A computational approach to primary healthcare information quality indicators : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand
In many countries around the world Information and Communication Technologies
(ICT) are being leveraged to produce e ciency gains and cost reductions in healthcare
by making health information more readily available in clinical contexts. This raises
issues as to the use of health information in clinical decision making at point of care,
as relying on poor quality information in this context can have serious consequences.
This thesis investigates quality criteria that are used when assessing health information,
with the objective of formalising those criteria for use with a prototype software system.
Literature, as well as standards and currently used forms of electronic health records,
were reviewed for what they o er for assessment of health information quality. A lack
of criteria from these sources necessitated interviewing practicing General Practitioners
(GPs) to determine criteria important to them, and how they assessed the information
they want to use. Interviews were of a semi-structured type using vignettes, for clinical
context. Recruitment used a Snowball methodology. Results were analysed and interpreted
using Thematic Analysis and showed the GPs assessed information using criteria
based on tacit knowledge, formed from community knowledge and past experience.
The Quality Criteria (QC), discovered to be integral to this process, were formalised
using the developed Quality Criteria Model (QCM). A prototype system was developed
to demonstrate that using a current health information standard, meta-data could be
used to detect the presence of QC within health information and capture these via
instantiation of the QCM. The results of successful detection of QC are then Health
Information Quality Indicators (HIQI). Contributions for this thesis include the following:
the set of discovered QC, thematic maps that capture the combination of criteria
and the process used when applying them, the formalised model for QC (the QCM), determination
that additional meta-data will be required to detect those QC categorised
as being subjectively evaluated, and the demonstration that a software system can detect,
and capture, QC found in health information. Implications are discussed such as
that just having access to information is insu cient, and subjectively evaluated QC
are problematic for implementation and use. Finally, conclusions are drawn and future
work suggested such as user interface development for HIQI representation, alternative
search algorithms for QC detection, and further development of the prototype toward
a production system.