IT teams are overextended, constantly “plugging in” new connections without time to optimize existing ones.
  
• Clinical staff are overwhelmed with fragmented interfaces and data overload.
  
• Vendors complain of limited support, changing specs, and moving targets.
  

Fatigue leads to shortcuts, unstable builds, and a lack of long-term governance, causing integration efforts to degrade over time.

7. Ecosystem Fragmentation from Niche Digital Health Tools

The explosion of health tech innovation has led to a fragmented digital ecosystem. Most clinics now use Electronic Health Records, EMR tools, two patient portals, a chronic care app, a telehealth platform, and a dozen other tools.

The problem is that each platform has its own login, data model, data privacy settings, and APIs. Most aren’t deeply integrated, meaning datasets lives in silos and doesn’t flow freely between platforms.

It creates a fractured care experience for patients and providers. Clinicians can’t obtain a unified view of patient information, patients must re-enter data across platforms, and analytics teams struggle to create meaningful dashboards.

You need stronger interoperability standards and better use of integration platforms (like FHIR-backed APIs and cloud data lakes) that act as connective tissue across the ecosystem.

Strategies for Migrating and Integrating Legacy Healthcare Systems

Migrating legacy healthcare systems to the cloud is a strategic transformation. These systems often involve years of custom development, fragmented integrations, and sensitive data that is critical to day-to-day operations. 

That’s why moving them to the cloud requires careful planning, in-depth domain knowledge, and a strong focus on patient privacy, uptime, and compliance in the healthcare industry.

1. Start With a Smart, Layered Migration Plan

Before anything moves, you need a clear view of what you’re working with. That means mapping out every system, integration, and data flow, then identifying what’s worth modernizing, what can be retired, and what needs secure handling during the transition. 

A phased plan works best: prioritize high-impact modules, address interdependencies early, and involve IT and clinical teams in risk assessment. Avoid rushing; measured execution reduces disruption and builds internal confidence.

2. When in Doubt, Go Hybrid

Legacy systems rarely fit neatly into a single-cloud model. Hybrid or multi-cloud approaches give you more control and flexibility, especially when uptime is non-negotiable. 

For example, a hospital may keep EHR systems on-premise for direct access while moving analytics and archiving to the cloud. 

If one provider experiences downtime, operations can be shifted to another, ensuring essential services continue to run without interruption.

3. Modernize Without Starting Over

Cloud migration doesn’t have to mean a complete rebuild. Often, the best results come from choosing the right modernization path based on the system’s age, value, and complexity:

• Optimize and upgrade. A simple upgrade to the latest cloud computing version for stable platforms can help reduce technical debt without requiring changes to the architecture.
• Partial redesign. If the system exhibits performance or scalability limitations, re-architecting key components to utilize cloud-native features such as auto-scaling or managed security services can deliver significant gains.
  
• Full rebuild. In cases where the legacy system is holding the business back entirely, a complete redevelopment using microservices, containers, and serverless infrastructure provides a clean break and long-term scalability.
  
• Platform swap. If a trusted SaaS product already solves the problem well, don’t reinvent the wheel. Replacing custom-built systems with commercial tools can save time and cost, especially when compliance and healthcare Interoperability are baked in.

Why Data Integration Fails Without People at the Center

A data integration strategy fails without people at the center because when healthcare organizations discuss seamless data integration, the conversation typically focuses on technical aspects. These include connecting APIs, syncing platforms, and ensuring everything flows through a clean process. 

Here are some reasons why:

• Conflicting priorities across departments. IT teams care about infrastructure, compliance teams want auditability, and healthcare professionals prioritize speed and usability, yet integration efforts often overlook these differences.
• Lack of shared definitions. What one team calls “relevant data” might look very different to another, causing inconsistencies and delays in workflows.
• Poor communication during rollouts. Integration plans are often built around systems, not users. Without clear communication and context, teams don’t understand how the changes benefit them.
• Change fatigue among users. Employees are frequently asked to adopt new tools or abandon workarounds they’ve grown comfortable with. Without proper support, they revert to old habits.

Best Practices for Successful Data Integration in Healthcare

Bringing together healthcare data helps ensure that information is transmitted in the correct format, at the right time, and to the intended recipients. With numerous data sources in play, including EHRs, lab health systems, billing software, and IoT devices, the challenge becomes even greater.

Here’s how to approach integration in a way that supports clinicians, improves patient outcomes, and strengthens operational decision-making.

1. ETL: Clean Before You Use

The classic Extract, Transform, Load (ETL) approach is still a cornerstone of healthcare data management and data governance.

• Extract. Pull structured and unstructured data silos from multiple EMR systems, like diagnostic labs, pharmacy records, and claims databases.
• Transform. Clean and standardize it. Normalize data formats, correct name spellings, and map medical codes to standard terminologies.
• Load. Push the clean, usable data into your warehouse or analytics platform.
  

2. Data Streaming

Waiting for daily batch updates won’t cut it in emergency care, telehealth, or remote patient monitoring. Data streaming allows you to integrate data the moment it’s generated from heart rate monitors, wearable devices, mobile apps, or active clinical workflows.

It’s the backbone of systems that need to trigger alerts, guide decisions on the spot, and reduce time-to-response. For example, if a patient’s vitals cross a critical threshold, the different systems can notify a clinician instantly.

3. Use Cloud Data Lakes

Healthcare data is complex, diverse, and vast. That’s where data lakes come in. They let you dump raw data (structured, semi-structured, or unstructured) from different sources into one ample, scalable storage space without needing to pre-format or cleanse it upfront.

However, without the right governance, a data lake can become a data swamp. So, yes, use data lakes to facilitate real-time access and machine learning, but layer in access controls, metadata tagging, and semantic consistency to make the data for a patient’s medical history usable for both engineers and analysts.

4. Data Virtualization

Sometimes, you don’t need to copy or store all your data in one place. That’s where data virtualization helps. It enables you to pull data from systems such as EHRs, lab software, and public health databases, and view it together without migrating or duplicating sensitive records.

This is especially useful when you need fast answers or lightweight reporting without the overhead of managing another data warehouse. It’s secure, efficient, and avoids unnecessary storage costs.

PatientCalls Helps With Easy Data Integration For Your Healthcare Business

Healthcare teams rely on accurate, up-to-date data to provide timely care, but that data isn’t always where it should be. If calls, voicemails, or messages don’t get adequately logged, your staff ends up chasing context instead of focusing on care, with different data formats present.

PatientCalls integrates communication into your data ecosystem. Their HIPAA-compliant medical answering service syncs every interaction directly into your system. This includes scheduled appointments and emergency callbacks. Your team can view everything in one place, eliminating the need for manual data entry or post-call summaries.

Here’s what you should be doing next:

1. Check how your current call handling and message routing integrate (or don’t) with your EHR or practice management system.
2. Identify the systems and workflows you want to keep in sync, such as patient messaging, call logs, appointment reminders, or prescription updates. Evaluate how well your tools support that.
3. Explore support with PatientCalls to handle medical answering and EMR integration. 

Get on a call with us to know more.

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About The Author

Jordan McGlone

Jordan has more than seven years of experience working for PatientCalls and a strong background in the healthcare answering service industry. He designs directive plans to fit the unique structure and activities of healthcare organizations, while ensuring that communications are efficient, compliant with HIPAA privacy and security regulations, and support optimal patient care.