Electronic Health Records (EHRs) are now an important part of healthcare in the United States. Almost 96% of U.S. non-federal hospitals report using certified EHR systems. Even with this, many healthcare providers still find it hard to get complete, timely, and correct patient information. Around 72% of providers face problems because systems do not work well together and patient data is kept separate in silos. For medical managers, owners, and IT staff, knowing these problems and ways to fix them is important for better patient care, smoother operations, and following the rules.
This article looks at the main problems caused by proprietary systems and data silos in U.S. healthcare. It also talks about the effects of old systems and ways to solve these issues. At the end, there is a section on how artificial intelligence (AI) and workflow automation can help.
One big cause of EHR system problems is proprietary platforms and formats. Many healthcare information systems use closed, vendor-specific technology. While these systems can fit the needs of some groups, they often cannot share data easily with other systems. This is true even inside the same hospital and much more difficult between different healthcare providers, labs, pharmacies, or insurance companies.
Proprietary systems create what are called “information silos.” These silos keep patient data locked in certain places or departments, making it hard to access from outside. For example, a specialist clinic’s EHR may not work well with a hospital’s system. Different labs might use different codes and data styles. Because of this, important patient information remains broken up, which can cause problems like:
The many different data formats make sharing harder. Different providers use structured data, free text, or both. They also use different coding systems like ICD-10 for disease names, SNOMED CT for clinical terms, and LOINC for lab results. Without one universal system, computers can’t automatically understand each other’s data.
Old systems, called legacy systems, are another big challenge. These are older software and hardware still used in many healthcare places. They are part of clinical and office work but cannot easily connect with modern systems that use common standards.
Legacy systems can be standalone or loosely linked with newer systems. They often have problems such as:
The difficulty of changing legacy systems depends on their setup. Some systems are isolated and easier to replace but come with risks. Others are packed with connected parts, making changes more costly and hard.
It is important to fully understand these old systems, their data, and how they work before trying to upgrade or replace them. Without this, updates may fail or disrupt patient care unexpectedly.
To fix the problems with proprietary systems and data silos, the healthcare industry is slowly adopting open standards and systems that can work well together. Important standards include:
By using these standards, healthcare systems can “speak the same language.” This helps them share data smoothly, improving real-time access to patient info.
APIs (Application Programming Interfaces) are also important. FHIR-based APIs let apps get, send, or update health data across systems quickly. Frameworks like SMART on FHIR let third-party apps work inside existing EHR portals.
The government supports interoperability efforts with rules too. The U.S. 21st Century Cures Act requires electronic medical records to stop information blocking and allow safe data sharing. Privacy laws like HIPAA make sure patient data stays secure while becoming easier to access.
Making systems work together better also means protecting data with strong security. Healthcare in the U.S. faces more cyberattacks. In 2022, about 1,410 attacks happened each week—an 86% increase from the year before. These attacks try to steal patient data, stop hospital work, or demand ransom money.
To protect patients’ data while sharing it safely, organizations use many security actions, including:
Providers also look at new security tools like blockchain to protect data accuracy and increase transparency. But this needs careful use because of healthcare’s special needs and rules.
Artificial intelligence (AI) and workflow automation offer ways to make EHR systems easier to use and help reduce paperwork for healthcare staff. These tools help with the many systems, different data types, and changing rules.
AI and automation help in these ways:
Using AI well needs technology, trained staff, and good processes. Training helps staff use AI right and accept new tools. Testing projects let teams improve use based on feedback.
One example is the Mayo Clinic. They made a system that trains AI models across hospitals without sharing raw patient data. This protects privacy but helps improve care by working together.
Besides technology, healthcare providers need to fix organization and policy issues to get real EHR data sharing and better integration.
Data silos not only cause inefficiencies but also hold back biomedical research and advanced AI use. When patient data stays trapped in different systems or places, it lowers the ability to get broad insights needed for personalized medicine, disease tracking, and public health work.
Fragmented data systems often lead to:
To fix this, some platforms like Elucidata’s Polly combine different biomedical data sets into shared, AI-ready formats. Automated processes handle differences in units, names, and data types. Breaking down silos speeds up discoveries and improves the insights doctors use during care.
Even though certified EHR systems are widely used in the United States, many challenges remain in connecting proprietary systems and breaking down data silos. These issues affect patient care, how well healthcare runs, and costs. Administrators, owners, and IT managers should focus on using open standards, carefully updating old systems, applying strong security rules, and using AI and automation to improve data sharing and workflows. By working together on technology upgrades, organizational changes, and following regulations, healthcare providers can move closer to a system where patient data moves smoothly to support better and more efficient care.
Semantic interoperability refers to the ability of different healthcare systems to exchange and interpret shared data, allowing for meaningful communication across organizational boundaries.
Health organizations often struggle with the integration and exchange of information due to proprietary systems and internal ecosystems that create information silos.
Breaking down information silos can lead to reduced medical errors, improved disease monitoring, personalized patient care, and more efficient data management.
Recent studies have identified various technologies and tools that facilitate data interoperability, enabling efficient communication among healthcare actors.
Organizations can avoid data silos by implementing systems that support semantic interoperability, allowing data access and sharing without vendor restrictions.
The review focused on semantic interoperability in electronic health records, assessing various methodologies, technologies, and tools initiated between 2010 and 2020.
The article proposes a taxonomy around semantic interoperability that categorizes different approaches and technologies used to solve interoperability challenges.
Legacy systems often pose challenges for data exchange due to their heterogeneous nature, which can hinder effective integration of health records.
The study outlines several approaches, including using standards-based methods, semantic web technologies, and fostering a common framework for data integration.
Integrating clinical decision support systems with electronic health record data enhances the capacity to provide accurate and timely healthcare recommendations through improved data access.
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