{"id":36046,"date":"2025-07-06T06:19:10","date_gmt":"2025-07-06T06:19:10","guid":{"rendered":""},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-30T00:00:00","slug":"addressing-the-challenges-of-electronic-health-records-integrating-proprietary-systems-and-overcoming-information-silos-484448","status":"publish","type":"post","link":"https:\/\/www.simbo.ai\/blog\/addressing-the-challenges-of-electronic-health-records-integrating-proprietary-systems-and-overcoming-information-silos-484448\/","title":{"rendered":"Addressing the Challenges of Electronic Health Records: Integrating Proprietary Systems and Overcoming Information Silos"},"content":{"rendered":"<p>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.<\/p>\n<p>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.<\/p>\n<h2>The Complexity of Proprietary Systems and Information Silos in U.S. Healthcare<\/h2>\n<p>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.<\/p>\n<p>Proprietary systems create what are called &#8220;information silos.&#8221; These silos keep patient data locked in certain places or departments, making it hard to access from outside. For example, a specialist clinic\u2019s EHR may not work well with a hospital\u2019s system. Different labs might use different codes and data styles. Because of this, important patient information remains broken up, which can cause problems like:<\/p>\n<ul>\n<li><strong>Duplicate Testing:<\/strong> Without past lab or imaging results, doctors may order the same tests again. This wastes resources and makes things harder for patients.<\/li>\n<li><strong>Misdiagnosis and Incomplete Treatment:<\/strong> When data is disconnected, doctors cannot see the full picture. This can lead to wrong diagnoses or incomplete care.<\/li>\n<li><strong>Patient Safety Risks:<\/strong> Missing or partial data can cause medication mistakes or allergic reactions.<\/li>\n<li><strong>Inefficient Workflow:<\/strong> Staff spend extra time trying to fix records or get data from outside sources.<\/li>\n<li><strong>Higher Costs:<\/strong> Health systems pay more because of repeated procedures and inefficiencies.<\/li>\n<\/ul>\n<p>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\u2019t automatically understand each other\u2019s data.<\/p>\n<p><!--smbadstart--><\/p>\n<div class=\"ad-widget case-study-ad\" smbdta=\"smbadid:sc_21;nm:UneQU319I;score:0.89;kw:data-entry_0.98_insurance-extraction_0.94_ehr_0.89_sm-process_0.78_form-automation_0.72;\">\n<h4>AI Call Assistant Skips Data Entry<\/h4>\n<p>SimboConnect extracts insurance details from SMS images &#8211; auto-fills EHR fields.<\/p>\n<div class=\"client-info\">\n    <!--<span><\/span>--><br \/>\n    <a href=\"https:\/\/simbo.ai\/schedule-connect\">Connect With Us Now \u2192<\/a>\n  <\/div>\n<\/div>\n<p><!--smbadend--><\/p>\n<h2>Legacy Systems: A Barrier to Digital Transformation in Healthcare<\/h2>\n<p>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.<\/p>\n<p>Legacy systems can be standalone or loosely linked with newer systems. They often have problems such as:<\/p>\n<ul>\n<li><strong>Incompatible Data Formats and Protocols:<\/strong> Older systems use outdated or private ways to handle data that new software cannot read.<\/li>\n<li><strong>Limited Flexibility:<\/strong> These systems are hard to change or update because of their design or vendor rules.<\/li>\n<li><strong>Integration Complexities:<\/strong> Connecting old systems with new digital tools requires expensive fixes like custom interfaces.<\/li>\n<\/ul>\n<p>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.<\/p>\n<p>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.<\/p>\n<h2>Standards and Technologies Facilitating Interoperability<\/h2>\n<p>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:<\/p>\n<ul>\n<li><strong>HL7 (Health Level Seven):<\/strong> A set of international rules for sharing and managing electronic health information.<\/li>\n<li><strong>FHIR (Fast Healthcare Interoperability Resources):<\/strong> A newer HL7 standard made for web-based data exchange, easier and faster to use.<\/li>\n<li><strong>DICOM (Digital Imaging and Communications in Medicine):<\/strong> A standard for handling medical images like X-rays and MRIs.<\/li>\n<\/ul>\n<p>By using these standards, healthcare systems can \u201cspeak the same language.\u201d This helps them share data smoothly, improving real-time access to patient info.<\/p>\n<p>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.<\/p>\n<p>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.<\/p>\n<p><!--smbadstart--><\/p>\n<div class=\"ad-widget checklist-ad\" smbdta=\"smbadid:sc_9;nm:AOPWner28;score:1.6099999999999999;kw:medical-record_0.98_record-request_0.95_record-automation_0.89_patient-data_0.63_data-retrieval_0.57;\">\n<div class=\"check-icon\">\u2713<\/div>\n<div>\n<h4>Automate Medical Records Requests using Voice AI Agent<\/h4>\n<p>SimboConnect AI Phone Agent takes medical records requests from patients instantly.<\/p>\n<p>    <a href=\"https:\/\/simbo.ai\/schedule-connect\" class=\"download-btn\"> Don\u2019t Wait \u2013 Get Started <\/a>\n  <\/div>\n<\/div>\n<p><!--smbadend--><\/p>\n<h2>Security Concerns: Balancing Access and Privacy<\/h2>\n<p>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\u2014an 86% increase from the year before. These attacks try to steal patient data, stop hospital work, or demand ransom money.<\/p>\n<p>To protect patients\u2019 data while sharing it safely, organizations use many security actions, including:<\/p>\n<ul>\n<li><strong>End-to-End Encryption:<\/strong> Data is encrypted while stored and when sent to stop unauthorized access.<\/li>\n<li><strong>Access Controls and Authentication:<\/strong> Only approved staff can see or change patient data. Multi-factor authentication adds more security.<\/li>\n<li><strong>Regular Security Audits:<\/strong> Checks find weak points and confirm rules like HIPAA are followed.<\/li>\n<li><strong>Data Governance Policies:<\/strong> Rules manage how data is shared, handle patient permission, and limit data use to proper purposes.<\/li>\n<\/ul>\n<p>Providers also look at new security tools like blockchain to protect data accuracy and increase transparency. But this needs careful use because of healthcare\u2019s special needs and rules.<\/p>\n<p><!--smbadstart--><\/p>\n<div class=\"ad-widget regular-ad\" smbdta=\"smbadid:sc_17;nm:AJerNW453;score:1.92;kw:hipaa_0.99_compliance_0.96_encryption_0.93_data-security_0.85_call-privacy_0.77;\">\n<h4>HIPAA-Compliant Voice AI Agents<\/h4>\n<p>SimboConnect AI Phone Agent encrypts every call end-to-end &#8211; zero compliance worries.<\/p>\n<p>  <a href=\"https:\/\/simbo.ai\/schedule-connect\" class=\"cta-button\">Start Your Journey Today \u2192<\/a>\n<\/div>\n<p><!--smbadend--><\/p>\n<h2>Workflow Automation and AI: Enhancing EHR Usability and Integration<\/h2>\n<p>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.<\/p>\n<p>AI and automation help in these ways:<\/p>\n<ul>\n<li><strong>Data Standardization and Normalization:<\/strong> AI tools clean and format healthcare data automatically. This makes data coding and terms consistent.<\/li>\n<li><strong>Smart Clinical Decision Support:<\/strong> AI in EHRs can give alerts, diagnosis tips, and treatment ideas based on current patient data.<\/li>\n<li><strong>Natural Language Processing (NLP):<\/strong> AI reads free-text notes, finds important info, and turns it into structured data usable by other software.<\/li>\n<li><strong>Automated Appointment Scheduling and Follow-Up:<\/strong> AI chatbots and voice assistants handle patient calls, reminders, and check-ins, helping front-office staff.<\/li>\n<li><strong>Call and Phone Automation:<\/strong> Some companies use AI to manage phone calls, gather patient info, and route calls properly.<\/li>\n<li><strong>Phased AI Integration:<\/strong> For places with old systems, AI tools can connect using APIs without big changes at once, avoiding disruptions.<\/li>\n<\/ul>\n<p>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.<\/p>\n<p>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.<\/p>\n<h2>Organizational Strategies to Overcome Integration Challenges<\/h2>\n<p>Besides technology, healthcare providers need to fix organization and policy issues to get real EHR data sharing and better integration.<\/p>\n<ul>\n<li><strong>Conduct Comprehensive System Audits:<\/strong> Check current systems, including old parts, data formats, and workflows to find strengths and gaps.<\/li>\n<li><strong>Prioritize Interoperability:<\/strong> Choose systems and vendors that use open standards, offer APIs, and want to share data.<\/li>\n<li><strong>Upgrade Legacy Systems Thoughtfully:<\/strong> Carefully plan legacy system replacements, considering risks and costs.<\/li>\n<li><strong>Invest in Staff Training:<\/strong> Teach employees about new tools, privacy rules, and goals to help change.<\/li>\n<li><strong>Foster Cross-Department Collaboration:<\/strong> IT, clinical, administrative, and compliance teams should work together.<\/li>\n<li><strong>Utilize Integration Engines and Middleware:<\/strong> Use technology that translates and connects data between different systems.<\/li>\n<li><strong>Implement Cloud-Based Solutions:<\/strong> Cloud platforms support scaling, flexibility, and security for data sharing across groups.<\/li>\n<li><strong>Establish Governance Frameworks:<\/strong> Set policies on data management, patient consent, and security oversight.<\/li>\n<\/ul>\n<h2>The Role of Data Silos in Biomedical Research and Clinical Care<\/h2>\n<p>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.<\/p>\n<p>Fragmented data systems often lead to:<\/p>\n<ul>\n<li>Incomplete or inconsistent metadata and notes<\/li>\n<li>Repeated or conflicting information that hurts research accuracy<\/li>\n<li>AI models that are biased because they rely on narrow data sets<\/li>\n<\/ul>\n<p>To fix this, some platforms like Elucidata\u2019s 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.<\/p>\n<h2>The Bottom Line<\/h2>\n<p>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.<\/p>\n<section class=\"faq-section\">\n<h2 class=\"section-title\">Frequently Asked Questions<\/h2>\n<div class=\"faq-container\">\n<details>\n<summary>What is semantic interoperability in healthcare?<\/summary>\n<div class=\"faq-content\">\n<p>Semantic interoperability refers to the ability of different healthcare systems to exchange and interpret shared data, allowing for meaningful communication across organizational boundaries.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What challenges do health organizations face regarding EHRs?<\/summary>\n<div class=\"faq-content\">\n<p>Health organizations often struggle with the integration and exchange of information due to proprietary systems and internal ecosystems that create information silos.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What are the benefits of overcoming information silos?<\/summary>\n<div class=\"faq-content\">\n<p>Breaking down information silos can lead to reduced medical errors, improved disease monitoring, personalized patient care, and more efficient data management.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What technologies aid in achieving semantic interoperability?<\/summary>\n<div class=\"faq-content\">\n<p>Recent studies have identified various technologies and tools that facilitate data interoperability, enabling efficient communication among healthcare actors.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>How can organizations avoid data silos in EHRs?<\/summary>\n<div class=\"faq-content\">\n<p>Organizations can avoid data silos by implementing systems that support semantic interoperability, allowing data access and sharing without vendor restrictions.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What was the focus of the systematic literature review?<\/summary>\n<div class=\"faq-content\">\n<p>The review focused on semantic interoperability in electronic health records, assessing various methodologies, technologies, and tools initiated between 2010 and 2020.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What is the taxonomy proposed in the study?<\/summary>\n<div class=\"faq-content\">\n<p>The article proposes a taxonomy around semantic interoperability that categorizes different approaches and technologies used to solve interoperability challenges.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>How do legacy systems affect interoperability?<\/summary>\n<div class=\"faq-content\">\n<p>Legacy systems often pose challenges for data exchange due to their heterogeneous nature, which can hinder effective integration of health records.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What approaches are suggested for solving interoperability issues?<\/summary>\n<div class=\"faq-content\">\n<p>The study outlines several approaches, including using standards-based methods, semantic web technologies, and fostering a common framework for data integration.<\/p>\n<\/p><\/div>\n<\/details>\n<details>\n<summary>What role do clinical decision support systems play?<\/summary>\n<div class=\"faq-content\">\n<p>Integrating clinical decision support systems with electronic health record data enhances the capacity to provide accurate and timely healthcare recommendations through improved data access.<\/p>\n<\/p><\/div>\n<\/details><\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-36046","post","type-post","status-publish","format-standard","hentry"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts\/36046","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/comments?post=36046"}],"version-history":[{"count":0,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts\/36046\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/media?parent=36046"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/categories?post=36046"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/tags?post=36046"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}