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Aim: To design, develop, and evaluate a clinical decision support system (CDSS) to decrease the adverse effects of treatment in Childhood Leukemia. Method: To achieve high accuracy, a knowledge base CDSS was designed based on the viewpoints provided by experts and clinical references. The system has the capability of medical report documentation, drug prescription, dosage determination, and displaying patients’ medical history. It is also able to eliminate the problems caused by prescription and drug dosage errors by considering the patient’s status. Results: By documenting the patients’ medical report, the system can provide comprehensive information and precise recommendations about the future readmission and drug dosage accuracy. The system achieved 94.5% sensitivity, 93% accuracy, and 80% specificity in the evaluation phase. Conclusion: Application of such systems in the process of prescribing drugs can improve the quality of patient care by reducing the probability of pharmaceutical errors.

Background and Purpose: Diagnosis of multiple sclerosis (MS) is complicated because of the lack of definite factor. Decision support systems are expert systems which help physicians in decision-making process. First step in designing the system is identification of a minimum dataset (MDS). This study aimed to determine minimum dataset required to design diagnosis decision support system.Materials and Methods: This research was a descriptive cross-sectional study. Data were gathered from medical guideline approved by Ministry of Health, Treatment and Medical Training, Multiple Sclerosis diagnosis, international guideline of Royal college of England, and McDonald Diagnostic criteria. Data collection tool was a designed checklist consisting of 100 items provided to 25 neurologists and MS fellowships of medical universities and private clinics in Iran.Results: Out of 100 designed information’s items, 10 items were omitted due to CVR less than 0.49. Employment status items, history of MS in 3rd grade relatives, history of viral diseases, orbital MRI, optical coherence tomography, brain CT-scan, ESR, CRP, visually evoked potentials, delay duration of P100 for each eyes are all examples of information elements that have been omitted.Conclusion: Determining the minimum dataset related to MS is an important step in designing diagnosis decision support system and medication follow-up. Therefore, MDSs can help those responsible for gathering standard information of patients with Multiple Sclerosis (MS), and causes improvement in management of information for this disease.

Wearable smart sensors are emerging technology for daily monitoring of vital signs with the reducing discomfort and interference with normal human activities. The main objective of this study was to review the applied wearable smart sensors for disease control and vital signs monitoring in epidemics outbreaks. A comprehensive search was conducted in Web of Science, Scopus, IEEE Library, PubMed and Google Scholar databases to identify relevant studies published until June 2, 2020. Main extracted specifications for each paper are publication details, type of sensor, disease, type of monitored vital sign, function and usage. Of 277 articles, 11 studies were eligible for criteria. 36% of papers were published in 2020. Articles were published in 10 different journals and only in the Journal of Medical Systems more than one article was published. Most sensors were used to monitor body temperature, heart rate and blood pressure. Wearable devices (like a helmet, watch, or cuff) and body area network sensors were popular types which can be used monitoring vital signs for epidemic trending. 65% of total papers (n = 6) were conducted by the USA, Malaysia and India. Applying appropriate technological solutions could improve control and management of epidemic disease as well as the application of sensors for continuous monitoring of vital signs. However, further studies are needed to investigate the real effects of these sensors and their effectiveness.

Reproductive health is vital for human and infertility is also one of the most important challenges in the reproductive system. Infertility is one of the most common chronic health disorders, regardless of age. The Minimum Data Set (MDS) helps to manage infertility by monitoring and evaluating infertility interventions based on collecting data. The development of MDS is an essential objective in order to implement an infertility monitoring system for the creation of standardized and effective data management through the provision of comprehensive and identical data elements for infertility. This is a descriptive cross-sectional study conducted in 2017. The data has been collected from infertility clinics in the world, as well as WHO, CDC, ASRM, and ESHRE reports. In order to decide on data elements, the Delphi technique was used using a questionnaire that contained data elements which were distributed among 12 experts including one reproductive endocrinology and infertility fellow, six obstetrician-gynecologists, two reproductive biologists, two urologists and one community medicine specialist using the 5 point Likert scale. The questionnaire was divided into two categories: managerial and clinical, each with 4 sections, and 60 and 940 data elements, respectively. MDS is an essential tool for evaluating the infertility process. Using this tool will provide an opportunity to develop a set of quality care criteria that can be used to ensure the quality of infertility care.

Background In recent years, mobile-based applications have become important technologies to the delivery of healthcare around the world. Mobile-based self-management systems with standard features for providing, evaluating, and improving HIV care are significantly required in developing countries. Objective To determine the common elements of a mobile-based self-management system for people living with HIV (PLWH). Methods This cross-sectional study was done in two main phases in 2017. In the first phase, a review was conducted in relevant databases such as; PubMed, Scopus, Up To Date, and Web of Science. The keywords used to search for resources were as follows; Self-care, Self-management, Data elements, Minimum data set, Mobile application, Mobile health, and HIV/AIDS. In the second phase, the infectious diseases specialists and health information managers affiliated with Tehran University of Medical Sciences were consulted to score identified elements by a questionnaire. Frequency and mean of collected data were calculated using SPSS software (version 19). Results By full-text reviewing of 9 related articles, the identified elements were justified in 3 main categories and 37 subcategories including: clinical data elements (17), technical capabilities (12) and demographic data elements (8). According to the findings, among the clinical category, 11 data elements were selected by the statistical population. Among the identified technical capabilities, 11 features were selected. Moreover, 6 data elements were selected as the demographic category. Conclusion We obtained data elements and technical capabilities of a mobile-based self-management system for people living with HIV. Using these elements and features, designing of self-management system architecture will be possible. Self-management skills of PLWH and their communication with healthcare providers will improve by using this system.

Background Colorectal cancer survivors face multiple challenges after discharge. eHealth may potentially support them by providing tools such as smartphone apps. They have lots of capabilities to exchange information and could be used for remote monitoring of these patients. Objective In this study, we addressed the required features for apps designed to follow up colorectal cancer patients based on survivors’ and clinical experts’ views. Methods A mixed methods study was conducted. Features of related apps were extracted through the literature; the features were categorized, and then, they were modified. A questionnaire was designed containing the features listed and prioritized based on the MoSCoW (Must have, Should have, Could have, Won’t have) technique and an open question for each category. The link to the questionnaire was shared among clinical experts in Iran. The answers were analyzed using the content validity ratio (CVR), and based on the value of this measure, the minimum feature set of a monitoring app to follow up patients with colorectal cancer was addressed. In addition, a telephone interview with colorectal cancer survivors was conducted to collect their viewpoints regarding a remote monitoring system for colorectal cancer cases. Results The questionnaire contained 10 sections evaluating 9 categories of features. The questionnaire was completed by 18 experts. The minimum set of features in the app was identified as patient information registration, sign and symptom monitoring, education, reminders, and patient evaluation (0.42 < CVR < 0.85). Features including physical activity, personalized advice, and social network did not achieve the minimum score (–0.11 < CVR < 0.39). We interviewed 9 colorectal cancer survivors. Information registration, sign and symptom monitoring, education, and personalized advice were the features with high priority from the survivors’ perspectives. Scheduling, shopping, and financial support features were emphasized by survivors in the interview. Conclusions The requirement set could be used to design an app for the targeted population or patients affected by other cancers. As the views from both survivors and clinical experts were considered in this study, the remote system may more adequately fulfill the need for follow-up of survivors. This eases the patients’ and health care providers’ communication and interaction.

Objectives Monitoring heart failure patients through continues assessment of sign and symptoms by information technology tools lead to large reduction in re-hospitalization. Agent technology is one of the strongest artificial intelligence areas; therefore, it can be expected to facilitate, accelerate, and improve health services especially in home care and telemedicine. The aim of this article is to provide an agent-based model for chronic heart failure (CHF) follow-up management. Methods This research was performed in 2013-2014 to determine appropriate scenarios and the data required to monitor and follow-up CHF patients, and then an agent-based model was designed. Results Agents in the proposed model perform the following tasks: medical data access, communication with other agents of the framework and intelligent data analysis, including medical data processing, reasoning, negotiation for decision-making, and learning capabilities. Conclusions The proposed multi-agent system has ability to learn and thus improve itself. Implementation of this model with more and various interval times at a broader level could achieve better results. The proposed multi-agent system is no substitute for cardiologists, but it could assist them in decision-making.

Objectives Given the importance of the follow-up of chronic heart failure (CHF) patients to reduce common causes of re-admission and deterioration of their status that lead to imposing spiritual and physical costs on patients and society, modern technology tools should be used to the best advantage. The aim of this article is to explain key points which should be considered in designing an appropriate multi-agent system to improve CHF management. Methods In this literature review articles were searched with keywords like multi-agent system, heart failure, chronic disease management in Science Direct, Google Scholar and PubMed databases without regard to the year of publications. Results Agents are an innovation in the field of artificial intelligence. Because agents are capable of solving complex and dynamic health problems, to take full advantage of e-Health, the healthcare system must take steps to make use of this technology. Key factors in CHF management through a multi-agent system approach must be considered such as organization, confidentiality in general aspects and design and architecture points in specific aspects. Conclusions Note that use of agent systems only with a technical view is associated with many problems. Hence, in delivering healthcare to CHF patients, considering social and human aspects is essential. It is obvious that identifying and resolving technical and non-technical challenges is vital in the successful implementation of this technology.

In the new millennium people are facing serious challenges in health care, especially with increasing non- communicable diseases (NCD). One of the most common NCDs is cancer which is the leading cause of death in developed countries and in developing countries is the second cause of death after heart diseases. Cancer registry can make possible the analysis, comparison and development of national and international cancer strategies and planning. Information technology has a vital role in quality improvement and facility of cancer registries. With the use of IT, in addition to gaining general benefits such as monitoring rates of cancer incidence and identifying planning priorities we can also gain specific advantages such as collecting information for a lifetime, creating tele medical records, possibility of access to information by patient, patient empowerment, and decreasing medical errors. In spite of the powerful role of IT, we confront various challenges such as general problems, like privacy of the patient, and specific problems, including possibility of violating patients rights through misrepresentation, omission of human relationships, and decrease in face to face communication between doctors and patients. By implementing appropriate strategies, such as identifying authentication levels, controlling approaches, coding data, and considering technical and content standards, we can optimize the use of IT. The aim of this paper is to emphasize the need for identifying positive and negative effects of modern IT on cancer registry in general and specific aspects as an approach to cancer care management.

Malignancy-associated mortality, decreased productivity, and spiritual, social and physical burden in cancer patients and their families impose heavy costs on communities. Therefore cancer prevention, early detection, rapid diagnosis and timely treatment are very important. Use of modern methods based on information technology in cancer can improve patient survival and increase patient and health care provider satisfaction. Robot technology is used in different areas of health care and applications in surgery have emerged affecting the cancer treatment domain. Computerized and robotic devices can offer enhanced dexterity by tremor abolition, motion scaling, high quality 3D vision for surgeons and decreased blood loss, significant reduction in narcotic use, and reduced hospital stay for patients. However, there are many challenges like lack of surgical community support, large size, high costs and absence of tactile and haptic feedback. A comprehensive view to identify all factors in different aspects such as technical, legal and ethical items that prevent robotic surgery adoption is thus very necessary. Also evidence must be presented to surgeons to achieve appropriate support from physicians. The aim of this review article is to survey applications, opportunities and barriers to this advanced technology in patients and surgeons as an approach to improve cancer care.

OBJECTIVE Intelligent data analysis has ability to prepare and present complex relations between symptoms and diseases, medical and treatment consequences and definitely has significant role in improving follow-up management of chronic heart failure (CHF) patients, increasing speed ​​and accuracy in diagnosis and treatments; reducing costs, designing and implementation of clinical guidelines. THE AIM The aim of this article is to describe intelligent data analysis methods in order to improve patient monitoring in follow and treatment of chronic heart failure patients as the best approach for CHF follow up management. METHODS Minimum data set (MDS) requirements for monitoring and follow up of CHF patient designed in checklist with six main parts. All CHF patients that discharged in 2013 from Tehran heart center have been selected. The MDS for monitoring CHF patient status were collected during 5 months in three different times of follow up. Gathered data was imported in RAPIDMINER 5 software. RESULTS Modeling was based on decision trees methods such as C4.5, CHAID, ID3 and k-Nearest Neighbors algorithm (K-NN) with k=1. Final analysis was based on voting method. Decision trees and K-NN evaluate according to Cross-Validation. CONCLUSION Creating and using standard terminologies and databases consistent with these terminologies help to meet the challenges related to data collection from various places and data application in intelligent data analysis. It should be noted that intelligent analysis of health data and intelligent system can never replace cardiologists. It can only act as a helpful tool for the cardiologist's decisions making.