M. Lipscomb, Alhefdi Mohammad, Alharthi Abdulrahman, L. Jololian
{"title":"Value-based modeling for mobile health application development.","authors":"M. Lipscomb, Alhefdi Mohammad, Alharthi Abdulrahman, L. Jololian","doi":"10.21037/mhealth-21-32","DOIUrl":null,"url":null,"abstract":"Background\nIn this paper is presented the use of value-based modeling, traditionally a business development tool, for the improvement of mobile health app design. The conceptual foundations for this work are design science, which is the scientific study and creation of artifacts, and convergence, which is a research method that in this case combines engineering with medicine. Relevant previous work done by the research team included the modeling of a case management system using process-based and information-based modeling techniques.\n\n\nMethods\nValue-based modeling represents actors who are exchanging with each other things of economic value, including service outcomes. The focus is on how value objects are offered, accepted, and exchanged in a network. Value-based models do not describe how transactions occur, but rather the net value of those transactions. This technique was applied to the design development of a mobile application system for the improvement of access to health services.\n\n\nResults\nSignificant value-based modeling was performed. These models highlighted the importance in healthcare delivery of effective value exchanges.\n\n\nConclusions\nThe results revealed a limitation on the net value of services delivery. These were related to constraints of time, cost, and responsibility. A design improvement was proposed: The development of an automated decision-making subsystem within the machine learning component of the app system. This subsystem would recommend between-visit micro adjustments to the plan of care based upon protocols established by the healthcare provider. Such would provide an agile response to the patient's changing needs as well as an amelioration to the challenges of access to services.","PeriodicalId":74181,"journal":{"name":"mHealth","volume":"8 1","pages":"16"},"PeriodicalIF":2.2000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"mHealth","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21037/mhealth-21-32","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Background
In this paper is presented the use of value-based modeling, traditionally a business development tool, for the improvement of mobile health app design. The conceptual foundations for this work are design science, which is the scientific study and creation of artifacts, and convergence, which is a research method that in this case combines engineering with medicine. Relevant previous work done by the research team included the modeling of a case management system using process-based and information-based modeling techniques.
Methods
Value-based modeling represents actors who are exchanging with each other things of economic value, including service outcomes. The focus is on how value objects are offered, accepted, and exchanged in a network. Value-based models do not describe how transactions occur, but rather the net value of those transactions. This technique was applied to the design development of a mobile application system for the improvement of access to health services.
Results
Significant value-based modeling was performed. These models highlighted the importance in healthcare delivery of effective value exchanges.
Conclusions
The results revealed a limitation on the net value of services delivery. These were related to constraints of time, cost, and responsibility. A design improvement was proposed: The development of an automated decision-making subsystem within the machine learning component of the app system. This subsystem would recommend between-visit micro adjustments to the plan of care based upon protocols established by the healthcare provider. Such would provide an agile response to the patient's changing needs as well as an amelioration to the challenges of access to services.