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Introduction Seasonal Patterns
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Acknowledgements
Method
Demographic by Clinic Type
Conrad Ng ([email protected]), Anatoliy Gruzd ([email protected]) – School of Information Management, Dalhousie University
Calvino Cheng, Bryan Crocker, Don Doiron, Kent Stevens – Capital District Health Authority, Halifax, Nova Scotia, Canada
Clinic to Clinic Network
Conclusions
Physician to Clinic Network
This project is funded by MITACS and CDHA. We also thank
the CDHA Pathology Informatics Group for assisting in the data
extraction and verification process.
This research uses data visualization techniques and
social network analysis to determine the status and
efficiency of laboratory ordering for the outpatient
system in Nova Scotia, Canada.
Currently, the Capital District Health Authority (CDHA)
model demonstrates that approximately 60% of
laboratory ordering originates in the outpatient setting
and is costing the province approximately $3.3 million
per month.
The goal of this pilot project is to turn the vast amount
of data in the CDHA’s laboratory information system
into usable information and allow the CDHA to identify
usage trends to better understand the future demands
on lab testing and allow policymakers more insight
into the Nova Scotia primary care landscape.
1. Extracted anonymized, outpatient lab test orders
from CDHA’s Laboratory Information Systems for
the period from May 2009 to May 2011
2. Re-indexed and cleaned records (e.g. assign
unique identifiers and work addresses to physicians
and clinics)
3. Descriptive analysis & visualization with Microsoft
Excel 2010
4. Network analysis & visualization with ORA 2.3.2
(developed by CASOS at Carnegie Mellon
University) based on the 3 networks: Clinic to Clinic (C2C), Physician to Clinic (P2C),
Physician to Physician (P2P)
Dataset Summary
# of Records 925,680
# of Clinics 196
# of Physicians 426
# of Patients 278,689
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# o
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Average Weekly Referrals
May 2009 - April 2010
May 2010 - April 2011
This chart confirms seasonal patterns based on
holidays and long weekends.
There are consistently less tests ordered during
major holidays (see the “valleys” in the chart), often
followed by a spike of these orders.
Connection = physician’s affiliation with a clinic(s)
Node Size = # of patients
Most physicians who work at the Family Focus and
Walk-in clinic groups also work at other clinics.
The nodes (dots) are clinics; the size of the nodes
represents the total number of unique referrals from
that clinic.
Two nodes (clinics) are connected if they share 50 or
more patients (“strong” connections).
While the Family Focus and Walk-in clinics only
account for about 10% of all lab testing referrals, they
appear to be relatively “central” in this network.
This network visualization can be used to identify
“well connected” clinics, ideal for disseminating new
information to physicians and patients.
Even relatively simple visualizations can offer useful
insights to managers and other health professionals
while helping them build a predictive model of
laboratory utilization.
The network visualizations uncovered hidden
connections between clinics and provided some
additional insights into the migration practices of
patients among clinics.
These visualizations can also be applied to make
more effective health spending and planning decisions
in other similar healthcare systems.
Walk-in, Family Focus, and Specialist type clinics are
more likely to refer younger patients (18-30 years of
age) to the outpatient laboratory testing facilities, while
General-type clinics are more likely to refer older
patients (48-66 years of age).
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Den
sit
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Patients' Age Group
Network Density of Clinic-to-Clinic Networks for Different Age Groups
Density = # of actual connections in the network
divided by the number of possible connections.
The densest networks corresponded to the age
group between ~20 and 35.
This suggests that young adults are less likely to
stay with the same clinic.
Funded by: