Development and Knowledge Translation of Decision-Support Tools

主要目标是继续开发信息技术系统，以提供知识，加强用户对正在研究的各种问题的决策。决策支持工具使用系统工程方法，结合Web服务，以简单直观的方式提供相关信息;我们研究计划的这一阶段侧重于在围产期护理方面向医生和患者/父母提供知识。值得注意的是，该研究项目中开发的方法可以应用于天气和气候测量等非临床数据库，以预测恶劣天气模式或股市变化，并帮助对非医疗应用的结果进行分类。这项研究的主要部分驻留在系统，计算机和软件工程领域;结果的评估（可用性研究）使用软件工程方法。这些系统在医疗环境中的应用约占所提出研究的10%。其目标是开发预测工具，以确定孕妇早产的风险，分娩方式（阴道或剖腹产）和Apgar评分（出生时婴儿的状态）;并使用同时从多达20名婴儿收集的实时数据，改进重症监护婴儿死亡率和一些并发症的风险预测模型。目前没有可用的方法预测这些结果。我们计划确定最能预测所研究的各种结果的变量，并在发生显著变化时纳入警告和警报。下一步将是使用网络服务以简单、安全的方式向医生和婴儿的父母提供信息，网络服务可以由多个用户在本地或远程访问。风险的特点和全面的产科和围产期信息需要以一种原始的方式整合，为医生和患者提供一个有用的工具。风险估计结果沿着对治疗的反应和患者预后将提供当前研究或商业系统中不可用的独特临床工具。医生对我们的方法有相当大的兴趣。这项研究是完全新颖的，团队拥有相当多的互补专业知识：Bariciak博士，临床方面的专家; Gilchrist博士，计算机科学家，软件问题; Frize博士，生物医学工程和研究方法。估计工具将有助于早期干预并改善患者的预后。主申请人在二十多年的时间里培养了五十多名高素质的人才;这项申请将使研究生在加拿大需求量很大的领域中接触到多学科的环境。所有以前的研究生在完成学位后都找到了相关的工作。 ******

The main goal is to continue to develop information technology systems that can provide knowledge that enhances decision-making by users for the various problems under study. The decision-support tools use a systems engineering approach, combined with web services, to provide relevant information in an easy and intuitive manner; this phase of our research program focusses on delivering knowledge to physicians and patients/parents in a perinatal care context. It is important to note that the methods developed in this research program can be applied to non-clinical databases like weather and climate measurements to predict severe weather patterns, or changes to the stock market, and help classify outcomes for non-medical applications. The major part of this research resides in the domain of systems, computer, and software engineering; the evaluation of the results (usability studies) use software engineering methods. The application of these systems to a medical environment represents around 10 % of the research proposed. The objectives are to develop prediction tools to identify the risk of premature birth in pregnant women, delivery mode (vaginal or Caesarian), and Apgar Score (state of infant at birth); and improve our risk prediction models for intensive care infants regarding mortality and a number of complications, using real-time data collected from up to twenty infants simultaneously. No currently available approaches predict these outcomes. We plan to identify the variables that best predict the various outcomes being studied as well as incorporate warnings and alarms when notable changes occur. The next step will be to present the information to physicians and parents of infants in an easy, secure manner using web services, which can be accessed locally or from remote locations, by several users. The characterisation of risks and the comprehensive obstetrical and perinatal information need to be integrated in an original manner to provide a useful tool for physicians and patients. The risk estimation results, along with response to treatments and patient prognosis, will offer a unique clinical tool not available in current research or commercial systems. There is a considerable level of interest by physicians for our approaches. The research is entirely novel and the team has considerable and complementary expertise: Dr Bariciak, neonatologist, for clinical aspects; Dr Gilchrist, a Computer Scientist, for software questions; Dr Frize, for biomedical engineering and research methodology. The estimation tools will help to intervene earlier and improve patient outcomes. The principal applicant has trained more than fifty highly qualified personnel over twenty years; this application will expose research students to a multidisciplinary environment in an area of great demand for Canada. All former research students found relevant employment after degree completion.  ******