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RWD 전략은 근거 생성, 의사 결정 지원, 적시 실행을 위해 지속적으로 갱신되고 에이전트가 활용할 수 있는 기반으로 진화하고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E라이프사이언스 조직에는 파이프라인 가속화, 더욱 개인화된 치료, 높아지는 성과 기대, 복잡한 글로벌 시장 전반의 출시 압박을 따라갈 수 있는 근거 전략이 필요합니다. 이는 근거 생성을 개발, 의학, 영업 및 마케팅, 시장 접근 전반에서 더 나은 의사 결정을 지원하는 보다 선제적인 비즈니스 역량으로 만들어야 한다는 의미입니다.\u003C/p\u003E\r\n\u003Cp\u003E근거 생성 전략을 고도화하기 위해 고려해야 할 다섯 가지는 다음과 같습니다.\u003C/p\u003E\r\n\u003Ch3\u003E1. 멀티모달 데이터 연결과 공통된 의미 체계를 고려한 설계\u003C/h3\u003E\r\n\u003Cp\u003E단일 데이터 소스만으로는 환자 여정, 질병 진행, 치료 관련 행동이나 시장 역학을 완전하게 파악할 수 없습니다. 청구 데이터는 이용 패턴을 보여 주며, 전자 건강 기록(EHR), 검사, 영상, 유전체, 레지스트리, 웨어러블, 환자 생성 데이터는 임상적 특성과 행동을 더 깊이 이해하는 데 도움이 됩니다. 이러한 유형의 데이터를 연결하는 것은 필요하지만, 단순히 모든 데이터를 하나의 리포지토리로 옮기는 것만으로는 충분하지 않습니다. 엄격한 개인정보 보호 규정 아래에서는 그렇게 하는 것이 불가능한 경우도 많습니다. 프로비넌스, 정의, 동의, 허용된 사용, 계보, 품질, 최신성, 목적 적합성은 항상 데이터와 함께 유지되어야 합니다. 환자 데이터를 연결하고 접근하며 공유하고 활용하는 방식은 처음부터 개인정보 보호와 데이터 주권을 반드시 준수하도록 설계해야 합니다.\u003C/p\u003E\r\n\u003Cp\u003E이를 위해서는 여러 팀이 환자, 질병, 치료, 시장에 대한 신뢰할 수 있고 연결된 관점을 바탕으로 일할 수 있는 엔터프라이즈 차원의 RWD 접근 방식이 필요합니다. 기술도 중요하지만, 전략적으로 먼저 던져야 할 질문이 있습니다. 이러한 데이터 소스가 연결될 때 어떤 의사 결정이 더 빨라지고, 더 반복 가능해지며, 더 환자 중심적으로 바뀌어야 할까요?\u003C/p\u003E\r\n\u003Cp\u003E이는 관련 에코시스템 참여자 모두가 함께 책임져야 할 공동의 과제입니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E데이터 생성자\u003C/b\u003E는 명확한 프로비넌스, 동의, 허용 사용 범위, 품질 신호, 개인정보 보존형 제어를 통해 데이터 가치를 높일 수 있습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E데이터 통합 제공자\u003C/b\u003E는 갱신되는 제품, 공통 정의, 투명한 한계, 근거 활용이 가능한 컨텍스트를 통해 정적 데이터 세트를 넘어 비즈니스 가치를 입증할 수 있습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E기반 기술 제공업체\u003C/b\u003E는 개인정보 보존형 연결, 안전한 협업, 정책 적용, 활용 가능한 애플리케이션을 지원할 수 있습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E라이프사이언스 조직\u003C/b\u003E은 질문이 생길 때마다 서로 단절된 데이터를 반복해서 추출하고 조합하는 대신, 재사용 가능한 근거 생성 역량을 구축할 수 있습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E목표는 데이터의 맥락을 보존하면서 근거의 전체 수명주기에 걸쳐 멀티모달 데이터를 규정에 맞게 활용할 수 있도록 하는 것입니다. 이는 화려하지는 않지만 대개 더 큰 영향을 미치는 AI 전환의 핵심 작업입니다. 정의, 계보, 액세스 정책, 데이터 품질 파이프라인을 제대로 정립해야 다음 사용 사례나 애플리케이션을 처음부터 다시 시작하지 않아도 됩니다. \u003C/p\u003E\r\n\u003Ch3\u003E2. 개별 프로젝트가 아닌 협업 기반 제품 생태계로의 RWE 전환\u003C/h3\u003E\r\n\u003Cp\u003ERWE는 그동안 R&D, 보건경제 및 성과 연구(HEOR), 역학, 메디컬 또는 시장 액세스 팀이 보유한 전문 역량으로 여겨지는 경우가 많았습니다. 이들 조직의 역할은 여전히 필수적이지만, 근거 생성의 전략적 가치는 이제 라이프사이언스 가치 사슬과 더 넓은 헬스케어 에코시스템 전반으로 확장되고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E프로토콜 설계와 사이트 선정부터 진료 격차 분석과 출시 계획에 이르기까지, R&D, Clinical Ops, Medical Affairs, Commercial 팀 모두가 RWD에 의존합니다. 이러한 사용 사례가 확대되면서 가치를 창출하는 기본 단위도 바뀌고 있습니다. 앞으로의 방향은 원시 파일을 바탕으로 일회성 연구를 쌓아 가는 것이 아닙니다. 다음과 같이 재사용 가능한 데이터 및 인사이트 제품의 포트폴리오를 구축하는 것입니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E시간의 흐름에 따른 환자 여정, 컴퓨터로 분석 가능한 환자 상태 정보, 치료 경로\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E진료 격차, 결과 코호트, 안전성 및 편익-위험 신호\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E의료 제공자와 진료기관, 보험자 보장 범위 및 의료 접근성 관련 패턴\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E임상시험 참여자와 유사한 환자 집단 및 외부 비교 코호트\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E재사용 가능한 표현형, 분석 계획, 근거 패키지, 인사이트 및 보고 애플리케이션\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E이러한 변화는 에코시스템 전반에 새로운 기회를 만듭니다. 그리고 RWE는 비로소 진정한 협업의 영역이 됩니다. 각 참여자의 기여가 더 재사용 가능하고 상호 운용 가능해질수록, 에코시스템은 그다음 질문에 더 빠르게 답할 수 있습니다. \u003C/p\u003E\r\n\u003Ch3\u003E3. 일회성 근거 생성에서 지속적 근거 생성으로 전환\u003C/h3\u003E\r\n\u003Cp\u003E많은 조직에서 전통적인 RWD 워크플로우는 여전히 일회성 요청에서 시작됩니다. 연구가 의뢰되고, 데이터가 수집되고, 분석이 수행되며, 결과가 보고되는 방식입니다. 오늘날 헬스케어 수요가 빠르게 변화하는 상황에서, 질문이 제기된 시점과 답을 제시할 근거가 확보되는 시점 사이에 몇 달에 이르는 지연이 발생하는 것은 더 이상 용납되기 어렵습니다.\u003C/p\u003E\r\n\u003Cp\u003E차세대 근거 생성 체계는 살아 있는 역량으로 유지될 것입니다. 지속적이고 거버넌스가 적용된 파이프라인은 새로운 데이터 슬라이스, 모집단, 모달리티가 사용 가능해질 때마다 갱신될 수 있습니다. 환자 상태와 경과 모델은 시간에 따른 멀티모달 이벤트를 구조화하여, 팀이 치료 노출, 반응, 진행, 이상 사례, 결과를 점점 더 높은 기여도와 함께 추적할 수 있도록 지원합니다.\u003C/p\u003E\r\n\u003Cp\u003E지속형 파이프라인은 준실시간 근거 생성을 가능하게 하며, 팀이 실제 환경의 신호와 그 검증, 다음 의사 결정 사이의 간극을 좁힐 수 있게 합니다. 실제로 이는 팀이 다음을 수행하는 데 도움이 됩니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E안전성 위험, 진료 격차, 누락된 진단, 새롭게 나타나는 여정 패턴과 같은 시판 후 신호를 더 일찍 포착\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E치료 반응, 액세스 장벽, 순응도 변화를 실시간으로 모니터링\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E환자 역학이 변화함에 따라 임상시험 실행 가능성, 모집 목표, 다양성 목표 조정\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E적합한 환자를 외부 대조군, 추적 연구 또는 지원 프로그램에 매칭\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E상황 변화에 맞춰 RWE를 출시 및 시장 액세스 전략에 반영\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E이를 실현하려면 스트리밍 데이터만으로는 충분하지 않습니다. 지속적 갱신에는 품질, 프로비넌스, 허용된 사용, 변경 관리에 대한 명확한 표준을 포함하는 지속적 거버넌스가 필요합니다. 에이전트는 반복 가능한 모니터링과 준비 작업을 처리할 수 있지만, 해석과 실행, 이전 결론이 더 이상 유효하지 않은 시점을 판단하는 책임은 인간 담당자에게 남아 있어야 합니다. 그 결과 실행 가능성 검토 주기는 짧아지고, 안전성 신호는 더 일찍 포착되며, 임상 근거가 축적될수록 더 나은 의사 결정이 가능해집니다.\u003C/p\u003E\r\n\u003Ch3\u003E4. 또 하나의 챗봇이 아니라 폐쇄 루프형 근거 생성 워크플로우 구축\u003C/h3\u003E\r\n\u003Cp\u003EAI는 팀이 대규모 RWD를 검색, 요약, 분류, 추출, 예측하고 추론하는 데 도움을 줄 수 있습니다. 더 큰 기회는 질문 정의와 실행 가능성 평가부터 분석, 검토, 커뮤니케이션에 이르기까지 근거 워크플로우 자체를 조율하는 데 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E에이전틱 워크플로우는 이를 반복 가능한 비즈니스 프로세스로 전환할 수 있습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E의사 결정과 근거 기준 정의\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E목적에 적합한 소스를 찾고 실행 가능성, 한계, 권한, 개인정보 보호 영향을 평가\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E분석 개발 및 검토\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E일회성 보고서로 끝나는 것이 아니라 다음 의사 결정에 활용될 수 있는 방어 가능하고 추적 가능한 근거 패키지 제공\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E중요한 게이트는 사람이 승인하고, 에이전트는 그 안에 정의된 계약에 따라 반복 가능한 작업을 수행합니다.\u003C/p\u003E\r\n\u003Cp\u003E이는 챗봇에게 데이터 세트를 요약해 달라고 요청하는 것과는 근본적으로 다릅니다. 기술적 구현도 중요하지만, 어디까지나 워크플로우를 뒷받침해야 합니다. 질문이 바뀌더라도 데이터, 방법, 모델이 진화하는 동안 재사용 가능한 프로세스와 의사 결정 권한은 유지되어야 합니다.\u003C/p\u003E\r\n\u003Cp\u003E그 결과는 전문성을 대체하거나 사람이 업무를 위임하는 구조를 없애는 것이 아니라 반복 개선을 가능하게 하는 것입니다. 도메인 전문성을 더 일관되게 적용하고, 더 빠르게 학습하며, 사람의 판단이 필요한 의사 결정에 집중하도록 하는 방식입니다. 이는 임상시험 설계, 액세스, 시장 차별화, 타기팅, 시장 신호 대응을 개선합니다.\u003C/p\u003E\r\n\u003Ch3\u003E5. 새로운 사일로가 아닌 비즈니스 의사결정 개선 도구로서의 AI 활용\u003C/h3\u003E\r\n\u003Cp\u003ERWD AI 전략은 가장 중요한 의사 결정과 워크플로우에서 출발해야 합니다. 임상시험에 적합한 환자를 찾고, 치료 반응을 이해하며, 안전성 신호를 감지하고, 순응도를 높이며, 출시를 준비하고, 시장을 확장하거나 지급자에게 가치를 입증하는 일입니다. AI 모델과 애플리케이션은 그 결과를 달성하기 위한 수단이지, 결과 그 자체가 아닙니다.\u003C/p\u003E\r\n\u003Cp\u003E라이프사이언스에서 이러한 워크플로우는 환자 개인정보 보호, IP, 규정 준수, 감사 가능성, 의학적 방어 가능성에 대한 높은 기준도 충족해야 합니다. 여기에는 적절한 비식별화 또는 토큰화, 역할 기반 액세스, 추적 가능한 데이터 및 모델 계보, 목적 적합성 검증, 환자 액세스와 결과에 중대한 영향을 미치는 의사 결정에 대한 명확한 인간 책임성이 포함됩니다. AI는 거버넌스가 적용된 데이터와 함께 작동하고, 조직의 기존 의사 결정 권한, 검토 프로세스, 운영 모델, 정책 적용 체계에 맞게 통합되어야 합니다.\u003C/p\u003E\r\n\u003Cp\u003E환자 경과 파운데이션 모델은 중요한 진화를 의미합니다. 이 모델은 진단, 약물, 진료 기록, 보험 청구, 영상, 검사 결과를 개별 기록으로 취급하는 대신, 시간의 흐름과 여러 데이터 유형을 아우르는 환자 상태 정보를 재사용 가능한 형태로 구성합니다. 이를 바탕으로 유사 환자 검색, 코호트 구성, 질병 진행 예측, 환자 경과 추적을 지원합니다. 핵심 이점은 명확합니다. 민감한 환자 데이터를 옮기지 않고도 개발부터 상용화에 이르는 전 과정에서 환자에 대한 맥락을 재사용할 수 있다는 것입니다. \u003C/p\u003E\r\n\u003Cp\u003E무엇보다 이 방식은 민감한 데이터를 모델로 옮기는 대신, AI와 근거 생성 모델을 거버넌스가 적용된 데이터가 있는 곳으로 가져옵니다. 모델이 단절된 추출물이 아니라 큐레이션된 도메인별 데이터 안에서 작동할 때, 팀은 더 강력한 제어, 더 풍부한 컨텍스트, 더 적은 데이터 이동을 확보할 수 있습니다. 팀은 근거를 조립하는 데 쓰는 시간을 줄이고 이를 실행에 옮기는 데 더 많은 시간을 할애함으로써, 개인정보 보호 및 규정 준수 요건을 지원하면서 임상시험 의사결정, 환자 결과, 시장 대응력을 개선할 수 있습니다.\u003C/p\u003E\r\n\u003Ch3\u003E데이터 축적을 넘어 근거 기반 경쟁 우위로\u003C/h3\u003E\r\n\u003Cp\u003E라이프사이언스의 다음 경쟁 우위는 단순히 가장 많은 RWD 자산을 확보하거나 최신 모델을 가장 먼저 도입한 조직에 돌아가지 않을 것입니다. 변화하는 관찰을 더 빠르고 반복 가능하며 자율적이고 확장 가능한 방식으로 신뢰할 수 있는 근거와 책임 있는 행동으로 전환하는 조직과 에코시스템이 그 우위를 차지할 것입니다. \u003C/p\u003E\r\n\u003Cp\u003E이를 위해서는 에코시스템 전체가 함께 다음과 같이 전환해야 합니다\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E데이터 확보에서 비즈니스 의사 결정과 연결된 근거 활용 역량으로\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E일회성 연구에서 지속적으로 갱신되는 개별 환자 및 환자 집단 정보 관점으로\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E수작업 분석 요청이 쌓이는 방식에서 사람의 감독하에 재사용할 수 있는 에이전틱 워크플로로\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E범용 AI 인터페이스에서 팀이 일하는 환경에 내재된 인사이트와 실행으로\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E기술 실험에서 개인정보 보호, 규제 준수, 책임 있는 근거 활용 원칙을 지키며 측정 가능한 환자 치료, 제품 포트폴리오, 시장 성과를 창출하는 단계로\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E실사용 데이터는 신뢰할 수 있는 맥락으로 전환될 때 더 큰 힘을 발휘합니다. 근거는 환자, 헬스케어 전문가, 보험자, 라이프사이언스 조직의 의사 결정을 개선하는 워크플로우에 도달할 때 비로소 영향력을 만들어냅니다.\u003C/p\u003E\r\n\u003Ch3\u003E규제 관련 추천 참고 자료\u003C/h3\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence\" target=\"_blank\"\u003EFDA: 실사용 근거(RWW)\u003C/a\u003E- 정의, 규제 목적의 사용 사례 및 주요 지침.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.fda.gov/regulatory-information/search-fda-guidance-documents/real-world-data-assessing-electronic-health-records-and-medical-claims-data-support-regulatory\" target=\"_blank\"\u003EFDA: 규제 의사 결정을 위한 전자의무기록(EHR) 및 의료 청구 데이터 평가\u003C/a\u003E- 데이터의 목적 적합성, 검증, 추적 가능성 및 연구 설계에 관한 기준.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.ema.europa.eu/en/about-us/how-we-work/data-regulation-big-data-other-sources/real-world-evidence/data-analysis-real-world-interrogation-network-darwin-eu\" target=\"_blank\"\u003EEMA: DARWIN EU\u003C/a\u003E- 네트워크 기반 RWE로 의약품 수명주기 전반의 의사 결정과 규제 의사 결정을 지원하는 글로벌 사례.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-6d396f67d3",":type":"snowflake-site/components/container",":items":{"author_chip":{"id":"author-chip-ab4528f089","title":{"id":"title","type":"heading2","lines":["Learn more about the 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href=\"https://www.snowflake.com/ko/legal/privacy/privacy-policy/#12\"\u003E개인 정보를 공유하지 않습니다.\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/ko/legal/\"\u003E법률\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E© 2026 Snowflake Inc. 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