從健保資料庫挖礦 終成臨床編碼唯一服務供應商
「我們始終想要用數據來做應用。」醫守科技執行長暨聯合創辦人龍安靖笑說，雖然曾被說不學無術，但團隊仍默默投入健保資料庫資料研究分析，靠著一股堅持，將數以百計的光碟片，一一讀取。也因為進得夠早、走得夠久、探得夠深，讓醫守得以成為臨床編碼唯一服務供應商，與臺灣的簽約數呈倍數成長，在數位發展部數位產業署的「資料經濟價值躍升計畫」支持下，也於美國傳來好消息，未來全球市場精彩可期。 由世界衛生組織建立的疾病分類系統，目的是把跨國家、地區在一定期間所蒐集的罹病或死亡資料，能夠系統化記錄、分析、解讀與比較，現已成為病歷書寫的重要部分。住院過程中，一位病患的病歷，作者群是經手過該病患治療歷程的所有醫師，他們會記錄病患主觀陳述、客觀檢查結果、曾經進行的手術或處置，最後進行病歷總結，再交給疾病分類人員分別進行疾病分類編碼，用以進行健保給付申報。 全球健保制度經過數十年的演進，申報制度大量引進各種編碼系統，為了整合各個複雜的系統，醫院需要耗費大量人力、物力，付出許多額外成本，更造成醫院的損失。臺灣健保疾病分類於2016年起以ICD-10 CM/PCS（International Statistical Classification of Disease and Related Health Problems, Tenth Revision, Clinical Modification / Procedure Coding System，國際疾病分類第十版）申報後，編碼數量大幅增加，也提高了臨床編碼的困難度。尤其，當醫師面對較不熟悉的處置碼和非本身專業科別的疾病，要選擇最正確且合適的編碼，相當挑戰與費時，導致疾病分類師必須花費更多的心力調整和修正醫師所選的編碼，更遑論進行在院編碼和品質稽核。
AESOP Technology, in collaboration with AstraZeneca Taiwan, has unveiled Medigator, an innovative AI software designed to manage immune-related adverse events (irAEs) and enhance the effectiveness of immunotherapy. With cancer being the second leading cause of death and immunotherapy offering improved survival rates, Medigator is specially designed to address the potential challenges associated with irAEs that may deter some patients from choosing this treatment. The American Cancer Society estimates that of the 2 million new cancer cases in the U.S. in 2023, about half could be eligible for immunotherapy treatment. While this treatment stimulates the patient's immune system to fight cancer, it can potentially trigger an overactive immune response, leading to irAEs. Managing irAEs is challenging due to the unique immune responses and variability in the reaction to immunotherapy. The intensified immune response that fights cancer cells can inadvertently harm normal tissues. Effective communication is crucial to educate patients about possible side effects, readiness for assistance, and timely medical intervention. Mild irAEs can often be managed symptomatically with topical treatments, while severe cases may require discontinuing immunotherapy and administering immune-suppressing medications. To address the complexities of managing irAEs, Medigator offers real-time assistance to physicians. This tool manages the risk of irAEs and is seamlessly integrated into the Computerized Physician Order Entry system. "By harnessing the power of big data analytics, Medigator analyzes real-world patient experiences with irAEs, physicians' management strategies, and patient responses to treatment based on a dataset of 197,921 claim-based prescriptions. Using these analytical parameters, Medigator goes a step further by predicting the risk levels of different irAEs in individual patients. It provides personalized medication options by considering factors such as patient age, gender, race, chronic medical conditions, and genetic history, empowering physicians to enhance their risk management strategies in the care plan," explained Jim Long, CEO of AESOP Technology. "Medigator is an immunotherapy medication navigator designed for physicians. It aims to minimize interruptions in immunotherapy, preserve valuable treatment time and resources, and alleviate the treatment burden," said Jim Long. "The origin of the partnership between AESOP Technology and AstraZeneca Taiwan traces back to 2019 at an international biomedical accelerator co-hosted by AstraZeneca A.Catalyst Network and the National Biotechnology Research Park in Taiwan, which is dedicated to exploring new possibilities to change patients' lives. Medigator has set a promising example demonstrating patient-centric innovation by advancing shared decision-making in precision medicine," said Ben Chen, Medical Director, AstraZeneca Taiwan. The research findings of Medigator, which were recently presented at the annual meeting of the American Society of Clinical Oncology, received notable attention. AESOP Technology continues its unwavering commitment to delivering physicians with precise and personalized solutions for irAEs as immunotherapy advances. Its ultimate goal is to enhance immunotherapy treatment outcomes and improve patients' overall quality of life. 
李宛庭
「solaxin」與「solian」兩個極其相似的藥名，實際卻是天差地遠的藥品，分別是肌肉鬆弛劑和抗精神病藥。另外，每家藥廠又都有著各自的命名方式，因此即使是聰明又小心的醫生，也可能不小心開錯藥。 開錯藥品可能帶來的風險，不只是患者無法得到妥善的治療，還有可能導致誤食過敏藥物、產生其他副作用等嚴重後果。 2019年，醫守科技看見了醫療產業開錯藥的問題，開發RxPrime藥御守協助解決，現今則推出DxPrime好完診進軍診斷系統服務，今（2022）年8月更完成了295萬美元（約新台幣8,850萬元）的Pre-A輪募資，由台杉資本領投、日本知名上市遊戲公司Colopl Next、美國創投500 Global和比翼生醫創投等跟投。 從原先的「藥物開立服務」跨足「診斷系統服務」，醫守科技究竟看見了哪些產業問題？ 從「用藥」到「診斷」，醫守科技如何從知名產品RxPrime藥御守轉型再出發？ 2019年成立的醫守科技，最先推出的產品是RxPrime藥御守，希望為醫生減少開錯藥的狀況。藥廠出廠藥品時，會為藥品命名，相似的藥名卻可能是天差地遠的藥品，於是醫守科技的RxPrime藥御守，透過機率模型與增強學習雙重人工智慧引擎，打造用藥安全演算法，在偵測到藥品可能開錯時，即時發出系統警告提醒醫生。 儘管推出RxPrime藥御守立意良好，醫守科技卻面臨到了醫院採購的系統性問題。
Digital health startup AESOP Technology has raised a $2.95 million series pre-A round to address the growing medical and billing errors problem. The round was led by Taiwania Capital with participation from Colopl Next, 500 Startups, and BE Capital.
Originally from Taiwan, AESOP started as a university spin-off from Taipei Medical University (TMU). Professor Yu Chuan (Jack) Li, the founder and current president of the International Medical Informatics Association, spent ten years before AESOP working on big data approaches to reduce medication errors. He initially applied the model to launch a product, RxPrime (previously known as MedGuard), that identifies wrong-drug errors. During the pandemic in 2020, Prof. Li officially established AESOP in the US with his former student, who grew to become CIO of a TMU-affiliated hospital, Dr. Jim Long, and former TMU Visiting Assistant Professor, Dr. Jeremiah Scholl. They worked together to broaden the types of errors the AI could identify and on products they could use to improve the US healthcare system. "Our solution is revolutionary and generalizable." CEO Jim Long described. "We have developed an AI model with an exceptional understanding of the association between diagnoses and structured clinical data like medications, lab results, and procedures." One of the first errors RxPrime identified was a 9-year-old girl accidentally prescribed an anti-schizophrenia drug for simple back pain. Another commonly prescribed error was Acetaminophen (pain killer), sometimes mistaken as Acetazolamide (glaucoma and altitude illness). "These mistakes might occur just because the two drugs have look-alike or sound-alike (LASA) names. It is horrible to think about, but errors like LASA happen in hospitals everywhere." Jim explained
AESOP Technology announced that they have been accepted into Mayo Clinic Platform_Accelerate, a 20-week program that helps early-stage health tech AI startups get market-ready.
Participants are selected through a competitive screening process where a panel of Mayo Clinic leaders reviews them from the clinical and operational perspective, led by John Halamka, MD., President of Mayo Clinic Platform. "It's an excellent opportunity for a medical AI startup like us. Data is the fuel from which everything grows into power, and this program provides de-identified patient datasets and tools to help us validate our solutions," says Jeremiah Scholl, the CPO of AESOP Technology. "This practical experience will help us go even further in developing better products. The fact that we get to be mentored by Mayo Clinic's reputable experts is inspiring." 'AESOP', which stands for 'AI-Enhanced Safety of Prescription', is working to make physician data entry easier, faster, and less error-prone using machine learning on 3.2 billion data sets. The company has developed products capable of this. One is RxPrime which detects wrong drug errors by checking if medications match patients' diagnoses, age, and gender. Errors can happen at any stage of the medication-use process, but more than 50% of them occur during the prescribing phase. RxPrime is able to detect potential and unexplained errors in prescriptions and provide optimal recommendations, even for the look-alike-sound-alike medication errors. It offers just-in-time decision support without interfering unnecessarily with the clinical consultation process.
ROCHESTER, Minn. — Mayo Clinic Platform_Accelerate has announced its second cohort of health tech startups, including national and international businesses. The program will help seven companies develop and validate their artificial intelligence-driven health care products or solutions and advance their business plans.
The immersive, 20-week program offers participants access to Mayo Clinic experts in regulatory, clinical, technology and business domains with a focus on AI model validation and clinical readiness. Technology experts from Google and Epic also will provide workshops for the participants. "The only way we can transform health care is by bringing together clinical experts with technology innovators," says John Halamka, M.D., president of Mayo Clinic Platform, a strategic initiative to improve health care through insights and knowledge derived from data. "Our Accelerate program combines emerging companies with breakthrough ideas, leaders from Mayo's clinical practice and our unique 'data behind glass' approach to algorithm development," Dr. Halamka says, describing the secure environment that allows companies to build algorithm models they can use for innovation, but the data never leave the Mayo Clinic Platform. The program will help participants explore ways to improve health care in a variety of areas:
Photo by: Helen King/Getty Images
California-based medical AI startup Aesop Technology, which has an R&D office in Taiwan, has recently unveiled its latest clinical documentation improvement tool that helps coders spot incorrectly coded diagnoses or procedures. DxPrime provides suggestions to support medical data entry. The CDI tool is based on a machine learning model that has been trained based on a data set of some 3.2 billion patient visits. According to Aesop Technology, their latest solution for medical coding harnesses AI to "efficiently compensate for traditional CDSS and NLP weaknesses to find correct or missed diagnoses". WHY IT MATTERS Now available on digital health marketplace Olive Library, DxPrime provides information on missing and wrongly coded diagnoses or procedures to easily correct patients' charts. With incorrect patient records, Aesop claims, patients could be given improper discharge instructions, thus receiving poor after-discharge care. For providers, this could lead to a wrong estimate of their patients' length of stay and wrong code insurance claims, which could ultimately result in denials and revenue losses. Aesop emphasized that errors in diagnosis input are difficult for physicians to avoid due to the gap in their knowledge of coding systems. Currently, the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) has 14,400 diseases included in its base classification, 68,000 diagnosis codes under ICD-10-CM and 87,000 procedural codes under ICD-10-PCS. 
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.
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. 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
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