Asthma Management Using the Mobile Asthma Evaluation and Management System in China

Continuous recording of daily symptoms constitutes an effective means of managing asthma patients. Daily management reduces the costs associated with hospitalization and improves the quality of patient care. We have implemented a Web-based mobile asthma management system. We used a pocket PC, mobile phone and desktop computer. The recorded items and individualized prescriptions were structured using Extensible Markup Language (XML) DTD (Data Type Definition). The mobile Web form was automatically adjusted to fit the different display resolutions of the terminal devices. The system provided reliable exchange of all relevant information between a doctor and the asthma patient using wireless mobile transmission. Email and Short Messaging Service (SMS) were used to send messages to patients, for example in the case of an automatically determined patient alert. Patients could obtain customized instructions according to their daily personal symptoms, peak expiratory flow (PEF), medications and activity restriction. The daily graph of PEF and the graphs of symptoms and medication were particularly useful for asthma patient control and self-awareness of the progress of the disease.

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Pediatric Asthma Management: Study of the Family Asthma Management System Scale with a Portuguese Sample

Children s Health Care ◽

10.1080/02739615.2013.837822 ◽

2014 ◽

Vol 43 (3) ◽

pp. 203-220 ◽

Cited By ~ 2

Author(s):

Cláudia M. da Silva ◽

Luísa Barros

Keyword(s):

Management System ◽

Asthma Management ◽

Pediatric Asthma ◽

Management Study ◽

The Family

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IMPELEMENTASI SEMI-SUPERVISED LEARNING PADA PERSONALIZED ASTHMA MANAGEMENT SYSTEM

KLIK - KUMPULAN JURNAL ILMU KOMPUTER ◽

10.20527/klik.v4i1.69 ◽

2017 ◽

Vol 4 (1) ◽

pp. 49

Author(s):

Cut Fiarni ◽

Evasaria Magdalena Sipayung ◽

Kevin Barry Moningka

Keyword(s):

Chronic Disease ◽

Supervised Learning ◽

Management System ◽

Asthma Management ◽

Asthma Severity ◽

Health Conditions ◽

Self Management ◽

Asthma Attack ◽

Learning Technique ◽

Asthma Triggers

Asthma is a chronic disease of the lungs that react on various stimuli that was found on the patient’s body. The various stimuli, which are Sensitization and Inflammatory are different for each patient and it also could lead to asthma attack on different severity degrees. Information and knowledge regarding the cause factors of asthma is very important for patient, so they could have a better control of their asthma triggers and health conditions. In this paper, we developed a personalized asthma management system by using semi-supervised learning technique. The main methodology is to find pattern from patient daily information, then system will extract rules regarding their asthma trigger and classify them to each of asthma severity degrees. There is a dashboard that contain all the factors noted by the patient and evaluation of their asthma management. The result of experiment evaluation shown that the proposed system have 80% of accuracy, which proves that system reliable for a better asthma self-management.Keywords: asthma management,semi- supervised learning, dashboard systemPenyakit asma adalah penyakit radang kronis pada paru-paru yang bereaksi pada berbagai rangsangan yang terdapat pada tubuh penderitanya. Penyakit ini memiliki berbagai macam faktor penyebab terjadinya serangan asma dan tidak dapat digeneralisasikan. Selain itu, penyakit asma memiliki derajatnya masing-masing sesuai dengan tingkat keparahannya. Informasi tentang faktor penyebab asma penting karena penderita asma cenderung lalai dalam memperhatikan gejala atau faktor-faktor penyebab terjadinya asma sehingga mengakibatkan manajemen asma yang tidak baik. Pada penelitian ini dikembangkan aplikasi manajemen penyakit asma yang bersifat personal untuk masing-masing penderita, dengan mengadopsi teknik supervised learning. Data dan infromasi aktivitas harian penderita akan direkam oleh system, kemudian system akan mencari pola terkait faktor-faktor pemicu dan pemacu asma, serta mengklasifikasikannya berdasarkan pada derajat serangan asma. Pada system usulan terdapat dashboard yang memberikan informasi dan hasil evaluasi kondisi historis penderita asma secara mudah dan efektif. Dari hasil pengujian didapat akurasi system sebesar 80%, hal ini menunjukan system mampu membantu pasien dalam melakukan manajemen asma secara mandiri.Kata kunci: manajemen penyakit asma, semi-supervised learning, dashboard system

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Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: A randomized clinical trial

International Journal of Medical Informatics ◽

10.1016/j.ijmedinf.2014.07.008 ◽

2014 ◽

Vol 83 (11) ◽

pp. 805-813 ◽

Cited By ~ 20

Author(s):

Judith W. Dexheimer ◽

Thomas J. Abramo ◽

Donald H. Arnold ◽

Kevin Johnson ◽

Yu Shyr ◽

...

Keyword(s):

Clinical Trial ◽

Emergency Department ◽

Randomized Clinical Trial ◽

Management System ◽

Asthma Management ◽

Pediatric Emergency ◽

Pediatric Emergency Department

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Using Formal Methods to Guide the Development of an Asthma Management System

2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT) ◽

10.1109/dessert.2019.8770017 ◽

2019 ◽

Author(s):

Juan Carlos Augusto ◽

Mario Jose Quinde ◽

Nawaz Kahn

Keyword(s):

Formal Methods ◽

Management System ◽

Asthma Management

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A system uptake analysis and GUIDES checklist evaluation of the Electronic Asthma Management System: A point-of-care computerized clinical decision support system

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocaa019 ◽

2020 ◽

Vol 27 (5) ◽

pp. 726-737 ◽

Cited By ~ 1

Author(s):

Jeffrey Lam Shin Cheung ◽

Natalie Paolucci ◽

Courtney Price ◽

Jenna Sykes ◽

Samir Gupta ◽

...

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Management System ◽

Point Of Care ◽

Action Plan ◽

Asthma Management ◽

Clinical Decision ◽

User Feedback ◽

Care Quality ◽

System Usability Scale

Abstract Objective Computerized clinical decision support systems (CCDSSs) promise improvements in care quality; however, uptake is often suboptimal. We sought to characterize system use, its predictors, and user feedback for the Electronic Asthma Management System (eAMS)—an electronic medical record system–integrated, point-of-care CCDSS for asthma—and applied the GUIDES checklist as a framework to identify areas for improvement. Materials and Methods The eAMS was tested in a 1-year prospective cohort study across 3 Ontario primary care sites. We recorded system usage by clinicians and patient characteristics through system logs and chart reviews. We created multivariable models to identify predictors of (1) CCDSS opening and (2) creation of a self-management asthma action plan (AAP) (final CCDSS step). Electronic questionnaires captured user feedback. Results Over 1 year, 490 asthma patients saw 121 clinicians. The CCDSS was opened in 205 of 1033 (19.8%) visits and an AAP created in 121 of 1033 (11.7%) visits. Multivariable predictors of opening the CCDSS and producing an AAP included clinic site, having physician-diagnosed asthma, and presenting with an asthma- or respiratory-related complaint. The system usability scale score was 66.3 ± 16.5 (maximum 100). Reported usage barriers included time and system accessibility. Discussion The eAMS was used in a minority of asthma patient visits. Varying workflows and cultures across clinics, physician beliefs regarding asthma diagnosis, and relevance of the clinical complaint influenced uptake. Conclusions Considering our findings in the context of the GUIDES checklist helped to identify improvements to drive uptake and provides lessons relevant to CCDSS design across diseases.

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