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Towards knowledge sharing and patient privacy in a clinical decision support system

Towards knowledge sharing and patient privacy in a clinical decision support system
Towards knowledge sharing and patient privacy in a clinical decision support system
Patient records and their disease and treatment history can be scattered among healthcare providers. Sharing the knowledge effectively and, at the same time, respecting patient privacy is crucial in providing safe and accurate clinical decision support systems (CDSSs). In this paper we reflect upon our experience in the HealthAgents project wherein a prototype system was developed and a novel approach employed that supports data transfer and decision making in human brain tumour diagnosis. Here we examine the capability of the lightweight coordination calculus (LCC), a process calculus-based language, in combining together distributed healthcare services and meeting security challenges in pervasive settings. The result is that various clinical specialisms, being captured in representational abstractions and making contribution to patient diagnosis and management, retain their autonomy. However, at the same time, the behaviour of specialists in sharing clinical knowledge about their patients and providing clinical support is constrained by policies and rules in respect of their own clinical duties and responsibilities. Being introduced into the programme of the HRB Centre for Primary Care Research, this novel approach has the potential to help the provision of optimal solutions in data linkage and sharing across the primary and cecondary care interface. As added value, its application also advances the process of integrating clinical prediction rules and implementing CDSSs in practice and, ultimately, the improvement of quality of care
99-104
IEEE
Xiao, L.
6072fdce-9bb5-4661-87ca-1d001dbf1171
Hu, B.
5df0c432-d182-4836-9bb9-2dbee47b60db
Hederman, L.
a4753acf-bb14-47a7-a6e0-baa926375a85
Lewis, P.
24408d77-a4e5-45af-9109-5010759c2c10
Dimitrov, B.D.
366d715f-ffd9-45a1-8415-65de5488472f
Fahey, T.
050e4cde-a5cf-4892-9728-b31c4e600429
Luzar-Stiffler, Vesna
Jarec, Iva
Bekic, Zoran
Xiao, L.
6072fdce-9bb5-4661-87ca-1d001dbf1171
Hu, B.
5df0c432-d182-4836-9bb9-2dbee47b60db
Hederman, L.
a4753acf-bb14-47a7-a6e0-baa926375a85
Lewis, P.
24408d77-a4e5-45af-9109-5010759c2c10
Dimitrov, B.D.
366d715f-ffd9-45a1-8415-65de5488472f
Fahey, T.
050e4cde-a5cf-4892-9728-b31c4e600429
Luzar-Stiffler, Vesna
Jarec, Iva
Bekic, Zoran

Xiao, L., Hu, B., Hederman, L., Lewis, P., Dimitrov, B.D. and Fahey, T. (2009) Towards knowledge sharing and patient privacy in a clinical decision support system. Luzar-Stiffler, Vesna, Jarec, Iva and Bekic, Zoran (eds.) In Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces. IEEE. pp. 99-104 . (doi:10.1109/ITI.2009.5196061).

Record type: Conference or Workshop Item (Paper)

Abstract

Patient records and their disease and treatment history can be scattered among healthcare providers. Sharing the knowledge effectively and, at the same time, respecting patient privacy is crucial in providing safe and accurate clinical decision support systems (CDSSs). In this paper we reflect upon our experience in the HealthAgents project wherein a prototype system was developed and a novel approach employed that supports data transfer and decision making in human brain tumour diagnosis. Here we examine the capability of the lightweight coordination calculus (LCC), a process calculus-based language, in combining together distributed healthcare services and meeting security challenges in pervasive settings. The result is that various clinical specialisms, being captured in representational abstractions and making contribution to patient diagnosis and management, retain their autonomy. However, at the same time, the behaviour of specialists in sharing clinical knowledge about their patients and providing clinical support is constrained by policies and rules in respect of their own clinical duties and responsibilities. Being introduced into the programme of the HRB Centre for Primary Care Research, this novel approach has the potential to help the provision of optimal solutions in data linkage and sharing across the primary and cecondary care interface. As added value, its application also advances the process of integrating clinical prediction rules and implementing CDSSs in practice and, ultimately, the improvement of quality of care

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Published date: 2009
Organisations: Primary Care & Population Sciences

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Local EPrints ID: 365880
URI: https://eprints.soton.ac.uk/id/eprint/365880
PURE UUID: ea72dea7-578f-4972-b6f1-a8e42beecb9e

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Date deposited: 20 Jun 2014 08:13
Last modified: 05 Oct 2018 11:35

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