- Email: [email protected]
49A Development of a centralized data management and computing resource at a large academic clinical research center
66S
Abstracts
material. The major training components supplementing our lectures are worksheets, games and a manual. The manual permits us to minimize the amount of formal lecturing required, since it contains much of the detailed information required on the job, including pathology conventions, toxicity criteria and definitions on the biology and treatments of cancer. Each DM receives a copy of the manual on the first day of the TP. An example of a learning tool is the worksheet on the Staging of Cancers. In order to utilize the information taught in the training sessions, we created mock paaent cases and designed them in the form of worksheets (e.g., Using the AJCC Staging Systems, stage the following patient). We have also developed games such as Cancer Jeopardy, which utilize terminology and definitions taught in our Introduction to Onco/ogy training session. The DMs evalua_tethe TP at the end of each session. Through this evaluation process we have received'positive feedback regarding our worksheets, tools and games. In this presentation we will focus on our reinforced learning techniques and provide you with examples of our manual, worksheets and games.
49A DEVELOPMENT OF A CENTRALIZED DATA MANAGEMENT AND COMPUTING RESOURCE AT A LARGE ACADEMIC CLINICAL RESEARCH CENTER Colin B. Begg, Coilette M. Houston, Elinor Miller, Danny Wu
Memorial Sloan Kettering Cancer Center New York, New York This project was motivated by recognition of the necessity for high quality, readily accessible data to the success of any clinical research study. The research programs of the Center are frequently multi-disciplinary, and comprise a large number of diverse protocols. Perceived problems at the outset included: a dispersed knowledge base; rapid staff turnover; data stored on multiple computer systems, typically poorly documented and inefficient; and lack of institutional funding. In 1990 we embarked on an ambitious program to resolve these problems. The initial step was to develop the consensus necessary for institutional recognition of the problems, and to obtain adequate funding to tackle them. The basic strategy was to develop a core of staff with expertise in research a~to management and relational a~t~hase technology. We then developed broad programs in a~t~base development, education and user support. The cornerstone is an institutional clinical research ttatAha$e with the following attributes: menu-driven user-interface; centralized registration and randomization;protocol-specific security; standardized protocol reports plus ad hoc reporting tools; and automatic a~t~ transfer from hospital databases. Our educational program includes an in-depth four-day training program for new data managers, a monthly seminar series, and a data manager's manual. Usersupport includes a consulting service to assist investigators in recruiting and training d~t~ managers, accurately predicting workloads, and developing forms and other protocol-specific tools. The biggest problems we faced were insgtutional skepticism about the costs and likely yield of the projects, concerns about data security, and technical challenges in creal~g the multi-puzI~ose database.
Abstracts
material. The major training components supplementing our lectures are worksheets, games and a manual. The manual permits us to minimize the amount of formal lecturing required, since it contains much of the detailed information required on the job, including pathology conventions, toxicity criteria and definitions on the biology and treatments of cancer. Each DM receives a copy of the manual on the first day of the TP. An example of a learning tool is the worksheet on the Staging of Cancers. In order to utilize the information taught in the training sessions, we created mock paaent cases and designed them in the form of worksheets (e.g., Using the AJCC Staging Systems, stage the following patient). We have also developed games such as Cancer Jeopardy, which utilize terminology and definitions taught in our Introduction to Onco/ogy training session. The DMs evalua_tethe TP at the end of each session. Through this evaluation process we have received'positive feedback regarding our worksheets, tools and games. In this presentation we will focus on our reinforced learning techniques and provide you with examples of our manual, worksheets and games.
49A DEVELOPMENT OF A CENTRALIZED DATA MANAGEMENT AND COMPUTING RESOURCE AT A LARGE ACADEMIC CLINICAL RESEARCH CENTER Colin B. Begg, Coilette M. Houston, Elinor Miller, Danny Wu
Memorial Sloan Kettering Cancer Center New York, New York This project was motivated by recognition of the necessity for high quality, readily accessible data to the success of any clinical research study. The research programs of the Center are frequently multi-disciplinary, and comprise a large number of diverse protocols. Perceived problems at the outset included: a dispersed knowledge base; rapid staff turnover; data stored on multiple computer systems, typically poorly documented and inefficient; and lack of institutional funding. In 1990 we embarked on an ambitious program to resolve these problems. The initial step was to develop the consensus necessary for institutional recognition of the problems, and to obtain adequate funding to tackle them. The basic strategy was to develop a core of staff with expertise in research a~to management and relational a~t~hase technology. We then developed broad programs in a~t~base development, education and user support. The cornerstone is an institutional clinical research ttatAha$e with the following attributes: menu-driven user-interface; centralized registration and randomization;protocol-specific security; standardized protocol reports plus ad hoc reporting tools; and automatic a~t~ transfer from hospital databases. Our educational program includes an in-depth four-day training program for new data managers, a monthly seminar series, and a data manager's manual. Usersupport includes a consulting service to assist investigators in recruiting and training d~t~ managers, accurately predicting workloads, and developing forms and other protocol-specific tools. The biggest problems we faced were insgtutional skepticism about the costs and likely yield of the projects, concerns about data security, and technical challenges in creal~g the multi-puzI~ose database.