In 2022, there were more than 33 million U.S. hospital admissions. These patients generate a vast amount of data every year; on average, they produce 50 million gigabytes. This includes lab tests, imaging, sensor readings, and clinical notes. And that is just the data generated from hospital admissions. It does not include data generated from medical research and non-hospital healthcare facilities. Hospitals and health systems need an efficient way to collect and synthesize this data.  

However, many are still using manual processes and unsupported internal staff to do so and are missing out on key clinical insights. In fact, 97% of the clinical data generated by hospitals in the U.S. goes unused.  

When supported by clinical data experts, automated clinical data abstraction can be an efficient solution for hospitals and health systems to capitalize on this data and help patients receive the most beneficial treatments. 

What is clinical data abstraction? 

Clinical data abstraction is the process of gathering data from medical records. This data can be from both electronic and paper records.  

It is used for various purposes, such as quality improvement, patient registration, clinical research, and administrative coding. The data elements extracted through this process include demographics, medical history, medications, allergies, lab results, and much more. 

Clinical data abstraction is essential for both clinical care and research. It enables hospitals and health systems to collect and extract data from a vast number of patients and use it to improve patient outcomes. 

How is data abstracted from clinical records? 

There are two main ways to abstract clinical data: manual and automated. 

Manual clinical data abstraction is the process by which trained abstractors review medical records and extract the required data elements. Manual abstraction includes the following steps: 

• • Identifying data elements needed for specific purposes. 
  • Creating a data abstraction protocol that outlines and standardizes the procedures used to extract data from medical records. 
  • Training the abstractors on this protocol. 
  • Reviewing the medical records to extract the required data and record it. 
  • Validating the data. 
  • Analyzing the data for a specific purpose. 

Manual abstraction can be time-consuming and fallible. A 2018 study has shown that manual medical record abstraction is associated with the highest rates of error. 

Automated clinical data abstraction allows clinical teams to reduce both the time-extensive and error-prone methods of manual abstraction. This technique uses clinical data management software, which is powered by artificial intelligence (AI), to support clinical data experts in their abstraction work. 

What is clinical data management software? 

Clinical data abstraction is a complex process, and clinical data management software powered by AI technologies can take on this complexity. Software like this can improve data integrity. It can also yield insights to improve business performance and efficiency. This can be done on both a macro and micro level. 

Clinical data management software features include: 

• • Data collection. 
  • Data storage. 
  • Data aggregation, curation, and transformation. 
  • Analysis and reporting, including dashboards and scorecards. 

What are the benefits of automating clinical data abstraction and management? 

Medical researchers, hospitals, and health systems can benefit from automating clinical data abstraction and management. This is especially true for those participating in value-based care programs. 

For instance, the number of hospital participants in clinical registries grows at a pace of 7% each year. Unfortunately, the industry’s inventory is plagued with misunderstandings and diminished data integrity. As a result, a treasure trove of data that could benefit patient care is largely untapped. 

Clinical data is becoming more plentiful and complex. High-end computing solutions, such as AI technologies, are often the best choice for analysis when supported by human clinical data expertise. But we know these technologies are only as good as the input they receive. Poorly organized or managed data will yield poor results. 

AI tools enable talented humans to explore data deeply. This exploration can uncover patterns and trends that form true business intelligence. Automation is just the beginning. When you combine data experts, data integrity, and AI-powered analytics through automated clinical data abstraction and management, you gain actionable insights. These insights turn into best practices that fuel ROI, operational excellence, compliance with regulations, agility, and growth. 

But it’s not just medical and healthcare professionals who benefit. Patients experience positive impacts from automated clinical data abstraction and management, including: 

• • Empowering patients to take ownership of their medical histories with easily accessible medical records. 
  • Informing providers of patients’ ongoing health status and using data-driven findings so they can assess treatment methods faster. 
  • Saving patients time and money. 
  • Improving care coordination by centralizing data across health systems. 

How do clinical data abstraction and management software centralize data from multiple health systems? 

According to a study published in the Journal of the American Medical Informatics Association, 79% of patients received care at more than one facility during the calendar year studied. The authors found that quality measures calculated using single-site electronic medical record data may be limited. Incomplete information can affect healthcare payments, patient safety, and care quality. 

Automated clinical data abstraction and management software can centralize data from multiple health systems. This includes electronic health records, clinical trials, and patient registries. It provides a single platform for the storage and management of data from across an entire healthcare enterprise. This can be beneficial for several reasons, including: 

• • Improved efficiency by making it easier for clinical data experts and abstractors to access and analyze data from multiple sources. 
  • Reduced costs by eliminating the need to maintain multiple data systems. 
  • Improved quality by making it easier to ensure that data is accurate and complete. 
  • Increased transparency by making it easier to share data with other researchers and the public. 

How is Q-Centrix automating clinical data abstraction? 

Q-Centrix understands the importance of clinical data and has built the Enterprise Clinical Data Management (eCDM™) platform in response. With its software, experienced team, analytics, data structure, and best practices adopted from over 1,200 hospitals, they provide meaningful, accurate, complete, and secure clinical data. 

Q-Centrix also partners with a company using AI and natural language processing (NLP) to capture unstructured clinical data, ensuring patient populations with heart conditions or cancer receive the most beneficial treatments. Q-Centrix brings a sophisticated AI and machine learning component to its industry-leading eCDM™ platform. 

The eCDM combines clinical expertise with market-leading technology, information, and analytics. This enables the extraction, curation, management, and analysis of high-fidelity clinical data. It covers healthcare systems and key clinical segments, such as: 

• Regulatory reporting: With more than two decades of experience, the Regulatory Reporting software was built to offer intuitive data capture; quality assurance; and action-oriented, on-demand analysis of new, retired, and custom measures. The Regulatory Reporting solution within the eCDM™ platform features capture and submission technology for core measures and electronic clinical quality measures plus market-leading reporting. 

• Cardiology software: Q-Centrix cardiology software is a modern and intuitive platform that is certified to submit to multiple cardiac registries — including NCDR, STS, and AHA programs. This partnership drives efficiency and returns on investment by centralizing all cardiac registry data management activities into one comprehensive solution. 

• Oncology technology: Q-Centrix introduced the first new technology to enter the cancer market in more than a decade. Its cloud-based oncology software captures and analyzes 250-plus data points. Featuring the same revolutionary technology that powers all the software within the eCDM™ platform, the SaaS structure and intuitive interface enable cancer experts to seamlessly capture data elements and identify actionable insights in real-time. 

The increasing value of clinical data in healthcare is undeniable, and automated clinical data abstraction is a promising tool to help providers and researchers make sense of this valuable resource. By automating portions of the data abstraction process, clinicians and researchers can free up more time to focus on improving patient care quality and outcomes.  

As the industry continues to face mounting pressure to improve outcomes and reduce costs, the use of automated clinical data abstraction is likely to become more widespread. By leveraging this powerful technology, health care providers can gain deeper insights into patient health and well-being, leading to more effective treatment options and improved patient outcomes.