Holzer Ralf, Ladusans Ed, Kitchiner Denise, Peart Ian, Gladman Gordon, Miles Gail
Department of Cardiology and Cardiac Surgery, Royal Liverpool Children's NHS Trust, Liverpool, United Kingdom.
Cardiol Young. 2006 Jun;16(3):289-99. doi: 10.1017/S1047951106000400.
Surgical waiting lists are of high importance in countries, where the national health system is unable to deliver surgical services at a rate that would allow patients to avoid unnecessary periods of waiting. Prioritization of these lists, however, is frequently arbitrary and inconsistent. The objective of our research was to analyze the medical decision-making process when prioritizing patients with congenital cardiac malformations for cardiac surgical procedures, identifying an appropriate representation of knowledge, and transferring this knowledge onto the design and implementation of an expert system ("PrioHeart"). The medical decision-making process was stratified into three stages. The first was to analyze the details of the procedure and patient to define important impact factors on clinical priority, such as the risk of adverse events. The second step was to evaluate these impact factors to define an appropriate "timing category" within which a procedure should be performed. The third, and final, step was to re-evaluate the characteristics of individual patients to differentiate between those in the same timing category. We implemented this decision-making process using a rule-based production system with support for fuzzy sets, using the FuzzyCLIPS inference engine and expert system shell as a suitable development environment for the knowledge base. The "PrioHeart" expert system was developed to give paediatric cardiologists a tool to allow and facilitate the prioritization of patients on the cardiosurgical waiting list. Evaluation of "PrioHeart" on limited sets of patients documented appropriate results of prioritization, with a significant correlation between the prioritization made using "PrioHeart" and those results obtained by the individual consultant specialist. We conclude that our study has demonstrated the feasibility of using an expert system approach with a fuzzy, rule-based production system to implement the prioritization of cardiac surgical patients. The approach may potentially be transferable to other medical subspecialities.
在那些国家卫生系统无法以让患者避免不必要等待期的速度提供外科手术服务的国家，外科手术等候名单至关重要。然而，这些名单的优先排序往往是任意且不一致的。我们研究的目的是分析在为先天性心脏畸形患者进行心脏外科手术时的医疗决策过程，确定合适的知识表示形式，并将这些知识应用于一个专家系统（“PrioHeart”）的设计和实施。医疗决策过程分为三个阶段。第一个阶段是分析手术和患者的细节，以确定对临床优先级有重要影响的因素，如不良事件的风险。第二步是评估这些影响因素，以确定手术应在其中进行的合适“时间类别”。第三个也是最后一个步骤是重新评估个体患者的特征，以区分处于同一时间类别的患者。我们使用基于规则的生产系统并支持模糊集来实现这个决策过程，使用FuzzyCLIPS推理引擎和专家系统外壳作为知识库的合适开发环境。开发“PrioHeart”专家系统是为了给儿科心脏病专家提供一种工具，以便对心脏外科手术等候名单上的患者进行优先排序并提供便利。对有限患者群体的“PrioHeart”评估记录了合适的优先排序结果，使用“PrioHeart”进行的优先排序与个别顾问专家获得的结果之间存在显著相关性。我们得出结论，我们的研究证明了使用具有模糊、基于规则的生产系统的专家系统方法来实现心脏外科手术患者优先排序的可行性。这种方法可能潜在地适用于其他医学亚专业。
