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Clinical Trials in Rare Diseases_공통보수

구분 온라인교육
카테고리온라인 > KoNECT International Conference_공통 > 2018 KoNECT International Conference
과정명Clinical Trials in Rare Diseases
신청기간 상시 교육기간 -
학습일수/시간 30 일 / 44 분 교육장소 미정
수강료 무료 교육정원인원제한없음
  • 소개
  • 목차
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학습개요
The Rare Diseases and Pediatric Clinical Research Networks in Europe

 

Jacques Demotes(ECRIN)

Machine learning in healthcare is rapidly expanding and will be one of the most impactful tools to effect significant improvements in care delivery. While clinical trials continue to be the gold standard for inferring causality, they are not adept at demonstrating small effect sizes across a population, particularly given the typical heterogeneity of treatment effect. With the proliferation of digital data, including monitors and EHR data, rigorous observational analysis can be a wellspring for process improvement and knowledge discovery.
However, the underlying challenge is that the potential of any analytics depends on the availability of data, more than the actual methods. While many institutions are generating massive amounts of data, very few have the infrastructure or resources in place to manage and leverage the data. There needs to be increased data access and sharing, but this in itself has significant technical and political challenges, not the least of which include privacy and security. A future health data ecosystem requires a widespread commitment of all the stakeholders across government and industry to realize the true value of health data.

 

Orphan Drug Development : A Regulatory Perspective

 

Changwon Park(MFDS)

In many areas, big data has driven a number of technological innovations, and now big data is no longer a buzzword. Unlike earlier there is still a big gap between the bench and the bed. The delay of big data's innovation in the medical arena may be due to many reasons including privacy issues, difficulties of proving causality, and other technical issues. Despite these difficulties, the gap between the bench and the bed is gradually being filled, and some of these efforts are introduced here.
Patient data stored in many hospitals is probably the most important source of medical big data, but unfortunately, these data
are scattered among hospitals. Patient data stored in each hospital is often too small to be used in artificial intelligence, so it is necessary to collect the data in one place. Collecting data from multiple hospitals to the one place is often very difficult due to technical reasons, such as the difference in the database structure of each hospital, as well as privacy and conflicting interests among hospitals. Many common data models have been proposed to overcome this difficulty. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) of Observational Health Data Sciences and Informatics (OHDSI) program is a representative example. OHDSI program, which is a multi-stakeholder, interdisciplinary collaborative effort to bring out the value of health data through large-scale analytics, has established an international network of researchers and observational health databases. Each hospital converts its electronic medical records to the OMOP CDM structure, stores it on a separate server, and periodically updates it. If a researcher wishes to analyze the data of the hospitals in the network, he or she can write a query statement according to the structure of the OMOP CDM and send it to each hospital. The medical institutions that have received the query statement independently determine whether or not to participate in the study, and at the hospitals that have decided to participate, the query statement is executed in the OMOP CDM server and the results are sent to the requesting researchers. The researcher who originally sent the query statement finishes the study by pooling the query results. With this system, the raw data of the hospital is kept within the hospital, and it is free from personal privacy issues and at the same time, it can prevent data being used for other than its intended purpose so that shared data can greatly contribute to the facilitation of research.
In hospitals, a huge amount of unstructured data is generated every day, but most of them are abandoned. It is absolutely
necessary to collect these data because analyzing these unstructured data can yield important insights for the patient. Recently many hospitals started to collect these kinds of data, such as continuous patient monitoring data, video and audio data, and so on.
Health insurance claims data are seen as a representative example of medical big data. Korea has nation-led health insurance for all citizens and it is possible to analyze by using health insurance data for a nation-wide aspect of any disease. Korea provides free, but obligatory, health screenings for all eligible citizens. Every year, two-thirds of the whole adult population of Korea is undergoing the health screening. These health screening data are shared with all eligible researchers through the National Health Insurance Sharing service.
So far, several examples of the big data usage in the medical field have been reviewed. At present, it is clear that we are not fully exploiting the advantage of medical big data. Through the technological innovations such as blockchain technology, we can overcome the limitation of medical big data and hope that medical big data research will improve human health. 

 

강사명

1. Jacques Demotes(ECRIN)

2. Changwon Park(MFDS)

교육대상

상기 과정은 보수과정으로 직능별, 단계별 인정가능시간이 상이함으로 관련규정 '[별표1] 임상시험등 종사자별 교육과정 및 이수시간'을 꼭 확인하시기 바랍니다.

[별표 1]

임상시험등 종사자별 교육과정 및 이수시간

(5조 관련)

교육과정

해당 분야 실시경험이

없는 종사자

해당 분야 실시경험이 있는 종사자2)

신규자 교육과정

(우선교육시간)1)

심화 교육과정

보수 교육과정

 . 임상시험등 시험책임자3), 시험담당자4)

8시간 이상

(4시간 이상)

6시간 이상

4시간 이상

 . 심사위원회 위원

의사등3)

8시간 이상

(4시간 이상)

6시간 이상

4시간 이상

그 밖의 위원

12시간 이상

(6시간 이상)

6시간 이상

4시간 이상

 . 관리약사

8시간 이상

(4시간 이상)

6시간 이상

4시간 이상

 . 임상시험등 모니터요원

40시간 이상

(20시간 이상)

24시간 이상

8시간 이상

 . 임상시험등 코디네이터

 . 임상시험등 실시기관 품질보증 담당자

 . 임상시험등 업무 담당자5)

4시간 이상

(2시간 이상)

3시간 이상

2시간 이상

1) 규칙 제38조의23항 후단 및 제5조제2항에 따라 임상시험등 업무 경력이 없는 사람이 그 업무를 시작하기 전에 받아야 하는 교육시간을 말한다.

2) 보수교육과정은 평가를 생략할 수 있고, 해당 교육과정의 교육대상이면서 교육을 실시한 경우에는 교육시간을 이수시간으로 인정하며, 심포지엄, 워크숍, 온라인 교육 등으로 이수할 수 있으며, 이수시간의 최대 50/100까지 인정된다.

3) 의사 등이 시험책임자?시험담당자 교육과정에서 이수 받은 교육시간은 심사위원회 교육과정에서 이수 받은 교육시간으로 보며, 반대의 경우도 동일하게 적용한다.

4) 의약품 등의 안전에 관한 규칙[별표 4] 의약품 임상시험 관리기준 제2호코목에 따른 시험당당자(Subinvestigator)를 말한다.

5) 시험담당자(Subinvestigator), 관리약사, 임상시험등 코디네이터를 제외한 시험책임자의 위임 및 감독에 따라 임상시험등 업무를 담당하는 사람(임상병리사, 방사선사 등)을 말한다.

 

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