Arturo González-Ferrer1, M Ángel Valcárcel2, Martín Cuesta3, Joan Cháfer2, Isabelle Runkle3. 1. Unidad de Innovación, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain. Electronic address: arturogf@ugr.es. 2. Unidad de Innovación, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain. 3. Servicio de Endocrinología, Metabolismo y Nutrición, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain.
Abstract
INTRODUCTION: Hyponatremia is the most common type of electrolyte imbalance, occurring when serum sodium is below threshold levels, typically 135mmol/L. Electrolyte balance has been identified as one of the most challenging subjects for medical students, but also as one of the most relevant areas to learn about according to physicians and researchers. We present a computer-interpretable guideline (CIG) model that will be used for medical training to learn how to improve the diagnosis of hyponatremia applying an expert consensus document (ECDs). METHODS: We used the PROForma set of tools to develop the model, using an iterative process involving two knowledge engineers (a computer science Ph.D. and a preventive medicine specialist) and two expert endocrinologists. We also carried out an initial validation of the model and a qualitative post-analysis from the results of a retrospective study (N=65 patients), comparing the consensus diagnosis of two experts with the output of the tool. RESULTS: The model includes over two-hundred "for", "against" and "neutral" arguments that are selectively triggered depending on the input value of more than forty patient-state variables. We share the methodology followed for the development process and the initial validation results, that achieved a high ratio of 61/65 agreements with the consensus diagnosis, having a kappa value of K=0.86 for overall agreement and K=0.80 for first-ranked agreement. CONCLUSION: Hospital care professionals involved in the project showed high expectations of using this tool for training, but the process to follow for a successful diagnosis and application is not trivial, as reported in this manuscript. Secondary benefits of using these tools are associated to improving research knowledge and existing clinical practice guidelines (CPGs) or ECDs. Beyond point-of-care clinical decision support, knowledge-based decision support systems are very attractive as a training tool, to help selected professionals to better understand difficult diseases that are underdiagnosed and/or incorrectly managed.
INTRODUCTION:Hyponatremia is the most common type of electrolyte imbalance, occurring when serum sodium is below threshold levels, typically 135mmol/L. Electrolyte balance has been identified as one of the most challenging subjects for medical students, but also as one of the most relevant areas to learn about according to physicians and researchers. We present a computer-interpretable guideline (CIG) model that will be used for medical training to learn how to improve the diagnosis of hyponatremia applying an expert consensus document (ECDs). METHODS: We used the PROForma set of tools to develop the model, using an iterative process involving two knowledge engineers (a computer science Ph.D. and a preventive medicine specialist) and two expert endocrinologists. We also carried out an initial validation of the model and a qualitative post-analysis from the results of a retrospective study (N=65 patients), comparing the consensus diagnosis of two experts with the output of the tool. RESULTS: The model includes over two-hundred "for", "against" and "neutral" arguments that are selectively triggered depending on the input value of more than forty patient-state variables. We share the methodology followed for the development process and the initial validation results, that achieved a high ratio of 61/65 agreements with the consensus diagnosis, having a kappa value of K=0.86 for overall agreement and K=0.80 for first-ranked agreement. CONCLUSION: Hospital care professionals involved in the project showed high expectations of using this tool for training, but the process to follow for a successful diagnosis and application is not trivial, as reported in this manuscript. Secondary benefits of using these tools are associated to improving research knowledge and existing clinical practice guidelines (CPGs) or ECDs. Beyond point-of-care clinical decision support, knowledge-based decision support systems are very attractive as a training tool, to help selected professionals to better understand difficult diseases that are underdiagnosed and/or incorrectly managed.