Development and Knowledge Translation of Decision-Support Tools

主要目标是继续发展信息技术系统，提供知识，提高用户对所研究的各种问题的决策能力。决策支持工具采用系统工程方法，结合网络服务，以简单直观的方式提供相关信息；我们的研究项目的这一阶段侧重于在围产期护理背景下向医生和患者/家长提供知识。值得注意的是，本研究项目中开发的方法可以应用于非临床数据库，如天气和气候测量，以预测恶劣天气模式，或股票市场的变化，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是在系统、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拟议研究的10%。目的是开发预测工具，以确定孕妇早产的风险、分娩方式（阴道分娩或剖腹产）和阿普加评分（婴儿出生状态）；改进重症监护婴儿死亡率和一些并发症的风险预测模型，使用同时从多达20个婴儿收集的实时数据。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正在研究的各种结果的变量，并在显著变化发生时纳入警告和警报。下一步将是使用web服务以一种简单、安全的方式向医生和婴儿的父母提供信息，这些信息可以由几个用户在本地或从远程位置访问。风险的特征和全面的产科和围产期信息需要整合在一个原始的方式，为医生和患者提供一个有用的工具。风险评估结果，以及对治疗的反应和患者预后，将提供当前研究或商业系统中无法获得的独特临床工具。医生们对我们的方法非常感兴趣。这项研究完全是新颖的，该团队拥有相当多的互补专业知识：Bariciak博士，新生儿学家，临床方面；计算机科学家吉尔克里斯特博士，软件问题；Frize博士，生物医学工程和研究方法。评估工具将有助于早期干预并改善患者的预后。二十年来，主申请人培养了五十多名高素质人才；这个应用程序将使研究生接触到一个多学科的环境，这个领域对加拿大有很大的需求。所有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.