Clinical Decision Support System

Please cite this article in press as: S.A. Zakane, et al., Guidelines for maternal and neonatal "point of care": Needs of and attitudes towards a computerized clinical decision support system in rural Burkina Faso, Int. J. Med. Inform. (2014), http://dx.

Background: A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and education. One of the big hurdles in developing such systems is the lack of automated mechanisms for knowledge acquisition to enable and educate clinicians in informed decision making. Materials and Methods: An automated knowledge acquisition methodology with a comprehensible knowledge model for cancer treatment (CKM-CT) is proposed. With the CKM-CT, clinical data are acquired automatically from documents. Quality of data is ensured by correcting errors and transforming various formats into a standard data format. Data preprocessing involves dimensionality reduction and missing value imputation. Predictive algorithm selection is performed on the basis of the ranking score of the weighted sum model. The knowledge builder prepares knowledge for knowledge-based services: clinical decisions and education support. Results: Data is acquired from 13,788 head and neck cancer (HNC) documents for 3447 patients, including 1526 patients of the oral cavity site. In the data quality task, 160 staging values are corrected. In the preprocessing task, 20 attributes and 106 records are eliminated from the dataset. The Classification and Regression Trees (CRT) algorithm is selected and provides 69.0% classification accuracy in predicting HNC treatment plans, consisting of 11 decision paths that yield 11 decision rules. Conclusion: Our proposed methodology, CKM-CT, is helpful to find hidden knowledge in clinical documents. In CKM-CT, the prediction models are developed to assist and educate clinicians for informed decision making. The proposed methodology is generalizable to apply to data of other domains such as breast cancer with a similar objective to assist clinicians in decision making and education.

Forensic medical practitioners and scientists have for several years sought improved decision support for determining and managing care and release of prisoners with mental health problems. Some of these prisoners can pose a serious threat of violence to society after release. It is, therefore, critical that the risk of violent reoffending is accurately measured and, more importantly, well managed with causal interventions to reduce this risk after release. The well-established predictors in this area of research are typically based on regression models or even some rule-based methods with no statistical composition, and these have proven to be unsuitable for simulating causal interventions for risk management. In collaboration with the medical practitioners of the Violence Prevention Research Unit (VPRU), Queen Mary University of London, we have developed a Bayesian network (BN) model for this purpose, which we call DSVM-P (Decision Support for Violence Management -Prisoners). The BN model captures the causal relationships between risk factors, interventions and violence and demonstrates significantly higher accuracy (cross-validated AUC score of 0.78) compared to well-established predictors (AUC scores ranging from 0.665 to 0.717) within this area of research, with respect to whether a prisoner is determined suitable for release. Even more important, however, the BN model also allows for specific risk factors to be targeted for causal intervention for risk management of future re-offending. Hence, unlike the previous predictors, this makes the model useful in terms of answering complex clinical questions that are based on unobserved evidence. Clinicians and probation officers who work in these areas would benefit from a system that takes account of these complex risk management considerations, since these decision support features are not available in the previous generation of models used by forensic psychiatrists.

Background and Objectives: Pharmacovigilance is central to the control of the menace of adverse drugs reactions.
Despite the fact that development of policy and practice framework to improve patients’ safety partly rely
on availability of authentic data on pharmacovigilance activities, knowledge about pharmacovigilance activities
among healthcare professionals in Nigeria is limited. To help fill this gap, this study explored the awareness, attitude
and practice of pharmacovigilance activities among the healthcare professionals in the Nigerian Nnamdi
Azikiwe University Teaching Hospital.
Methods: A descriptive cross-sectional survey was carried out among healthcare professionals in the Nnamdi
Azikiwe University Teaching Hospital, Nnewi. The participants were doctors, pharmacists, nurses and health
records officers employed in the teaching hospital. The sample was selected using stratified random sampling. A
structured, self-administered questionnaire was used as the survey instrument. Key informant interview was also
conducted among hospital’s administrative officers using standard interviewer guide. Descriptive statistics were
calculated for the demographic variables. Quantitative data were compared using inferential statistics.
Findings: Low level of awareness among the healthcare professionals about pharmacovigilance activities was
observed. About half of them, 130 (50.4%), stated that they were not aware of the Nigerian National Pharmacovigilance
tool that is used for documenting and reporting of adverse drug reactions. Only about one tenth of the
respondents, 35 (13.7%), mentioned that they use this tool for documenting and reporting of adverse drug reactions
whereas the majority of them, 220 (86.3%), stated that they had not used the tool.
Conclusions: The study indicated that the healthcare professionals in Nnamdi Azikiwe University Teaching Hospital
have a limited awareness about pharmacovigilance. There is also low frequency of utilization of the Nigeria
NPV tool for documentation and reporting of adverse drug reactions. Our findings highlight the need for educational
and managerial interventions to improve monitoring and reporting of adverse drug reactions within an all-inclusive
pharmacovigilance system in this country.

В статье представлен обзор различных возможностей поддержки принятия врачебных решений в медицинских информационных системах медицинских организаций. Дается описание функциональных требований и перспектив в части повышения эффективности медицинских информационных систем в информатизации клинической работы врачей.

Debido al gran interés, manifestado en las últimas décadas, hacia la mejora de los sistemas de detección y diagnóstico de cáncer de mama, se presenta un modelo de características con factores de peso implementados por funciones no lineales adecuado para auxiliar en la toma de decisiones destinadas al prediagnóstico de las glándulas mamarias, posterior a la realización de la prueba termográfica con el fin de detectar patologías en las mismas. Los termogramas fueron obtenidos de la base de datos generada por el Grupo de Detección Óptica Tumoral del Centro de Física Aplicada y Tecnología Avanzada (CFATA) de la UNAM. El procesamiento de la minería de datos y la clasificación estadística de las patologías se implementó según los valores térmicos apreciados en termogramas de mujeres sanas y con padecimientos diagnosticados mediante otras técnicas de imagen y confirmados con biopsia, así como su correlación con otros parámetros fisiológicos. La corroboración clínica se realizó en las instalaciones de la Unidad de Especialidades Médicas en Detección y Diagnóstico de Cáncer de Mama (UNEME-DEDICAM) del Estado de Querétaro y otras dependencias del Sector Salud. La determinación de los parámetros cualitativos se llevó a cabo, de manera heurística, de acuerdo al historial clínico y familiar de la paciente, aunado con los hábitos de vida y los patrones térmicos observados en cada caso de estudio. Éstos, en su conjunto, suponen un indicador ponderable de la probabilidad de desarrollo y/o padecimiento de las enfermedades mamarias más comunes. Se muestran resultados de la efectividad de prediagnóstico en mujeres sanas, con patologías benignas y con cáncer de seno. La principal contribución del modelo desarrollado es su capacidad de catalogar, con base en datos estadísticos y elementos probabilísticos, las características termográficas en contexto con la situación fisiológica individual.