The relationship of COVID-19 severity with laboratory findings and neutrophil-to-lymphocyte ratio in patients admitted to a large teaching hospital in Iran: A cross-sectional study.

Background and Aims: COVID-19 patients might be admitted to the hospital based on their clinical manifestations or to the intensive care unit (ICU) due to the severity of their symptoms or critical situation. Our main objective was to investigate clinical and demographic factors influencing COVID-19 patients' admission to the ICU and length of stay (LOS) using extracted data from the hospital information systems in Iran. Methods: The data of hospitalized patients with confirmed COVID-19 were retrieved from the health information system of Imam Khomeini Hospital Complex, Tehran, Iran between March 2020 and February 2022. The primary outcome was the ICU admission, and the secondary outcome was the LOS. The correlation analysis between laboratory findings and demographic data with ICU admission and LOS was done using SPSS 21.0, and p < 0.05 was considered significant. Results: Of all the 4156 patients, 2391 (57.5%) were male and the mean age was 58.69 ± 8.19 years. Of these, 9.5% of patients were admitted to ICU at any time point during their hospital stay. Age and laboratory variables such as neutrophil-to-lymphocyte ratio (NLR), ALT (U/L), albumin (g/dL), plasma glucose (mg/dL), ferritin levels (ng/mL), and phosphorous levels (mg/dL) shown the significant relationship with ICU admission. Also, being a smoker and having hypoxemia had a significant relationship with longer stays in the hospital. In this study, we validated a cut-off value of 4.819 for NLR, calculated at hospitalization, as a useful predictor of disease progression and occurrence of serious clinical outcomes, such as ICU admission. Conclusion: The study examined various clinical factors associated with ICU admission in COVID-19 patients. The findings suggest that certain factors can increase the risk of ICU admission and influence the length of hospital stay which should be focused in future studies.

Conceptual framework for establishing twins prevention and continuous health promotion programme: a qualitative study.

BACKGROUND: Twin registries and cohorts face numerous challenges, including significant resource allocation, twins' recruitment and retention. This study aimed to assess expert feedback on a proposed pragmatic idea for launching a continuous health promotion and prevention programme (HPPP) to establish and maintain twin cohorts. DESIGN: A qualitative study incorporating an inductive thematic analysis. SETTING: Tehran University of Medical Sciences. PARTICIPANTS: Researchers with expertise in twin studies participated in our study. ANALYSIS AND DESIGN: Expert opinions were gathered through focus group discussions (FGDs). Thematic analysis was employed to analyse the findings and develop a model for designing a comprehensive, long-term health promotion programme using ATLAS.ti software. Additionally, a standardised framework was developed to represent the conceptual model of the twin HPPP. RESULTS: Eight FGDs were conducted, involving 16 experts. Thematic analysis identified eight themes and seven subthemes that encompassed the critical aspects of a continuous monitoring programme for twin health. Based on these identified themes, a conceptual framework was developed for the implementation of an HPPP tailored for twins. CONCLUSION: This study presented the initial endeavour to establish a comprehensive and practical solution in the form of a continuous HPPP designed to tackle the obstacles of twins' cohorts.

Concordance between ARISCAT risk score and cardiopulmonary exercise test values in risk prediction of postoperative pulmonary complications of major abdominal surgeries in a tertiary cancer hospital: A cross-sectional study.

Background and Aims: Pulmonary complications are common after surgery. They include vascular thrombosis, pneumonia, respiratory failure (RF), and pain-related atelectasis. There are a number of models to predict the risk of postoperative respiratory events other than thrombosis. The aim of this study was to explore the correlation of assess respiratory risk in surgical patients in Catalonia (ARISCAT) scoring and cardiopulmonary exercise test (CPET) values in prediction of postoperative pulmonary complications (PPCs). Methods: Cancer patients referred to a tertiary hospital for elective major abdominal surgeries were studied. Patients were evaluated by ARISCAT score and then CPET was performed to determine the risk of surgery based on maximal oxygen consumption (VO2) value. Patients were followed for RF occurrence up to 72 h after surgery. Finally, the concordance of ARISCAT score and CPET values was evaluated in risk prediction of PPCs. Results: The results showed that parameters VO2, ARISCAT score, and anaerobic threshold could predict postoperative RF. Of these parameters, ARISCAT showed the highest sensitivity (100%) and the highest specificity (90.5%) compared with other parameters (Youden's J statistic = 0.905). However, VO2 value showed the highest validity. The percentage of agreement between different subgroups (low, medium, and high) of both criteria (VO2 and ARISCAT) was equal to 81.45% (p < 0.001) and the Ï° coefficient of the given weight was equal to 0.54 (p < 0.001), indicating a good agreement between these two criteria. Conclusion: ARISCAT scoring showed high sensitivity and specificity to PPCs in cancer patients and good correlation with CPET value for prediction of PPCs. Therefore, it is a reliable and robust risk prediction tool in major abdominal surgeries on cancer patients.

Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow.

BACKGROUND: Accurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates. METHOD: An iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design. RESULTS: According to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7). CONCLUSION: Through a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.

Feasibility study and determination of prerequisites of telecare programme to enhance patient management in lung transplantation: a qualitative study from the perspective of Iranian healthcare providers.

BACKGROUND: Non-adherence to treatment plans, follow-up visits and healthcare advice is a common obstacle in the management of lung transplant patients. This study aims to investigate experts' views on the needs and main aspects of telecare programmes for lung transplantation. DESIGN: A qualitative study incorporating an inductive thematic analysis. SETTING: Lung transplant clinic and thoracic research centre. PARTICIPANTS: Clinicians: four pulmonologists, two cardiothoracic surgeons, two general physicians, two pharmacotherapists, one cardiologist, one nurse and one medical informatician. METHOD: This study adopted a focus group discussion technique to gather experts' opinions on the prerequisites and features of a telecare programme in lung transplantation. All interviews were coded and combined into main categories and themes. Thematic analysis was performed to extract the key concepts using ATLAS.Ti. Ultimately, all extracted themes were integrated to devise a conceptual model. RESULTS: Ten focus groups with 13 participants were conducted. Forty-six themes and subthemes were extracted through the thematic analysis. The main features of the final programme were extracted from expert opinions through thematic analysis, such as continuous monitoring of symptoms, drug management, providing a specific care plan for each patient, educating patients module, creating an electronic medical record to collect patient information, equipping the system with decision support tools, smart electronic prescription and the ability to send messages to the care team. The prerequisites of the system were summarised in self-care activities, clinician's tasks and required technologies. In addition, the barriers and benefits of using a telecare system to enhance the quality of care were determined. CONCLUSION: Our investigation recognised the main factors that must be considered to design a telecare programme to provide ideal continuous care for lung transplant patients. Users should further explore the proposed model to support the development of telecare interventions at the point of care.

The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease.

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.

Requirements, Challenges, and Key Components to Improve Onboard Medical Care Using Maritime Telemedicine: Narrative Review.

Introduction: Telemedicine has been able to bring healthcare services to all people in far locations such as the sea. Our main objective was to overview the main features, challenges, and requirements of applying telemedicine at sea. Methods: The electronic search includes all types of papers published in English. It was performed in four databases with keywords to Feb 2023. Next, main categories were defined to extract major concepts. By mapping extracted themes, maritime telemedicine concepts were represented in two conceptual models. Results: After screening the papers based on title and abstract, 18 articles remained. They can be divided into 13 categories based on their clinical domains. Out of 18 reviewed articles, six articles were published in 2020. The greatest number of studies with five articles was conducted in France. Evidence showed that maritime telemedicine service can be provided to all kinds of ships. Regarding clinical domains, the greatest demand belonged to primary care problems (5 papers) and general health assessment (4 papers). Challenges were divided into four main categories. Moreover, the required services and equipment in four categories were described too. Finally, a conceptual model is represented for providing telemedicine services at sea using satellite Internet. Conclusion: Despite the existing challenges in providing the required equipment and resources for the implementation of maritime medicine, it has an important role in providing better care for seafarers without time limitations.

Iranian school-aged twin registry: preliminary reports and project progress.

BACKGROUND: National Persian school-aged twin registry was established to provide a platform for twin studies. In this report, we describe defining registry characteristics, database design, and preliminary results regarding gathered data in the first phase of the registry program. METHOD: Through focus group discussions, the required data elements to design the database and data collection process were defined. First, a list of twins in school-aged groups was retrieved from the electronic database of the Ministry of Education. Tehran schools were selected for the first phase of our registry. Standard "Pea-in-Pods" questionnaire and twins' similarity questionnaires were filled out by the parents themselves in addition to demographic information. Data were analyzed using SPSS v.22. RESULTS: The first national school-aged twin registry was established in 2018. Firstly, the required data sets and data collection process were defined using focus group discussions. At the country level, the initial information on 189,738 students was retrieved from the national database of the Ministry of Education. They were born between 2003 and 2017, of which 94,997 are boys (50.1%) and 94,741 are girls (49.9%). Of them, a total of 5,642 pairs of school-aged twins participated in the first phase of our program. Our sample size comprised 9772 twins, 906 triples, and 92 quadruplets. The analysis of the zygosity questionnaire showed that 14% of twin pairs were identified as monozygotic twins. CONCLUSION: Recruiting school-aged twins through school health assistants leads to high enrollment and decreasing costs for the twin registry. The study showed a high rate of dizygotic twins that need to be verified by twin bio-sample in the next phase of studies.

The application of wearable smart sensors for monitoring the vital signs of patients in epidemics: a systematic literature review.

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.

An evaluation of the quality of COVID-19 websites in terms of HON principles and using DISCERN tool.

BACKGROUND: As many people relied on information from the Internet for official scientific or academically affiliated information during the COVID-19 pandemic, the quality of information on those websites should be good. OBJECTIVE: The main purpose of this study was to evaluate a selection of COVID-19-related websites for the quality of health information provided. METHOD: Using Google and Yahoo, 36 English language websites were selected, in accordance with the inclusion criteria. The two tools were selected for evaluation were the Health on the Net (HON) Code and the 16-item DISCERN tool. RESULTS: Most websites (39%) were related to information for the public, and a small number of them (3%) concerned screening websites in which people could be informed of their possible condition by entering their symptoms. The result of the evaluation by the HON tool showed that most websites were reliable (53%), and 44% of them were very reliable. Based on the assessment results of the Likert-based 16-item DISCERN tool, the maximum and minimum values for the average scores of each website were calculated as 2.44 and 4.25, respectively. CONCLUSION: Evaluation using two widely accepted tools shows that most websites related to COVID-19 are reliable and useful for physicians, researchers and the public.

Prediction of COVID-19 Patients' Survival by Deep Learning Approaches.

Background: Despite many studies done to predict severe coronavirus 2019 (COVID-19) patients, there is no applicable clinical prediction model to predict and distinguish severe patients early. Based on laboratory and demographic data, we have developed and validated a deep learning model to predict survival and assist in the triage of COVID-19 patients in the early stages. Methods: This retrospective study developed a survival prediction model based on the deep learning method using demographic and laboratory data. The database consisted of data from 487 patients with COVID-19 diagnosed by the reverse transcription-polymerase chain reaction test and admitted to Imam Khomeini hospital affiliated to Tehran University of Medical Sciences from February 21, 2020, to June 24, 2020. Results: The developed model achieved an area under the curve (AUC) of 0.96 for survival prediction. The results demonstrated the developed model provided high precision (0.95, 0.93), recall (0.90,0.97), and F1-score (0.93,0.95) for low- and high-risk groups. Conclusion: The developed model is a deep learning-based, data-driven prediction tool that can predict the survival of COVID-19 patients with an AUC of 0.96. This model helps classify admitted patients into low-risk and high-risk groups and helps triage patients in the early stages.

Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review.

BACKGROUND: Machine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high volume of collected data in the lung transplant process makes it possible to extract hidden patterns by applying machine learning methods. Our study aims to investigate the application of machine learning methods in lung transplantation. METHOD: A systematic search was conducted in five electronic databases from January 2000 to June 2022. Then, the title, abstracts, and full text of extracted articles were screened based on the PRISMA checklist. Then, eligible articles were selected according to inclusion criteria. The information regarding developed models was extracted from reviewed articles using a data extraction sheet. RESULTS: Searches yielded 414 citations. Of them, 136 studies were excluded after the title and abstract screening. Finally, 16 articles were determined as eligible studies that met our inclusion criteria. The objectives of eligible articles are classified into eight main categories. The applied machine learning methods include the Support vector machine (SVM) (n = 5, 31.25%) technique, logistic regression (n = 4, 25%), Random Forests (RF) (n = 4, 25%), Bayesian network (BN) (n = 3, 18.75%), linear regression (LR) (n = 3, 18.75%), Decision Tree (DT) (n = 3, 18.75%), neural networks (n = 3, 18.75%), Markov Model (n = 1, 6.25%), KNN (n = 1, 6.25%), K-means (n = 1, 6.25%), Gradient Boosting trees (XGBoost) (n = 1, 6.25%), and Convolutional Neural Network (CNN) (n = 1, 6.25%). Most studies (n = 11) employed more than one machine learning technique or combination of different techniques to make their models. The data obtained from pulmonary function tests were the most used as input variables in predictive model development. Most studies (n = 10) used only post-transplant patient information to develop their models. Also, UNOS was recognized as the most desirable data source in the reviewed articles. In most cases, clinicians succeeded to predict acute diseases incidence after lung transplantation (n = 4) or estimate survival rate (n = 4) by developing machine learning models. CONCLUSION: The outcomes of these developed prediction models could aid clinicians to make better and more reliable decisions by extracting new knowledge from the huge volume of lung transplantation data.

Rapid implementation of telepharmacy service to improve patient-centric care and multidisciplinary collaboration across hospitals in a COVID era: A cross-sectional qualitative study.

Background and Aim: The COVID-19 pandemic forced healthcare systems to apply new technology-based solutions. The main objective of our study was to describe the conceptual model for rapid implementation of telepharmacy service and the main steps that should be considered. Method: In response to a limited number of on-site clinical pharmacy specialists and a lack of technology infrastructure, a cross-sectional telepharmacy program was established to support major teaching hospitals. A store and forward model of teleconsultation was employed using WhatsApp messenger to cover various aspects of multidisciplinary collaboration in COVID-19 management. All identifiable personal information was removed from all exchanged messages of collaborative consultations. The thematic analysis of consultations was performed to extract the main themes and subthemes that should be considered for designing future telepharmacy systems. Results: Through telepharmacy service, 600 intensive care unit teleconsultations for COVID-19 cases were conducted in the residence center and nonresidence centers. In total, 1200 messages were exchanged between specialists in 3 months. The average time taken to respond to a message was 1.30 h. Thematic analysis revealed four main concepts and 15 subconcepts that should be considered in telepharmacy consultations for COVID-19 management. Based on the extracted themes, a conceptual model for developing a telepharmacy program was devised. Conclusion: The results showed that by utilizing telehealth, clinical pharmacists could cover critically ill patients who need pharmacotherapy counseling through interdisciplinary collaboration. Moreover, the main features of our service that are represented through this survey can be employed by other researchers for developing telepharmacy services.

Telemedicine in lung transplant to improve patient-centered care: A systematic review.

OBJECTIVES: Long-term care combined with complex follow-up processes is among the essential needs of lung transplantation. Therefore, Telemedicine-based strategies can provide an effective approach for both patients and clinicians by applying remote patient monitoring. Hence, the main objective of this study was to investigate Telemedicine and telehealth usage in lung transplantation. METHOD: A systematic review was conducted in four databases using keywords. Eligible studies were all English papers that developed Telemedicine-based programs to enhance patient care in lung organ transplantation. The interventions were analyzed analysis to determine the main descriptive areas. The quality of the included articles was evaluated using Mixed Methods Appraisal Tool (MMAT) tool by two authors. RESULTS: Of the 261 retrieved articles, 27 met our inclusion criteria. Of these, 22 studies were devoted to the post-transplantation phase. All articles were published from 2002 to 2021 and the trend of publications has increased in recent years. Most of the studies were conducted in the United States and Canada. All eligible studies can be categorized into five types of Telemedicine interventions, 15 (55.56%) articles devoted to Telemonitoring, four (14.81%) for Teleconsultation, four (14.81%) articles for Telerehabilitation, three (11.11%) articles for Telespirometery, and one (3.70%) article were done regarding Tele-education. CONCLUSION: This integrated review provides researchers with a new understanding of Telemedicine-based care solutions. Findings show that remote patient care in lung transplantation includes various aspects, especially self-care improvement.

Medical Informaticians' Skills and Competencies to Achieve Roles and Opportunities: A Qualitative Study.

For Medical Informatics graduates, there is no compatibility between the training knowledge and skills at universities and the job requirements. This study aimed to determine the skills and competencies requirements for medical informatics graduates and possible job positions in an emerging discipline. This qualitative study was conducted using a questionnaire developed by the researchers. Nine independent medical informatics professionals assessed the initial draft of this tool to determine its face and content validity, and reliability. The questionnaire was distributed among 80 medical informaticians with a doctorate or a master's degree. In this study, items with an average of 4 and higher were confirmed; out of the 78 items, 66 were approved. The highest number of unapproved items was related to managerial knowledge and skills. Research knowledge, training skills, individual skills, technical capacities, specific skills in the health industry, and managerial skills are the main areas that graduates must learn. This survey can help develop a curriculum and job descriptions for medical informatics.

Pragmatic solutions to enhance self-management skills in solid organ transplant patients: systematic review and thematic analysis.

BACKGROUND: In organ transplantation, all patients must follow a complex treatment regimen for the rest of their lives. Hence, patients play an active role in the continuity of the care process in the form of self-management tasks. Thus, the main objective of our study was to investigate the pragmatic solutions applied by different studies to enhance adherence to self-management behaviors. METHOD: A systematic review was conducted in five databases from 2010 to August 2021 using keywords. Eligible studies were all English papers that developed self-management programs to enhance patient care in solid organ transplantation. The interventions were analyzed using thematic analysis to determine the main descriptive areas. The quality of the included articles was evaluated using the research critical appraisal program (CASP) tool. RESULTS: Of the 691 retrieved articles, 40 met our inclusion criteria. Of these, 32 studies were devoted to the post-transplantation phase. Five main areas were determined (e-health programs for telemonitoring, non-electronic educational programs, non-electronic home-based symptom-monitoring programs, electronic educational plans for self-monitoring, and Telerehabilitation) according to thematic analysis. Most studies (72.5%) declared that developed programs and applied solutions had a statistically significant positive impact on self-management behavior enhancement in transplant patients. CONCLUSION: The results showed that an effective solution for improving organ transplantation needs patient collaboration to address psychological, social, and clinical aspects of patient care. Such programs can be applied during candidate selection, waiting list, and after transplantation by putting the patient at the center of care.

Persian mobile health applications for COVID-19: A use case-based study.

BACKGROUND: Since the beginning of the COVID-19 outbreak, a significant number of mobile health apps have been created around the world and in Iran to help consequence reduction of this emerging pandemic. OBJECTIVES: This study aimed to review the characteristics of Persian Android and iOS apps related to COVID-19 and determine their use-cases based on a reference model. METHODS: This was a cross-sectional descriptive study conducted in three main steps. First, a systematic search was conducted via Iranian mobile apps' markets using the keywords related to COVID-19 in January 2021. Then, the retrieved apps were analyzed according to their characteristics. Finally, the use-cases of the given apps were determined and categorized based on a reference model. RESULTS: Based on our inclusion criteria, 122 apps were selected and evaluated. Most of these apps (87.7%) was free. Small proportions (5%) of reviewed apps have been developed with participation of clinical expert and half of the apps mentioned the references they used. Furthermore, about half of the apps (50.8%) were provided contact information of the developers. The studied apps were classified into four use-case major categories, including educational (98%), fulfilling a contextual need (18%), communicating, and/or sharing the information (0.83%), and health-related management (2%). CONCLUSION: The results showed that the Persian mobile apps for COVID-19 are not in a satisfying situation. Furthermore, although these apps are significant in quantity but in terms of use-cases, they are not widespread.

Determining chronic pain data elements as a first step towards improving quality of care and research in chronic pain.

BACKGROUND: Chronic pain is a significant clinical problem in the world.  There is still no quite effective treatment for this pain due to its complex nature. Timely retrieval of accurate and comprehensive information through organized clinical and epidemiological studies is an essential prerequisite for providing high quality clinical care and more accurate health planning.  We aimed to determine minimum set of data needed as a first step in design and development of a chronic pain registry system. MATERIALS AND METHODS: This descriptive-applied study was carried out in three phases; identifying necessary minimum data, preparing a primary minimum dataset, and surveying experts by questionnaire.  Result: The literature review revealed that, the primary minimum dataset consisted of 51 elements, which were reduced to 41 after applying the experts' opinion. This dataset covered six areas: demographic information (8 elements), initial pain assessment (12 elements), medical history (8 elements), mental health and well-being (6 elements), diagnostic measures (3 elements), and diagnosis and treatment plan (4 elements). CONCLUSION: Determining minimum set of chronic pain data will be an effective step towards integrating and improving information management of patients with chronic pain. It will also allow for proper storage and retrieval of information related to these patients.

An information-based framework for development national twin registry: Scoping review and focus group discussion.

BACKGROUND: Registries in various clinical domains have been established in the last decades. The specific genetic structure of twins has enabled researchers to find answers to the role of genetics and the environment in medical sciences. Thus, twin registries were developed across the world to support twin studies. Our main objective was to devise a conceptual model for developing the national twin registry to ensure the success of this registry. METHODS: In this descriptive and qualitative study, the combination of literature review and focus group discussions was applied to achieve suitable models for developing a national twin registry based on lessons learned from founded registries. The qualitative synthesis and reporting results were conducted based on the COREQ checklist. RESULTS: According to a systematic literature review, the characteristics and employed strategies employed by established twin registries were recognized. Moreover, based on our objectives, suitable models for registry development were defined. The source of information, the different levels of data, and the information flow were determined based on this model. CONCLUSION: Suggesting a conceptual framework for twin registry development at the national level based on the experiences of other countries could contribute to a greater understanding of twin registry implementation efficiently.

Applied techniques for putting pre-visit planning in clinical practice to empower patient-centered care in the pandemic era: a systematic review and framework suggestion.

BACKGROUND: One of the main elements of patient-centered care is an enhancement of patient preparedness. Thus, pre-visit planning assessment tools was emerged to prepare and involve patients in their treatment process. OBJECTIVE: The main objective of this article was to review the applied tools and techniques for consideration of putting pre-visit planning into practice. METHODS: Web of Science, Scopus, IEEE, and PubMed databases were searched using keywords from January 2001 to November 2020. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, qualitative analysis was done to suggest an appropriate framework by mapping the main concepts. RESULTS: Out of 385 citations were retrieved in initial database searches, 49 studies from ten countries were included. Applied pre-visit techniques can be classified into eight categories. Our results showed that almost 81% of studies were related to procedures that were done between each visit, while 42% of articles were related to before visits. Accordingly, the main approach of included articles was patient preparedness. While 38 studies reported this approach is effective, three studies reported the effectiveness of such tools as moderate, only two articles believed it had a low effect on improving patient-centered care. CONCLUSION: This survey summarized the characteristics of published studies on pre-visit planning in the proposed framework. This approach could enhance the quality of patient care alongside enhancement patient-provider communication. However, such an approach can also be helpful to control pandemic diseases by reducing unnecessary referrals.