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. 2024 Mar 8;24(1):69.
doi: 10.1186/s12911-024-02471-w.

Developing an integrated clinical decision support system for the early identification and management of kidney disease-building cross-sectoral partnerships

Gillian Gorham  1 , Asanga Abeyaratne  2   3 , Sam Heard  4 , Liz Moore  5 , Pratish George  6 , Paul Kamler  3 , Sandawana William Majoni  2   3   7 , Winnie Chen  2 , Bhavya Balasubramanya  2 , Mohammad Radwanur Talukder  2 , Sophie Pascoe  2 , Adam Whitehead  8 , Cherian Sajiv  6   7 , Louise Maple-Brown  2   9 , Nadarajah Kangaharan  10 , Alan Cass  2
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Developing an integrated clinical decision support system for the early identification and management of kidney disease-building cross-sectoral partnerships

Gillian Gorham et al. BMC Med Inform Decis Mak. .

Erratum in

Abstract

Background: The burden of chronic conditions is growing in Australia with people in remote areas experiencing high rates of disease, especially kidney disease. Health care in remote areas of the Northern Territory (NT) is complicated by a mobile population, high staff turnover, poor communication between health services and complex comorbid health conditions requiring multidisciplinary care.

Aim: This paper aims to describe the collaborative process between research, government and non-government health services to develop an integrated clinical decision support system to improve patient care.

Methods: Building on established partnerships in the government and Aboriginal Community-Controlled Health Service (ACCHS) sectors, we developed a novel digital clinical decision support system for people at risk of developing kidney disease (due to hypertension, diabetes, cardiovascular disease) or with kidney disease. A cross-organisational and multidisciplinary Steering Committee has overseen the design, development and implementation stages. Further, the system's design and functionality were strongly informed by experts (Clinical Reference Group and Technical Working Group), health service providers, and end-user feedback through a formative evaluation.

Results: We established data sharing agreements with 11 ACCHS to link patient level data with 56 government primary health services and six hospitals. Electronic Health Record (EHR) data, based on agreed criteria, is automatically and securely transferred from 15 existing EHR platforms. Through clinician-determined algorithms, the system assists clinicians to diagnose, monitor and provide guideline-based care for individuals, as well as service-level risk stratification and alerts for clinically significant events.

Conclusion: Disconnected health services and separate EHRs result in information gaps and a health and safety risk, particularly for patients who access multiple health services. However, barriers to clinical data sharing between health services still exist. In this first phase, we report how robust partnerships and effective governance processes can overcome these barriers to support clinical decision making and contribute to holistic care.

Keywords: Chronic disease; Clinical algorithms; Clinical decision support; Clinical information systems; Clinical safety; Integrated patient care; Remote health.

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Conflict of interest statement

The authors report no conflicts of interest.

Figures

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Fig. 1
System architecture diagram
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Fig. 2
Extract of a patient synopsis report
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Fig. 3
TKC development timeline
See this image and copyright information in PMC

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References

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