409806
JHIXXX10.1177/1460458211409806Schuurman et al.Health Informatics Journal
Article
A spatial decision support
tool for estimating population
catchments to aid rural and
remote health service allocation
planning
Health Informatics Journal
17(4) 277­–293
© The Author(s) 2011
Reprints and permission: sagepub.
co.uk/journalsPermissions.nav
DOI: 10.1177/1460458211409806
jhi.sagepub.com
Nadine Schuurman and Ellen Randall
Simon Fraser University, Canada
Myriam Berube
University of British Columbia, Canada
Abstract
There is mounting pressure on healthcare planners to manage and contain costs. In rural regions, there is a
particular need to rationalize health service allocation to ensure the best possible coverage for a dispersed
population. Rural health administrators need to be able to quantify the population affected by their allocation
decisions and, therefore, need the capacity to incorporate spatial analyses into their decision-making process.
Spatial decision support systems (SDSS) can provide this capability. In this article, we combine geographical
information systems (GIS) with a web-based graphical user interface (webGUI) in a SDSS tool that enables
rural decision-makers charged with service allocation, to estimate population catchments around specific
health services in rural and remote areas. Using this tool, health-care planners can model multiple scenarios
to determine the optimal location for health services, as well as the number of people served in each instance.
Keywords
GIS, healthcare rationing, health service allocation, informed decision-making, spatial decision support
systems, webGUI
Introduction
The growing need for rationalized health service allocation
Governments worldwide are grappling with the combined pressures of escalating healthcare costs
and decelerated economic growth. In 2010, the Organisation for Economic Co-operation and
Corresponding author:
Nadine Schuurman, Department of Geography, RCB 7123, Simon Fraser University, Burnaby, BC Canada
Email: nadine@sfu.ca
278
Health Informatics Journal 17(4)
Development (OECD) reported that its member countries were experiencing a trend toward
healthcare expenditures representing an increasing share of GDP.1 While circumstances may vary
from country to country, certain common drivers have been identified for this growth in healthcare spending, including a shifting demographic profile, increasing consumer expectations, and
the evolution of technology.1–5 Globally, therefore, contemporary healthcare planners are beset by
a number of competing and, at times, conflicting demands: to evolve service delivery to meet the
needs of a population that is aging and increasingly contending with chronic disease; to embrace
and deliver technological and procedural advances; and to practice sound fiscal management in
the face of soaring costs.
Against this backdrop, there is a growing emphasis being placed on innovative and informed
health services planning.6–8 Further, there is a heightened need, given current economic constraints,
for this planning to be prescient. Sound decision-making must be grounded in current reality, yet
employ a forward looking lens:9 specific goals must be strategized and the means to attain those
goals methodically developed. A critical component of contemporary healthcare planning is the
rationalization of resource allocation in order to configure the supply of healthcare to maximize
efficiency and coverage, while supporting system sustainability.6,9 Responsible decision-making
about health service allocation must make every effort to realize the greatest return on increasingly
limited resources, not only in the short term, but in the long term as well.9
Therefore, healthcare decision-makers are under mounting pressure to anticipate both immediate and far-reaching solutions when rationalizing health service allocation. Their mandate is formidable: to balance the goal of maintaining a system that is effective and provides optimal quality of
care, while ensuring that funding and resources are apportioned in a fiscally responsible and integrated manner, and do so in a manner that will render the greatest possible sustainable good across
a variety of population needs.10 For policy-makers and administrators at every level, the opposing
forces of increased demand for basic and state-of-the-art services, and the decreased ability of
governments to provide sufficient funding, has heightened the “. . . need for systematic, evidenceinformed, transparent approaches to setting healthcare priorities”.8 As healthcare administrators
strive to find ways to stretch their healthcare dollars in an era of intensified demand for accountability, there is a requirement that decisions about health service allocations be founded on defensible, relevant data. Ready access to reliable, high quality data that delineates the needs of the
population being served, as well as the existing capacity of the system to meet those needs, is vital
for planning.6 Armed with a comprehensive understanding of the status quo, planners must not
only assess the current equation of supply and demand, but endeavour to anticipate how this equation might shift in the future.6,11
Understanding how current and future service capacity intersects with the demographic profile
of the population is a critical component of forecasting.11 This ability rests on having access to
analytical tools that enable healthcare administrators to weigh options and identify the relative
merits of a decision in advance of implementation in order to increase the likelihood that allocation
decisions will support a health system’s on-going sustainability.6,12 Faced with the challenge of
allocating scarce resources within a health region or jurisdiction, health services administrators
need tools that allow them to explore options in service configuration planning so as to optimize
their chance of identifying sustainable, long term solutions.
The added challenge of geography: Rural health service allocation
The need for rigorous, informed planning takes on a particular significance in nations who
must rationalize health service allocation for a significant rural and remote population. These
Schuurman et al.
279
administrators are tasked with distributing healthcare services to provide the best possible coverage to a dispersed population. In doing so, they must make allowances for the actuality that
many of these communities are somewhat isolated, often have limited health human resources
and service infrastructure, and are typically geographically remote from secondary or tertiary
care hubs.2,13
While not always sufficient to ensure access to care, geographic accessibility is, nonetheless, a
necessary prerequisite.14 Given economic, resource and geographic constraints, however, rural and
remote administrators are not typically in a position to deploy a full range of healthcare services in
each community. The current climate of fiscal conservancy encourages consideration of facility
closures and service reallocations;15,16 rural facilities are often a target in rationalization exercises
as they tend to be limited in scope of services and relatively expensive to run.16–18 It is critical,
however, that discretionary decisions about the strategic siting of health services in rural regions
factor in an understanding of the systemic interdependence of rural communities. Services must be
situated to ensure the greatest number of people can access care in a timely manner. Investment in
one site may necessarily be at the expense of another: this trade-off is apparent in the phenomenon
of centralization of delivery, where key services are concentrated in a larger service hub in an effort
to contain costs.15 However, centralization necessitates greater travel, expense and disruption for
people in the surrounding smaller communities.2,19
Given all of these considerations, rural healthcare administrators must endeavor to gauge the
systemic ramifications of any service allocation decision: whether it be to open, expand, reduce or
close a service, these decisions must consider the entire population affected, which typically
extends far beyond local residents to encompass an outlying population as well. Understanding the
size and spatial distribution of the rural population is crucial for effective healthcare planning, as
these two factors can “. . . significantly influence the mode and form of service delivery”.2 Having
a tool that would allow these administrators to accurately estimate the population served by any
given facility would be an invaluable asset in comprehensive regional service planning.
The role of spatial decision support systems (SDSS) in supporting rural health
service allocation
Rural healthcare planners, charged with allocating services and managing resources over a geographic region, must have the capacity to incorporate spatial analyses into their decision-making
process. With so much at stake and relatively few resources, it is paramount that these administrators have ready access to data needed to inform these decisions. Consequently, they have a need for
a tool that provides the capability to draw on, and integrate, existing data on the distribution of their
population, and the healthcare infrastructure and services in place to provide for that population.
Further, in the interests of methodical, future-focused planning, such a tool should enable planners
to configure and model various options in service allocations.
A spatial decision support system (SDSS) can provide this capability.20,21 Based on geographic
information system (GIS) technology, SDSS tools are designed to collect, integrate and model
spatial data.22 Use of such a tool can allow healthcare decision-makers to explore questions with a
critical spatial component, deciding, for example, where best to site a specific service in order to
most effectively serve a widely distributed target population. An effective SDSS tool would provide rural decision-makers with a means of accessing relevant data from existing datasets and
model prospective service configurations within and across their regions.23–25 An SDSS tool, powered by the intelligence and functionality of GIS, is an invaluable support in mapping the relationship between healthcare service delivery and geographic accessibility to those services.
280
Health Informatics Journal 17(4)
Making SDSS accessible: the web-based graphical user interface (webGUI)
Having the ability to integrate accurate, timely spatial analyses into their decision-making processes is important for rural healthcare planners. Successful decision-making is founded on having
the requisite skills, technology and administrative infrastructure to support the process.6,26 In order
to incorporate an SDSS tool into their process, however, many healthcare planners have had to
rationalize contracting with a GIS consultant. Alternatively, making the investment to bring the
conventional GIS technology in-house has depended on the administrators having the adequate
financial and technical capacity. In a highly pressurized environment of cost containment, these
options can render the benefits of SDSS beyond the reach of many healthcare planners. A further
consideration in a rural context is that team-based decision-making may require the facilitation of
remote participation across multiple sites, creating a need for easy information sharing. In order to
realize the potential of SDSS, rural administrators need a tool that is accessible in the broadest
sense: it would require a minimum of technical expertise to employ; need only a basic technologic
platform on which to run; and be designed in such a way as to facilitate the dissemination of findings across the decision-making team. This would afford health services administrators the opportunity to readily combine current, relevant data with the expertise and experience of their
decision-making team, greatly improving the odds of creating a sound tactical plan to meet their
region’s healthcare delivery strategy.
With data-informed decision-making, the extent to which a jurisdiction is resourced with both
information systems and people trained to use those systems, can serve as a constraint.27 For
regional healthcare planners to readily adopt an SDSS tool, the threshold for both specialized
resources and expertise must be lowered. Developing an SDSS tool that incorporates a userfriendly graphical user interface (GUI) can facilitate critical spatial modeling, employing fairly
basic user interfaces requiring minimal training.22,27,28 Further, an SDSS tool that is web-based and
runs on standard desktop platforms would be both highly accessible and cost-effective, allowing
greater access to and participation in a GIS-informed decision-making process.22,29,30
Having the capability to draw on appropriate datasets and model service configuration scenarios
as part of the standard decision-making process for rural health service allocation would provide
decision-makers with greater flexibility to explore relevant options.31–33 For team-based decisionmaking processes, using an intuitive, web-based SDSS tool to model options and share information
has been found to increase team participation and interaction,30,34 decrease the need for training,30,35 and reduce the need for overall facilitation of the decision-making process.22,36 In addition,
having a tool that will graphically render this key relationship between rural populations and existing or potential services will also enable its users to incorporate these data, in a readily accessible
format, into their regional performance reporting. In these ways, a web-based SDSS tool that
allows rural healthcare planners to model the implications of proposed service modifications will
go a long way to assisting in the management of the twin challenges of geography and
population.
The Health Services Locator (HSL) webGUI
The Health Services Locator (HSL) webGUI is a web-based decision-support tool developed specifically to assist rural health administrators charged with making decisions about the location and
allocation of health service resources in the Canadian province of British Columbia. By enabling
them to map population catchments, it allows these decision-makers to generate answers to service
allocation questions in-house, even if the team includes multiple stakeholders across multiple sites.
Schuurman et al.
281
Canada is a country with a large rural and remote population; the 2006 national census found
that roughly 20% of Canadians live in rural settings.37 In the province of British Columbia, approximately 15% of the population is designated as rural.37 Canada’s healthcare system is multi-tiered,
with provincial governments bearing the overarching responsibility for the administration and
delivery of healthcare for their populace. In British Columbia (a region covering an area larger than
France), this responsibility is shared by five regional health authorities that must balance supply
and demand in a manner that reflects both their constituency and their resources. The challenges
facing these health authorities differ according to the size and distribution of their population and
the extent to which their region is either urban or rural.
Under constant pressure to contain costs, Canadian healthcare decision-makers have placed
hospitals under close scrutiny because of the magnitude of their cost to the system. As a result,
hospitals deemed to be inefficient have been selected for closure or downsizing and repurposing.15
In some rural Canadian jurisdictions, where the operating costs for smaller hospitals are often proportionately higher, and the capabilities more limited than their urban counterparts, closures have
taken place, or hospitals have had their services reduced.16 While these tactics may meet the
demand for cost containment, they do not always facilitate equitable access for residents in regions
with geographic barriers: those that are rural and remote and, in the case of British Columbia, often
mountainous.19,38,39 For health authority administrators with a large rural population, ensuring
timely access to services is a critical component of regional service allocation, especially for certain acute, time-sensitive services such as obstetrics, cancer treatment and trauma care.
Working to balance fiscal restraint with the provision of adequate services across a dispersed
geographical base has created a need for these administrators to understand the critical relationship
between location of services, depth of services provided and the distribution of the population.25 To
that end, the HSL webGUI has the capability of integrating existing datasets for rural hospitals and
services with those of regional populations and road networks, making it possible for users to
determine the percentage of population in a specific catchment served by a specific health service
or resource.
With the current version of the HSL webGUI, policy-makers are able to select individual hospitals and clinics in non-metropolitan British Columbia and determine total populations within a one,
two and four hour drive time of the services. In addition, the first release of the HSL webGUI has
service level information for trauma services, obstetric services and provincial cancer clinics. This
feature allows the selection of specific service levels (e.g. full obstetrics, general practitioner surgeon or midwife only) and the subsequent calculation of population catchments for these specific
service levels. This permits quantification of service capacity by region, based on proportion of the
population served.
Data and methods
Population catchments
The HSL webGUI works with population catchments, based on the percentage of the rural population within given travel times of the hospital or facility in question. It is important to note that in
rural and remote settings, a service or hospital catchment is often congruent with a population
catchment, reflecting the reality that there is typically “. . . only one ‘natural’ choice for patients,
and it is dependent on travel time and geographic proximity”.25
The catchments for the HSL webGUI quantify the population around a service, within one, two
and four hour drive times—times based on the standards set by the British Columbia Ministry of
282
Health Informatics Journal 17(4)
Health, which specified the maximum acceptable distances to any given service in order that this
service be accessible to 98% of the population within its respective health authority.40 While the
Ministry of Health originally defined these time limits (one hour for emergencies; two hours for
acute inpatient services; and four hours for speciality services) based on ‘crow-fly’ or straight line
distances,40 the catchment areas for the HSL webGUI have been calculated using road travel time
to better capture the experience of actual patients.
Data
The HSL webGUI integrates five existing datasets providing decision-makers with access to the
most current data for British Columbia hospitals and services, roads, and population size. The
British Columbia hospital data indicates presence or absence of various health services (e.g. neurosurgery, Intensive Care Unit). The British Columbia road network data provides real vehicle
travel times, extracted from a value-added GIS data set, for travel in both directions. The British
Columbia census block population data determines the populations within specific travel times of
a hospital. Census blocks are the smallest demographic unit for which population counts are
released to the research community by Statistics Canada; they correspond to approximately a half
city block. Spatial data files corresponding to the five regional health authorities are used to enable
users to contain their search to a specific health authority. Lastly, data from the British Columbia
Census Metropolitan Area dataset were used to confine analysis to rural areas. From these data, a
new suite of datasets was derived which include a health authority population table, and an origindestination cost table that lists populations within one, two and four hour travel times to each
hospital.
Analyzing the Health Services Locator (HSL) webGUI catchments
An origin-destination cost table containing census block to hospital travel times was generated
using ESRI’s Network Analyst New OD Cost Matrix tool (in ArcMap 9.1). The catchments, or
service areas, were created in ArcMap 9.1 using the Network Analyst New Service Area tool.
Three maximum travel times were designated, defining the catchments as one, two and four hours.
The original ArcMap version of the GUI could only be utilized by users with ArcGIS Desktop
(software which includes the ArcMap application) installed on their personal computer. The data
required for the hospital catchment maps and calculations also had to be located on the user’s computer, clearly limiting the functionality for the vast majority of potential users. The HSL webGUI
circumvents these requirements.
From a server side perspective, the webGUI was created using ESRI’s ArcIMS 9.1 software.
ArcIMS is a software application designed specifically for publishing interactive maps on the
Internet. An in-house web server was used to serve the website. In addition, Java was used to
develop the web application. The ArcIMS software was used in conjunction with Apache HTTP
Server. As many of the ArcIMS components were built with Java, a Java Development Kit (JDK)
and a servlet engine (Apache Tomcat) was also required.
It is possible to create a website using an ArcIMS template but this requires using javascript and
ArcXML to add functionality. As creating the Hospital Catchment webGUI involved considerable
coding for custom functionality, it was decided to create a custom web application using Java
Server Pages. The Java Connector, which contains the ArcIMS Java Connector API JavaBeans,
was used for this purpose. These JavaBeans and their associated methods can be used in Java code
to implement map-related functions. The ArcSDE Java API JavaBeans were also used to query the
Schuurman et al.
283
tables in the Microsoft structured query language (SQL) database. The ArcSDE Java API was
obtained from the ArcSDE Client software. The data were copied to a SQL database, which allows
concurrent multi-user access, and an ArcSDE server functioned as the gateway between the database and the GIS software.
Significantly more information on the technical details of the analytical process is provided in
Schuurman et al.41 The goal of this paper is to demonstrate the utility of the webGUI to aid in making health services allocation decisions in rural and remote areas. This utility is demonstrated in the
results below.
Results: Based on three sample scenarios
Access to obstetrical services
The HSL webGUI illustrates service capacity by region and by catchment, allowing hospital
resource allocators to map obstetric, trauma and cancer care services against rural populations
within pre-specified travel distances. The province of British Columbia mandates that access to
core specialty services, including obstetrics, be available to 98% of a health authority’s population
within a four hour travel time.40 The HSL webGUI provides a means of ascertaining the extent to
which this mandate is served within non-metropolitan areas of British Columbia. A query to the
HSL webGUI revealed that at the provincial level, the target of 98% has not quite been realized,
with only 94% of the rural population living within the mandated four hour travel time to obstetric
services. The webGUI can calculate and display the population that is within four hours of any
established obstetrical services, as illustrated in the Catchment Report shown in Figure 1.
In addition to the Catchment Report, the results are displayed in map format in the main window
of the webGUI. In Figure 2, the map of British Columbia is displayed with four hour travel time
catchments shown around all hospitals that offer obstetrical services. From the map, decisionmakers can quickly discern that the north-western coast of the province, including the Haida Gwai
(Queen Charlotte) islands, is not well served by maternity services. The user can also click on the
‘i’ (information) symbol in the upper right of the webGUI and click on a square hospital icon on
the map to determine the level of the maternity services, as well as the levels of other services,
offered at that hospital. This information is the basis for subsequent modelling in which the user
can hypothetically add or remove obstetrical services from a hospital and re-run the analysis to
determine how well a new service configuration may serve the population.
Access to trauma services
A second sample query addresses areas in the Northern Health Authority that are outside of onehour-to-trauma services. There is evidence to support better outcomes associated with arrival at
definitive trauma care within one hour of injury.42 Termed the ‘golden hour’, this window represents the optimal total time from a severe injury event to care. In British Columbia, there are eight
designated trauma hospitals, and trauma service levels are assigned based on the baskets of services available at designated hospitals. The HSL webGUI can calculate levels of access by population for any of the five health regions in the province, or for an aggregate area incorporating the
entire province. In this sample query, we determined the catchment for the lowest level of trauma
care (level 3 centre) in the Northern Health Authority. A level 3 centre has basic trauma certification with an emergency room, an intensive care unit, general surgery and orthopaedics, but lacks
neurology services. There is no higher level of trauma care offered in the Northern Health Authority.
284
Health Informatics Journal 17(4)
Hospital Catchments
Rural population/area of interest: all British Columbia
Maximum travel time: 2 hours
Service type: maternity
Levels: 1
Hospital
ID
Hospital name
14
Campbell River and District General Hospital
94,462
6.26
70
Cariboo Memorial Hospital
52,592
3.49
18
Chilliwack General Hospital
90,575
6.01
22
Cowichan District Hospital
67,380
4.47
21
Dawson Creek and District Hospital
24,519
1.63
East Kootenay Regional Hospital
64,302
4.26
25
Fort St John General Hospital
30,804
2.04
30
Kelowna General Hospital
149,789
9.93
Kootenay Lake Hospital
29,888
1.98
Lions Gate Hospital
24,821
1.65
7186
0.48
8
2
39
1
Matsqui-Sumas-Abbotsford General Hospital
60
Mills Memorial Hospital
38
Nanaimo Regional General Hospital
41
Rural
population
served
Percent
of British
Columbia rural
population
34,712
2.3
121,408
8.05
Penticton Regional Hospital
78,768
5.22
47
Powell River General Hospital
17,865
1.18
48
Prince George Regional Hospital
96,250
6.38
49
Prince Rupert Regional Hospital
13,210
0.88
29
Royal Inland Hospital
126,180
8.37
89
St. Mary’s Hospital
27,280
1.81
62
Trail Regional Hospital
34,609
2.3
64
Vernon Jubilee Hospital
120,172
7.97
68
Victoria General Hospital
2783
0.18
42
West Coast General Hospital
48,443
3.21
1,357,998
90.06
149,800
9.94
Total rural population served
Rural population not served
British Columbia rural population
1,507,798
100
Figure 1. Catchment report generated by a request to the Health Services Locator (HSL) web-based
graphical user interface (webGUI) to view all catchments in the province within a two hour road travel
time of level 1 obstetrical services. Note that, although the province’s accessibility standards dictate that:
“Access to basic inpatient hospital services will be available within two hours travel time for 98% of
residents within the region”, just over 90% are actually within the mandated service area.
Schuurman et al.
285
Figure 2. The resulting catchment map displayed in the main Health Services Locator (HSL) web-based
graphical user interface (webGUI)window when the query to show two hour travel time catchments to
obstetrical services is chosen. The user can identify which hospitals do not offer obstetrical services from
the map. When a user zooms in, they can also view roads outside the two hour catchment. Note that the
catchments have a snake like appearance: this is because they follow the road networks in the province.
Figure 1 provides the population figures associated with the travel time catchments.
As Figure 3 illustrates, access to trauma services is limited in this large geographical area, with
close to 70% of the population outside the recommended one hour travel time to trauma care.
Access to provincial cancer centres
A final example of the utility of the HSL webGUI in generating data to inform allocation decisions is based on access to provincial cancer centres. Presently, the province of British Columbia
has five fully functioning cancer centres. In addition, one is near completion and promises to be
fully functional in the next two years. For the purposes of the webGUI, existing centres are considered ‘level 1’, while proposed centres are considered ‘level 2’. While cancer centres do not
286
Health Informatics Journal 17(4)
Hospital Catchments
Rural population/area of interest: Northern Health Authority
Maximum travel time: 1 hour
Service type: trauma
Levels: 3
Hospital ID
Hospital name
48
Prince George Regional
Hospital
Rural population
served
Percent of northern
rural population
82,812
30.02
Total rural population served
82,812
30.02
Rural population not served
193,040
69.98
Northern rural population
275,852
100
Figure 3. Catchment report generated by the query to find one hour travel time catchments for level 3
trauma services (limited to emergency room, intensive care unit, general surgery and orthopaedics) in the
Northern Health Authority. Note that only 30% of the population in this Heath Authority has access to
trauma services within the ‘golden hour’ for treatment of severe injury.
offer emergency care per se, it is clear that proximity is a priority for cancer patients and their
families, so provincial queries were run for both one hour and two hour travel time. An analysis
of current access to cancer treatment centres using the HSL webGUI reveals that only 19% of the
provincial non-metropolitan population is within one hour of a cancer centre. A second query
further reveals that just over 34% of this population has access to specialized cancer care within
a two hour road travel time, as shown in Figure 4. Clearly, the relatively low level of access has
policy implications. For instance, a study in New Mexico revealed that increased travel distance
was associated with a lower likelihood of patients receiving recommended radiation therapy following breast conserving surgery for cancer.43 Other studies have shown that patients who had
greater distances to travel to radiation centres were less likely to undergo breast conserving surgery and more likely to have mastectomies.44,45
The corresponding catchment map shown in Figure 5 reveals that even when a relatively long
road travel time (e.g. two hours) is selected to existing cancer care, the northern part of the province of British Columbia is underserved.
The sixth provincial cancer centre, soon to be opened, is located in Prince George, a city within
this northern provincial health region. Notably, when the cancer centre in Prince George is included
in the calculations, the proportion of the non-metropolitan population served within one hour will
increase from 19% to 24%. The proportion of the non-metropolitan population within two hours
road travel time also increases from 34% to almost 42%, a significant increase. While these
increases in serviced population mark a move in the right direction, the HSL webGUI presents
compelling visual confirmation of the on-going failure to achieve adequate service levels for a key
health service within the travel timeframes mandated by the provincial access guidelines.40
Discussion
Against the backdrop of growing costs, decreasing coffers, increasing demand for accountability
and the need to deliver the best possible healthcare, rural healthcare planners have to invest in
287
Schuurman et al.
Hospital Catchments
Rural population/area of interest: all British Columbia
Maximum travel time: 2 hours
Service type: cancer
Levels: 1
Hospital ID
Hospital Name
99
Abbotsford Cancer
Centre
98
Cancer Centre for the
Southern Interior
88
97
Rural Population
Served
Percent of BC Rural
Population
97,526
6.47
322,314
21.38
Vancouver Cancer
Centre
18,673
1.24
Vancouver Island
Cancer Centre
78,683
5.22
Total rural population served
517,196
34.3
Rural population not served
990,602
65.7
British Columbia rural population
1,507,798
100
Figure 4. Accessibility to cancer centres in the province of British Columbia within two hours road travel
time. There is a strikingly low level of access, with only 34.30% of the rural population able to access care
within this window.
decision-making processes that will actively support their efforts to allocate services for their
regions. A tool that allows easy access to relevant data—paired with the ability to use that data to
model service options—can provide healthcare administrators with a vital assist to their decisionmaking process. For planners who must manage scarce resources within a rural context, the capability of undertaking accurate, timely spatial analyses of service allocations or reallocations,
without the time and expense of contracting specialists or purchasing complex technology, is
increasingly important. In settings where key stakeholders are likely to be geographically scattered
across multiple sites rather than concentrated in metropolitan hubs, the capacity to readily share
information across a decision-making team offers the opportunity to maximize participation, while
at the same time reducing the overall time in administrating the process.
For these reasons, SDSS tools that enable healthcare administrators to work directly on scenario
development are the most effective mechanisms for generating targeted, applicable information to
assist the decision-making process. The ability of SDSS to capture data and provide spatial modelling functionality, employing a user-friendly web-based interface, will allow decision-makers to
model service configuration options in-house. A tool like the HSL webGUI enables healthcare
planners to both generate and disseminate information, and overcomes many of the barriers traditionally associated with data-informed decision-making. A web-based GUI has the advantage of
being cost-effective and familiar to most users, as well as having the ability to link people across
sites and platforms.46 As decision-makers are already familiar with web browsers, there is the benefit of encountering a new tool within a highly familiar environment. These tools run on a standard
288
Health Informatics Journal 17(4)
Figure 5. Map generated by a query to show the two hour road travel time catchment for access to
cancer care in rural British Columbia using existing centres.
platform, eliminating costs and other barriers associated with proprietary software and hardware:
access is therefore possible at any time, in any place. The browser’s document mark-up language
ensures that the visual representation of the information remains consistent across sites, regardless
of platforms—a consistency that facilitates information sharing across distributed groups.
With the benefit of GIS technology, this webGUI provides the ability to map populations within
preset road travel times, thereby providing metrics that directly pertain to the goal of ensuring a
widely distributed population is served in an acceptably timely manner. While the British Columbia
government currently defines standards for rural healthcare service access in terms of ‘crow-fly’
distance,40 the HSL webGUI provides a much more realistic and meaningful measure based on
actual road travel times. The standards based on straight-line estimates of driving distances have
proven inadequate for predicting actual patient access time, especially in regions of British
Columbia that are particularly mountainous.47,48 With a more realistic model at hand, decisionmakers are better equipped to weigh the pros and cons of adding and subtracting services from
existing hospital sites. The evaluation of current drive-time proximity to three core services
Schuurman et al.
289
revealed certain gaps in access that may warrant consideration of alternative service configurations. The HSL webGUI is also highly expandable; new modules reflecting other services, such as
tubercolisis monitoring or pediatric care, can easily be added.
The HSL webGUI was designed to maximize its utility for rural healthcare decision-makers.
Web-based SDSS tools can facilitate acceptance and uptake, and encourage a broader consideration of possible options. Stakeholders are more inclined to explore a range of options because they
are able to control the definition of scenarios,29,33 and are able to do so with relative ease.31,32,49 A
webGUI facilitates enhanced communication of the concept in question,50,51 especially important
across a multi-site team. As a tool that offers both accessibility and affordability, a webGUI can
reduce the time needed to orchestrate the decision-making process and it can encourage greater
stakeholder involvement in health service decision-making.22,35,36,52 In addition, this tool provides
users with the opportunity of integrating these data, in an easily understood visual format, into their
performance reporting, thereby supporting the requirements for administrators to rationalize and
account for their decisions. Beyond internal reporting, there is also the potential for these service
configuration data and maps to be used to inform the public about service accessibility, through
health authority publications and websites.
Limitations
A web-based SDSS tool of this nature is not without limitations. As a consequence of being generally accessible, there is necessarily a degree of compromise in terms of the number of possible
scenarios that can be generated. There are limits to the tool’s ability to accommodate multiple
variations. A webGUI may not, therefore, be capable of representing all forms of evidence desired
by the team of decision-makers. There is also the potential for bias in the model, or models, selected
in that they may represent relationships which are considered fixed in one discipline, while not
being so in others.53,54 These trade-offs can impose constraints on the objective of comprehensive
decision-making.28,51 Experts familiar with using SDSS tools may be frustrated by the lack of control they will have in designing problem representation.35,55 This shortcoming, however, can usually be overcome by the development of an expert webGUI that offers that increased functionality
and a greater ability to control modelling.28
A further consideration is that the current version of the HSL webGUI only models services that
are facility-based, for which service data are readily available. There may be other key services
under consideration that are community-based, for which service data are not available. Finally,
while the HSL webGUI provides rural healthcare planners with invaluable information about the
spatial distribution of services, it is important to remember that establishing good geographical
access is not always sufficient in ensuring equitable access to care for all members of the population. To make a sound service allocation decision, health planners also have to consider other factors that affect a population’s ability to realize access, including its relative level of deprivation.
Conclusions
Contemporary healthcare planning is beset by many challenges, not least of which is rationalizing
service allocations in order to responsibly provide the most equitable and comprehensive health
service coverage possible. In rural and remote regions, health administrators must balance the
sometimes conflicting demands of cost containment and timely access to care for a geographically dispersed population. Rural planners must be able to predicate their decisions about siting
and resourcing services on a thorough, population-based spatial understanding of their region,
290
Health Informatics Journal 17(4)
necessitating access to relevant, high quality data about their populations, their facilities and their
services. Further, these administrators require the means of using these data to model the implications of various service configurations in order that healthcare planning stands the best chance of
contributing to equitable, sustainable delivery of care.
The HSL webGUI harnesses the strengths of GIS technology in a highly operable manner that
makes spatial analysis of access readily available for healthcare decision-makers. With the pressures of increasing demand and constrained budgets necessitating a fiscally sound, carefully calibrated approach to healthcare delivery, reconsidering locations of services is often a means of
achieving heightened efficiency by concentrating both human and technological resources.16,38
The cost-benefits of locating or relocating services must, however, always be balanced against
ensuring certain services are accessible within a given timeframe. This consideration is particularly relevant for trauma, cancer care and obstetrical services. The HSL webGUI, therefore, allows
those charged with allocating these services in regions where travel time is a key factor, to model
service-specific scenarios to determine what proportion of the population will be served within
mandated timeframes. With the capability of removing or adding hospitals altogether, decisionmakers can get an immediate visual representation of the impact on the population currently
served within a catchment area. Designed to be expandable, the HSL webGUI has the capacity to
add additional service modules and, for more seasoned users, can be developed into an expert
version, allowing for greater user control and customization. Intuitive, platform-independent, and
accessible round-the-clock from any location, the HSL webGUI signals a new potential for SDSS
tools in healthcare planning, enabling healthcare decision-makers, regardless of experience, to
make informed decisions about the most effective distribution of healthcare services for populations in rural and remote regions.
Acknowledgements
The authors would like to thank the Canadian Institutes for Health Research (CIHR) Grant #149353 for
invaluable assistance, as well as the Michael Smith Foundation for Health Research (MSFHR) for its continued research support. In addition, Justin Song provided invaluable technical services, without which the
webGUI would not be possible.
Conflict of interests
The authors have no competing interests.
References
1. Organisation for Economic Co-operation and Development (2010). Growing health spending puts pressure on government budgets, according to OECD Health Data 2010. 2010 Available at: http://www.
oecd.org/documentprint/0,3455,en_2649_34631_45549771_1_1_1_37407,00.html (accessed 27 January 2011).
2. Humphreys JS. Key considerations in delivering appropriate and accessible health care for rural and
remote populations: Discussant overview. Aust J Rural Health 2009; 17 (1): 34–38.
3. Christiansen T, Bech M, Lauridsen J and Nielsen P (2007). ENEPRI Policy Brief No. 4: Demographic
Changes and Aggregate Healthcare Expenditure in Europe. Brussels: European Network of Economic
Policy Research Institutes.
4. World Heath Organization (2010). The World Health Report 2010. Health Systems Financing: the path
to universal coverage. Geneva: World Health Organization.
5. Bodenheimer T. High and Rising Health Care Costs. Part 2: Technologic innovation. Ann Intern Med
2005; 142 (11): 932–937.
Schuurman et al.
291
6. Fazekas M, Ettelt S, Newbould J and Nolte E (2010). Framework for assessing, improving and enhancing health service planning: RAND Europe, Bertelsmann Foundation.
7. Canada Health Action (1997). Building on the Legacy. Final Report of the National Forum on Health.
Ottawa: Health Canada.
8. Menon D, Stafiniski T and Martin D. Priority-setting for healthcare: Who, how, and is it fair? Health
Policy 2007; 84 (2–3): 220–233.
9. Green A (2007). An introduction to health planning for developing health systems. Oxford: Oxford University Press.
10. Baker GR, Brooks N, Anderson G, Brown A, McKillop I, Murray M, et al. Healthcare performance
measurement in canada: who’s doing what? Healthcare Quarterly 1998/1999; 2 (2): 22–26.
11. Layte R, Barry M, Bennett K, Brick A, Morgenroth E, Normand C, et al. (2009). Projecting the impact
of demographic change on the demand for and delivery of health care in Ireland. Dublin: The Economic
and Social Research Institute.
12. Ettelt S, McKee M, Nolte E, Mays N and Thomson S. Planning health care capacity: whose responsibility? In: Rechel B, Wright S, Edwards N, Dowdeswell B and McKee M (eds). Investing in hospitals of
the future. Copenhagen: World Health Organization, 2009, pp. 47–66.
13. The Scottish Government (2008). Remote and Rural Workstream. Delivering for Remote and Rural
Healthcare: the Final Report of the Remote and Rural Workstream. Edinburgh.
14. Glazier RH, Gozdyra P and Yeritsyan N (2011). Geographic Access to Primary Care and Hospital
Services for Rural and Northern Communities: Report to the Ontario Ministry of Health and Long-Term
Care. Toronto: Institute for Clinical Evaluative Sciences.
15. Barer ML, Morgan SG and Evans RG. Strangulation or rationalization? Costs and access in Canadian
hospitals. Longwoods Review 2003; 1 (4): 10–19.
16. Liu L, Hader J, Brossart B, White R and Lewis S. Impact of rural hospital closures in Saskatchewan,
Canada. Soc Sci Med 2001; 52 (12): 1793–1804.
17. Office of Inspector General (2003). United State Deparment of Health and Human Services. Trends in
rural hospital closures: 1990–2000. Washington, DC.
18. Rossbarnett J. Rationalising hospital services: reflections on hospital restructuring and its impacts in
New Zealand. New Zeal Geogr 2000; 56 (1): 5–21.
19. British Columbia Ministry of Health Services (2007). Rural Health Care. Part II: Summary of Input on
the Conversation on Health. Victoria.
20. Geertman SCM and Van Eck JRR. GIS and models of accessibility potential: an application in planning.
Int J Geogr Inf Sci 1995; 9 (1): 67–80.
21. Higgs G. A Literature review of the use of GIS-based measures of access to health care services. Health
Serv Outcomes Res Method 2004; 5 (2): 119–139.
22. Shim JP, Warkentin M, Courtney JF, Power DJ, Sharda R and Carlsson C. Past, present, and future of
decision support technology. Decis Support Syst 2002; 33 (2): 111–126.
23. Gatrell A and Senior M (2005). Health and health care applications. In: Longley PA, Goodchild MF,
Maguire DJ and Rhind DW (eds). Geographical Information Systems: Principles, Techniques, Management and Applications 2nd Abridged edition. New York: John Wiley & Sons.
24. McLafferty SL. GIS and health care. Annu Rev Publ Health 2003; 24: 25–42.
25. Schuurman N, Fiedler RS, Grzybowski SC and Grund D. Defining rational hospital catchments for nonurban areas based on travel-time. Int J Health Geogr 2006; 5: 43.
26. Boyne GA, Gould-Williams JS, Law J and Walker RM. Problems of rational planning in public organizations. Admin Soc 2004; 36 (3): 328–350.
27. Broemeling A, Watson D, Black C and Reid R (2006). Measuring the Performance of Primary Health
Care: existing capacity and future information needs. Vancouver: UBC Centre for Health Services and
Policy Research.
28. Bhargava HK, Power DJ and Sun D. Progress in Web-based decision support technologies. Decis Support Syst 2007; 43 (4): 1083–1095.
292
Health Informatics Journal 17(4)
29. Elvins TT and Jain R. Engineering a human factor-based geographic user interface. IEEE Comput Graph
Appl Mag 1998; 18 (3): 66–77.
30. Carver S, Evans A, Kingston R and Turton I. Public participation, GIS, and cyberdemocracy: evaluating
on-line spatial decision support systems. Environ Plann Plann Des B 2001; 28: 907–922.
31. Bedard Y, Gosselin P, Rivest S, Proulx M-J, Nadeau M, Lebel G, et al. Integrating components with
knowledge discovery technology for environmental health decision support. Int J Med Inform 2003; 70
(1): 79–94.
32. Jarupathirun S and Zahedi FM. Exploring the influence of perceptual factors in the success of web-based
spatial DSS. Decis Support Syst 2007; 43 (3): 933–951.
33. Nyerges T, Jankowski P, Tuthill D and Ramsey K. Collaborative water resource decision support: results
of a field experiment. Ann Assoc Am Geogr 2006; 96 (4): 699–725.
34. Boulos MNK and Wheeler S. The emerging Web 2.0 social software: an enabling suite of sociable technologies in health and health care education. Health Info Libr J 2007; 24 (1): 2–23.
35. Westmacott S. Developing decision support systems for integrated coastal management in the tropics: Is
the ICM decision-making environment too complex for the development of a useable and useful DSS? J
Environ Manage 2001; 62 (1): 55–74.
36. Matthew S and Bambang P. Development of SOVAT: A numerical-spatial decision support system for
community health assessment research. Int J Med Inform 2006; 75 (10): 771–784.
37. Statistics Canada. Population urban and rural, by province and territory (Canada) (2009) Available at:
http://www40.statcan.gc.ca/l01/cst01/demo62a-eng.htm (accessed 15 January 2011).
38. Church J and Barker P. Regionalization of health services in Canada: a critical perspective. Int J Health
Serv 1998; 28 (3): 467–486.
39. Hanlon N and Halseth G. The greying of resource communities in northern British Columbia: Implications for health care delivery in already-underserviced communities. Can Geogr 2005; 49 (1): 1–24.
40. BC Ministries of Health Services and Health Planning (2002). Standards of Accessibility and Guidelines
for Provision of Sustainable Acute Care Services by Health Authorities. Victoria: Province of British
Columbia.
41. Schuurman N, Leight M and Berube M. A Web-based graphical user interface for evidence-based decision making for health care allocations in rural areas. Int J Health Geogr 2008; 7: 49.
42. Crews MG and Holbrook SE. New education delivery system plays vital role in getting patients to emergency department within ‘Golden Hour’. Ann Emerg Med 2005; 46 (3): 88.
43. Athas WF, Adams-Cameron M, Hunt WC, Amir-Fazli A and Key CR. Travel distance to radiation
therapy and receipt of radiotherapy following breast-conserving surgery. J Natl Cancer Inst 2000; 92
(3): 269–271.
44. Nattinger AB, Kneusel RT, Hoffmann RG and Gilligan MA. Relationship of distance from a radiotherapy facility and initial breast cancer treatment. J Natl Canc Inst 2001; 93 (17): 1344–1346.
45. Meden T, St. John-Larkin C, Hermes D and Sommerschield S. Relationship between travel distance and
utilization of breast cancer treatment in rural northern Michigan. JAMA 2002; 287 (1): 111.
46. Deshpande A and Jadad AR. Web 2.0: Could it help move the health system into the 21st century?
J Mens Health Gend 2006; 3 (4): 332–336.
47. Martin D, Roderick P, Diamond I, Clements S and Stone N. Geographical aspects of the uptake of renal
replacement therapy in England. Intl J Popul Geogr 1998; 4 (3): 227–242.
48. Martin D, Wrigley H, Barnett S and Roderick P. Increasing the sophistication of access measurement in
a rural healthcare study. Health Place 2002; 8 (1): 3–13.
49. Dye AS and Shaw S-L. A GIS-based spatial decision support system for tourists of Great Smoky Mountains National Park. J Retailing Consum Serv 2007; 14 (4): 269–278.
50. Hu PJ-H, Ma P-C and Chau PYK. Evaluation of user interface designs for information retrieval systems:
a computer-based experiment. Decis Support Syst 1999; 27 (1–2): 125–143.
51. Saade GR and Otrakji AC. First impressions last a lifetime: effect of interface type on disorientation and
cognitive load. Comput Hum Behav 2007; 23 (1): 525–535.
Schuurman et al.
293
52. French S. Web-enabled strategic GDSS, e-democracy and Arrow’s theorem: A Bayesian perspective.
Decisi Support Syst 2007; 43 (4): 1476–1484.
53. Boulos MNK. Towards evidence-based, GIS-driven national spatial health information infrastructure
and surveillance services in the United Kingdom. Int J Health Geogr 2008; 3: 2.
54. Hausman AJ. Implications of evidence-based practice for community health. Am J Community Psychol
2002; 30 (3): 453–467.
55. Dobrow MJ, Goel V, Lemieux-Charles L and Black NA. The impact of context on evidence utilization:
A framework for expert groups developing health policy recommendations. Soc Sci Med 2006; 63 (7):
1811–1824.