Optimizing the Quality of Clinical Data in an Australian Aged Care and Disability Service to Improve Care Delivery and Clinical Outcomes: Protocol for an Agile Lean Six Sigma Study
Bibliographic record
Abstract
Background In Australia, aged care and disability service providers are legally required to maintain comprehensive and accurate clinical documentation to meet regulatory and funding requirements and support safe and high-quality care provision. However, evidence suggests that poor-quality clinical data and documentation are widespread across the sector and can substantially affect clinical decision-making and care delivery and increase business costs. Objective In the Optimizing the Quality of Clinical Data in an Australian Aged Care and Disability Service to Improve Care Delivery and Clinical Outcomes (OPTIMISE) study, we aim to use an Agile Lean Six Sigma framework to identify opportunities for the optimization of clinical documentation processes and clinical information systems, implement and test optimization solutions, and evaluate postoptimization outcomes in a large postacute community-based health service providing aged care and disability services in Western Australia. Methods A 3-stage prospective optimization study will be conducted. Stage 1 (baseline [T0]) will measure existing clinical data quality, identify root causes of data quality issues across services, and generate optimization solutions. Stage 2 (optimization) will implement and test changes to clinical documentation processes and information systems using incremental Agile sprints. Stage 3 (evaluation) will evaluate changes in primary and secondary outcomes from T0 to 12 months after optimization. The primary outcome is the data quality measured in terms of defects per unit, defects per million opportunities, and Sigma level. The secondary outcomes are care delivery (direct care time), clinical incidents, business outcomes (cost of quality and workforce productivity), and user satisfaction. Case studies will be analyzed to understand the impact of optimization on clinical outcomes and business processes. Results As of June 1, 2022, stage 1 commenced with T0 data quality audits conducted to measure current data quality. T0 data quality audits will be followed by user consultations to identify root causes of data quality issues. Optimization solutions will be developed by May 2023 to inform optimization (stage 2) and evaluation (stage 3). Results are expected to be published in June 2023. Conclusions The study findings will be of interest to individuals and organizations in the health care sector seeking novel solutions to improve the quality of clinical data, support high-quality care delivery, and reduce business costs. International Registered Report Identifier (IRRID) DERR1-10.2196/39967
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.064 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".