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:
Chosen from a competitive process, each company receives a benefit package that offers:
As part of this in-kind investment, Mayo Clinic Platform will have an equity position in the companies.
"In our first cohort, we already have seen these companies receive attention from potential investors, health care providers and others who want to support their work," says Eric Harnisch, vice president, Partner Programs, Mayo Clinic Platform. "We are excited to further these efforts with the second group of companies."
The inaugural cohort of four companies recently finished the program. Applications for the third Mayo Clinic Platform_Accelerate cohort will be open soon and reviewed on a rolling basis.
About Mayo Clinic Platform
Founded on Mayo Clinic’s dedication to patient-centered care, Mayo Clinic Platform. enables new knowledge, new solutions and new technologies through collaborations with health technology innovators to create a healthier world. To learn more, visit Mayo Clinic Platform.
About Mayo Clinic
Mayo Clinic is a nonprofit organization committed to innovation in clinical practice, education and research, and providing compassion, expertise and answers to everyone who needs healing. Visit the Mayo Clinic News Network for additional Mayo Clinic news.
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.
THE LARGER TREND
Last month, Aesop's medication decision support tool RxPrime was launched on Olive Helps, a desktop platform for healthcare IT developers. The solution analyses inpatient data using patterns from prescriptions and flags potentially inappropriate prescriptions that do not match a patient's diagnosis.
In other news, Aesop partnered with Taipei Medical University, Harvard Medical School and Brigham and Women's Hospital last year for a study that ran its machine learning model in EHR systems in the United States. It was found that the model, which provides adaptive suggestions to help doctors better complete their prescriptions, had demonstrated good international transferability.
ON THE RECORD
Jim Long, CEO of Aesop, said: "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. 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 using DxPrime, he claimed, doctors were able to notice incorrect code complications. "By assisting them in inputting the proper diagnoses, our partners have seen an increase in revenue of 5-10% per inpatient," Long said.
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: yahoo finance
U.S.A., Jan. 31, 2022 - Olive, the automation company creating the Internet of Healthcare, today announced the winners of the first-ever Hack for Health contest. In partnership with Rotera, Olive held the contest to encourage developers to build Loops (think apps in an app store) that will change the way healthcare employees work by improving efficiency, reducing the scope for errors and burnout, and enhancing productivity — all to optimize the patient experience.
Olive designed its Hack for Health contest to solve healthcare’s biggest problems through collaboration with the developer community. Participation is open to any individual developer, startup, health system, or enterprise technology company. Winning Loop submissions are published on Olive’s platform, giving developers full access to Olive’s growing payer-provider network.
While any developer who publishes a loop on Olive’s platform receives 85% of the revenue generated from their Loop(s) once published, Hack for Health grand prize winners also receive tiered cash prizes up to $10,000, industry speaking engagement opportunities, a 60-minute pitch meeting with the Olive Ventures team, an Oculus Quest 2, YubiKey, and a 30” curved monitor. Olive also provides opportunities for its employees to develop Loops as part of an internal contest with its own set of prizes.
“Olive created healthcare’s first true platform. We wanted to create an event that encouraged a wide range of developers to come build on it to grow our ecosystem of people working together to transform healthcare,” said Patrick Jones, Executive Vice President, Partnerships at Olive. “Our first-ever Hack for Health contest, in partnership with Rotera, was the perfect way to introduce developers of all sizes to Olive and create new solutions that will help change the way healthcare workers work.”
Olive selected the following five Loops as the grand prize winners based on their functionality, innovation, outcome improvement, and bettering of diversity, equity, and inclusion among underrepresented healthcare workers and patients:
針對市場需求，陳兆煒執行長指出，首要任務是瞭解使用者的需要，定義出需解決的問題，藉此幫助團隊找出創業初期市場中尚未滿足的需求（Unmet Need），「經過十年後，就要致力於找到高價值需求True need，才能幫助台灣的生醫市場發展起來。」同時，北醫加速器在做的，就是協助新創團隊在複雜的利害關係中，調整出最合適的經營模式。
Taiwan Tech Arena to Host Its First Global Innovation Pitch Showcase in Partnership with Berkeley SkyDeck
Ten startups from the Taiwan Tech Arena (TTA) will participate in the program's first Global Innovation Pitch Showcase, produced in partnership with Berkeley SkyDeck, UC Berkeley's highly competitive global startup accelerator. Silicon Valley VCs, angel investors and industry professionals will attend the virtual event. Interested investors may register to join the pitch Showcase here.
TTA, funded by Taiwan's Ministry of Science and Technology, is focused on building a vibrant tech ecosystem of Asian startups. Each year, they select a cohort of up to 30 startups to participate in the TTA Silicon Valley accelerator, with eight of the startups participating with SkyDeck as part of its Global Innovation Partners program. This event marks the first time TTA is producing a pitch showcase with SkyDeck.
"We are proud to connect the outstanding Taiwanese tech talent with the impressive entrepreneurial community of Berkeley SkyDeck," said the TTA Silicon Valley office. "Since 2016, we have brought more than 150 innovators from Taiwan to the U.S. to build strong international relationships and connections and attract investment. And it's noteworthy that most of the startups' businesses stem from academia. To date more than half of these Taiwanese startups have raised money. With the new Showcase, we're thrilled we can share their talents, ideas and innovations on a global stage."
SkyDeck's Global Innovation Partner Program serves as a bridge for global startup teams as they participate in the SkyDeck entrepreneurial ecosystem and bring their ideas to the U.S. market. A limited number of startups from outside the U.S. are selected to participate in the partner program alongside the SkyDeck Batch (cohort) and Pad-13 (incubator) teams.
"Working closely with TTA has been a wonderful experience for all of us," said Caroline Winnett, Executive Director, Berkeley SkyDeck "Not only are the teams from Taiwan getting an immersive learning and networking experience at SkyDeck, they will return home ready to launch and create economic opportunities in their communities. We look forward to helping jumpstart these startups here in the U.S. and then seeing how they grow."
The Aug. 19 Showcase will feature the following startups:
The study also found that applying a federated learning approach can further improve accuracy of the model.
A study has demonstrated the international transferability of a Taiwanese artificial intelligence model for detecting medication errors in EHR systems in the United States.
The study was jointly conducted by Taiwan-based medical AI startup Aesop Technology, Taipei Medical University, Harvard Medical School and Brigham and Women's Hospital. Its results were announced last week in a press release.
WHY IT MATTERS
The "biggest challenge" in data-driven medicine is the successful implementation of data-driven applications in clinical practice from local to global settings without compromising patient safety and privacy, according to Dr Yu-Chuan Jack Li, a professor at Taipei Medical University.
The study, whose findings were published in the Journal of Medical Internet Research - Medical Informatics in January, found "good" transferability of Aesop's machine learning model in the EHR systems of two training schools under Harvard Medical School – Brigham and Women’s Hospital and Massachusetts General Hospital.
A federated learning (FL) approach was applied to the model which enhanced its performance. The said approach is an emerging technique that addresses the issues of isolated data islands and privacy.
"FL provides the solution by training algorithms collaboratively without exchanging the data itself," Dr Yu-Chuan said.
"The study has shown that the model trained by federated learning achieves remarkable performance comparable to the other two models trained by individual data sets," Aesop Technology co-founder and CEO Jim Long also said.
Incorporated in Aesop's MedGuard system, the AI medication safety model was trained using the 1.3 billion prescription data set from the National Health Insurance Administration in Taiwan.
In the statement, Aesop said its system can "immediately" provide adaptive suggestions to help doctors better complete their prescriptions. The AI model has since been expanded to eastern and western hospitals in the US.
THE LARGER TREND
Despite the wide adoption and optimisation of EHR systems in US hospitals, those systems still pose risks given varying safety performance, according to a 2020 study by researchers from the University of Utah and the Brigham and Women’s Hospital.
Medical errors are costing the US around $20 billion each year, leading to over 250,000 deaths. These can occur at any stage of the medication process with errors in prescribing happening half of the time.
The use of an AI system in preventing medication errors was already validated as early as 2017 by researchers at Harvard Medical School. In the same year, MedAware, the Israel-based startup which developed the algorithmic system, raised $8 million to scale its AI-powered solutions.
ON THE RECORD
"Reducing medication errors at the source is crucial. However, to help physicians be better informed and make better decisions, they need more accurate suggestions and alerts. This is where machine learning can help to make better decisions and improve patient safety and quality of care," said Dr David W. Bates, Chief of General Internal Medicine and Primary Care at Brigham and Women's Hospital and Professor of Medicine at Harvard Medical School.
New study paves the way for collaboration on artificial intelligence modelling and medication error reduction globally
Researchers at Harvard Medical School, Brigham and Women's Hospital, Taipei Medical University, and Aesop Technology, a Taiwan-based startup, announced today the results of a new joint study into the international transferability of machine learning (ML) models to detect medication errors. The results were recently published in the Journal of Medical Internet Research - Medical Informatics.
Working to Reduce Medication Errors
Medication errors are a growing financial and healthcare burden that results in economic costs of around US$ 20 billion and more than 250,000 deaths annually in the U.S. alone.
Medication errors can occur during any stage of the medication process, including prescribing, dispensing, administration, and monitoring, with errors in prescribing accounting for 50% of the total.
When medicating patients, physicians go through complex decision-making processes to accurately write a prescription. First, they must clearly define the patient's problem and list the therapeutic objective before selecting an appropriate drug therapy based on age, gender, and possible allergies. They must also consider dosing, drug-drug interaction, potential discontinuation of the drug, drug cost, and other therapies — and all of these need to be done instantly and simultaneously.
"Reducing medication errors at the source is crucial. However, to help physicians be better informed and make better decisions, they need more accurate suggestions and alerts. This is where machine learning can help to make better decisions and improve patient safety and quality of care," said Dr. David W. Bates, Chief of General Internal Medicine and Primary Care at Brigham & Women's Hospital and Professor of Medicine at Harvard Medical School.
For technology to assist in solving these problems, it is critical that machine learning understands these variables. For this to be successful, data must be properly collected, organized, and maintained.
「 台灣每年光是把止痛藥開錯，開成青光眼用藥的案例，就多達3,000多次。 」醫守科技共同創辦人暨執行長龍安靖說，「止痛藥通常又會吃好幾顆，對老人、小孩來說，吃錯是非常危險的。」
今年7月，北醫正式開幕生醫加速器。這個台灣首家醫療大學國際級加速器，聚焦「 數位醫療 」、「 人工智慧 」與「 醫療器材 」3大主題，除導入Biodesign訓練課程、從需求找商機外，更運用1校7院的臨床資源，提供試驗規畫、降低開發風險，輔導團隊研究成果商品化與鏈結國際。
第一階段，北醫已和比翼資本共同輔導10家新創公司，並分別投資10萬美元給較成熟的復健醫學智慧軟體開發公司龍骨王、醫療新創醫守科技、美國健康監測解方公司Rhythm Diagnostic System（RDS）等3團隊。
Digging for data
Any AI system needs to learn from data – the more the better. Taiwan has a trove of data to feed on thanks to its National Health Insurance (NHI)system, which was launched in 1995 and covers more than 99% of the population. In recent years, the government has allowed access to this extensive data for research and development.
One startup to take advantage of this opportunity is AESOP Technology, which has offices in Taipei, Berkeley, California, and Cambridge, Massachusetts. AESOP is trying to reduce a major risk to patients’ health globally: medical errors, specifically prescription errors. In Taiwan, they analyzed NHI data and found at least 3 million incorrect prescriptions each year, from incorrectly filled out forms to prescribing the wrong drugs or dosages.
“Everyone thinks hospitals are high-tech, but in hospitals, the staff records patients’ status on white boards,” said Jim Long, CEO of AESOP. “Doctors spell drug names wrong – even common drug names that sound like and look like other drug names.”
生病就醫原本是為了獲得治療，但如果醫師開錯藥，不僅無助病症，更可能造成嚴重後果！根據統計發現，美國醫院因醫療錯誤的死亡數，僅次於心臟病、癌症，尤其有 51% 的用藥錯誤，來自醫生一開始就開錯處方。過去曾任萬芳醫院資訊室主任，在醫療與製造業累積達 20 年經驗的龍安靖， 2019 年與醫療人工智慧權威李友專教授共同創立醫守科技，打造智慧型藥物安全系統「藥御守」（ MedGuard ），透過醫療大數據以及 AI 機器學習的人工智慧科技，改善用藥安全，降低醫師開錯藥物的機率。
事實上，台灣非常適合發展醫療 AI 技術，因為台灣健保制度上線多年，病歷電子化程度高，而且資料結構完整，加上臺灣人愛看醫生，進而累積全世界少有的高品質醫療大數據。醫守科技蒐集最近五年高達 13 億筆的處方資料，運用人工智慧從中探索複雜的診斷與用藥關聯以建立模型，並且每年持續更新模型。
龍安靖說，大多數醫師看診時，病患身上 80% 的問題，醫師可透過長年累積的醫學知識開立正確的藥物，但有 20% 的病患由於病因複雜或其他特殊狀況，可能會不當地導致醫師開錯藥。龍安靖長期待在醫療產業，他說開錯藥多半來自系統性問題(註1)，但過去缺乏有效的解決方案。
他舉例，某家教學醫院導入用藥錯誤的預防安全系統高達 40 多個，但是傳統的警示系統只能從大原則提醒，無法針對患者個人實際狀況，從上億種藥物與病因之間的組合給予精確建議，導致大約僅有 5% 的第一線醫師會參考這類只能警示大原則的傳統系統。
7月23日，比翼資本與北醫攜手在2020-Bio Asia亞洲生技大會期間，舉辦新一期輔導新創成果展「TMU X BE Demo Day」，展示來自美國、加拿大國內外10家獨具潛力的新創公司，同時，比翼與北醫選出3家最具海外市場擴展的新創公司分別注入10萬美金的投資，包含龍骨王(LongGood)、醫守科技(AESOP)與美國健康監測解方公司Rhythm Diagnostic System(RDS)。
國家級加速器產學研鏈結中心 ( TSI ) 與國家級投資公司台杉投資管理顧問，聯手舉辦「 Deep Tech 跨界整合論壇」，力邀聚焦人工智慧 ( AI ) 題目的台灣新創團隊，分享智慧城市及智慧醫療兩大領域佈局現況，並邀請創投觀點分享 AI 科技應用產業現況，與新創團隊對話討論，共同激盪具市場潛力的創新解決方案。
產學研鏈結中心執行長楊涵淳表示，加入價創計畫的新創團隊，大多數累積多年研發能量，同時搭配創新商業模式及產品解決方案，相信海外競爭對手在三、五年內是無法複製且超越。目前 TSI 輔導新創團隊三年半時間，培育超過 100 多個團隊，其中 20 多個團隊的募資金額累積新台幣 20 億元，成績斐然。
TAITRA launched the first #InnoVEXOnlineDemo through COMPUTEX Facebook and YouTube channels today to show the achievements of tech startups in Taiwan. Partnering with MOST (The Ministry of Science and Technology) of Taiwan, TAITRA invited five outstanding startups to demonstrate their innovative solutions of healthcare, AI, image analysis and blockchain at #InnoVEXOnlineDemo.
The COVID-19 pandemic sparked off crisis to the world, but it also acts as a massive disruptor that precipitates new opportunities for tech startups to thrive. According to Numbeo, Taiwan ranks #1 in the world’s health care index and has built a solid value chain in the biomedical industry. #InnoVEXOnlineDemo hence presents three medical startups in Taiwan with different solutions.
北醫新創公司醫守科技推出「藥御守MedGuard」，分析13億筆電子病歷、健保資料庫等醫療Real World Data，引入機器學習技術偵測錯誤或不適當處方，並於醫師開藥時給予警示。
工業技術研究院院長劉文雄、科技部政務次長許有進、科技部產學及園區業務司司長邱求慧與2020 CES TTA台灣創新館中的82家團隊共同向全球科技與會者展現台灣智慧應用實力。
Taipei, Jan. 6 (CNA) A delegation of 28 Taiwanese startups organized by the Ministry of Science and Technology (MOST) will attend the 2020 Consumer Electronics Show (CES) in Las Vegas to gain exposure for their brands, the ministry said Monday.
The 28 startups (including Aesop Technology) are largely involved in providing solutions in health care, internet security and smart city development, and their presence at the upcoming CES will demonstrate Taiwan's capability for innovation, the ministry said in a statement.
醫療錯誤，全美第三大死因？！AI提供解藥！ 醫守科技 龍安靖執行長與主持人路怡珍對談。