CPOE in Iran-A viable prospect?. Physicians' opinions on using CPOE in an Iranian teaching hospital

Background: In recent years, the theory that on-line clinical decision support systems can improve patients' safety among hospitalised individuals has gained greater acceptance. However, the feasibility of implementing such a system in a middle or low-income country has rarely been studied. Understanding the current prescription process and a proper needs assessment of prescribers can act as the key to successful implementation. Objectives: The aim of this study was to explore physicians' opinions on the current prescription process, and the expected benefits and perceived obstacles to employ Computerised Physician Order Entry in an Iranian teaching hospital. Methods: Initially, the interview guideline was developed through focus group discussions with eight experts. Then semi-structured interviews were held with 19 prescribers. After verbatim transcription, inductive thematic analysis was performed on empirical data. Forty hours of on-looker observations were performed in different wards to explore the current prescription process. Results: The current prescription process was identified as a physician-centred, top-down, model, where prescribers were found to mostly rely on their memories as well as being overconfident. Some errors may occur during different paper-based registrations, transcriptions and transfers. Physician opinions on Computerised Physician Order Entry were categorised into expected benefits and perceived obstacles. Confidentiality issues, reduction of medication errors and educational benefits were identified as three themes in the expected benefits category. High cost, social and cultural barriers, data entry time and problems with technical support emerged as four themes in the perceived obstacles category. Conclusions: The current prescription process has a high possibility of medication errors. Although there are different barriers confronting the implementation and continuation of Computerised Physician Order Entry in Iranian hospitals, physicians have a willingness to use them if these systems provide significant benefits. A pilot study in a limited setting and a comprehensive analysis of health outcomes and economic indicators should be performed, to assess the merits of introducing Computerised Physician Order Entry with decision support capabilities in Iran. © 2008 Elsevier Ireland Ltd. All rights reserved

What they fill in today, may not be useful tomorrow: Lessons learned from studying Medical Records at the Women hospital in Tabriz, Iran

<p>Abstract</p> <p>Background</p> <p>The medical record is used to document patient's medical history, illnesses and treatment procedures. The information inside is useful when all needed information is documented properly. Medical care providers in Iran have complained of low quality of Medical Records. This study was designed to evaluate the quality of the Medical Records at the university hospital in Tabriz, Iran.</p> <p>Methods</p> <p>In order to get a background of the quality of documentation, 300 Medical Records were randomly selected among all hospitalized patient during September 23, 2003 and September 22, 2004. Documentation of all records was evaluated using checklists. Then, in order to combine objective data with subjective, 10 physicians and 10 nurses who were involved in documentation of Medical Records were randomly selected and interviewed using two semi structured guidelines.</p> <p>Results</p> <p>Almost all 300 Medical Records had problems in terms of quality of documentation. There was no record in which all information was documented correctly and compatible with the official format in Medical Records provided by Ministry of Health and Medical Education. Interviewees believed that poor handwriting, missing of sheets and imperfect documentation are major problems of the Paper-based Medical Records, and the main reason was believed to be high workload of both physicians and nurses.</p> <p>Conclusion</p> <p>The Medical Records are expected to be complete and accurate. Our study has unveiled that the Medical Records are not documented properly in the university hospital where the Medical Records are also used for educational purposes. Such incomplete Medical Records are not reliable resources for medical care too. Some influencing factors external to the structure of the Medical Records (i.e. human factors and work conditions) are involved.</p

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

<p>Abstract</p> <p>Background</p> <p>Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.</p> <p>Methods</p> <p>Models based on Bayes rule, <it>k-</it>nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.</p> <p>Results</p> <p>Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. <it>k</it>-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.</p> <p>Conclusion</p> <p>Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.</p

The effect of computerized physician order entry and decision support system on medication errors in the neonatal ward: Experiences from an Iranian teaching hospital

Medication dosing errors are frequent in neonatal wards. In an Iranian neonatal ward, a 7.5 months study was designed in three periods to compare the effect of Computerized Physician Order Entry (CPOE) without and with decision support functionalities in reducing non-intercepted medication dosing errors in antibiotics and anticonvulsants. Before intervention (Period 1), error rate was 53, which did not significantly change after the implementation of CPOE without decision support (Period 2). However, errors were significantly reduced to 34 after that the decision support was added to the CPOE (Period 3; P<0.001). Dose errors were more often intercepted than frequency errors. Over-dose was the most frequent type of medication errors and curtailed-interval was the least. Transcription errors did not reduce after the CPOE implementation. Physicians ignored alerts when they could not understand why they appeared. A suggestion is to add explanations about these reasons to increase physicians' compliance with the system's recommendations. © 2009 Springer Science+Business Media, LLC