Memory assessment software in a dementia clinic: a pilot study using improvement science
Bibliographic record
Abstract
Background: The pressure on memory assessment services in the UK to undertake an increasing number of new patient assessments is growing. Digital assessment tools delivered on hand-held electronic devices, such as tablets, may optimise the efficiency, accuracy and usefulness of remote cognitive assessments. Aim: To pilot the introduction of cognitive assessment software delivered on tablets in domiciliary-based memory assessments. Methods: Using the principles of improvement science (IS), the authors undertook a 12-month prospective technology introduction study, using a change package with an interrupted time series design. New tablet-based auto-scoring assessment software was systematically introduced in one mental health Trust in the North West UK to improve the efficiency of the assessment and report preparation process. Changes involved incrementally adapting the software with assessment nurses in a series of ‘Plan Do Study Act’ (PDSA) cycles evaluated with time series run charts. Results: Application of the final version of the software reduced the mean time taken to score and prepare the clinical assessment report by nearly 84% from the baseline (a median improvement of 77.5 minutes). This equates to a substantial increase in the number of new assessments that could be undertaken per year due to increased availability of front-line care. Conclusion: Introducing tablet-compatible assessment software for home-based memory clinics significantly increases the efficiency of the process and may facilitate more rapid turnover of new patient referrals. IS methods have potential to aid the introduction of new technology to improve dementia care.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".