Accepted for/Published in: JMIR Research Protocols
Date Submitted: May 30, 2022
Date Accepted: Dec 21, 2022
Date Submitted to PubMed: Feb 15, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Optimising the Quality of Clinical Data in an Australian Aged Care and Disability Service to Improve Care Delivery and Clinical Outcomes (OPTIMISE): Protocol for an Agile Lean Six Sigma Implementation Study
ABSTRACT
Background:
Poor quality clinical data and documentation is widespread across the Australian aged care and disability services sector and can significantly affect clinical decision-making and care delivery. The OPTIMISE study aims to improve the quality of clinical data in a large post-acute health service providing aged care and disability services in Western Australia, through design and implementation of a novel, purpose-built clinical information system.
Objective:
The OPTIMISE study aims to improve the quality of clinical data in a large post-acute health service providing aged care and disability services in Western Australia, through design and implementation of a novel, purpose-built
Methods:
A three-stage prospective implementation study using an Agile Lean Six Sigma framework will be undertaken. Stage 1 (Pre-Implementation) will measure existing clinical data quality and identify root causes of data quality issues across the service. Stage 2 (Implementation) will design, test and implement a novel purpose-built clinical information system using incremental Agile sprints, and Stage 3 (Post-Implementation) will evaluate change in primary and secondary outcomes from baseline to 12 months after system implementation. The primary outcome is data quality measured in terms of Defects Per Unit and Defects Per Million Opportunities. Secondary outcomes are care delivery (direct care time), clinical incidents, business outcomes (cost of quality, workforce productivity), and user satisfaction. Case studies will be analysed to understand impacts of the purpose-built clinical information system on client outcomes and business processes.
Results:
As of 1 June 2022, Stage 1 is currently underway with baseline data quality audits in progress to identify current data quality and system strengths and limitations. Baseline data quality audits will be followed by user consultations to identify root causes of data quality issues. Clinical information requirements will be developed by August 2022 to inform the new system build (Stage 2) and evaluation (Stage 3).
Conclusions:
Study findings will be of interest to individuals and organisations in the healthcare sector seeking novel technology solutions to improve the quality of clinical data and support high quality care delivery and reduce business costs. Clinical Trial: N/A
Citation
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