Determinants of continuance intention in a post-adoption satisfaction evaluation of a clinical information system.

Purpose: The evaluation of end-user satisfaction is an essential part of any clinical information system (CIS) project. The purpose of this study is to evaluate the determinants of CIS continuance intention in a late post-adoption phase at the Georges Pompidou University Hospital (HEGP) in Paris.

Methods: We designed an electronic survey instrument based on an IT post-adoption model (ITPAM) developed from three previous models, i.

Clinical information system post-adoption evaluation at the georges pompidou university hospital.

The evaluation of a clinical information system (CIS) at different stages of deployment and routine use is a key factor to improve acceptability and use by health professionals. This paper examines on an expectation-confirmation model (ITPAM) the relationships between the determinants of success of a CIS in a cross-sectional survey performed at the Georges Pompidou University Hospital (HEGP). Results for the groups of physicians and nurses that replied to the survey (n=312) suggest that health professional satisfaction (overall R(2)=0.

Electronic implementation of guidelines in the EsPeR system: a knowledge specification method.

Despite initiatives to standardize methods for the development of clinical guidelines, several barriers hinder their integration in daily clinical practice: failure to fulfil quality criteria, poor effectiveness of their dissemination. Computerization of guidelines can favor their dissemination. The initial step of computerization is the knowledge specification from the text of the guideline.

Computerization of guidelines: a knowledge specification method to convert text to detailed decision tree for electronic implementation.

The initial step for the computerization of guidelines is the knowledge specification from the prose text of guidelines. We describe a method of knowledge specification based on a structured and systematic analysis of text allowing detailed specification of a decision tree. We use decision tables to validate the decision algorithm and decision trees to specify and represent this algorithm, along with elementary messages of recommendation.