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- Andersson, Henrik, et al.
(författare)
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Using optimization to provide decision support for strategic emergency medical service planning - Three case studies
- 2020
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Ingår i: International Journal of Medical Informatics. - : ELSEVIER IRELAND LTD. - 1386-5056 .- 1872-8243. ; 133
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Tidskriftsartikel (refereegranskat)abstract
- To achieve high performing emergency medical services (EMS), planning is of vital importance. EMS planners face several challenges when managing ambulance stations and the fleet of ambulances. In this paper, three strategic cases for EMS planners are presented together with potential solutions. In the first case, the effects of closing down a local emergency room (ER) are analyzed together with how adding an ambulance station and an ambulance to the area affected by the closing of the ER can be used to mitigate the negative consequences from the closing. The second case investigates a change in the organization of EMS. Currently, many non-urgent transport assignments are performed by ambulances which make them unavailable for more urgent calls. The potential for a more effective utilization of the ambulances is explored through transferring these assignments to designated transport vehicles. The third case is more technical and challenges the common practice regarding how time dependent demand is handled. Looking at the busiest hour or the average daily demand, is compared with taking time varying demand into account. The cases and solutions are studied using a recently developed strategic ambulance station location and ambulance allocation model for the Maximum Expected Performance Location Problem with Heterogeneous Regions (MEPLP-HR). The model has been extended to also include multiple time periods. This article demonstrates an innovative use of the model and how it can be applied to find and evaluate solutions to real cases within the field of strategic planning of EMS. The model is found to be a useful decision support tool when analyzing the cases and the expected performance of potential solutions.
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- Belsti, Yitayeh, et al.
(författare)
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Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population : the Monash GDM Machine learning model
- 2023
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Ingår i: International Journal of Medical Informatics. - : Elsevier. - 1386-5056 .- 1872-8243. ; 179
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Tidskriftsartikel (refereegranskat)abstract
- Background: Early identification of pregnant women at high risk of developing gestational diabetes (GDM) is desirable as effective lifestyle interventions are available to prevent GDM and to reduce associated adverse outcomes. Personalised probability of developing GDM during pregnancy can be determined using a risk prediction model. These models extend from traditional statistics to machine learning methods; however, accuracy remains sub-optimal.Objective: We aimed to compare multiple machine learning algorithms to develop GDM risk prediction models, then to determine the optimal model for predicting GDM.Methods: A supervised machine learning predictive analysis was performed on data from routine antenatal care at a large health service network from January 2016 to June 2021. Predictor set 1 were sourced from the existing, internationally validated Monash GDM model: GDM history, body mass index, ethnicity, age, family history of diabetes, and past poor obstetric history. New models with different predictors were developed, considering statistical principles with inclusion of more robust continuous and derivative variables. A randomly selected 80% dataset was used for model development, with 20% for validation. Performance measures, including calibration and discrimination metrics, were assessed. Decision curve analysis was performed.Results: Upon internal validation, the machine learning and logistic regression model's area under the curve (AUC) ranged from 71% to 93% across the different algorithms, with the best being the CatBoost Classifier (CBC). Based on the default cut-off point of 0.32, the performance of CBC on predictor set 4 was: Accuracy (85%), Precision (90%), Recall (78%), F1-score (84%), Sensitivity (81%), Specificity (90%), positive predictive value (92%), negative predictive value (78%), and Brier Score (0.39).Conclusions: In this study, machine learning approaches achieved the best predictive performance over traditional statistical methods, increasing from 75 to 93%. The CatBoost classifier method achieved the best with the model including continuous variables.
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- Chirambo, Griphin Baxter, et al.
(författare)
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End-user perspectives of two mHealth decision support tools : Electronic Community Case Management in Northern Malawi
- 2021
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Ingår i: International Journal of Medical Informatics. - : Elsevier BV. - 1386-5056 .- 1872-8243. ; 145
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Tidskriftsartikel (refereegranskat)abstract
- Background: The introduction of a paper-based Community Case Management (CCM) in Malawi has contributed to a reduction of child morbidity and mortality rates. In addition, the introduction of electronic Community Case Management (eCCM) (smartphones with built in CCM apps) may help to reduce the under-five mortality rates even further. Purpose: It is not uncommon for Apps with a similar area of interest to develop different features to assist the end users. Such differences between Apps may have a significant role to play in its overall adoption and integration. The purpose of this research was to explore end users perspectives of two eCCM decision support tools developed and implemented by the Supporting LIFE project (SL eCCM App) and D-Tree International's (Mangologic eCCM App)in Northern Malawi. Methods: A mixed methods approach was applied, involving a survey of 109 users (106 Health Surveillance Assistants (HSAs), and 3 Integrated Management of Childhood Il6lnesses (IMCI) coordinators). This was followed up with semi-structured interviews with 34 respondents (31 HSAs, and 3 IMCI coordinators). Quantitative data was analyzed using SPSS version 20 where descriptive statistics and Chi-Squared tests were generated. Qualitative data were analyzed based on thematic analysis. Results: Participants reported that both Apps could assist the HSAs in the management of childhood illnesses. However, usability differed between the two apps where the Supporting LIFE eCCM App was found to be easier to use (61%) compared to the Mangologic eCCM App (4%). Both Apps were perceived to provide credible and accurate information. Conclusion: It is essential that the quality of the data within Mobile Health (mHealth) Apps is high, however even Apps with excellent levels of data quality may not succeed if the overall usability of the App is low. Therefore it is essential that the Apps has high levels of data quality, usability and credibility. The results of this study will help inform mobile Health (mHealth) App designers in developing future eCCM Apps as well as researchers and policy makers when considering the adoption of mHealth solutions in the future in Malawi and other LMICs.
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- Drissi, Nidal, et al.
(författare)
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An analysis on self-management and treatment-related functionality and characteristics of highly rated anxiety apps.
- 2020
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Ingår i: International Journal of Medical Informatics. - : Elsevier BV. - 1386-5056 .- 1872-8243. ; 141, s. 104243-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND AND OBJECTIVE: Anxiety is a common emotion that people often feel in certain situations. But when the feeling of anxiety is persistent and interferes with a person's day to day life then this may likely be an anxiety disorder. Anxiety disorders are a common issue worldwide and can fall under general anxiety, panic attacks, and social anxiety among others. They can be disabling and can impact all aspects of an individual's life, including work, education, and personal relationships. It is important that people with anxiety receive appropriate care, which in some cases may prove difficult due to mental health care delivery barriers such as cost, stigma, or distance from mental health services. A potential solution to this could be mobile mental health applications. These can serve as effective and promising tools to assist in the management of anxiety and to overcome some of the aforementioned barriers. The objective of this study is to provide an analysis of treatment and management-related functionality and characteristics of high-rated mobile applications (apps) for anxiety, which are available for Android and iOS systems.METHOD: A broad search was performed in the Google Play Store and App Store following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol to identify existing apps for anxiety. A set of free and highly rated apps for anxiety were identified and the selected apps were then installed and analyzed according to a predefined data extraction strategy.RESULTS: A total of 167 anxiety apps were selected (123 Android apps and 44 iOS apps). Besides anxiety, the selected apps addressed several health issues including stress, depression, sleep issues, and eating disorders. The apps adopted various treatment and management approaches such as meditation, breathing exercises, mindfulness and cognitive behavioral therapy. Results also showed that 51% of the selected apps used various gamification features to motivate users to keep using them, 32% provided social features including chat, communication with others and links to sources of help; 46% offered offline availability; and only 19% reported involvement of mental health professionals in their design.CONCLUSIONS: Anxiety apps incorporate various mental health care management methods and approaches. Apps can serve as promising tools to assist large numbers of people suffering from general anxiety or from anxiety disorders, anytime, anywhere, and particularly in the current COVID-19 pandemic.
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- Ehn, Maria, et al.
(författare)
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User-centered requirements engineering to manage the fuzzy front-end of open innovation in e-health : A study on support systems for seniors’ physical activity
- 2021
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Ingår i: International Journal of Medical Informatics. - : Elsevier Ireland Ltd. - 1386-5056 .- 1872-8243. ; 154
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Tidskriftsartikel (refereegranskat)abstract
- Background: Although e-health potentials for improving health systems in their safety, quality and efficiency has been acknowledged, a large gap between the postulated and empirically demonstrated benefits of e-health technologies has been ascertained. E-health development has classically been technology-driven, often resulting in the design of devices and applications that ignore the complexity of the real-world setting, thus leading to slow diffusion of innovations to care. Therefore, e-health innovation needs to consider the mentioned complexity already from the start. The early phases of innovation, fuzzy front-end (FFE) defined as “the period between when an opportunity is first considered and when an idea is judged ready for development” has been identified to have the highest impact on the innovation process and its outcome. The FFE has been recognized as the most difficult stage to manage in the innovation process as it involves a high degree of uncertainty. Such a phase becomes even more difficult when different sectors and organizations are involved. Therefore, effective methods for involving different organizations and user groups in the FFE of innovation are needed. Objective: The aim of this study was to manage the FFE of a collaborative, open innovation (OI) process, to define a software system supporting seniors’ physical activity (PA) by applying a framework of methods from software requirements engineering (RE) to elicit and analyze needs and requirements of users and stakeholders, as well as the context in which the system should be used. Methods: Needs and requirements of three future user groups were explored through individual- and focus group interviews. Requirements were categorized and analyzed in a workshop with a multidisciplinary team: a system overview was produced by conceptual modelling using elicited functional requirements; high-level non-functional requirements were negotiated and prioritized. Scenario descriptions of system's supportive roles in different phases of a behavioral change process were developed. Results: User-centered RE methods were successfully used to define a system and a high-level requirements description was developed based on needs and requirements from three identified user groups. The system aimed to support seniors’ motivation for PA and contained four complementary sub-systems. The outcome of the study was a Concept of Operations (ConOps) document that specified the high-level system requirements in a way that was understandable for stakeholders. This document was used both to identify and recruit suitable industrial partners for the following open innovation development and to facilitate communication and collaboration in the innovation process. Conclusions: Applying software RE methods and involving user groups in the early phases of OI can contribute to the development of new concepts that meet complex real-world requirements. Different user groups can complement each other in conveying needs and requirements from which systems can be designed. Empirical studies applying and exploring different methods used to define new e-health solutions can contribute with valuable knowledge about handling innovation FFE.
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