The effect of personalized intelligent digital systems for self-care training on type II diabetes: a systematic review and meta-analysis of clinical trials.

AIMS: Type 2 diabetes (T2D) is rising worldwide. Self-care prevents diabetic complications. Lack of knowledge is one reason patients fail at self-care. Intelligent digital health (IDH) solutions have a promising role in training self-care behaviors based on patients' needs. This study reviews the effects of RCTs offering individualized self-care training systems for T2D patients. METHODS: PubMed, Web of Science, Scopus, Cochrane Library, and Science Direct databases were searched. The included RCTs provided data-driven, individualized self-care training advice for T2D patients. Due to the repeated studies measurements, an all-time-points meta-analysis was conducted to analyze the trends over time. The revised Cochrane risk-of-bias tool (RoB 2.0) was used for quality assessment. RESULTS: In total, 22 trials met the inclusion criteria, and 19 studies with 3071 participants were included in the meta-analysis. IDH interventions led to a significant reduction of HbA1c level in the intervention group at short-term (in the third month: SMD = - 0.224 with 95% CI - 0.319 to - 0.129, p value < 0.0; in the sixth month: SMD = - 0.548 with 95% CI - 0.860 to - 0.237, p value < 0.05). The difference in HbA1c reduction between groups varied based on patients' age and technological forms of IDH services delivery. The descriptive results confirmed the impact of M-Health technologies in improving HbA1c levels. CONCLUSIONS: IDH systems had significant and small effects on HbA1c reduction in T2D patients. IDH interventions' impact needs long-term RCTs. This review will help diabetic clinicians, self-care training system developers, and researchers interested in using IDH solutions to empower T2D patients.

The Minimum Feature Set for Designing Mobile Apps to Support Bipolar Disorder-Affected Patients: Proposal of Essential Functions and Requirements.

Research conducted on mobile apps providing mental health services has concluded that patients with mental disorders tend to use such apps to maintain mental health balance technology may help manage and monitor issues like bipolar disorder (BP). This study was conducted in four steps to identify the features of designing a mobile application for BP-affected patients including (1) a literature search, (2) analyzing existing mobile apps to examine their efficiency, (3) interviewing patients affected with BP to discover their needs, and 4) exploring the points of view of experts using a dynamic narrative survey. Literature search and mobile app analysis resulted in 45 features, which were later reduced to 30 after the experts were surveyed about the project. The features included the following: mood monitoring, sleep schedule, energy level evaluation, irritability, speech level, communication, sexual activity, self-confidence level, suicidal thoughts, guilt, concentration level, aggressiveness, anxiety, appetite, smoking or drug abuse, blood pressure, the patient's weight and the side effects of medication, reminders, mood data scales, diagrams or charts of the collected data, referring the collected data to a psychologist, educational information, sending feedbacks to patients using the application, and standard tests for mood assessment. The first phase of analysis should consider an expert and patient view survey, mood and medication tracking, as well as communication with other people in the same situation are the most features to be considered. The present study has identified the necessity of apps intended to manage and monitor bipolar patients to maximize efficiency and minimize relapse and side effects.

Developing a mobile health application for wound telemonitoring: a pilot study on abdominal surgeries post-discharge care.

BACKGROUND: Many early signs of Surgical Site Infection (SSI) developed during the first thirty days after discharge remain inadequately recognized by patients. Hence, it is important to use interactive technologies for patient support in these times. It helps to diminish unnecessary exposure and in-person outpatient visits. Therefore, this study aims to develop a follow-up system for remote monitoring of SSIs in abdominal surgeries. MATERIAL AND METHODS: This pilot study was carried out in two phases including development and pilot test of the system. First, the main requirements of the system were extracted through a literature review and exploration of the specific needs of abdominal surgery patients in the post-discharge period. Next extracted data was validated according to the agreement level of 30 clinical experts by the Delphi method. After confirming the conceptual model and the primary prototype, the system was designed. In the pilot test phase, the usability of the system was qualitatively and quantitatively evaluated by the participation of patients and clinicians. RESULTS: The general architecture of the system consists of a mobile application as a patient portal and a web-based platform for patient remote monitoring and 30-day follow-up by the healthcare provider. Application has a wide range of functionalities including collecting surgery-related documents, and regular assessment of self-reported symptoms via systematic tele-visits based on predetermined indexes and wound images. The risk-based models embedded in the database included a minimum set with 13 rules derived from the incidence, frequency, and severity of SSI-related symptoms. Accordingly, alerts were generated and displayed via notifications and flagged items on clinicians' dashboards. In the pilot test phase, out of five scheduled tele-visits, 11 (of 13) patients (85%), completed at least two visits. The nurse-centered support was very helpful in the recovery stage. Finally, the result of a pilot usability evaluation showed users' satisfaction and willingness to use the system. CONCLUSION: Implementing a telemonitoring system is potentially feasible and acceptable. Applying this system as part of routine postoperative care management can provide positive effects and outcomes, especially in the era of coronavirus disease when more willingness to telecare service is considered.

Data Sharing Platform for MIMIC-IV and MIMIC-ED Data Marts: Designing a Data Retrieving System Based on the Intra-Hospital Patient Transfer Pathway.

Accessibility to high-quality historical data for patients in hospitals may facilitate related predictive model development and data analysis experiments. This study provides a design for a data-sharing platform based on all possible criteria for Medical Information Mart for Intensive Care (MIMIC) IV and Emergency MIMIC-ED. Tables containing columns of medical attributions and outcomes were studied by a team of 5 experts in Medical Informatics. They completely agreed about the columns connection using subject-id, HDM-id, and stay-id as foreign keys. The tables of two marts were considered in the intra-hospital patient transfer path with various outcomes. Using the constraints, queries were generated and applied to the backend of the platform. The suggested user interface was drawn to retrieve records based on various entry criteria and present the output in the frame of a dashboard or a graph. This design is a step toward platform development that is useful for studies aimed at patient trajectory analysis, medical outcome prediction, or studies that require heterogeneous data entries.

The effect of m-health applications on self-care improvement in older adults: A systematic review.

PARTICIPANTS: Four electronic databases were searched on March 6, 2020 including Scopus, PubMed, ISI, and Embase. METHODS: Our search consisted of concepts of "self-care," "elderly" and "Mobile device." English journal papers and, RCTs conducted for individuals older than 60 in the last 10 years were included. A narrative approach was used to synthesize the data due to the heterogeneous nature of the data. RESULTS: Initially, 3047 studies were obtained and finally 19 studies were identified for deep analysis. 13 outcomes were identified in m-health interventions to help older adults' self-care. Each outcome has at least one or more positive results. The psychological status and clinical outcome measures were all significantly improved. CONCLUSION: According to the findings, it is not possible to draw a definite positive decision about the effectiveness of interventions on older adults because the measures are very diverse and have been measured with different tools. However, it might be declared that m-health interventions have one or more positive results and can be used along with other interventions to improve the health of older adults.

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms.

BACKGROUND: This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD). METHODOLOGY: This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers. DISCUSSION: This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences. TRIAL REGISTRATION: Systematic review registration: PROSPERO CRD42022334988.

Remote Monitoring of Colorectal Cancer Survivors Using a Smartphone App and Internet of Things-Based Device: Development and Usability Study.

BACKGROUND: Patients with colorectal cancer who undergo surgery face many postoperative problems. These problems include the risk of relapse, side effects, and long-term complications. OBJECTIVE: This study sought to design and develop a remote monitoring system as a technological solution for the postdischarge care of these patients. METHODS: This research was conducted in 3 main steps: system feature extraction, system design, and evaluation. After feature extraction from a systematic review, the necessary features were defined by 18 clinical experts in Iran. In the next step, the architecture of the system was designed based on the requirements; the software and hardware parts of the system were embedded in the architecture, then the software system components were drawn using the unified modeling language diagrams, and the details of software system implementation were identified. Regarding the hardware design, different accessible hardware modules were evaluated, and suitable ones were selected. Finally, the usability of the system was evaluated by demonstrating it over a Skype virtual meeting session and using Nilsen's usability principles. RESULTS: A total of 21 mandatory features in 5 main categories, including patient information registration, periodic monitoring of health parameters, education, reminders, and assessments, were defined and validated for the system. The software was developed using an ASP.Net core backend, a Microsoft SQL Server database, and an Ionic frontend alongside the Angular framework, to build an Android app. The user roles of the system included 3 roles: physicians, patients, and the system administrator. The hardware was designed to contain an Esp8266 as the Internet of Things module, an MLX90614 infrared temperature sensor, and the Maxim Integrated MAX30101 sensor for sensing the heartbeat. The hardware was designed in the shape of a wristband device using SolidWorks 2020 and printed using a 3D printer. The firmware of the hardware was developed in Arduino with the capability of firmware over the air. In evaluating the software system from the perspective of usability, the system received an average score of 3.8 out of 5 from 4 evaluators. CONCLUSIONS: Sensor-based telemonitoring systems for patients with colorectal cancer after surgery are possible solutions that can make the process automatic for patients and caregivers. The apps for remote colorectal patient monitoring could be designed to be useful; however, more research regarding the developed system's implementation in clinic settings and hospitals is required to understand the probable barriers and limitations.

A Mixed Model Approach for Estimating the Optimal Food Fortification of Vitamin D: Experiment Based on Mashhad Cohort Study in Iran.

BACKGROUND: Vitamin D deficiency is a prevalent problem in worldwide healthcare related to several system disorders. Food fortification as a solution is associated with several challenges including insufficient coverage of the entire population, required degree of fortification, the vehicles used for fortification and potential toxicity. This study aimed to determine the optimal amount of vitamin D for fortification without surpassing the upper intake level (UL) of intake at the 95th percentile of the Iranian population and compare two methods of food fortification. METHODS: This study is aimed to develop a model of two different fortifying approaches related to an available dataset called MASHAD cohort study. The dataset comprised demographic and nutritional data of 9704 Iranian individuals living in the Greater Mashhad region. The first approach was a computational method necessary to implement a range of eight foods and calculate the optimal approach. In the second case, we used the European formula method called ILSI. RESULTS: To find the appropriate value for fortification, we calculated the consumption of 400 IU and 1000 IU supplements of vitamin D. Three micrograms per 100 g in each food was the optimal output. We also used Flynn and Rasmussen's formula on our data. Using these methods, we found that 2.1 micrograms per 100 kcal provides the best result. Hence, using the two different approaches, the results appear to be consistent and promising. CONCLUSION: One interesting finding was that supplement consumption did not greatly affect the impact of fortification. This observation may support the hypothesis to determine the amount of fortification, and we can ignore the study population's supplement consumption.

Requirement Analysis for an Intelligent Warning System to Alarm the Rapid Response Team Prior to Patient Deterioration.

The early warning system alarms the rapid response team (RRT) for clinical deterioration monitoring and prediction. Available systems do not perform well to decrease the number of ICU transfers or death. This study aimed to address the requirement of an intelligent warning system for timely and accurate RRT activation. METHODOLOGY: A literature review was conducted in scientific databases to extract data. Then, a questionnaire was developed for experts' views collection (N=12). The collected data were analyzed using the Content Validity Ratio (CVR). According to the Lawshe table for the corresponding number of experts, the cut-off=0.56 for items to be accepted/rejected was considered. A schematic structure was suggested. FINDINGS: The analysis of the extracted papers (N=24) and qualitative analysis addressed 44 requirements in the frame of five involved sub-systems, including a patient monitoring system, electronic health record, clinical decision support system, remote monitoring patient, and dashboard ®istries. They were confirmed by meeting the least cut-off value (CVR= 0.86). CONCLUSION: An integrated approach and technologies of IoT, deep and machine learning techniques, big data, advanced databases, and standards to create an intelligent EWS are required.

Towards the Application of Machine Learning in Emergency Informatics.

Emergency care is one of the cornerstone parts of the world health organization's action plan. Rapid response and immediate care are considered in agile emergency care. Artificial intelligence (AI) and informatics have been applied to fulfill these requirements through automated emergency technology. Machine learning (ML) is one of the main parts of some of these proposed technologies. There are various ML algorithms and techniques which are potentially applicable for different purposes of emergency care. AI-based approaches using classification and clustering algorithms, natural language processing, and text mining are some of the possible techniques that could prove useful for investigating models of emergency prevention and management and proposing improved procedures for handling such critical situations. ML is known as a field of AI which attempts to automatically learn from data and applies that learning to make better decisions. Decision-support tools can apply the results of either supervised or various semi-supervised or unsupervised learning methods to tackle the how decisions about emergency situations are typically handled by the best professionals at the scene of an emergency, in the pre-hospital, and in healthcare facility settings. Enhanced and rapid communication at the moment of emergency, with the most effective decision making for triaging to estimate the acute nature of injuries and possible complications, how to keep a patient stable on the way to the care facility, and also avoiding adverse drug reactions, are some of the possible directions for exploring how ML can help to gather the data and to make emergency management more efficient and effective. The wide range of scenarios present in emergency situations and the complexity of different legal and ethical constraints on what responding personnel are allowed to perform on an injured subject before reaching a hospital makes for a most challenging set of problems for investigating the components of "intelligent" decision support that could help in these highly interactive and humanly tragic situations.

Early Warning System for Emergency Care: Designing a Timely Monitoring Mobile-Based System.

Early Warning Scores (EWSs) systems support the timely detection of patient deterioration and rapid response of the care team. Due to the mobility nature of healthcare settings, there has been a growing tendency to use mobile-based devices in these settings. This chapter aimed to design a mobile-based EWS application (app). This was a descriptive study to design the architecture of the proposed EWS app. The design of architecture was done using the Unified Modeling Language diagrams including a class diagram, use-case diagram, and activity diagram. We evaluated the architecture using the ARID scenario-based evaluation method. The proposed EWS application (app) was the integration of three EWSs, including NEWS2, PEWS, and MEOWS. The workflow of these EWSs systems was designed and integrated into a single app. Also, the static structure of the proposed EWS app was designed by class diagram and the behavioral structure was depicted by use-case and activity diagrams. The class diagram showed the system components and their relationships. However, the use-case diagram displayed the app's interaction with its environment, and the activity diagram illustrated how the EWS app processes were carried out. Evaluation results showed the possibility of designing the architecture for the proposed EWS app. In our app, the EWSs were designed in the clinician's workflow, and it was integrated with the patient's Electronic Health Record (EHR). These factors may lead to more use of EWSs. Considering the frequency of alerts represented to clinicians and the user-friendly design of the app, some suggestions can be considered by EWS systems developers in the future.

Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine.

INTRODUCTION: Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes. Thus, the main goal of this study was to review the application of health information technology and especially artificial intelligence (AI) methods for the treatment of childhood disease using PM. METHODS: PubMed, Scopus, Web of Science, and EMBASE databases were searched up to December 18, 2019. Articles that focused on informatics applications for childhood disease PM included in this study. Included papers were classified for qualitative analysis and interpreting results. The results were analyzed using Microsoft Excel 2019. RESULTS: From 341 citations, 62 papers met our inclusion criteria. The number of published papers that used AI methods to apply for PM in childhood diseases increased from 2010 to 2019. Our results showed that most applied methods were related to machine learning discipline. In terms of clinical scope, the largest number of clinical articles are devoted to oncology. Besides, the analysis showed that genomics was the most PM approach used regarding childhood disease. CONCLUSION: This systematic review examined papers that used AI methods for applying PM approaches in childhood diseases from medical informatics perspectives. Thus, it provided new insight to researchers who are interested in knowing research needs in this field.

Neonatal abstinence syndrome: a systematic review of current databases and registries.

OBJECTIVES: Registries are considered as rich sources of data for determination of infants with neonatal abstinence syndrome (NAS), the improvement of provided care and research. The aims of this study were: (1) to investigate the existing studies including NAS registries, (2) to identify and extract the required data elements. METHODS: The following electronic databases were searched: PubMed, Scopus, Web of Science, ProQuest, Embase/Medline, and Psych Info. In addition, a review of gray literature was undertaken to identify relevant studies in English covering the period from 1 January 2009 to 1 November 2018 including registries and databases. Screening of titles, abstracts, and full-texts were conducted independently by two researchers based on PRISMA guidelines. The basic registry information, scope, registry type, data source, the purpose of registry, important variables were extracted and analyzed. RESULTS: Twenty-five articles were eligible and included in the review; they reported 37 registries and databases related to NAS at the national and state levels in 11 countries from 1876 to 2013. We proposed a NAS registry design framework based on well-known data-information-knowledge (DIK) structure due to Ackoff's DIK hierarchy has a defined role as a central model of information systems, information management, and knowledge management. CONCLUSIONS: To the best of our knowledge, this is the first study which has systematically reviewed NAS-related registries. Since there are no international standards to develop new NAS registries, the proposed framework in this article can be beneficial. This framework is essential not only to facilitate the NAS registry design but also to help the collection of high-value clinical data necessary for the acquisition of better clinical knowledge.

Nursing process education: A review of methods and characteristics.

The concepts of the nursing process have been assigned as important plans for the curricula and continuing education programs in the nursing discipline. This paper focuses on the main characteristics of the educational methods applied in training programs in the nursing process or nursing care plan. The original studies were extracted from the PubMed, ProQuest, Scopus, Science Direct and, Web of Science databases, following two-step screening by two reviewers. Accordingly, 21 papers were included in the study based on inclusion criteria. According to the results, 42.85% of educational methods were computer-based while 57.15% were non-computer based. Computers were used as the main educator (14.28%) or as an assisted-tool (25.57%). The lecture-based training, as one of the non-computer based educational tools, was the most frequent method observed across the studies (33.3%). There are two frequently used measures of evaluating the educational programs learner's knowledge of nursing process (28.75%) and their attitude toward the program (23.80%). The results suggested that in order to improve educational methods for the nursing process, computers, mobile phones, and other available technologies should be integrated with the traditional educational methods. Also, comprehensive measures should be applied for evaluating them.

A study of factors related to patients' length of stay using data mining techniques in a general hospital in southern Iran.

PURPOSE: The length of stay (LOS) in hospitals is a widely used indicator for goals such as health care management, quality control, utilizing hospital services and resources, and determining the degree of efficiency. Various methods have been used to identify the factors influencing the LOS. This study adopts a comparative approach of data mining techniques for investigating effective factors and predict the length of stay in Shahid-Mohammadi Hospital, Bandar Abbas, Iran. METHODS: Using a dataset consists of 526 patient records of the Shahid-Mohammadi Hospital from March 2016 to March 2017, factors affecting the LOS were ranked using information gain and correlation indices. In addition, classification models for LOS prediction were created based on nine data mining classifiers applied with and without feature selection technique. Finally, the models were compared. RESULTS: The most important factors affecting LOS are the number of para-clinical services, counseling frequency, clinical ward, the specialty and the degree of the doctor, and the cause of hospitalization. In addition, regarding to the classifiers created based on the dataset, the best accuracy (83.91%) and sensitivity (80.36%) belongs to the Logistic Regression and Naïve Bayes respectively. In addition, the best AUC (0.896) belongs to the Random Forest and Generalized Linear classifiers. CONCLUSION: The results showed that most of the proposed models are suitable for classification of the length of stay, although the Logistic Regression might have a slightly better performance than others in term of accuracy, and this model can be used to determine the patients' Length of Stay. In general, continuous monitoring of the factors influencing each of the performance indicators based on proper and accurate models in hospitals is important for helping management decisions.

Systematic Review of Hospital Based Cancer Registries (HBCRs): Necessary Tool to Improve Quality of Care in Cancer Patients

Introduction: Incidence and mortality rate of cancer is increasing in all countries including low and middle-income countries. Hospital based cancer registry (HBCR) is an important tool for administration purpose and improvement of the quality of care. It is also important resource for population based cancer registries. In this study we reviewed HBCRs in different countries. Methods: We searched the published literature using the MEDLINE (PubMed), Google scholar, Scopus, ProQuest and Google. We also reviewed websites of the HBCRs in different countries. The search was carried out based on proper keywords in English for all motor engines including "hospital-based", "clinical" and "data quality" combined with "registry", "cancer" and "tumor" including all subheadings. We reviewed objectives, developer institutions, minimum datasets, data sources, quality control indicators and processes. Results: In total we found 163 papers in the first step. After screening of the titles, abstracts and the full texts, 14 papers remained for analysis. Analysis of the 14 papers showed that the improvement of the quality of the care were the most important objectives among the registries. HBCRs collect information about patients, tumor diagnosis, treatment and follow-up. Generally, indicators such as completeness and validity were used for quality control. Conclusion: Because of the increases in cancer burden in the world, more attention is needed to be paid on cancer surveillance systems, including HBCRs. We evaluated and highlighted the importance and characteristics HBCRs and believe that this paper would help the hospitals and policy makers for planning and establishment of new HBCRs. We suggest the establishment of a worldwide network for coordination and collaboration between HBCRs.