RESUMO
This paper presents the design of a unifying infrastructure for clinical decision support systems (CDSSs) and medical data relating to the perinatal life cycle. The diverse CDSSs designed for deployment within the perinatal life cycle to improve care, such as Artificial Neural Networks and Case-Based Reasoners, are integrated using the eXtended Markup Language (XML) and are subsequently offered as a secure web service. These web services are accessible from anywhere within the hospital information system and from remote authorized sites. The goal of such an infrastructure is to provide integrated CDSS processing in a complex distributed environment, in order to support real-time physician decision-making. This design provides a novel web services infrastructure implementation and offers a strong case study for deploying and evaluating the web services paradigm within a health care environment.
RESUMO
Recent studies in neonatal medicine, clinical nursing, and cognitive psychology have indicated the need to augment current decision-making practice in neonatal intensive care units with computerized, intelligent decision support systems. Rapid progress in artificial intelligence and knowledge management facilitates the design of collaborative ethical decision-support tools that allow clinicians to provide better support for parents facing inherently difficult choices, such as when to withdraw aggressive treatment. The appropriateness of using computers to support ethical decision-making is critically analyzed through research and literature review. In ethical dilemmas, multiple diverse participants need to communicate and function as a team to select the best treatment plan. In order to do this, physicians require reliable estimations of prognosis, while parents need a highly useable tool to help them assimilate complex medical issues and address their own value system. Our goal is to improve and structuralize the ethical decision-making that has become an inevitable part of modern neonatal care units. The paper contributes to clinical decision support by outlining the needs and basis for ethical decision support and justifying the proposed development efforts.