Track 5 : 临床情报和医疗情报

Clinical and Medical Informatics

分子医疗的革新的实现: caBIG™网路(Track 1共同发表)

This talk will explore strategies for embracing evidence-based patient care, using caBIG™ as a case study example. caBIG™ is the cancer Biomedical Informatics Grid, an unprecedented initiative led by the NCI to create a seamless technology network that accelerates information and data translation, and enables molecular approaches to research, as well as adaptive clinical trials. caBIG™ connects its collaborators as a voluntary, open-source network of infrastructure, tools, and ideas that enables the collection, analysis, and sharing of data and knowledge along the entire research pathway from laboratory bench to patient bedside. This discussion will help to illustrate how information technology is at work to bridge the gap between clinical and research informatics.

Kenneth Buetow, Ph.D., Associate Director for Bioinformatics and Information Technology and Director, NCI Center for Biomedical Informatics and Information Technology, National Cancer Institute

以癌的创药为目标之协同研究团体及生医情报GRID远端摇控资料収集

The National Cancer Institute (NCI) is implementing a nationwide interoperable remote date capture solution of clinical trial information for cancer drug development. This talk will explore the major NCI efforts to harmonize clinical trial data amongst the broader cancer research community to enable better collaboration to improve the clinical trials process and accelerate drug development.

George Redmond, M.Sc., M.B.A., Bioinformatician, Cancer Therapy Evaluation Program (CTEP), Division of Cancer Treatment and Diagnosis, National Cancer Institute (NCI)

联结临床资料、实验室、供给者: 为了实现个人化医疗的应用网路之构筑

As a growing numbers of genetic and genomic based diagnostics are preformed, it will be increasing difficult for clinicians to track and fully leverage these test results.丂 Clinical decision support systems can assist in this area.丂 Genetic based clinical decision support systems require the integration of丂genetic data, phenotypic data and genetic knowledge.丂 At times all of this information exists within a single institution. We will discuss examples of self contained clinical decision support that operates within our institution. However, the information required for these systems to meet their potential is often spread out across multiple databases in multiple institutions.丂 We will also discuss the types of secure data networks that need to be established to provide broader clinical decision support.

Samuel (Sandy) Aronson,丂Executive丂Director of IT, Harvard Partners Center for Genetics and Genomics (HPCGG)

针对转译研究(translational research)的情报解决方案

An information management system has been developed to assist clinical and translational researchers in storing, querying and integrating such diverse data sets efficiently. We have also developed analysis tools to help biomedical researchers and those in the pharmaceutical industry to manage and analyze large volumes of data effectively. This talk will help researchers and clinicians how to: 1) efficiently manage and track laboratory samples, chips, gels and biospecimens; 2) effectively analyze, and interpret high-throughput data from microarrays; 3) visualize microarray, proteomics and sequence data; 4) integrate data and annotations from disparate data sources; and, 4) correlate patient data with experimental data as well as annotations from public databases.

Rakesh Nagarajan, Ph.D., Assistant Professor, Pathology and Immunology, Division of Laboratory and Genomic Medicine, Washington University School of Medicine
Aditya Phatak, Strategic Relationship Manager, Life Sciences, Persistent Systems Inc.

EDC和资料统合

This presentation will discuss an approach that we are using at the Dana-Farber cancer institute for electronic collection and aggregation of clinical trial results data. The 'bench to bedside and back' paradigm for achieving personalized medicine requires the seamless and timely exchange, sharing, aggregation, integration and analysis of clinical research data across the cancer research community. However even when sources of large datasets exist, data integration is challenging because of the lack of completeness of information or lack of semantic integrity and resulting ambiguity. This talk will describe the problem and present an example strategy to resolve some of the issues.

Jomol Mathew, Ph.D., Director, Clinical Research Information Technology, Dana-Farber Cancer Institute

导引临床实验医療方案(Protocol)的从医性（compliance）之以本体论为基础(ontology-based)的推论服务

The Immune Tolerance Network (ITN) has been in collaboration with Stanford Medical Informatics for the last 2 years to increase support for automated clinical trials management. At the core of such efforts is formal modeling of knowledge found in clinical protocols. We are modeling this knowledge in the Epoch clinical trial ontologies developed at Stanford, to encapsulate the knowledge in a computable formalism. Managing protocol compliance, and the large amounts of data associated with a clinical trial requires a study design beyond that of a traditional CTIS. We believe that the work being done with Clinical Trial Ontologies provides such a design. We will present ways of actively using the trail design to support ontology and non-ontology based applications.

Keith Boyce, Associate Director, Bioinformatics, Immune Tolerance Network / UCSF

临床试验中患者识别付著技术和直接取得资料以提升医药品开发

Patient compliance (adherence) to long-term therapy is emerging as a critical challenge across the drug development and commercial continuum. Implications of poorly characterized patient adherence in clinical trials affect interpretation of PK, dosing, efficacy, and safety. Opportunities exist for new technologies to improve our understanding of patient adherence in clinical trials, as well as to favorably influence adherence.

This talk will review existing and emerging technology for monitoring adherence (RFID, printed electronics, drug tagging, etc.), as well as the range of applications of the resulting data.

Craig Lipset, Director, Health Technologies, Pfizer

临床监视的相互运用性: 利用HER之治验者之募集

Clinical Data acquired in the patient care process and stored in Electronic Health Records can be re-used to enable a wide variety of applications such as Patient Recruitment, Drug Safety Surveillance, Post Launch Drug Use and Virtual Phase IV Clinical Trials. A critical component is the ability to interoperate and exchange clinical observations across the Clinical Research, Clinical Trials and Clinical Practice. This presentation will focus on the problem of Patient Recruitment and illustrate, with the help of detailed examples, the use of Semantic Web Technologies for enabling interoperability between the EHR and Clinical Trials applications. Results of an ongoing collaborative effort in the framework of the W3C Healthcare and Life Sciences Interest Group will be presented.

Vipul Kashyap, Ph.D., Senior Medical Informatician, Clinical Informatics R&D, Partners Healthcare System

二个世界的联结: HER系统和临床调查

eClinical Forum was formed in 2000 by members of the U.S. and European pharmaceutical industry. This presentation will describe a current project that the eClinical Forum and PhRMA EDC/eSource Taskforce are partnering on to address requirements needed to determine how electronic health record systems can be used for clinical research. The EHR clinical research profile team is working toward producing a set of functions and criteria in order for EHR systems to be used as a source of data for clinical research. A draft of the first release of this profile is expected to be distributed for wide-industry comment in 4Q 2007 with work continuing toward HL7 and QRec submissions in 2008. We will describe the EHR/CR Functional Profile and its importance to industry, as well as, certification, applications of use cases in real world environment, and lessons learned.

Linda King, Team Leader, Data Management, Eli Lilly and Company

为了临床实验的迅速化的复合式医疗方案设计(Protocol design): EDC系统中复数资料库的锁定的实施

Pharmaceutical companies tirelessly search for the most efficient way to implement their clinical trials. One of the ways to simultaneously reduce time and costs is to conduct several studies or portions of a study (e.g.

double blind, randomized withdrawal, and open-label portions) as "one protocol". In this scenario, it is very common that a part of the trial is being locked, unblinded, analyzed and reported, while the subjects continue to participate in the study and the data continue to be collected. This approach makes perfect scientific and business sense and will likely become the main stream of protocol design in the future. This type of complex protocol design brings new challenges to current Electronic Data Capture (EDC) structure, especially the external data import, common Data Management process such as database (db) lock. There are enormous implications in Clinical and Regulatory affairs. In order to prevent inadvertent or unauthorized changes of data once the final analysis and reporting of the trial data have begun, db lock has become a well-defined process for closing a database and change control procedures in data management of a clinical trial. In a complex protocol design consisting of multiple studies, the traditional db lock occurring ONCE at the end of the trial is not sufficient because the data is usually unblinded, analyzed and submitted in the middle of the trial. Therefore, implementing multiple db locks is necessary. We will discuss the new requirements for multiple db locks and using a case study to illustrate the challenges and our solutions. In this case, we have implemented three partial locks for a very complex study, which is comprised of four distinct parts to answer different clinical questions.

Olive Yuan, Ph.D., Senior Clinical Data Manager, Regeneron Pharmaceuticals. Inc.

现今及未来的CDISC

This talk will review the industry乫s efforts to establish a standard platform for data capture and its impact on EDC-based studies. Currently, Dr. El Emam is leading a CDISC task force for the Society of Clinical Data Management, with the goal of educating members on CDISC and contributing to the not-for-profit organization's reference material on the application of the data standard to good clinical practices. Discussed will be an 1) an overview of CDISC standards current adoption in clinical research; 2) the relationship between CDISC and other data interchange standards; 3) approaches to importing and exporting ODM data sets in clinical trials; 4) privacy and security requirements when exchanging CDISC data; and, 5) what the future holds for CDISC as an industry-wide data standard.

Khaled El Emam, Ph.D., Associate Professor in the Faculty of Medicine and Canada Research Chair in Electronic Health Information, University of Ottawa

EDC利用的优点

Can organizations fully realize the larger business benefit that EDC and eClinical trials can bring? 丂So far, most sponsors are using EDC so that standard, current-state information technology can correct basic clinical trial process inefficiencies. Only a few companies have found how to really shorten development timelines or accelerate decision-making — two other assumed EDC benefits. But even more is made possible by the catalyst that eClinical Development provides: re-considering clinical development governance and resourcing; radically altering protocol design and development; routinely designing for and benefiting from interim analyses and adaptive design; re-examining the relationships between medical affairs, clinical research and marketing; cultivating higher-quality sites; reducing overall IT expense through cross-application integration; changing fundamental assumptions in site monitoring philosophies; increasing investigational drug supply efficiency; and more. This session will present perspectives and case study examples of organizations that have successfully leveraged their use of EDC and the actionable knowledge it creates.

Ronald Waife, President, Waife & Associates, Inc.