Commercial off-the-shelf systems (COTS) such as enterprise resource planning (ERP) systems are becoming mature technologies to support inter- and intra-company business processes even in intermediate and small organizations. However, such systems are complex and expensive. The decision to install a COTS necessitates a choice of mechanisms to determine whether it fits firm¡¦s requirements. This paper presents a misfit diagnosis decision principle grounded on the COTS misfit analysis methodology proposed by Wu, et al., 2008. We propose a systematic diagnosis decision principle to add on to the misfit analysis methodology by providing support to decision-making in customization or Business Process Engineering (BPR). Our research contributes to the misfit analysis methodology by proposing a systematic diagnosis decision principle that will help both software vendors and organizations when the misfits between firm requirements and COTS functionality are encountered. The results indicate that with this approach, organizations can more easily and systematically determine whether the misfits should be customized or conducted BPR. They also help to evaluate the efforts needed for COTS customization and business process reengineering for each misfit and thereby help to support decision making in customization or BPR, thereby reducing the risk in implementing COTS.

Sistemas de Suporte a Decisão Clínica (CDSS) são sistemas baseados em conhecimento que utilizam dados de pacientes para gerar um parecer médico com objetivo de apoiar o usuário na decisão diagnóstica. Geralmente os sistemas hospitalares e clínicos fornecem informações criadas apenas por usuários locais ao CDSS para que uma base de conhecimento possa ser criada. Consequentemente, o CDSS apoiará seus usuários com o aprendizado realizado apenas em experiências locais. Por outro lado, uma situação interessante é quando os sistemas normalmente legados podem alimentar um CDSS com informações clínicas provenientes também de outros sistemas remotos, e criadas por médicos de outras instituições. Neste cenário, um médico poderia ser apoiado de forma colaborativa na decisão diagnóstica por vários outros médicos localizados remotamente, a partir do compartilhamento de bases de conhecimento. Neste cenário de computação distribuída, é comum a aparição de problemas de interoperabilidade técnica causados principalmente pelo uso de diferentes protocolos de comunicação para a criação e chamada de serviços clínicos remotos. Ainda no cenário apresentado anteriormente, podem surgir problemas de interoperabilidade semântica justificados pelo emprego de sistemas de terminologia e de ontologia clínica heterogêneos por parte das aplicações médicas. Assim sendo, estes problemas de interoperabilidade precisam ser explorados e solucionados de modo a permitir que a atividade de apoio à decisão diagnóstica colaborativa seja possível. Este trabalho investiga e discute os problemas de interoperabilidade semântica e técnica durante a troca de informações entre CDSSs e sistemas em saúde. A partir desta discussão, é proposta uma solução interoperável, baseada na UMLS, para apoiar a decisão diagnóstica colaborativa na Web. Esta solução é composta da criação de uma ontologia denominada DDSOnt, que extende a \\emph{UMLS Semantic Network}, e define um novo conjunto de tipos semânticos para a criação de uma estrutura que suporte o compartilhamento de bases de conhecimento de modo que possam ser utilizadas por médicos no apoio à decisão clínica colaborativa. A construção da ontologia busca promover a compatibilidade ontológica. Ainda, são confeccionados softwares que auxiliam os desenvolvedores na utilização da ontologia DDSOnt na construção de aplicações clínicas que precisam ser apoiadas na tomada de decisão diagnóstica: uma API denominada JDDSOnt para a definição e construção de documentos da Web Semântica em conformidade com a DDSOnt, e um conjunto de serviços Web, denominado DDSOntWs, que permite que esta troca de experiência clínica ocorra em ambiente distribuído, orientado a serviços, e independente de plataforma, promovendo assim a compatibilidade técnica. Desta maneira, um CDSS é projetado na forma de um serviço Web. Objetivando promover a compatibilidade terminológica, um dos serviços Web da DDSOntWs desenvolvido no trabalho faz uso da \\emph{UMLS Metathesaurus}. Um estudo de caso para a construção de aplicativos médicos que façam uso da solução proposta também foi realizado para teste e validação da metodologia apresentada. / Clinical Decision Support Systems (CDSS) are knowledge based systems that make use of patient data to generate a diagnosis response aiming to support a clinical decision. Usually, hospital and clinical systems supply CDSS with information generated by local users. Thus, CDSS\'s knowledge base may be created based on local experiences learning and will support users with this type of experience. A different and interesting situation would be if legacy systems could supply CDSS with clinical information deriving from physically remote systems, and created by physicians from others affiliations. At this scenario, a physician could be supported in a collaborative environment during a clinical decision with physicians working remotely, and with shared clinical knowledge bases. Concerning this distributed computer scenario technical interoperability, communication problems may appear mainly due to different communication protocol usage to create and invoke remote clinical services. Still considering the scenario presented before, semantic interoperability problems can occur because heterogeneous terminology and ontology clinical systems may be used by distinctive medical applications. Therefore, these interoperability problems must be explored and solved to allow a collaborative clinical decision support activities. This paper aims to investigate and discuss these technical and semantic interoperability problems caused by clinical information exchanged between CDSSs and health institutions systems. Based on that discussion, an interoperability and UMLS based solution is proposed to support a collaborative clinical decision environment on the Web. The solution is composed by a novel DDSOnt (Diagnosis Decision Support Ontology) ontology, that extends UMLS Semantic Network to create a new set of semantic types needful to define a structure that supports a shared knowledge databases to be used by physicians during the collaborative clinical decision. Ontology construction aims to promote ontology compatibility. Still, softwares have been developed to make DDSOnt useful by clinical applications that needs to be supported during clinical decision: a JDDSOnt API to define and to create semantic documents DDSOnt conformed, and a set of web services named DDSOntWs to enable a collaborative, service oriented, distributed computer environment, and plataform independent. Therefore, a CDSS is created as a web service. Aiming to promote terminology compatibility, one DDSOnt web service use the UMLS MetaThesaurus. An acute abdominal pain use case is also presented to demonstrate DDSOnt usability.

Sistemas de Suporte a Decisão Clínica (CDSS) são sistemas baseados em conhecimento que utilizam dados de pacientes para gerar um parecer médico com objetivo de apoiar o usuário na decisão diagnóstica. Geralmente os sistemas hospitalares e clínicos fornecem informações criadas apenas por usuários locais ao CDSS para que uma base de conhecimento possa ser criada. Consequentemente, o CDSS apoiará seus usuários com o aprendizado realizado apenas em experiências locais. Por outro lado, uma situação interessante é quando os sistemas normalmente legados podem alimentar um CDSS com informações clínicas provenientes também de outros sistemas remotos, e criadas por médicos de outras instituições. Neste cenário, um médico poderia ser apoiado de forma colaborativa na decisão diagnóstica por vários outros médicos localizados remotamente, a partir do compartilhamento de bases de conhecimento. Neste cenário de computação distribuída, é comum a aparição de problemas de interoperabilidade técnica causados principalmente pelo uso de diferentes protocolos de comunicação para a criação e chamada de serviços clínicos remotos. Ainda no cenário apresentado anteriormente, podem surgir problemas de interoperabilidade semântica justificados pelo emprego de sistemas de terminologia e de ontologia clínica heterogêneos por parte das aplicações médicas. Assim sendo, estes problemas de interoperabilidade precisam ser explorados e solucionados de modo a permitir que a atividade de apoio à decisão diagnóstica colaborativa seja possível. Este trabalho investiga e discute os problemas de interoperabilidade semântica e técnica durante a troca de informações entre CDSSs e sistemas em saúde. A partir desta discussão, é proposta uma solução interoperável, baseada na UMLS, para apoiar a decisão diagnóstica colaborativa na Web. Esta solução é composta da criação de uma ontologia denominada DDSOnt, que extende a \\emph{UMLS Semantic Network}, e define um novo conjunto de tipos semânticos para a criação de uma estrutura que suporte o compartilhamento de bases de conhecimento de modo que possam ser utilizadas por médicos no apoio à decisão clínica colaborativa. A construção da ontologia busca promover a compatibilidade ontológica. Ainda, são confeccionados softwares que auxiliam os desenvolvedores na utilização da ontologia DDSOnt na construção de aplicações clínicas que precisam ser apoiadas na tomada de decisão diagnóstica: uma API denominada JDDSOnt para a definição e construção de documentos da Web Semântica em conformidade com a DDSOnt, e um conjunto de serviços Web, denominado DDSOntWs, que permite que esta troca de experiência clínica ocorra em ambiente distribuído, orientado a serviços, e independente de plataforma, promovendo assim a compatibilidade técnica. Desta maneira, um CDSS é projetado na forma de um serviço Web. Objetivando promover a compatibilidade terminológica, um dos serviços Web da DDSOntWs desenvolvido no trabalho faz uso da \\emph{UMLS Metathesaurus}. Um estudo de caso para a construção de aplicativos médicos que façam uso da solução proposta também foi realizado para teste e validação da metodologia apresentada. / Clinical Decision Support Systems (CDSS) are knowledge based systems that make use of patient data to generate a diagnosis response aiming to support a clinical decision. Usually, hospital and clinical systems supply CDSS with information generated by local users. Thus, CDSS\'s knowledge base may be created based on local experiences learning and will support users with this type of experience. A different and interesting situation would be if legacy systems could supply CDSS with clinical information deriving from physically remote systems, and created by physicians from others affiliations. At this scenario, a physician could be supported in a collaborative environment during a clinical decision with physicians working remotely, and with shared clinical knowledge bases. Concerning this distributed computer scenario technical interoperability, communication problems may appear mainly due to different communication protocol usage to create and invoke remote clinical services. Still considering the scenario presented before, semantic interoperability problems can occur because heterogeneous terminology and ontology clinical systems may be used by distinctive medical applications. Therefore, these interoperability problems must be explored and solved to allow a collaborative clinical decision support activities. This paper aims to investigate and discuss these technical and semantic interoperability problems caused by clinical information exchanged between CDSSs and health institutions systems. Based on that discussion, an interoperability and UMLS based solution is proposed to support a collaborative clinical decision environment on the Web. The solution is composed by a novel DDSOnt (Diagnosis Decision Support Ontology) ontology, that extends UMLS Semantic Network to create a new set of semantic types needful to define a structure that supports a shared knowledge databases to be used by physicians during the collaborative clinical decision. Ontology construction aims to promote ontology compatibility. Still, softwares have been developed to make DDSOnt useful by clinical applications that needs to be supported during clinical decision: a JDDSOnt API to define and to create semantic documents DDSOnt conformed, and a set of web services named DDSOntWs to enable a collaborative, service oriented, distributed computer environment, and plataform independent. Therefore, a CDSS is created as a web service. Aiming to promote terminology compatibility, one DDSOnt web service use the UMLS MetaThesaurus. An acute abdominal pain use case is also presented to demonstrate DDSOnt usability.

Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases. The proposed framework comprises of two novel key components: (1) ODCRARS (2) MLDPS. The ODCRARS is developed under the close supervision of consultant cardiologists Professor Calum MacRae from Harvard Medical School and Professor Stephen Leslie from Raigmore Hospital in Inverness, UK. The ODCRARS comprises of various components, which include: (a) Ontology-driven intelligent context-aware information collection for conducting patient interviews which are driven through a novel clinical questionnaire ontology. (b) A patient semantic profile, is generated using patient medical records which are collated during patient interviews (conducted through an ontology-driven context aware adaptive information collection component). The semantic transformation of patients’ medical data is carried out through a novel patient semantic profile ontology in order to give patient data an intrinsic meaning and alleviate interoperability issues with third party healthcare systems. (c) Ontology driven clinical decision support comprises of a recommendation ontology and a NICE/Expert driven clinical rules engine. The recommendation ontology is developed using clinical rules provided by the consultant cardiologist from the US hospital. The recommendation ontology utilises the patient semantic profile for lab tests and medication recommendation. A clinical rules engine is developed to implement a cardiac risk assessment mechanism for various cardiovascular conditions. The clinical rules engine is also utilised to control the patient flow within the integrated cardiovascular preventative care solution. The machine learning-driven prognostic system is developed in an iterative manner using state of the art feature selection and machine learning techniques. A prognostic model development process is exploited for the development of MLDPS based on clinical case studies in the cardiovascular domain. An additional clinical case study in the breast cancer domain is also carried out for the development and validation purposes. The prognostic model development process is general enough to handle a variety of healthcare datasets which will enable researchers to develop cost effective and evidence based clinical decision support systems. The proposed clinical decision support framework also provides a learning mechanism based on machine learning techniques. Learning mechanism is provided through exchange of patient data amongst the MLDPS and the ODCRARS. The machine learning-driven prognostic system is validated using Raigmore Hospital's RACPC, heart disease and breast cancer clinical case studies.