Q. Wang, Y. C. Shen / J. Biomedical Science and Engineering 6 (2013) 1072-1076 1073
Thus, there is a need to build a data warehouse that al-
lows full analysis towards strategy development [6].
2. DATA ANALYSIS AND MODEL
BUILDING
In cooperation with rural community health service cen-
ters, we analyzed data sources towards promoting com-
munity health management for osteoporosis by means of
a data warehouse.
2.1. Data Research
We invited staff from Yushan Xinglong Community Health
Service Center, the Departments of Orthopedics, Gyne-
cology and Obstetrics, and Endocrinology of Changshu
Hospital of Tradition al Chinese Medicin e, Health Bureau
of Changshu, and software engineers to compose the
group for our data research. The outpatient clinic of
Changshu Hospital of Traditional Chinese Medicine was
responsible for the organization and communications re-
lated to this study, and the data warehouse was construct-
ed for physicians from Yushan Xinglong Community
Health Service Center and the indicated departments of
Changshu Hospital of Traditional Chinese Medicine and
health management institutions. The data warehouse us-
ers were provided with basic training with regards to th e
study concept and required levels of analysis.
2.2. Data Scope
Data source was divided according to subjects and data
research to integrate information models of various busi-
ness systems for macroscopic merge, abstraction, and da-
ta scope to confirm that all required data were extracted
from business systems and well organized. The data scope
was defined as community residents with osteoporosis or
at a high risk of osteoporosis; it mainly comprised post-
menopausal females and elderly males (older than 60
years). The data of all residents in the data scope were
used in the study except those of residents who moved
out of the community or died.
2.3. Subject Elements of Community
Management for Osteoporosis
The subject elements for community management of os-
teoporosis included dimension (content of business), sub-
jects (includes data of subjects), particle size (dimension
levels to extract data details), and storage limit (of data).
Based on a comprehensiv e analysis of data conducted by
physicians from community health service centers and
the indicated departments of Changshu Hospital of Tra-
ditional Chinese Medicine, several dimensions were con-
firmed; these included baseline information, osteoporo-
sis-related high-risk factors, bone density, and assess-
ments of interventions. Each dimension was divided into
several levels, and particle size was used to confirm and
elucidate the dimension levels.
Osteoporosis-related high-risk factors are complex.
Among males, 21 high-risk factors have been identified,
whereas 26 have been identified among females [7]. We
screened the high-risk factors and provided options for
data entry, analysis, and minin g. Evaluated osteoporosis-
related high-r isk factors comprised age, body mass, fam-
ily history, and nutritional factors; intervention measure-
ments included appropriate diet, exercise, sufficient cal-
cium intake, vitamin D intake, and correction of poor life-
style habits; bone density examination items included quan-
titative computed tomography, dual energy X-ray absorp-
tiometry, and ultrasound bone intensity examination; treat-
ment measurements included calcium agents, vitamin D,
alendronate sodium tablets, calcitonin, Acla sta, a nd est ro-
gen replacement therapy. The dose and duration of each
intervention, examination, and treatment were recorded.
The snowflake model was used to summarize the a b o v e
multidimensional data relationships; the model consisted
of a fact table and a group of dimension tables. This al-
lowed the dimension factors to be further divided; for
example, intervention factors included sports, amount of
sunlight, appropriate diet, and medication. The “interven-
tion” dimension could be snow flaked, that is, the dimen-
sions could be decomposed in terms of the attributes for
sports, amount of sunlight, appropriate diet, and medica-
tion to form four-dimension tables. For the “female” di-
mension, time since menopause had to be recorded as the
particle size.
2.4. Dynamic Loading
Community management needs to be continuously up-
dated so as to provide dynamic data loading when con-
structing a database. Traditional data warehouses store
diachronic, resting, and integrated business data, which
initially load data and then support business searches.
However, data loading with a dynamic data warehouse
can load data while simultaneou sly allowing users to co n-
duct searches. Moreover, dynamic loading does not af-
fect the use of the data warehouse, which allows the im-
mediate analysis of loading data. The intervention measures
and bone density measurements of subjects could be con-
tinuously recorded.
3. CONSTRUCTION OF DATABASE AND
PHYSICAL ACHIEVEMENT
3.1. Fact Table of Data Warehouse
The fact table contains all the osteoporosis health data.
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