Information and Knowledge Management using ArcGIS ModelBuilder

Jose Teixeira and Reima Suomi
The authors were the two observers, performing the observation and classification independently in
constant communication and results control. The observation was performed in Turku University using
simple and wide available tools such as a Internet browser, a Usenet newsreader and spreadsheet
software; it started on the seventh of February 2011 taking a non-continuous period of five days. On
the google search engine and using the three open-source project names and official websites as
keywords, we identified the patient to patient online communities presented in Table 2 , after manually
filtering query results. Google found a total of 1353 of web-pages mentioning the studied open-source
projects, from where we have been able to identify the eight mentioned patient to patient online
communities. All the eight online communities provided search and indexing mechanisms, enabling
an easy search for relevant users-feedback on the studied chronic care software projects.
We performed the observations on patient-community information following six different behavioural
classes that guided the structured observation. In Error! Reference source not found. we describe
how we classified the expressed feedback by carefully analysing the patient-provided free-form text
feedback. Using the simple evernote software available at http://www.evernote.com/, two individuals
classified the captured free-form text sentences in parallel obtaining the identical results, therefore
reducing the risk ambiguous behavioural classifications. Note that a single patient phrase could
express multiple behavioural attitudes towards the software.
Table 3: Structured observation classification system (authors).
Behavioural classification Description
User looks for use information Patient looks for information/help on how to use the software.
User express positive feedback Patient expresses satisfaction with the software or gratifies its developer.
User express negative feedback Patient expresses dissatisfaction with the software or blames its
developer.
User reports a bug Patient reports a bug/malfunction on the software.
User requests a new feature Patient suggest a new feature/requirement to the software.
User requests a competitor
feature
Patient suggest a new feature/requirement to the software explicitly
mentioning a competitor product already implementing it.
During our research we identified several open-source healthcare applications targeting chronic
patients; however it is extremely hard to find evidence in the public available information about opensource
projects where the developers manifest their chronic patient condition. This condition limited
the set of analysed projects, even if the research authors strongly believe that many software
applications are developed by the patients themselves, very few publicly reveal themselves as chronic
patients. From an universe of 36 chronic care healthcare software projects analysed, just three
projects contained public information where the lead developer revealed his patient status.
These three projects formed a sample of many other existing healthcare applications developed by
patients. The three projects were started by geographically distributed developers; project’s lead
developers that publicly revealed their nationalities were from Canada, US, Germany and Slovenia.
Two of the applications address the diabetes chronic disease and one other address fat-related
diseases.
The three cases studied are shortly introduced below.
Project A: GNU Gluco Control
The first of the three studied open-source software projects is named GGC- GNU Gluco Control and
both the software application and its source code can be downloaded from http://ggc.sourceforge.net/.
Current software available version is 0.4 Beta and its software developers provide language
translation for English, German and Slovene suggesting that those developers contribute from
different geographical locations, a typical pattern in open-source development teams. From the
sizable set of project’s software artefacts we found a research relevant text file, in which the two
leading developers clearly admit their chronic disease “since we are diabetics ourselves, we are trying
to make this software the best it can be” (GGC software project text file README.en, 2010).
Project B: My Self Health Information
The second observed project is named MSHI- My Self Health Information; it is a web-based
application that allows tracking and learning about personal health. The base of the software comes
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