Information and Knowledge Management using ArcGIS ModelBuilder

Maryati Mohd Yusof
Further framework validation and identification of HIS adoption factors were undertaken through a
qualitative systematic review. A number of the following steps were taken to identify the adoption
factors of Health Information Systems in the selected studies (Adapted from Wyatt (personal
communication, October 11, 2005)):
1. Assess the adoption status of each study. This was done by classifying the study outcome as
positive, negative, mixed positive and negative or unclear for the following adoption criteria (that are
based on the phases in the system development life cycle): system installation, clinical acceptance,
clinical use at specified months, impact on clinical practice, impact on patient care and impact on
health organisation.
2. For each study with at least one definite positive, mixed or negative outcome, assess the presence
of the following potential success factors according to the HOT-fit framework and emerging themes:
Technology factors such as ease of system use, system flexibility and information accessibility.
Human factors such as user training and user attitude towards system.
Organisation factors such as leadership, system champion and clinical process.
All three factors of technology, human and organisation that influence the net benefits.
Factors that influence specific type of system
The fit between human, organisation and technology.
3. Identify the most critical adoption factors according to their occurrences in the case studies and
their association with the success and failure of HIS adoption cases.
4. Examine the relationship between these critical factors using a conceptual map.
4. Results
The research identifies and highlights the following dominant adoption factors: technology (ease of
use, system usefulness, system flexibility, response time/turnaround time, information accessibility,
information relevancy, technical support); human (clarity of system purpose, user involvement, system
design, user training, user perception, user skill/knowledge, user role); organisational (clinical
process, leadership, champion, medical sponsorship, project committee, bridger/mediator), internal
communication, resources, organisational readiness, Inter-organisational Systems as well as fit
between them.
5. Discussion and conclusions
The multi-disciplinary approach of HOT-fit addresses the essential components of IS, namely human,
organisation and technology, and the fit between them using a comprehensive, continuous and
systematic approach. The use of HOT-fit framework can unfold the complexity and can greatly
increase the understanding of the whole human, organisational and technology issues, their
relationships and fit with each other, system problems and performance. In addition, the HOT-fit
framework can also be used to identify the process and information requirement of complex
application. HOT-fit provides comprehensive evaluation factors, dimensions and measures to analyse
the overall view of the essential system components, development, use, and net benefits. It also
incorporates the system development dimension to enable a continuous and systematic evaluation.
These are the elements that should be identified to ensure the realisation of HIS.
We evaluate the adoption of two case studies and carried out a systematic review to justify their
realisation and relative contributing factors. The findings offer a strong indication of the applicability
of the framework to HIS evaluation where the adoption factors in the human, organization, technology
and fit between them identified in both research strategies confirmed with the essence of the
proposed framework – the three factors, their respective dimensions, measures and the fit between
them. A number of challenges have been encountered while applying the HOT-fit framework in data
analysis. First, there are a number of data that share the same evaluation measures; careful
consideration has to be taken in selecting the most appropriate measures for each data. Second, the
classification of data analysis according to the three evaluation factors has affected the flow of the
narrative approach of presenting the data and can result in confusion to the reader.
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