=Paper=
{{Paper
|id=None
|storemode=property
|title=Coordinating Social Care and Healthcare using Semantic Web Technologies
|pdfUrl=https://ceur-ws.org/Vol-1035/iswc2013_demo_43.pdf
|volume=Vol-1035
|dblpUrl=https://dblp.org/rec/conf/semweb/KotoulasLSTSHSBKREESMXA13
}}
==Coordinating Social Care and Healthcare using Semantic Web Technologies==
https://ceur-ws.org/Vol-1035/iswc2013_demo_43.pdf
Coordinating Social Care and Healthcare using
Semantic Web Technologies
Spyros Kotoulas, Vanessa Lopez, Martin Stephenson, Pierpaolo Tommasi,
Wei Jia Shen, Gang Hu, Marco Luca Sbodio, Veli Bicer, Anastasios Kementsietsidis,
M. Mustafa Rafique, Jason Ellis, Thomas Erickson, Kavitha Srinivas,
Kevin McAuliffe, Guo Tong Xie, and Pol Mac Aonghusa
IBM Research
1 Introduction
Healthcare and Social Care are unique domains in terms of cultural importance, eco-
nomic magnitude and complexity. On a cultural level, the level of advancement of a
society is often measured in terms of protection of the less able. In economic terms,
for 2009, total expenditure on healthcare in the United States was 2.6 trillion USD or
17.4% of the GDP1 . Total expenditure on social care was 2.98 trillion USD or 19.90%
of the GDP2 . In terms of US Federal government expenditure, social security, medi-
care and medicaid amount to 45% of total spending. In terms of complexity, organiza-
tions that are involved in providing social and medical care are numerous and span a
very wide domain. For example, AHIP, the trade association of health insurers numbers
some 1300 members3 ; the number of hospitals registered with the American Hospital
Association is 57244 and the number of homeless shelters surpasses 40005 . In addition,
medical information is vastly complex: Nuance reports that LinkBase R 6 contains more
than 1 million concepts. Social care depends on information from a very broad domain,
ranging from criminal records to housing.
Coordinating social care and health care has been identified both as a major pain
point and a significant opportunity in modern health and social systems [1]. Several
studies have shown that costs can be contained and outcomes improved with a more
holistic approach to care [2]. As a simple motivating example, consider an individual
quartered in inappropriate housing while suffering from a relatively minor health issue,
aggravated by the housing condition. As a result, the given individual frequently re-
sorts to visiting emergency rooms, resulting in significant cost to the healthcare system
and a less effective treatment. By itself, the housing situation does not warrant state
intervention. Nevertheless, resolving it would dramatically improve the health situa-
tion, resulting in a better quality-of-life for the individual and lower costs for the health
system.
1
http://dx.doi.org/10.1787/888932523215
2
http://www.oecd.org/els/social/expenditure
3
http://www.ahip.org
4
http://www.aha.org/research/rc/stat-studies/fast-facts.shtml, retrieved 19/04/2013
5
http://www.shelterlistings.org/
6
http://www.nuance.com/for-healthcare/resources/clinical-language-
understanding/ontology/index.htm
Application IBM WebSphere Data Sources
administrator
Application Server
SeDA RDBMS
Feder.
Data source Node
Query
management registry ... ...
Compon
View ent
Context SPARQL RDF Store
management REST definitions
IBM Tivoli API
View Access Metadata Repository
Provenance
management Manager Link
Exploration and Repo.
interface WebSEAL Linker Feder. Mgt.
Query Info DB2
Context Compon RDF
Search Prov.
ent IBM
IBM
IBM
Ref. Storage
Visual Storage
Storage
IBM
IBM HTTP Ontol. (SAN)
Analytics IBMHTTP
HTTP (SAN)
(SAN)
Server
Server
Server Ancillary Indexes
User
Feder.
Full-text
Query
Compon RDF
Proprietary
ent Store
Fig. 1: System architecture
Even in this simple example, the challenges presented are significant: How do we
access information in disparate systems, storing vastly heterogeneous information on
various infrastructures? How do we cope with policy constraints disallowing replica-
tion or centralization of data? How do we abstract from the information and represen-
tation complexity?
In this paper, we propose a novel technical solution to augment applications with
cross-domain context, in the domain of Social Care and Healthcare based on business
rules and contextual exploration. We claim that Semantic Technologies can uniquely
address these problems because: (a) The distributed nature of RDF allows access to in-
tegrated information across silos. (b) Explicit and global semantics allow us to ground
business rules across systems. (c) The distributed and incremental data integration paradigm
advocated by linked data can help coping with the complexity of the data.
We present a demonstrator of a system that supports two key use-cases for this do-
main: (a) Displaying a view of the combined needs across several dimensions for a
given person and people in their social context, based on a set of business rules. This
allows a social/health worker to quickly assess the situation of an individual. From a
knowledge management perspective, it requires grounding a set of business rules across
several ontologies and instance data in several data sources. (b) Exploration of the con-
text to surface information not directly covered by the business rules. Given the het-
erogeneity of the domain, the user will most likely need additional information around
a given individual. Our demonstrator uses the business rules as a navigational aid to
explore the semi-structured information.
2 Approach
Key Performance Indicators (KPIs) are used to ground business rules to data, offering
a tree-based view over the factors that contributed to a given KPIs and the weight of
2
each factor (influence) to the global vulnerability score, helping us understand relations
across different needs. For example, being homeless (or living under poor housing con-
ditions) is an aggravating factor for health. These views can be applied to an individual,
family members, or socially/geographically organized groups. Each node in a KPI is
associated to two SPARQL queries. The first one is to calculate the score of a given
contributing factor (if present) obtained from a given data source(s) and with a given
weight. The second one is a CONSTRUCT query to retrieve the set of triples provid-
ing additional context in the ontology(-ies) associated to the data that contributed to
the score, as well as the justification on the values from which this KPI factor was de-
rived. The score of a KPI node is the sum of its own score and that of its children. For
both types of queries, rather than being tied to a specific model, we abstract from the
particular representation using a set of query patterns.
An enterprise architecture supporting our approach is shown in Fig. 1. Due to space
restrictions, we describe only the components necessary to understand the basic oper-
ation of the system. Web-facing services use a set of REST services, implemented on
a custom application running on IBM WebSphere Application Server. The main com-
ponents for these services are the Node registry, which tracks nodes in the Federated
Query Engine, the View definitions, that are used to project information out of the graph
model for use by analytics widgets and UI elements. Data Sources are exposed as virtual
RDF, using SeDA, an IBM technology to execute R2RML mappings. The virtual RDF
Data Sources, the Metadata Repository and the Ancillary Indexes are accessed through
the Federated Query Engine, providing transparent access to the distributed informa-
tion. All core components in this architecture can be clustered, for high availability and
performance.
3 Deployment
We have internally deployed a proof of concept based on the above architecture, inte-
grating a set of IBM solutions for clinical and social program information: IBM soft-
ware Patient Care and Insights provides data driven population analysis to support
patient centered care processes. It integrates and analyzes the full breadth of patient
information sourced from multiple systems and different care providers. It stores three
categories of data: extracted patient medical history called clinical summary; medical
data analytics results from an analytics component called care insights and personalized
electronic care plans. IBM Cúram is a business and technology solution to help social
program organizations provide optimal outcomes for citizens, satisfy increasing de-
mand, and lower costs for organizations. In connection to this paper, the information of
interest mainly regards social relationships, known problems concerning employment,
substance abuse, participation in social assistance programs and information concerning
housing, education and safety.
Figure 2 shows some UI components from our proof of concept. Since our approach
is meant to be deployed as part of a existing application, in order to augment them
with information from other systems, we have opted to focus on the context that can
be retrieved, rather than trying to replicate the enterprise application: (a) Genogram,
Fig. 2, floating frame on top-left. We have adapted the genogram visualization [3] to
3
Fig. 2: Screenshot
explore the family environment of a person and associated problems. (b) Hierarchical
KPI, Fig. 2, right. The tree allows the user to explore the vulnerabilities of a person us-
ing information coming from several sources. The KPIs themselves are tree structures.
Clicking on a node brings up a contextual exploration view. (c) Contextual exploration,
Fig. 2, bottom-left. The user is able to investigate information related to a node in the
KPI tree based on a graph exploration interface. In addition to elements shown in the
figure, our proof of concept supports exploration and analysis based on the spatial com-
ponent and family relations.
From internal feedback, the main strong points of our approach lie in the ability to
consume data from heterogeneous sources without complicated data warehouses, asso-
ciated ETL processes and setting up related infrastructures, although it remains to be
seen whether the tooling required for a semantic approach will reach the sophistication
of what is currently found in the enterprise domain. In addition, tabular or tree-like vi-
sualizations are strongly preferred to graphs. Future work lies in better-informed data
exploration, mining the RDF graphs to identify meaningful relationships and data in-
consistency checking across silos.
References
1. Rigby, M., Hill, P., Koch, S., Keeling, D.: Social care informatics as an essential part of
holistic health care: A call for action. I. J. Medical Informatics 80(8) (2011) 544–554
2. Peikes, D., Chen, A., Schore, J., Brown, R.: Effects of care coordination on hospitalization,
quality of care, and health care expenditures among medicare beneficiaries. JAMA: the journal
of the American Medical Association 301(6) (2009) 603–618
3. Jolly, W., Froom, J., Rosen, M., et al.: The genogram. The Journal of family practice 10(2)
(1980) 251
4