바이오인포메틱스: 의사가 알아야 할 것, 의사가 배우는 방법의 변화(Acad Med, 2011)

바이오인포메틱스: 의사가 알아야 할 것, 의사가 배우는 방법의 변화(Acad Med, 2011)

Biomedical Informatics: Changing What Physicians Need to Know and How They Learn (Acad Med, 2011)

William W. Stead, MD, John R. Searle, PhD, Henry E. Fessler, MD,

Jack W. Smith, MD, PhD, and Edward H. Shortliffe, MD, PhD

바이오인포메틱스는 다학제간interdisciplinary과학의 한 분야로서 과학적 탐구/문제해결/의사결정/의사소통을 위하여 자료/정보/지식을 효과적으로 활용하는 것을 목적으로 한다. 비록 이 분야가 1950년대부터 시작되긴 했지만, 의과대학에서는 자주 다뤄지지 않았는데, 비교적 최근까지 의과대학 학장들은 공학이나 컴퓨터과학 분야에서 주로 다루는 것이라고 생각했기 때문이다.

Biomedical informatics is the interdisciplinary scientific field that studies and pursues the effective use of data, information, and knowledge for scientific inquiry, problemsolving, decision making, and communication. Although the field dates to the 1950s,1 until relatively recently because deans of medical schools saw it as a disciplinary priority of other schools such as engineering or computer science.

생의학의 복잡성이 크게 늘어나면서 의학적 의사결정의 패러다임이 '한 사람의 두뇌'에서 'systems of brains의 협력적 힘'으로 옮겨가기 시작했다

At this juncture, the explosive growth of biomedical complexity calls for a shift in the paradigm of medical decision making—from a focus on the power of an individual brain to the collective power of systems of brains.

한 사람의 두뇌에서 두뇌의 시스템으로

Shifting the Paradigm From Individual Brains to Systems of Brains

오늘날 의학교육 프로세스와 교육과정은 개개인을 전문가로 발달하게 한다.

Today’s medical education processes and curricula lead to the development of individual experts.

"개인 전문가individual expertise"에 의존하는 의료행위는 자율성/자신감/다양한 현실에서의 우아한 적응 등을 발생시킨engender다

This practice of depending on individual expertise engenders autonomy, self-confidence, and gracious acceptance of variability in practice.3

그러나 한 사람의 두뇌가 가진 인지적 용량은 한 차례의 의사결정 당 다섯 세트의 팩트만을 관련지을 수 있을 뿐이고, 이는 전문성-기반 의료의 한계로 작용한다. 하나의 돌연변이와 하나의 질병간의 관계만을 보여주는 single genetic test와 달리, full genetic sequence는 개인의 질병에 대한 취약성과 어떤 사람이 특정 진단을 받을 가능성을 변화시킬 수 있는 다수의 low-power association 정보를 제공한다.

However, the cognitive capacity of individual brains, which can correlate only about five sets of facts in a single decision,4 limits expert-based medicine. Unlike single genetics tests, which strongly associate one mutation with one disease phenotype, the full genetic sequence will provide many low- power associations that in combination change prior probabilities about both an individual’s susceptibility to disease states and the likelihood of that individual carrying a specific diagnosis. Specialization is not a viable approach to managing this complexity.

 

 

 

'전문성-기반 의료Expert-based practice'의 초점은 개인의 능력에서 시스템-기반 진료로 옮겨갈 것이며, 이는 시스템의 능력에 초점을 둔다는 것을 의미한다. 여러 사람으로 이뤄진 팀과, 잘 정의된 프로세스와 IT가 합해져서 하나의 시스템으로서 결과를 도출할 것이다. 무엇인가를 빼먹거나 실수가 있다면 이 역시 정보로서 제공되어 시스템의 향상 방향을 알려줄 것이다. 학문-중심 교육과정은 환자돌봄을 위한 systems approach를 활용하는 학습 프로세스와 align되는 방향으로 바뀔 것이다. 성공적으로 도입된다면, 다학제간팀interdisciplinary team이 자신의 일을 하는 동안 학습은 필수불가결하게 이뤄질 것이다. 팀은 개인과 팀의 역량을 평가할 것이며, 학습모듈, 전문가 원격접속, 시뮬레이션 등을 활용하여 부족한 부분을 매워갈 것이다. 성과 데이터는 곧바로 역량평가와 개선점 탐색에 활용될 것이다. 인포메틱스 토대가 의료와 의학교육을 모두 지원해줄 것이다.

Expert-based practice, with its focus on the individual’s performance, will shift to system- supported practice, with a focus on the system’s performance. Teams of people, well-defined processes, and IT will work as a system to produce the desired result. Each omission or error will provide data to guide iterative improvement of the system.7 Discipline-specific curricula will shift to align with a learning process that utilizes the systems approach to care.8 If successful, learning will become an unavoidable outcome as interdisciplinary teams go about their work. The teams will assess individual and team competency against upcoming work, and they will be able to use learning modules, remote access to experts, and simulation to close gaps. Outcomes data will be readily available to assess competency and identify areas for improvement. An informatics foundation will support both medical practice and medical education.7,8

인포메틱스 환경에서의 학슴, 환자돌봄, 연구

Learning, Clinical Care, and Research in Informatics-Rich Environments

 

초창기의 컴퓨터-기반 학습환경은 단순히 기존의 교수법을 따라하는 것에 불과했으며, 테크놀로지 없이도 가능한 교육에 테크놀로지를 활용하여 동일한 것을 한 것에 불과했다.

Early computer-based learning environments merely mimicked established teaching methods, using technology to perform the same teaching tasks that had been possible without it;

더 근래의 접근법은 현대의 인포메틱스와 학습을 도입하였다. Beaumie and Reeves는..

More recent approaches have attempted to couple modern informatics and learning. Beaumie and Reeves9 propose that a 

학습자가 (학습)도구는 낮은 수준의 과제(자료 보여주기, 의사결정 옵션 제공하기)를 수행하는 동안 높은 수준의 인지활동(방향 결정하기, 의사결정, 평가결과를 기반으로 접근법을 바꾸는 것)을 하는 것

learner performs higher-level cognitive activities (e.g., executing directions, making decisions, and/or changing approach based on assessment results), while the tool performs lower-level tasks (e.g., visually representing data or providing decision options).

인포메틱스는 교육과정과 학습(과정)을 서포트 할 수 있다. Denny 등은 교육과정 내용을 검색할 수 있는 웹-기반 리소스를 묘사한 바 있다. 교수와 학생은 리소스로부터 교육프로그램이나 학년의 경계를 넘나들며 어떤 개념에 대해서 검색할 수 있으며 가상과목virtual course처럼 관련된 자료만 볼 수도 있다.

Informatics can support curricula and learning more broadly as well. Denny and colleagues describe a Web-based resource to search curricular content.10 Faculty and students may then search the resource to find concepts across programand school year boundaries, and they can browse the related material as a virtual course.10

 

어떤 사람들은 EHR을 의료행위와 의학교육을 연결시켜주는 용도로 제안하기도 했다. Stead는 EHR과 임상인포메틱스 도구를 활용하여 학습을 서포트하는 네 단계 프레임워크를 제안하였다.

Others, too, have suggested uses for the EHR to link medical practice and medical education. Stead proposes a framework of four tiers through which the EHR and related clinical informatics tools could support learning.13

• 리소스 소모를 측정하고 그룹 내intragroup 차이를 identify 한다.
  The processes that both measure variation in resource consumption (e.g., length of inpatient stays, tests performed, medications used) and identify intragroup variation in practice provide the framework’s foundation.

• EHR자료를 활용하여 인지적 에너지를 확보하여 정보의 통합synthesis에 초점을 둔다. 인포메틱스는 의사가 자신의 의료행위와 환자outcome을 연결시킬 수 있게 도와주며, open-loop practice에 피드백을 줌으로써 closed-loop 으로 만들어준다.
  In the next tier, physicians use data from the EHR to free their cognitive energy to focus on synthesis; the informatics allows them to tie their own practices to their own patient outcomes, converting their open-loop practice into a closed loop with feedback.14

• 예상하지 못한 상황의 탐지와, 의사결정지원시스템의 활용가능성(경보, 환자-특이적 변화, 근거 link)
  The capacity to detect unexpected events and the availability of decision-support systems (with alerts and reminders, patient-specific information about changes in practice, and links to evidence) together make up the third tier.

• EHR과 바이오뱅크의 추출물extract을 통해서 상관관계를 찾고 가설을 설정함
  In the fourth tier, extracts from the EHR and bio-banks combine to support correlation and hypothesis generation.

다른 요약

• 의료행위의 variation을 측정 That is, first, measure practice variation;

• 개개인에게 맞춘 피드백을 제공 second, provide individual feedback on practice and outcomes;

• 의사의 손바닥fingertips에 지식과 정보를 제공하여 진료 향상 third, improve individual practice by placing knowledge and information at physicians’ fingertips; and

• Care의 향상을 위해 검증가능한 가설을 제공하여 의학을 진보시킴 finally, support advancement of medical science by suggesting testable hypotheses to improve care.

일부에서는 이미 EHR이 광범위하게 활용되고 있다. Veterans Health Administration (VHA) 에서는 EHR을 활용해서 학습을 더 빠르게 하려는 목적으로 national laboratory를 제공하고 있다. VHA는 시스템 차원의 장기적 자료를 활용하여 care management의 효과를 정량화하고 site-to-site varation이 physician-to-physician variation보다 더 크다는 것을 보여주었다. 더 나아가서 EHR은 시판 후 약제의 pharmacovigilence를 도와주어서 시판 전에 알지 못했던 약물유해사건을 감지할 수 있게 도와준다. EHR은 임상가설을 생성해주기도 한다. Hanauer 등은 유전자-매핑 소프트웨어를 활용하여 자연어free-text 임상문제 기술서로부터 잠재적 연관성을 찾기도 하였다.

Some have already begun to use the EHR extensively. The Veterans Health Administration (VHA) has provided a national laboratory on the use of EHRs to accelerate learning. The VHA has used system-wide, longitudinal data to quantify the impact of care management and to show that site-to-site variation is more significant than physician-to- physician variation within sites.15 Further, EHRs have the potential to support comprehensive postmarket pharmacovigilence and to accelerate detection of unrecognized adverse drug events.16 EHRs also have the potential to generate clinical hypotheses. Hanauer and colleagues demonstrated the feasibility of using gene-mapping software to identify potential associations among free-text clinical problem statements in their EHR.17

인포메틱스 환경의 장점과 단점

Beneficial and Deleterious Effects of Informatics-Rich Environments

이 많은 것들이 EHR로의 변화를 겪어본 사람에게는 친숙할 것이다.

Many of these are familiar to those who have experienced a transition to EHRs.

(학생들이) 주치의의 source data에 직접 접근이 가능해지면 정보를 통합하기 전에 비판적으로 생각해야 할 필요성이 줄어들고, 노트를 copy and paste하는 능력 등이 줄어드는데, 이런 것들은 학생들이 무엇을 어떻게 기록으로 남길지 결정해야 할 필요성이 줄어들게 만들고, 의료기록에서 narrative를 위협한다.

Some potential problems include attendings’ direct access to source data, which reduces the need for students to think critically beforehand to synthesize and present the data, and the abilities to copy and paste notes and to access results instantaneously, both of which, first, reduce the need for students to decide what and how to document and, second, threaten the narrative in the medical record.

 

문제는 테크놀로지 그 자체가 아니라 그것을 활용하는 방식이다.

We believe the problems lie not in the technology per se, but in its application.

Patel 등은 인지와 의사결정지원 사이의 관계를 살펴보았다. IT가 단순히 의사결정 프로세스를 향상시킬 뿐만 아니라, 인지용량에 지속되는 영향을 주어서 의사결정 프로세스 자체를 완전히 바꾸어놓기도 한다. Eddy and Gigerenzer의 연구결과를 바탕으로 의사에게 자료를 제시하는 구조가 의사의 판단에 영향을 줌을 보여주었다. 즉, 자료가 제시되거나 시각화되는 방식이 의사가 그 자료를 해석하는 방식에 영향을 준다는 것이며, 그 자료에 기반하여 이뤄는 당장의 의사결정 뿐 아니라 비슷한 유형의 자료에 의해서 내려지는 미래의 의사결정도 영향을 받게 된다.

Patel and colleagues review the relationship between cognition and decision support. They show that IT does not merely support or enhance the decision process, but fundamentally transforms it, having an enduring effect on cognitive capacity.21 They draw on work by Eddy22 and Gigerenzer22 to show that the very structure of the presentation of data to a physician affects the physician’s judgment. That is, the way data are shown or visualized strongly influences the way physicians interpret the data, the decisions they make based on the data, and even future decisions they will make based on similar data they later encounter.

미래 의사를 위한 인포메틱스 역량

Informatics Competencies for Future Health Professionals

2003년 IOM의 HPES에서는 의료전문직이 갖춰야 할 핵심 역량 영역 중 하나로 인포메틱스 활용을 꼽았다.

In 2003 the Institute of Medicine’s Health Professions Education Summit identified utilizing informatics as one of the five domains of core competency for health care professionals.24

EHR을 이상적으로 활용하기 위해서는 특별한 능력을 필요로 한다.

The optimal use of the EHR at the bedside does require some special skills,

의사들은 확실히 이러한 능력을 습득할 수 있다. Morrow 등은 학생을 두 그룹으로 무작위로 구분하고 한 그룹만 EHR-특이적 커뮤니케이션 스킬을 훈련하였는데, intervention group이 10개의 EHR-특이적 스킬 중 6개에서 더 나은 수행능력을 보여주었다.

but physicians can certainly learn these. Morrow and colleagues 25 The researchersthen randomized students into two groups: one group received training in EHR-specific communication skills, and the other did not. The intervention group performed better on 6 of 10 EHR-specific skills

2008년 AMIA와 AAHC는 공통의 의사결정역량을 개발하였다.

In 2008, the American Medical Informatics Association and the Association of Academic Health Centers convened representatives of 14 health professions in a DesignShop at the Vanderbilt Center for Better Health to develop a common informatics competency framework.26

 

 

 

교수개발을 위한 단계들

Broad Steps for Faculty Development

computational techniques의 강점과 약점을 충분히 이해함으로써 의사들이 인포메틱스 도구가 제시한 결과를 언제 수용하고, 언제 기각해야 하는가를 결정하는데 도움이 될 수 있다.

Sufficient understanding of the strength and weaknesses of the computational techniques will help physicians decide when to accept, and when to override, the results of their informatics tools.

 

의과대학의 투자와 지원이 인포메틱스-강화 의료의 진화를 더 빠르게 만들 수 있다. 다음의 네 가지 큰 단계를 제안한다.

Investment and support by medical schools can hasten the evolution to informatics-enhanced patient care and learning. We suggest the following four broad steps:

 

(1) 바이오메디컬 인포메틱스를 위한 학문단위academic unit을 만들라
create academic units in biomedical informatics;

(2) AHC의 IT인프라를 연구실testing laboratories로 개조adapt하라 adapt the IT infrastructure of academic health centers (AHCs) into testing laboratories;

(3) 의학교육자들이 바이오메디컬 인포메틱스의 활용모델을 만들 수 있게 충분히 설명해주라 introduce medical educators to biomedical informatics sufficiently for them to model its use; and

(4) AHC교수들이 바이오메디컬 인포메틱스에 의해서 가능해진 systems approach에 기반한 헬스케어로의 전환을 이끌도록 하라
retrain AHC faculty to lead the transformation of health care based on a new systems approach enabled by biomedical informatics.

1. 새로운 학문단위

Create academic units in biomedical informatics that have a seat at both the academic and operational tables.

A critical mass of faculty who understand biomedical informatics can provide the nucleus to teach noninformatics faculty and students how to use informatics techniques and tools in their work.

2. IT인프라

Adapt the IT infrastructure of AHCs into testing laboratories to evaluate and utilize emerging biomedical informatics techniques for data aggregation, systems analysis, and visualization support.

AHCs with informatics units can supplement simple automation by using

• computational techniques such as connectivity, social networks that connect people to one another and to systems;

• statistical decision support, in which multiple weak signals contribute to robust answers; and

• data mining, through which relationships are discovered among data from diverse highly dimensional data sets.

These approaches allow the clinical information system infrastructure to serve as a laboratory where physicians, physician educators, and physicians-in-training may evaluate and apply informatics “interventions.”29

3. 의학교육자
Introduce medical educators to biomedical informatics sufficiently for them to model its clinical and research uses, to modernize curricula appropriately, and to evaluate trainees and teaching methods.

This shift changes curricular priorities as well as learning and evaluation strategies. One strategy, for example, is using clinical outcomes to measure the effectiveness of a learning intervention.

4. 교수
Retrain faculty in AHCs to lead the transformation to health care that incorporates systems approaches enabled by biomedical informatics.

 The first step is to build awareness within the leadership and faculty of academic medicine that the change is unavoidable. Like many changes, this one offers opportunities— Growing dissatisfaction with current roles, under the dual pressures of cognitive overload and payment reform, may ignite the burning platform and motivate the leap needed to reach a sustainable next generation model for the profession.

26 Designing the informatics component of an IOMchasmhealth professions core competencies curriculum. Vanderbilt Center for Better Health. American Medical Informatics Association (AMIA) Design Session, AMIA Academic Forum, and American College of Medical Informatics. Vanderbilt University, Nashville, TN, 2008. Available at: https://www.mc.vanderbilt.edu/ vcbh/ds/081001amia/index.html. User id: 081001_amia, Password: grant.

 

 

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Acad Med. 2011 Apr;86(4):429-34. doi: 10.1097/ACM.0b013e3181f41e8c.

Biomedical informatics: changing what physicians need to know and how they learn.

Stead WW1, Searle JR, Fessler HE, Smith JW, Shortliffe EH.

Author information

• 1McKesson Foundation Professor of Biomedical Informatics, and professor of Medicine, Vanderbilt University, Nashville, Tennessee 37232-2104, USA. bill.stead@vanderbilt.edu

Abstract

The explosive growth of biomedical complexity calls for a shift in the paradigm of medical decision making-from a focus on the power of an individual brain to the collective power of systems of brains. This shift alters professional roles and requires biomedical informatics and information technology (IT) infrastructure. The authors illustrate this future role of medical informatics with a vignette and summarize the evolving understanding of both beneficial and deleterious effects of informatics-rich environments on learning, clinical care, and research. The authors also provide a framework of core informatics competencies for health professionals of the future and conclude with broad steps for faculty development. They recommend that medical schools advance on four fronts to prepare their faculty to teach in a biomedical informatics-rich world: (1) create academic units inbiomedical informatics; (2) adapt the IT infrastructure of academic health centers (AHCs) into testing laboratories; (3) introduce medical educators tobiomedical informatics sufficiently for them to model its use; and (4) retrain AHC faculty to lead the transformation to health care based on a new systems approach enabled by biomedical informatics. The authors propose that embracing this collective and informatics-enhanced future of medicine will provide opportunities to advance education, patient care, and biomedical science.

© by the Association of American Medical Colleges.

PMID:

 

20711055

 

[PubMed - indexed for MEDLINE]