Mobile Decision Support Research Articles

Developing interventions to improve detection of depression in primary healthcare settings in rural Ethiopia.

The poor detection of depression in primary healthcare (PHC) in low- and middle-income countries continues to threaten the plan to scale up mental healthcare coverage. To describe the process followed to develop an intervention package to improve detection of depression in PHC settings in rural Ethiopia. The study was conducted in Sodo, a rural district in south Ethiopia. The Medical Research Council's framework for the development of complex interventions was followed. Qualitative interviews, observations of provider-patient communication, intervention development workshops and pre-testing of the screening component of the intervention were conducted to develop the intervention. A multicomponent intervention package was developed, which included (a) manual-based training of PHC workers for 10 days, adapted from the World Health Organization's Mental Health Gap Action Programme Intervention Guide, with emphasis on depression, locally identified depressive symptoms, communication skills, training by people with lived experience and active learning methods; (b) screening for culturally salient manifestations of depression, using a four-item tool; (c) raising awareness among people attending out-patient clinics about depression, using information leaflets and health education; and (d) system-level interventions, such as supportive supervision, use of posters at health facilities and a decision support mobile app. This contextualised, multicomponent intervention package may lead to meaningful impact on the detection of depression in PHC in rural Ethiopia and similar settings. The intervention will be pilot tested for feasibility, acceptability and effectiveness before its wider implementation.

A mobile decision support system for diagnosis, treatment and follow-up of vascular ulcers

A mobile decision support system for diagnosis, treatment and follow-up of vascular ulcers

Experience of primary healthcare workers in using the mobile app-based WHO mhGAP intervention guide in detection and treatment of people with mental disorders: A qualitative study in Nepal

This study assessed perception and experience of trained primary health care workers in using a mobile app-based version of the WHO mental health gap action programme (mhGAP) Intervention Guide in the assessment and management of people with mental health conditions in primary care in Nepal. A qualitative study was conducted with primary healthcare workers who were trained in using the WHO mhGAP mobile application in Jhapa, a district in eastern Nepal. Semi-structured interviews were carried out with 15 healthcare workers (3 females and 12 males) nine months after the training. Interviews were audio recorded, transcribed and translated into English for the thematic qualitative data analysis. Health care workers (HCWs) found the mobile app helpful to verify and confirm their assessment and diagnosis. The other benefits reported by the participants were: bringing uniformity in diagnosis and management of mental health conditions across primary healthcare facilities; reminding the HCWs specific assessment questions; creating awareness on the importance of psychosocial intervention; improving the interaction between patients and service providers; and the importance of follow-up care. Despite these advantages, lack of clarity about report submission modality; lack of electricity or internet connectivity; and low technology literacy among older HCWs were some of the barriers in using the app. The major recommendation made by the HCWs for the guide included revision of the assessment process and system; inclusion of a chat function with supervisors; minimizing the amount of patients’ data to be collected in the app; and addition of conversion and anxiety disorders to the app. This study concludes that the mobile decision support application could be acceptable and feasible in primary care settings if the suggested modifications are incorporated, in addition to addressing other systemic issues facing the primary care-based delivery of mental health services.

Architecture of a mobile transport route selection system by a team of volunteers in the conditions of the military state

The work is devoted to the issue of designing the architecture of a mobile system for choosing the route of cargo transportation and passenger transportation by a team of volunteers under martial law. The system provides support to the decision-maker -the coordinator of the volunteer team in conditions of information uncertainty and dynamic changes in the external environment. The system allows you to receive and take into account operational information about the state of the route segments, which is provided by experts in the role of volunteers.The architecture of the mobile system for the volunteer team in the conditions of dynamic changes is client-server. The client sends data entered by volunteer experts to the server using a mobile phone or Internet connection. The volunteer team coordinator's mobile device is the server of the volunteer team's mobile decision support system. The server has a three-tier architecture: the presentation tier, the application logic tier, and the data tier. At the program logic level, a decision-making subsystem based on the multi-criteria modified Smart method is implemented. As a backup, data from the database on the volunteer team coordinator's device is uploaded to the database located on a remote web server.Based on the developed architecture, a mobile system for building a recommended route for a team of volunteers was developed in Java in Android Studio. As an application of the developed mobile system, a practical example of determining the best of five existing cargo transportation routes is considered

Development and evaluation of a decision support mobile application for cotton irrigation management

Irrigation decision support tools are a means by which crop producers can improve the efficiency of water they apply. However, the available tools generally have poor adoption rates due to high costs, requirement for technical background and skills, need for extensive input data, lack of economic analysis support, and/or low accuracy. Here, we aimed to develop an inexpensive and easy-to-use decision support mobile app called Irrigation Decision-support for Conserving Resources and Optimizing Production (idCROP), to aide cotton producers in the Texas Rolling Plains and High Plains regions to increase water-use efficiency while maintaining higher crop yields. The app was built based on the crop simulation model, Decision Support System for Agrotechnology Transfer (DSSAT), and a newly developed economic model to provide real-time irrigation schedules and projected economic outcomes based on water use and production goals. The app integrates real-time management information from users with historic, real-time, and forecasted short-term and seasonal weather data. This integration enables output of efficient and situationally relevant irrigation schedules and forecasts of associated cotton yield and economic returns. This enables users to choose an irrigation strategy that best suits their irrigation capacities and expected returns. The irrigation schedules can be improved by optional remote detection of plant water stress using a sensor platform mounted on a pivot irrigation system, but the app can be used without the sensors in conjunction with any type of irrigation system. The crop model-based irrigation scheduling in idCROP was validated at the Texas A&M AgriLife Chillicothe Research Station in the Texas Rolling Plains region and acceptable results were obtained. The idCROP app provides producers with a simple but powerful tool that takes the guess work out of irrigation management, helping them apply limited irrigation water more efficiently.

Grab A Tutor: A Decision Support Mobile App for Student Tutoring

Grab A Tutor: A Decision Support Mobile App for Student Tutoring

Designing a Novel Clinician Decision Support Tool for the Management of Acute Diarrhea in Bangladesh: Formative Qualitative Study.

BackgroundThe availability of mobile clinical decision support (CDS) tools has grown substantially with the increased prevalence of smartphone devices and apps. Although health care providers express interest in integrating mobile health (mHealth) technologies into their clinical settings, concerns have been raised, including perceived disagreements between information provided by mobile CDS tools and standard guidelines. Despite their potential to transform health care delivery, there remains limited literature on the provider’s perspective on the clinical utility of mobile CDS tools for improving patient outcomes, especially in low- and middle-income countries.ObjectiveThis study aims to describe providers’ perceptions about the utility of a mobile CDS tool accessed via a smartphone app for diarrhea management in Bangladesh. In addition, feedback was collected on the preliminary components of the mobile CDS tool to address clinicians’ concerns and incorporate their preferences.MethodsFrom November to December 2020, qualitative data were gathered through 8 web-based focus group discussions with physicians and nurses from 3 Bangladeshi hospitals. Each discussion was conducted in the local language—Bangla—and audio recorded for transcription and translation by the local research team. Transcripts and codes were entered into NVivo (version 12; QSR International), and applied thematic analysis was used to identify themes that explore the clinical utility of an mHealth app for assessing dehydration severity in patients with acute diarrhea. Summaries of concepts and themes were generated from reviews of the aggregated coded data; thematic memos were written and used for the final analysis.ResultsOf the 27 focus group participants, 14 (52%) were nurses and 13 (48%) were physicians; 15 (56%) worked at a diarrhea specialty hospital and 12 (44%) worked in government district or subdistrict hospitals. Participants’ experience in their current position ranged from 2 to 14 years, with an average of 10.3 (SD 9.0) years. Key themes from the qualitative data analysis included current experience with CDS, overall perception of the app’s utility and its potential role in clinical care, barriers to and facilitators of app use, considerations of overtreatment and undertreatment, and guidelines for the app’s clinical recommendations. Participants felt that the tool would initially take time to use, but once learned, it could be useful during epidemic cholera. Some felt that clinical experience remains an important part of treatment that can be supplemented, but not replaced, by a CDS tool. In addition, diagnostic information, including mid-upper arm circumference and blood pressure, might not be available to directly inform programming decisions.ConclusionsParticipants were positive about the mHealth app and its potential to inform diarrhea management. They provided detailed feedback, which developers used to revise the mobile CDS tool. These formative qualitative data provided timely and relevant feedback to improve the utility of a CDS tool for diarrhea treatment in Bangladesh.

A Bayesian belief data mining approach applied to rice and shrimp aquaculture.

In many parts of the world, conditions for small scale agriculture are worsening, creating challenges in achieving consistent yields. The use of automated decision support tools, such as Bayesian Belief Networks (BBNs), can assist producers to respond to these factors. This paper describes a decision support system developed to assist farmers on the Mekong Delta, Vietnam, who grow both rice and shrimp crops in the same pond, based on an existing BBN. The BBN was previously developed in collaboration with local farmers and extension officers to represent their collective perceptions and understanding of their farming system and the risks to production that they face. This BBN can be used to provide insight into the probable consequences of farming decisions, given prevailing environmental conditions, however, it does not provide direct guidance on the optimal decision given those decisions. In this paper, the BBN is analysed using a novel, temporally-inspired data mining approach to systematically determine the agricultural decisions that farmers perceive as optimal at distinct periods in the growing and harvesting cycle, given the prevailing agricultural conditions. Using a novel form of data mining that combines with visual analytics, the results of this analysis allow the farmer to input the environmental conditions in a given growing period. They then receive recommendations that represent the collective view of the expert knowledge encoded in the BBN allowing them to maximise the probability of successful crops. Encoding the results of the data mining/inspection approach into the mobile Decision Support System helps farmers access explicit recommendations from the collective local farming community as to the optimal farming decisions, given the prevailing environmental conditions.

Implementation of App-Based Diabetes Medication Management: Outpatient and Perioperative Clinical Decision Support.

Outpatient and perioperative therapeutic decision making for patients with diabetes involves increasingly complex medical-decision making due to rapid advances in knowledge and treatment modalities. We sought to review mobile decision support tools available to clinicians for this essential and increasingly difficult task, and to highlight the development and implementation of novel mobile applications for these purposes. We found 211 mobile applications related to diabetes from the search, but only five were found to provide clinical decision support for outpatient diabetes management and none for perioperative decision support. We found a dearth of tools for clinicians to navigate these tasks. We highlight key aspects for effective development of future diabetes decision support. These include just-in-time availability, respect for the five rights of clinical decision support, and integration with clinical workflows including the electronic medical record.

Usefulness of a mobile app to improve performance of specialists in responding correctly to CRC screening and surveillance clinical scenarios.

Background and study aims The adherence to and knowledge of physicians about colorectal cancer (CRC) screening and surveillance guidelines is still suboptimal, threatening the effectiveness of CRC screening. This study assessed the usefulness of a mobile decision support system (MDSS) to improve physician ability to recommend proper timing of and intervals for CRC screening and surveillance. Patients and methods This was a binational, single-blinded, randomized clinical trial including gastroenterologists and colorectal surgeons from Argentina and Uruguay. The specialists were invited to respond to a questionnaire with 10 CRC screening and surveillance clinical scenarios, randomized into two groups, with and without access to a dedicated app (CaPtyVa). The main outcome measure was the proportion of physicians correctly solving at least 60 % of the clinical cases according to local guidelines. Results A total of 213 physicians were included. The proportion of physicians responding correctly at least 60 % of the vignettes was higher in the app group as compared to the control group (90 % versus 56 %) (relative risk [RR] 1.6 95 % confidence interval [CI] 1.34–1.91). The performance was also higher in the app group for both vignette categories: CRC screening (93 % vs 75 % RR 1.24, 95 %CI 1.01–1.40) and surveillance (85 % vs 47 % RR 1.81 95 %CI 1.46–2.22), respectively. Physicians considered the app easy to use and of great utility in daily practice. Conclusions A MDSS was shown to be a useful tool that improved specialist performance in solving CRC screening and surveillance clinical scenarios. Its implementation in daily practice may facilitate the adherence of physicians to CRC screening and surveillance guidelines.

P-219 A two-cycle audit of imaging requests by presenting symptom and modality associated with implementation of a mobile imaging decision support tool 'app’

P-219 A two-cycle audit of imaging requests by presenting symptom and modality associated with implementation of a mobile imaging decision support tool 'app’

Impact of a mobile decision support tool on antimicrobial stewardship indicators in St. John's, Canada.

Inappropriate antibiotic use contributes to antimicrobial resistance. The SpectrumTM app provides antibiotic decision support, based on local antimicrobial resistance rates. We determined the impact of regional implementation of the app on inpatient antimicrobial appropriateness, inpatient antimicrobial usage (AMU), population-based Clostridioides difficile infection (CDI) rates and cost, using a retrospective, before and after quasi-experimental design, including a one-year study period. The SpectrumTM app was released to prescribers in February, 2019. We performed two one-day inpatient point prevalence surveys using the National Antimicrobial Prescribing Survey tool, six months before (June 25, 2018) and six months after (June 25, 2019) app dissemination. Inpatient AMU in Defined Daily Dose/1000 patient days and CDI incidence were compared, before and after app dissemination. The pre-survey included 184 prescriptions, and the post-survey included 197 prescriptions. Appropriateness was 97/176 (55.1%) pre, and 126/192 (65.6%) post (+10.5%, p = 0.051). Inpatient AMU declined by 6.6 DDD/1000 patient days per month, and CDI declined by 0.3 cases per month. Cost savings associated with reduced AMU were $403.98/bed/year and associated with reduced CDI were $82,078/year. We observed improvement in antimicrobial stewardship indicators following SpectrumTM implementation. We cannot determine the cause of these improvements.

An IoT Based Water Quality Testing Device: An Approach to Modelling a Geographical Map Based on Water Quality Data and Decision Support System

According to the World Health Organization, 30% people lack access to safe water. Normally water quality is evaluated in a scientific laboratory which is expensive, time-consuming and impractical for real-time implementation. Thus, researchers have introduced different electronic devices to test water quality in real-time. However, these methods focused only on testing device development rather than disseminating resultant information for real-life applications. Moreover, these devices did not have any correlation with mobile decision support system and notification. To address these limitations, an integrated decision support system has been developed for instantaneous water quality detection by hardware configuration, smartphone application and web-based mapping. The evolved device automatically calculates six types of water parameter values, while the smartphone application categorizes water quality and notifies its consumption suitability by decision support system. The smartphone application also stores the GPS location of the water source and water quality data and also sends to the server for generating geographical map concerning its quality. Smartphone application’s stored data and central stored data are synchronized based on location. The smartphone also generates notification even without any access to the internet when a user enters into a lowerwater quality region. This package of solutions can be implemented all over the world for ensuring safe water.

TOPSIS for mobile based group and personal decision support system

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an algorithm that can be used for alternative design in a decision support system (DSS). TOPSIS provides recommendation so that users can get information that support their decision, for example a tourist wants to visit a tourist destination in Malang, then TOPSIS provides recommendations of tourist destinations in the form of ranking recommendation, with the highest rank is the most recommended recommendation. TOPSIS-based Mobile Decision Support System (DSS) has relatively low algorithm complexity. However, there are some cases that require development from personal DSS to group DSS, for example tourists rarely come alone, in which case most of them invite friends or family. For users who are more than 1 person, the TOPSIS algorithm can be combined with the BORDA algorithm. This study explains about the implementation & testing of TOPSIS and TOPSIS-BORDA as algorithms for personal and group DSS in mobile-based tourism recommendation system in Malang. Correlation testing was conducted to test the effectiveness of TOPSIS in mobile-based recommendation system. In previous study, correlation testing for personal DSS showed that there was a relationship between the recommendation and user choice, with correlation value of 0.770769231. In this study, correlation testing for group DSS showed there is a positive correlation of 0.88 between the recommendations of the group produced by TOPSIS-BORDA and personal recommendations for each user produced by TOPSIS.

Mobile Healthcare Design Research: A Special Issue for Information Systems Researchers

Abstract Special Section Editorial - Mobile Decision Support and Analytics for Healthcare: Citizen, Organization, Governmental and Technological Perspectives

Exploring the influence of security/privacy, trialability, output quality and anxiety on the adoption of mobile decision support systems among nurses: a developing country context

Nursing staff need to be highly mobile in executing their routine work. Therefore, they may need to catch, deliver and/or receive critical information, orders or alerts via mobile devices at any point of care to help them take immediate decisions/actions or orders to accomplish their tasks quickly. This paper investigates the factors that affect the adoption of mobile decision support systems among nurses in Jordan. Experience and voluntariness as moderators in the proposed model were also investigated. The model was analysed and tested using WarpPLS 5.0 software. The findings of this study have demonstrated that perceived usefulness, perceived ease-of-use, security/privacy, trialability, output quality, and anxiety are important constructs in predicting and affecting intentional behaviour to adopt decision support systems among nurses in Jordan. The model has explained 65% of the variance in behavioural intention. Theoretical contributions and practical implications are outlined. Limitations and suggestions for future studies are discussed.

Exploring the influence of security/privacy, trialability, output quality and anxiety on the adoption of mobile decision support systems among nurses: a developing country context

Exploring the influence of security/privacy, trialability, output quality and anxiety on the adoption of mobile decision support systems among nurses: a developing country context

Mobile-based Decision Support System for Poultry Farmers: A Case of Tanzania

Poultry farms in Tanzania are characterized by inadequate management practices which are mainly caused by the lack of adequate systems to guide the small-scale poultry farmers in decision making. It is well-established that information is a key factor in making effective decisions in numerous sectors including poultry farming. Furthermore, various researchers have identified the use of mobile decision support tools to be an effective way of aiding farmers in making informed decisions. In this paper, we present a mobile-based decision support system that will aid rural and small-scale poultry farmers in Tanzania to obtain reliable information that is crucial for making proper decisions in their farming activities. In this context, a mobile-based decision support system was achieved through a mobile application integrated with a chatbot assistant to provide a solution to various poultry farming-related problems and simplify their decision-making process. We used a data-driven approach towards developing an informational chatbot assistant for Android smartphones that is capable of interacting with small-scale poultry farmers through natural conversations by utilizing the RASA framework.

How to Develop a Risk Prediction Smartphone App.

Purpose. Powered by big data, predictive models provide individualized risk stratification to inform clinical decision-making and mitigate long-term morbidity. We describe how to transform a large institutional dataset into a real-time, interactive clinical decision support mobile user interface for risk prediction. Methods. A clinical decision point ideal for risk stratification and modification was identified. Demographics, medical comorbidities, and operative characteristics were abstracted from the electronic medical record (EMR) using ICD-9 codes. Surgery-specific predictive models were generated using regression modeling and corroborated with internal validation. A clinical support interface was designed in partnership with an app developer, followed by subsequent beta testing and clinical implementation of the final tool. Results. Individual, specialty-specific, and preoperatively actionable models incorporating clustered procedural codes were created. Using longitudinal inpatient, outpatient, and office-based data from a large multicenter health system, all patient and operative variables were weighted according to ß-coefficients. The individual risk model parameters were incorporated into specialty-specific modules and implemented into an accessible iOS/Android compatible mobile application. Conclusions. As proof of concept, we provide a framework for developing a clinical decision support mobile user interface, through the use of clinical and administrative longitudinal data. Point-of-care applications, particularly ones designed with implementation and actionability in mind, have the potential to aid clinicians in identifying and optimizing risk factors that impact the outcome of interest's occurrence, thereby enabling clinicians to take targeted risk-reduction actions. In addition, such applications may help facilitate counseling, informed consent, and shared decision-making, leading to improved patient-centered care.

Research Design of Mobile Based Decision Support for Early Flood Warning System

Flooding has become one of the most rapidly growing types of natural disaster that has spread around the globe. It is is one of the major natural hazards in many countries and mostly affected in the low-lying or flood prone areas. In order to minimize loss of life and economic losses, a detailed and comprehensive decision making tool is necessary for both flood control planning and emergency service operations. In this paper, we demonstrate our research design for mobile based decision support of Flood Early Warning System (FEWS). We outlined four research objectives. Firstly, critical criteria for flood risk assessment will be identified and the second step will involve develop measurement model for relative flood risk using Geographic Information System (GIS), Multi Attribute Decision Making (MADM) and data mining technique. In the third objectives, the holistic architectural design is develop by incorporating the communication technology and other related ICT requirements for the mobile decision support. The fourth objective is to validate the mathematical model and architectural design. Case study approach is chosen in order to understand the flood event and validate the decision support model. Following well-defined procedures, flood maps were drawn based on the data collected from expert responses to a questionnaire, the field survey, satellite images, and documents from flood management agencies. It is anticipates that by integrating of mathematical model, GIS and mobile application in flood risk assessment could provide useful detailed information for flood risk management, evacuation, communication. The decision support design from this study is perhaps to improve the warning system and contribute to reduction of casualties.