Developing fracture liaison service framework based on comparative analysis and scoping review.

This research conducts a comparative analysis and scoping review of 105 studies in the field of Fracture Liaison Service (FLS). The resulting two-dimensional framework represents a significant step toward FLS implementation. PURPOSE: The primary goal is to review interventions in real world settings in order to provide the FLS framework that specifies the essential elements of its implementation and offers different perspectives on that. METHOD: This study encompasses two phases: a comparative analysis of existing FLS models, including "Capture the Fracture," "5IQ," and "Ganda," and a scoping review from 2012 to 2022 in PubMed, Web of Science, Scopus, ProQuest, and IEEE databases limited to publications in English. RESULTS: The resulting model of comparative analysis identifies patient identification, investigation, intervention and integration or continuity of care as the four main stages of FLS. Additionally, the elements of quality and information span across all stages. Following comparative analysis, the framework is designed to be used for content analysis of the included studies in the scoping review. The intersection of columns (Who, Where, When, What, How, Quality) with rows (Identification, Investigation, Intervention, and continuity of care) yields a set of questions, answered in tabular form based on the scoping review. CONCLUSION: The framework offers potential benefits in facilitating the adoption of effective approaches for FLS implementation. It is recommended to undertake an in-depth review of each of these components in order to uncover novel and innovative approaches for improving their implementation.

Prediction of subsequent fragility fractures: application of machine learning.

BACKGROUND: Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive power of different ML algorithms in this area and identify key features associated with the risk of subsequent fragility fractures in osteoporotic patients. METHODS: We retrospectively analyzed data from patients presented with fragility fractures at our Fracture Liaison Service, categorizing them into index fragility fracture (n = 905) and subsequent fragility fracture groups (n = 195). We independently trained ML models using 27 features for both male and female cohorts. The algorithms tested include Random Forest, XGBoost, CatBoost, Logistic Regression, LightGBM, AdaBoost, Multi-Layer Perceptron, and Support Vector Machine. Model performance was evaluated through 10-fold cross-validation. RESULTS: The CatBoost model outperformed other models, achieving 87% accuracy and an AUC of 0.951 for females, and 93.4% accuracy with an AUC of 0.990 for males. The most significant predictors for females included age, serum C-reactive protein (CRP), 25(OH)D, creatinine, blood urea nitrogen (BUN), parathyroid hormone (PTH), femoral neck Z-score, menopause age, number of pregnancies, phosphorus, calcium, and body mass index (BMI); for males, the predictors were serum CRP, femoral neck T-score, PTH, hip T-score, BMI, BUN, creatinine, alkaline phosphatase, and spinal Z-score. CONCLUSION: ML models, especially CatBoost, offer a valuable approach for predicting subsequent fragility fractures in osteoporotic patients. These models hold the potential to enhance clinical decision-making by supporting the development of personalized preventative strategies.

Patient safety classifications, taxonomies and ontologies, part 2: A systematic review on content coverage.

BACKGROUND: Content coverage of patient safety ontology and classification systems should be evaluated to provide a guide for users to select appropriate ones for specific applications. In this review, we identified and compare content coverage of patient safety classifications and ontologies. METHODS: We searched different databases and ontology/classification repositories to identify these classifications and ontologies. We included patient safety-related taxonomies, ontologies, classifications, and terminologies. We identified and extracted different concepts covered by these systems and mapped these concepts to international classification for patient safety (ICPS) and finally compared the content of these systems. RESULTS: Finally, 89 papers (77 classifications or ontologies) were analyzed. Thirteen classifications have been developed to cover all medical domains. Among specific domain systems, most systems cover medication (16), surgery (8), medical devices (3), general practice (3), and primary care (3). The most common patient safety-related concepts covered in these systems include incident types (41), contributing factors/hazards (31), patient outcomes (29), degree of harm (25), and action (18). However, stage/phase (6), incident characteristics (5), detection (5), people involved (5), organizational outcomes (4), error type (4), and care setting (3) are some of the less covered concepts in these classifications/ontologies. CONCLUSION: Among general systems, ICPS, World Health Organization's Adverse Reaction Terminology (WHO-ART), and Ontology of Adverse Events (OAE) cover most patient safety concepts and can be used as a gold standard for all medical domains. As a result, reporting systems could make use of these broad classifications, but the majority of their covered concepts are related to patient outcomes, with the exception of ICPS, which covers other patient safety concepts. However, the ICPS does not cover specialized domain concepts. For specific medical domains, MedDRA, NCC MERP, OPAE, ADRO, PPST, OCCME, TRTE, TSAHI, and PSIC-PC provide the broadest coverage of concepts. Many of the patient safety classifications and ontologies are not formally registered or available as formal classification/ontology in ontology repositories such as BioPortal. This study may be used as a guide for choosing appropriate classifications for various applications or expanding less developed patient safety classifications/ontologies. Furthermore, the same concepts are not represented by the same terms; therefore, the current study could be used to guide a harmonization process for existing or future patient safety classifications/ontologies.

Machine learning-based mortality prediction models for smoker COVID-19 patients.

BACKGROUND: The large number of SARS-Cov-2 cases during the COVID-19 global pandemic has burdened healthcare systems and created a shortage of resources and services. In recent years, mortality prediction models have shown a potential in alleviating this issue; however, these models are susceptible to biases in specific subpopulations with different risks of mortality, such as patients with prior history of smoking. The current study aims to develop a machine learning-based mortality prediction model for COVID-19 patients that have a history of smoking in the Iranian population. METHODS: A retrospective study was conducted across six medical centers between 18 and 2020 and 15 March 2022, comprised of 678 CT scans and laboratory-confirmed COVID-19 patients that had a history of smoking. Multiple machine learning models were developed using 10-fold cross-validation. The target variable was in-hospital mortality and input features included patient demographics, levels of care, vital signs, medications, and comorbidities. Two sets of models were developed for at-admission and post-admission predictions. Subsequently, the top five prediction models were selected from at-admission models and post-admission models and their probabilities were calibrated. RESULTS: The in-hospital mortality rate for smoker COVID-19 patients was 20.1%. For "at admission" models, the best-calibrated model was XGBoost which yielded an accuracy of 87.5% and F1 score of 86.2%. For the "post-admission" models, XGBoost also outperformed the rest with an accuracy of 90.5% and F1 score of 89.9%. Active smoking was among the most important features in patients' mortality prediction. CONCLUSION: Our machine learning-based mortality prediction models have the potential to be adapted for improving the management of smoker COVID-19 patients and predicting patients' chance of survival.

Patients' perspectives and preferences toward telemedicine versus in-person visits: a mixed-methods study on 1226 patients.

INTRODUCTION: Despite the fact that telemedicine can eliminate geographical and time limitations and offer the possibility of diagnosing, treating, and preventing diseases by sharing reliable information, many individuals still prefer to visit medical centers for in-person consultations. The aim of this study was to determine the level of acceptance of telemedicine compared to in-person visits, identify the perceived advantages of telemedicine over in-person visits, and to explore the reasons why patients choose either of these two types of visits. METHODS: We developed a questionnaire using the rational method. The questionnaire consisted of multiple-choice questions and one open-ended question. A total of 2059 patients were invited to participate in the study. Chi-square tests and descriptive statistics were employed for data analysis. To analyze the data from the open-ended question, we conducted qualitative content analysis using MAXQDA 18. RESULTS: Out of the 1226 participants who completed the questionnaire, 865 (71%) preferred in-person visits, while 361 (29%) preferred telemedicine. Factors such as education level, specific health conditions, and prior experience with telemedicine influenced the preference for telemedicine. The participants provided a total of 183 different reasons for choosing either telemedicine (108 reasons) or in-person visits (75 reasons). Avoiding infectious diseases, saving cost, and eliminating and overcoming geographical distance barriers were three primary telemedicine benefits. The primary reasons for selecting an in-person visit were: more accurate diagnosis of the disease, more accurate and better examination of the patient by the physician, and more accurate and better treatment of the disease. CONCLUSION: The results demonstrate that despite the numerous benefits offered by telemedicine, the majority of patients still exhibit a preference for in-person visits. In order to promote broader acceptance of telemedicine, it becomes crucial for telemedicine services to address patient preferences and concerns effectively. Employing effective change management strategies can aid in overcoming resistance and facilitating the widespread adoption of telemedicine within the population.

Establishment of Iran Musculoskeletal Tumor Registry: A Study Protocol and Lessons Learned from Implementation and the Pilot Phase.

Background: The number of available musculoskeletal tumor registries is relatively small. We developed a registry system focused on the clinical aspects of musculoskeletal tumors to improve quality of care indexes through the development of updated national protocols. In this study, we describe our protocol, challenges, and the data collected during the implementation of the registry system in a single-specialty orthopedic center in Iran. Methods: Three main malignant bone tumors, including osteosarcoma, Ewing sarcoma, and chondrosarcoma, were included in the registry. After establishing a steering committee, we defined the minimum data set based on a literature review and suggestions from an expert panel. Accordingly, the data collection forms and the web-based software were developed. The collected information was categorized into 9 classes, including demographics, socioeconomic data, signs and symptoms, past medical history, family history, laboratory tests, tumor characteristics, primary treatment, and follow-up. Data collection was performed both retrospectively and prospectively. Results: Until September 21, 2022, a total of 71 patients were registered (21 patients prospectively and 50 patients retrospectively) and consisted of 36 (50.7%) cases of osteosarcoma, 13 (18.3%) cases of Ewing sarcoma, and 22 (31%) cases of chondrosarcoma. The implementation of the registry demonstrated promising data regarding the tumor characteristics, delay patterns, and socioeconomic status of the patients. Conclusion: The main lessons learned were to develop a monitoring system to make sure that the new staff is adequately trained for the registration process as well as avoid the inclusion of time-consuming useless data in the minimum data set.

Patient safety classifications, taxonomies and ontologies: A systematic review on development and evaluation methodologies.

INTRODUCTION: Patient safety classifications/ontologies enable patient safety information systems to receive and analyze patient safety data to improve patient safety. Patient safety classifications/ontologies have been developed and evaluated using a variety of methods. The purpose of this review was to discuss and analyze the methodologies for developing and evaluating patient safety classifications/ontologies. METHODS: Studies that developed or evaluated patient safety classifications, terminologies, taxonomies, or ontologies were searched through Google Scholar, Google search engines, National Center for Biomedical Ontology (NCBO) BioPortal, Open Biological and Biomedical Ontology (OBO) Foundry and World Health Organization (WHO) websites and Scopus, Web of Science, PubMed, and Science Direct. We updated our search on 30 February 2021 and included all studies published until the end of 2020. Studies that developed or evaluated classifications only for patient safety and provided information on how they were developed or evaluated were included. Systems with covered patient safety terms (such as ICD-10) but are not specifically developed for patient safety were excluded. The quality and the risk of bias of studies were not assessed because all methodologies and criteria were intended to be covered. In addition, we analyzed the data through descriptive narrative synthesis and compared and classified the development and evaluation methods and evaluation criteria according to available development and evaluation approaches for biomedical ontologies. RESULTS: We identified 84 articles that met all of the inclusion criteria, resulting in 70 classifications/ontologies, nine of which were for the general medical domain. The most papers were published in 2010 and 2011, with 8 and 7 papers, respectively. The United States (50) and Australia (23) have the most studies. The most commonly used methods for developing classifications/ontologies included the use of existing systems (for expanding or mapping) (44) and qualitative analysis of event reports (39). The most common evaluation methods were coding or classifying some safety report samples (25), quantitative analysis of incidents based on the developed classification (24), and consensus among physicians (16). The most commonly applied evaluation criteria were reliability (27), content and face validity (9), comprehensiveness (6), usability (5), linguistic clarity (5), and impact (4), respectively. CONCLUSIONS: Because of the weaknesses and strengths of the development/evaluation methods, it is advised that more than one method for development or evaluation, as well as evaluation criteria, should be used. To organize the processes of developing classification/ontologies, well-established approaches such as Methontology are recommended. The most prevalent evaluation methods applied in this domain are well fitted to the biomedical ontology evaluation methods, but it is also advised to apply some evaluation approaches such as logic, rules, and Natural language processing (NLP) based in combination with other evaluation approaches. This research can assist domain researchers in developing or evaluating domain ontologies using more complete methodologies. There is also a lack of reporting consistency in the literature and same methods or criteria were reported with different terminologies.

Smartphone-based application for self-management of patients with colorectal cancer: development and usability evaluation.

PURPOSE: Self-management is considered essential for improving the treatment and management of colorectal cancer patients. This study was conducted to develop and evaluate the usability of a smartphone-based application for the self-management of patients with colorectal cancer. METHODS: We developed an Android-based application called "Colorectal Cancer Along (ColorectAlong)" for the self-management of colorectal cancer. Seventeen patients, from the Cancer Institute of Iran, evaluated the usability of this application for 2 weeks using a standard questionnaire for user interaction satisfaction (QUIS) version 5.5. The obtained data was analyzed using SPSS software version 26. RESULTS: The ColorectAlong application was developed with features covering several areas of colorectal cancer self-management. The areas available for evaluation in the app are patient profile; medication management; nutrition and diet management; pain management; mental health; smoking cessation and alcohol reduction; educational content; and adding reminders, questions, and notes. Our results indicate that the usability of this application is good with an average QUIS score of 8.03 out of 9. CONCLUSION: The ColorectAlong application can improve self-management, assist patients, and facilitate access to required information without the need for internet access.

Barriers and facilitators for disease registry systems: a mixed-method study.

BACKGROUND: A Disease Registry System (DRS) is a system that collects standard data on a specific disease with an organized method for specific purposes in a population. Barriers and facilitators for DRSs are different according to the health system of each country, and identifying these factors is necessary to improve DRSs, so the purpose of this study was to identify and prioritize these factors. METHODS: First, by conducting 13 interviews with DRS specialists, barriers and facilitators for DRSs were identified and then, a questionnaire was developed to prioritize these factors. Then, 15 experts answered the questionnaires. We prioritized these factors based on the mean of scores in four levels including first priority (3.76-5), second priority (2.51-3.75), third priority (1.26-2.50), and the fourth priority (1-1.25). RESULTS: At first, 139 unique codes (63 barriers and 76 facilitators) were extracted from the interviews. We classified barriers into 9 themes, including management problems (24 codes), data collection-related problems (8 codes), poor cooperation/coordination (7 codes), technological problems and lack of motivation/interest (6 codes for each), threats to ethics/data security/confidentiality (5 codes), data quality-related problems (3 codes), limited patients' participation and lack of or non-use of standards (2 codes for each). We also classified facilitators into 9 themes including management facilitators (36 codes), improving data quality (8 codes), proper data collection and observing ethics/data security/confidentiality (7 codes for each), appropriate technology (6 codes), increasing patients' participation, increasing motivation/interest, improving cooperation/coordination, and the use of standards (3 codes for each). The first three ranked barriers based on mean scores included poor stakeholder cooperation/coordination (4.30), lack of standards (4.26), and data quality-related problems (4.06). The first three ranked facilitators included improving data quality (4.54), increasing motivation/interest (4.48), and observing ethics/data security/confidentiality (4.36). CONCLUSION: Stakeholders' coordination, proper data management, standardization and observing ethics, security/confidentiality are the most important areas for planning and investment that managers must consider for the continuation and success of DRSs.

Prediction of neonatal deaths in NICUs: development and validation of machine learning models.

BACKGROUND: Prediction of neonatal deaths in NICUs is important for benchmarking and evaluating healthcare services in NICUs. Application of machine learning techniques can improve physicians' ability to predict the neonatal deaths. The aim of this study was to present a neonatal death risk prediction model using machine learning techniques. METHODS: This study was conducted in Tehran, Iran in two phases. Initially, important risk factors in neonatal death were identified and then several machine learning models including Artificial Neural Network (ANN), decision tree (Random Forest (RF), C5.0 and CHART tree), Support Vector Machine (SVM), Bayesian Network and Ensemble models were developed. Finally, we prospectively applied these models to predict neonatal death in a NICU and followed up the neonates to compare the outcomes of these neonates with real outcomes. RESULTS: 17 factors were considered important in neonatal mortality prediction. The highest Area Under the Curve (AUC) was achieved for the SVM and Ensemble models with 0.98. The best precision and specificity were 0.98 and 0.94, respectively for the RF model. The highest accuracy, sensitivity and F-score were achieved for the SVM model with 0.94, 0.95 and 0.96, respectively. The best performance of models in prospective evaluation was for the ANN, C5.0 and CHAID tree models. CONCLUSION: Using the developed machine learning models can help physicians predict the neonatal deaths in NICUs.

Effect of Implementation of Picture Archiving and Communication System on Radiologist Reporting Time and Utilization of Radiology Services: A Case Study in Iran.

We aimed to determine the effect of PACS implementation on the radiologist reporting time and utilization of radiology services in a hospital in Iran. This study was conducted in three 6-month periods (before PACS implementation, immediately after the implementation, and 1 year after implementation). Data related to reporting time of CT examinations were collected from 5074 patients with 6613 examinations and compared using the Friedman test. The utilization rate of radiology services was conducted about CT scans and radiographies. Therefore, 17,862 patients with 36,321 radiographies and 7155 patients with 10,571 CT scans were evaluated, and the ratio of the number of examinations to the number of patients and patient days was compared in three periods. The mean of reporting time on CT scan examinations in the period of immediately after PACS was changed compared to the period of before PACS from 13.05 to 24.18 days and compared to 1 year after PACS implementation, to 4.14 days (P value < 0.05). The utilization rate in CT scans, 1 year after PACS increased at least 10% to 25% compared to the immediately after PACS and before implementing PACS. The utilization rate in radiographies, 1 year after PACS, increased at least 16% to 78% compared to the immediately after PACS and before PACS implementation. In conclusion, the mean of the radiologist reporting time for CT scans is significantly decreased by PACS in the long-term. Additionally, the utilization rate of radiology services is increased in the short- and long-term after PACS implementation in most examinations of CT scan and radiography examinations.

Implementation of regional COVID-19 registry in Hormozgan (RCovidRH), Iran: Rationale and study protocol.

Background: The rapid outbreak of COVID-19 has resulted in a global pandemic in 2020. Information sources such as disease registries through accessing quality, valid, accurate, and timely data empower researchers and health authorities to study and develop appropriate actions. Our study describes the protocol for implementation of regional COVID-19 registry in Hormozgan province (RCovidRH). Methods: We followed approved phases for the development of RCovidRH to cover the population in Hormozgan. Missioned to develop and implement the protocol, the registry's steering committee was made up of 10 members from subject fields of the registry at the core and 5 subgroups. The main purpose of the registry is to provide a comprehensive information profile of demographic, clinical, laboratory, imaging, and treatment data of confirmed and probable COVID-19 patients in Hormozgan. The data is retrospectively and prospectively collected. Case report form (CRF) was mainly based on International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) CRF. A web-based, 2-language software was also developed to facilitate data collection and storage. Data analysis is to be conducted with collaboration of clinical physicians, data-mining specialists, and epidemiologists after reaching appropriate sample size. Results: We included data related to demographic and identification, onset and admission, signs and symptoms at hospital admission, admission signs and symptoms, comorbidities, pathogen testing, assessment, laboratory, imaging, complications, treatment and medication, and outcomes. We found this registry was limited by incomplete clinical data for small fraction of outpatients, incomplete or inaccurate address by referred people due to fear of social rejection, delay in data entry at the facilities due to workload. Conclusion: This registry via organizing clinical and epidemiological COVID-19 data increases the potentiality of joint studies. Recognition and coordination of a registry is highly important to solve its limitations to collect data. Other universities and provinces can apply our model to develop COVID-19 registries or data sets for this disease.

Monitoring of external predisposing factors for Diabetic Foot: A literature review and physicians' perspectives.

Background: Diabetic foot is one of the most important complications of diabetes caused by the existence of some destructive factors in different anatomical locations of feet. Management and monitoring of these factors are very important to decrease or avoid ulcerating lesions of the foot. The purpose of this study is to identify and introduce the predisposing factors and anatomical locations associated with these destructive factors. Methods: First, we conducted a comprehensive review of different databases to identify the factors and associated anatomical locations from the previous studies. Then, we designed a questionnaire and invited physicians and specialists to express their perspectives on these factors and locations. The data were analyzed using SPSS version 23. Frequency, percentage, mean and standard deviation of these variables were calculated. Results: Based on the literature review, four factors, including pressure, moisture and sweat, temperature, and acceleration were identified as factors destructive to the tissues of the diabetic foot and worsen ulcers. The view of specialists approved the results of the literature review. Besides, there was an insignificant difference between the results of the literature review and the specialists' view in terms of anatomical locations that need to be continuously monitored. Conclusion: Monitoring the pressure in heel, first metatarsal, and first metatarsal head; moisture and sweat under the fingers, hallux and heels as well as the temperature at the first metatarsal, first metatarsal head, and the third metatarsal head are important in preventing ulceration, destructing the foot tissue, and accelerating the treatment process.

Improving Cancer Literacy for the Deaf Using Deaf-Tailored Educational Interventions: a Review of the Literature.

To date, there have been many strategies, including educational interventions, for cancer prevention and control, but most of them are not deaf-tailored ones. This narrative review aimed to examine cancer educational programs to improve the deaf individuals' knowledge and attitude toward cancer. The design of this study is a narrative review. We searched ISI Web of Science, Scopus, Science Direct, and MEDLINE/PubMed using the following search strategy: ("cancer education" AND "deaf") OR ("cancer" AND "deaf" AND literacy). Publication years ranged from 1983 to 2016 for studies on cancer educational interventions for the deaf. Included studies were analyzed regarding research methodologies, types of intervention, and major findings. In total, 12 included studies were classified into three research methodologies. Although short-term and long-term knowledge improvement has been reported, since there is limited evidence on the types of cancer-related educational interventions and there are insufficient studies, longterm effectiveness of educations in improving cancer knowledge of the deaf has to be reported cautiously. Current deaf-tailored education interventions are limited, but included functional features which facilitate communicating cancer health information to the deaf community. In fact, cancer literacy might improve considering deaf community preferences such as using a short open caption, sign language, and plain language in educational interventions, but further research is recommended.

Nurses' knowledge and performance of the patients' bill of rights.

BACKGROUND: Observance of the patients' bill of rights is one of the main features of moral codes in hospitals. In this regard, nurses bear great responsibility because they spend a long time with patients. Therefore, the continuous evaluation of the nurses' performance and assessing their knowledge about the patients' bill of rights are a need. OBJECTIVES: We aimed to determine the nurses' awareness of the patients' rights and measure their performance in this regard. Research design and participants: This cross-sectional study was carried out in 2013. To measure the nurses' knowledge and performance, 250 nurses and 300 patients were surveyed. The participants were selected randomly from five teaching hospitals in Tehran, Iran. Two questionnaires, one for nurses (17 questions) and the other for patients (11 questions), were applied. The data were analyzed in SPSS software using descriptive and inferential statistics. Ethical consideration: The research protocol was submitted and approved by the research and ethics committees of the participating hospitals. Additionally, the consent of all of the participants was obtained before the study. FINDINGS: The mean score of the nurses' knowledge regarding the patients' rights was acceptable (69.85 ± 11.7 of 85). Furthermore, the mean score of nurses' performance in observing the patients' rights was relatively acceptable (11.2 ± 4.6 of 22). More experienced and educated nurses had higher knowledge regarding the patients' rights, and patients with higher education level or experience of being hospitalized were less satisfied with the nurses' performance. CONCLUSION: Nurses' knowledge of the patients' bill of rights was acceptable; however, observance of the patients' rights was not. It seems that notification of the patients' bill of rights has increased the nurses' awareness of the patients' rights, although improvement of the nurses' performance needs more extensive measures.

Online survey software as a data collection tool for medical education: A case study on lesson plan assessment.

Background: There are no general strategies or tools to evaluate daily lesson plans; however, assessments conducted using traditional methods usually include course plans. This study aimed to evaluate the strengths and weaknesses of online survey software in collecting data on education in medical fields and the application of such softwares to evaluate students' views and modification of lesson plans. Methods: After investigating the available online survey software, esurveypro was selected for assessing daily lesson plans. After using the software for one semester, a questionnaire was prepared to assess the advantages and disadvantages of this method and students' views in a cross-sectional study. Results: The majority of the students (51.7%) rated the evaluation of classes per session (lesson plans) using the online survey as useful or very useful. About 51% (n=36) of the students considered this method effective in improving the management of each session, 67.1% (n=47) considered it effective in improving the management of sessions for the next semester, and 51.4% (n=36) said it had a high impact on improving the educational content of subsequent sessions. Finally, 61.4% (n=43) students expressed high and very high levels of satisfaction with using an online survey at each session. Conclusion: The use of online surveys may be appropriate to improve lesson plans and educational planning at different levels. This method can be used for other evaluations and for assessing people's opinions at different levels of an educational system.

The effect of an android-based application on the knowledge of the caregivers of children with cerebral palsy.

Background: Mobile health and e-learning may have a significant impact on training patients, physicians, students and caregivers. This study aimed to evaluate the effect of using an educational mobile application on the knowledge of the caregivers of children with cerebral palsy (CP). Methods: We used a previously developed mobile application. The knowledge of 17 caregivers of children with CP (including parents) about the daily care of their children was evaluated through a self-assessment and a test with multiple-choice and true-false questions. Next, the application, which included several educational modules for the daily care of the children with CP, was given to the caregivers to use continually for two months. After this period, the knowledge of the caregivers was evaluated by the same tools. Data analysis was performed by SPSS-16, using paired-sample t-test or Wilcoxon test. Results: The effect of the use of this educational application on the knowledge of caregivers in all childcare domains, except for eating, was reported to be significant (p<0.05). Furthermore, the results of the multiplechoice test revealed that this application increased the knowledge of caregivers in all domains except playing (p<0.05). Conclusion: Training through novel technologies such as Smartphone along with their applications can improve the knowledge of caregivers about the daily care of children with cerebral palsy.

Perceived Quality of Informed Refusal Process: A Cross-Sectional Study from Iranian Patients' Perspectives.

Patients have the right to refuse their treatment; however, this refusal should be informed. We evaluated the quality of the informed refusal process in Iranian hospitals from patients' viewpoints. To this end, we developed a questionnaire that covered four key aspects of the informed refusal process including; information disclosure, voluntariness, comprehension, and provider-patient relationship. A total of 284 patients who refused their treatment from 12 teaching hospitals in the Isfahan Province, Iran, were recruited and surveyed to produce a convenience sample. Patients' perceptions about the informed refusal process were scored and the mean scores of the four components were calculated. The findings showed that the practice of information disclosure (9.6 ± 6.4 out of 22 points) was perceived to be moderate, however, comprehension (2.3 ± 1.4 out of 4 points), voluntariness (8.7 ± 1.5 out of 12 points) and provider-patient relationship (10.2 ± 5.2 out of 16 points) were perceived to be relatively good. We found that patients, who refused their care before any treatment had commenced, reported a lower quality of information disclosure and voluntariness. Patients informed by nurses and those who had not had a previous related admission, reported lower scores for comprehension and relationship. In conclusion, the process of obtaining informed refusal was relatively satisfactory except for levels of information disclosure. To improve current practices, Iranian patients need to be better informed about; different treatment options, consequences of treatment refusal, costs of not continuing treatment and follow-ups after refusal. Developing more informative refusal forms is needed.

Prioritizing factors influencing nurses' satisfaction with hospital information systems: a fuzzy analytic hierarchy process approach.

Our aim was to use the fuzzy analytic hierarchy process approach to prioritize the factors that influence nurses' satisfaction with a hospital information system. First, we reviewed the related literature to identify and select possible factors. Second, we developed an analytic hierarchy process framework with three main factors (quality of services, of systems, and of information) and 22 subfactors. Third, we developed a questionnaire based on pairwise comparisons and invited 10 experienced nurses who were identified through snowball sampling to rate these factors. Finally, we used Chang's fuzzy extent analysis method to compute the weights of these factors and prioritize them. We found that information quality was the most important factor (58%), followed by service quality (22%) and then system quality (19%). In conclusion, although their weights were not similar, all factors were important and should be considered in evaluating nurses' satisfaction.

Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.