PTU-139 The need to work ‘smart’ on acute wards: could mobile phone applications be the answer?
Bibliographic record
Abstract
Introduction Due to rising service pressures there is a need for innovation to optimise efficiency on acute medical and gastroenterology wards. Using smartphone applications to achieve this is an attractive proposition, but the utility of such technologies in clinical practice is unclear. Here, we report the experience of multidisciplinary staff after a pilot of a smartphone application at our hospital. Methods Two acute medical wards (including a 24 bed gastroenterology ward), were selected for a two-week pilot trial of a smartphone application, Listrunner (Desma Health, Canada), during October 2017. Multidisciplinary team members including; doctors, pharmacists, and nurses, were given access to Listrunner via dedicated secure mobile devices. During daily consultant ward rounds and throughout the working day, all tasks were uploaded onto Listrunner. A Control Centre lead reviewed all uploaded tasks, identified non-medical tasks and either completed these or reassigned them to more appropriate team members. At the end of the pilot, staff provided feedback on their experience via a structured questionnaire. Results During the pilot, whilst a total of 1080 tasks were uploaded onto Listrunner, 20% of these were non-medical tasks managed by the Control Centre. The most common tasks managed by Control Centre were chasing specialty reviews (42%) and chasing investigations (33%). At the end of the pilot, staff from both wards (n=19; Junior Doctors n=9, Nurses n=4, Consultants n=2, Pharmacists n=2 and Occupational Therapists n=2), completed questionnaires. Most doctors (73%) found Listrunner easy to use and 56% of juniors felt that it improved the relevance of their work by reassigning non-medical tasks. Overall, 42% rated Listrunner as ‘useful’, whereas 21% did not find it useful and 56% felt it improved communication between team members. When asked how Listrunner affected the conduct of ward work, the most popular responses selected were; ‘it improved patient flow/discharges’ (n=8), ‘it speeded up allocation of tasks’ (n=7) and ‘it prolonged the ward round’ (n=6). Whilst 12/19 (63%) felt it would be worth adopting Listrunner, 8/19 (42%) expressed some reservations about using smartphones in front of patients. The main barrier to adopting this technology more widely (according to 58%) would be the staffing levels and related costs required to replicate the pilot experience. Conclusions Our data suggest that a large number of tasks currently performed by doctors on acute wards are non-medical tasks. Smartphone technology appears to have potential to improve efficiency and streamline clinical activities, and our early experience may help inform future adoption and further development of this technology.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.013 |
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".