Journal of Health and Biomedical Informatics

Introduction: The cancellation of a surgery operation not only imposed too much expenditure on the healthcare system, but also waste lots of energy and time of patient and health care provider. The aim of this study was to assess reasons of surgery cancellation, and to provide a comprehensive list of cancellation reasons to implement in hospital information system for improving documentation.
Method: This study is an applied-descriptive study which was conducted in two parts. In first part, all medical records of hospitalized patients in Shafa educational hospital during 6 months period which had been assigned a cancellation code based on ICD-10, were assessed. The data were collected through a researcher-made checklist and have been analyzed using SPSS.V18 software. The second part was literature review which provided a list of surgery cancellation reasons. To retrieve the relevant papers PubMed and Google Scholar databases were searched. Finally the results of two parts of the study were combined and a comprehensive list of surgery cancellation reasons was developed.
 Results: Of 7529 surgical operation, 1229 operations (16.4%) were cancelled. Most of the cancelled surgical operations were related to inappropriate patient’s clinical condition before surgery. Extracted cancellation reasons were categorized into 8 groups. Totally a list of 54 reasons for cancellation was developed.
Conclusion: Precise documentation of surgery cancellation reasons in each health care institution in order to determine the reasons and plan to prevent them is essential. Designing an information system with proper information content can support the above mentioned issues.

Introduction: The development of terminological systems is one of the requirements of the exchange of information in health information systems. A group of these terminological systems is drug terminology. In this study, we reviewed and introduced one of the drug terminological systems called NDF-RT's (National Drug File-Reference Terminology) and explained its applications systematically.
Method: This research has been done using two methods: a systematic review to identify studies concerning the application of NDF-RT and a non-systematic review to introduce this system. Articles were searched in Pub Med and Google Scholar databases. In the systematic review, the papers information including title, author's affiliation, year of publication, journal's name, and application of NDF-RT, were extracted and analyzed using Microsoft excel 2010.
Results: NDF-RT classification system classifies drugs based on their structure, ingredients, and their physiologic effects. In our systematic review, thirteen papers were retrieved, nine of which have evaluated NDF-RT system for usability and deployment of the system in health care environments, diagnosis and drug relationships, and accuracy of drugs classification.
Conclusion: NDF-RT drug terminology is one of the most comprehensive terminological systems for the classification of drugs which can be used for drugs information standardization in health information systems.

Introduction: Primary liver cancer (­HCC), is the fifth most common type of cancer and the third leading cause of death in the world. Symptoms of liver cancer will progress rapidly after the onset of the disease, and unfortunately, the patients' survival rate is very low. One of the main problems for gastroenterologists is the prediction and early detection of liver cancer. Data mining techniques can be used to understand and predict cancer. The aim of this study was to identify the best model based on intelligent data mining to predict and diagnose liver cancer in an early stage.

Method: In the present article, a retrospective study was conducted on 516 cases of primary and secondary liver cancer, and 22 risk factors were examined. Data were collected from the patients' files and analyzed using 5 data mining models including VFI Classifier, Regression Classifier, Hyper Pipes Classifier, Functional trees with logistic regression, and Meta Multi Class Classifier with the highest precision (Precision). These models were compared.

Results: The precision, sensitivity, specificity, and the area under the curve of VFI Classifier model were respectively 71.29%, 49%, 50%, and 63.31%, and VFI Classifier model is the best model based on intelligent data mining to predict and diagnose liver cancer in an early stage.

Conclusion: If properly designed, data mining model VFI Classifier can predict liver cancer or detect it in an early stage.

Introduction: In order to the implementation of “SEPAS" which is the development of electronic health Records in Iran, the creation and documentation of the essential data in the HIS seems necessary. The purpose of this study was to evaluate comptability of the hospital information systems (HIS) in Kerman teaching, Private and Social Security hospitals with minimum clinical data set developed by Iran’s Ministry of Health.

Method: This study was a descriptive cross-sectional study. The checklist developed by the Ministry of Health was used to evaluate the compliance with minimum data set (MDS) in 5 HISs of 9 teaching, private and social security hospitals. The study was conducted in the second half of 2014 in the City of Kerman, Iran.

Results: The results showed that, software No.2 had the highest conformity level (%100) for the “medication and medical consumables data”, and (%97) in “Paraclinical data”, software No.5 had the lowest conformity levels (%79) and (%86) for the two forgoing indexes, respectively. It was also found that none of the HISs contained sub-categories of “diagnosis & death” data set including data on the “morphology of neoplasm” as well as “the place and time of death”.

Conclusion: Given the importance of the MDS in integrating the data relating to the patients, implementation of the MDS developed by the Ministry of Health in the HISs is quite substantial by the system’s developers as well as ensuring its registration by the system users in the medical centers.

Introduction: Considering the importance of accurate documentation in psychiatric history and mental assessment and its effect on the current and future treatment among patients, this study aimed to determine data elements in the psychiatric history and mental assessment forms.

Method: This is a descriptive-comparative study. Using psychiatric history and assessment forms, the required data were gathered in this study. Finally, all psychiatric history and mental assessment forms gathered from the selected countries including the United States (n=9), Australia (n=1), France (n=1), Iran (n=2) were assessed. After preparing the comparative tables, a comprehensive list of data elements related to the psychiatric history and assessment was provided. Using bottom up approach the extracted data elements were categorized.

Results: In total, 58 data elements including demographic data, patient history, current symptoms of disease, evaluation of psychological states, suicide risk assessment, behavioral/emotional conditions, thinking process, drug abuse assessment, family safety/violence assessment, multi-axial diagnosis and treatment type/plan were extracted from psychiatric history and assessment forms.

Conclusion: The present study provides a general view of important data elements considered for documentation in psychiatric history and assessment forms.