The growing demand for personalized medicine has accelerated the adoption of Real-World Data (RWD) in healthcare. The Tungs' Taichung MetroHarbor Hospital in Taiwan, in collaboration with AESOP Technology, conducted research on an AI-driven Clinical Decision Support System (CDSS) that leverages RWD to enable safer and more effective clinical decision-making, significantly reducing the risk of potentially inappropriate medications. The findings were published in the Journal of Medical Internet Research recently.

RWD encompasses various data types, including electronic health records (EHR), insurance claims, wearable devices, environmental factors, and social determinants of health. It offers a comprehensive view of patient conditions and treatment outcomes. However, effectively utilizing RWD remains a significant challenge.

As a key component of RWD, the EHR system is often constrained by the poor design of traditional CDSS. These systems frequently fire irrelevant or low-priority alerts and fail to provide specific recommendations for complex scenarios, such as off-label drug use, multimorbidity, and polypharmacy. This results in alert fatigue among physicians, causing critical alerts and reminders to be overlooked. Consequently, the completeness and accuracy of medical records are compromised, increasing the risk of inappropriate diagnoses or treatments and potentially threatening patient safety.

This study addresses these challenges with an integrated AI-powered CDSS that combines MedGuard (now called RxPrime) for prescription appropriateness and DxPrime for diagnostic recommendations. By analyzing 438,558 prescriptions during a year-long trial, the system delivered 10,006 actionable recommendations, achieving a nearly 60% acceptance rate by physicians. Compared to traditional systems, this AI-enhanced approach demonstrated superior precision and practical applicability in real-world clinical settings.

The results also revealed high acceptance rates in specialties such as ophthalmology (96.59%) and obstetrics/gynecology (90.01%), indicating strong applicability. In contrast, lower acceptance rates in neurology (38.54%) and hematology-oncology (10.94%) underscore the need for specialty-specific customization to address diverse clinical demands.

This research highlights the transformative potential of RWD-driven AI systems with actionable recommendations to improve patient safety and support complex treatment decisions. These advancements foster greater trust and adoption of CDSS by physicians. Furthermore, by enhancing the completeness and accuracy of medical records, these systems elevate the quality of RWD, fostering a positive feedback loop that drives future medical advancements and consistently provides a reliable foundation for data-driven healthcare.

PRNewswire

Medical AI start-up Aesop Technology announced a new partnership that made their new product, DxPrime, available in the Olive Library. DxPrime provides physicians and clinical documentation improvement (CDI) teams with information about missing and wrongly coded diagnoses and procedures to correct the patient's chart in just a few clicks. It makes completing discharge summaries, prioritizing work for CDI teams, and medical coding much easier, faster, and less error-prone.
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If the patient record is incorrect, you cannot code correctly.

Completeness, precision, and validity of medical documentation are critical for all healthcare stakeholders. Without correct patient records, patients could receive improper discharge instructions and a sub-optimal continuum of care. Providers also can struggle to estimate the length of stay and code insurance claims correctly, resulting in denials and loss of revenue.

Approximately 10% of inpatient claims are denied, of which more than 85% (or about $35 billion) result in unnecessary losses. Many of these denials occur because of errors in the patient record that occur upstream from the claims process. Diagnosis input errors are difficult for physicians to avoid because the knowledge of coding systems is different from what they need to learn to provide great patient care. Modern medicine's complexity has caused 14,400 diseases to be included in ICD-10, further classified into 68,000 ICD-10-CM and 87,000 ICD-10-PCS codes.

"Physicians, CDI team, and coders have to spend a lot of time poring through medical records to find the key clinical diagnoses among the vast amount of information available," said Jim Long, CEO of AESOP. "After that, they have to follow a series of inefficient steps on the computer to complete the input process, and search functionality for ICD codes often is not helpful. The whole process is complex, time-consuming, and error-prone.

When the physicians input the improper diagnosis, it also has downstream implications. "When using DxPrime, we have helped physicians often notice they did not correctly code complications such as urinary tract infections and respiratory failure. By assisting them in inputting the proper diagnoses, our partners have seen an increase in revenue of 5-10% per inpatient."

State-of-the-art machine learning assisted physician data entry.

DxPrime provides high-quality suggestions to support physician data entry based on a machine learning model (published in the Healthcare journal) that has been run on top of data from 3.2 Billion patient visits, including vast amounts of structured information. It allows DxPrime to use items from the patient record like lab test results and medications ordered when predicting a diagnosis.
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This comprehensive model utilizes artificial intelligence to efficiently compensate for traditional CDSS and NLP weaknesses to find correct or missed diagnoses.

Referred from: PR Newswire