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Archive (2015–2005)

A Pictorial Schema for a Comprehensive User-oriented Identification of Medical Apps

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
Topic:

Focus Theme
Using Data from Ambient Assisted Living and Smart Homes in Electronic Health Records
Guest Editors: P. Knaup, L. Schöpe

DOI: http://dx.doi.org/10.3414/ME13-01-0093
Issue: 2014 (Vol. 53): Issue 3 2014
Pages: 208-224

A Pictorial Schema for a Comprehensive User-oriented Identification of Medical Apps

Original Article

S. Bonacina (1), S. Marceglia (1), F. Pinciroli (1)

(1) eHealthLAB – Laboratorio di Informatica BioMedica e Sanità Digitale, DEIB – Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy

Keywords

Quality Control, Computers, Media tablet apps, handheld/trends, credentialing, software/utilization

Summary

Objectives: The huge amount of released medical apps prevents medical app users from believing that medical scientific societies and other accreditation bodies as well, have the resources and the power for assigning to any medical app a quality score. By the time being, any medical app user has to take the risks related to the frequently insufficient accreditation of that app. Providing clear user-oriented schemas, to be adopted both when putting a medical App on the market and when an App comes to be evaluated by a cohort or single users, becomes crucial. The aim of our research was to define a pictorial identification one-shot schema for a comprehensive user-oriented identification of medical apps.

Methods: Adopting a pictorial approach is common in software design modeling. To build up our identification schema we started from the limited number of Apps already available on a web site of app reviews (iMedicalApps.com), and we identified an appropriately large set of attributes for describing medical apps. We arranged the attributes in six main families. We organized them in a one-shot comprehensive pictorial schema. We adopted a traffic light color code for assessing each attribute, that was sufficient to provide simple elements of alerts and alarms regarding a single App. Then, we considered apps from iMedicalApps.com web site belonging to three medical specialties: cardiology, oncology, and pharma and analyzed them according to the proposed pictorial schema.

Results: A pictorial schema having the attributes grouped in the families related to “Responsible Promoters”, “Offered Services”, “Searching Methods”, “Applications Domains”, “Envisaged Users”, and “Qualifiers and Quantifiers” has been identified. Furthermore, we produced a one-shot pictorial schema for each considered app, and for each medical specialty, we produced it also in an aggregated form.

Conclusions: The one-shot pictorial schema provides a useful perception of when and where to use a considered app. It fits positively the expectations of potential but different user’s profiles. It can be a first step towards a systematic assessment of apps from the user viewpoint.

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