An Evidence-Based Antimicrobial Stewardship Smartphone App for Hospital Outpatients: Survey-based Needs Assessment Among Patients
Bibliographic record
Abstract
BACKGROUND: Current advances in modern technology have enabled the development and utilization of electronic medical software apps for both mobile and desktop computing devices. A range of apps on a large variety of clinical conditions for patients and the public are available, but very few target antimicrobials or infections. OBJECTIVE: We sought to explore the use of different antimicrobial information resources with a focus on electronic platforms, including apps for portable devices, by outpatients at two large, geographically distinct National Health Service (NHS) teaching hospital trusts in England. We wanted to determine whether there is demand for an evidence-based app for patients, to garner their perceptions around infections/antimicrobial prescribing, and to describe patients' experiences of their interactions with health care professionals in relation to this topic. METHODS: A cross-sectional survey design was used to investigate aspects of antimicrobial prescribing and electronic devices experienced by patients at four hospitals in London and a teaching hospital in the East of England. RESULTS: A total of 99 surveys were completed and analyzed. A total of 82% (80/98) of respondents had recently been prescribed antimicrobials; 87% (85/98) of respondents were prescribed an antimicrobial by a hospital doctor or through their general practitioner (GP) in primary care. Respondents wanted information on the etiology (42/65, 65%) and prevention and/or management (32/65, 49%) of their infections, with the infections reported being upper and lower respiratory tract, urinary tract, oral, and skin and soft tissue infections. All patients (92/92, 100%) desired specific information on the antimicrobial prescribed. Approximately half (52/95, 55%) stated it was "fine" for doctors to use a mobile phone/tablet computer during the consultation while 13% (12/95) did not support the idea of doctors accessing health care information in this way. Although only 30% (27/89) of respondents reported on the use of health care apps, 95% (81/85) offered information regarding aspects of antimicrobials or infections that could be provided through a tailored app for patients. Analysis of the comments revealed the following main global themes: knowledge, technology, and patient experience. CONCLUSIONS: The majority of respondents in our study wanted to have specific etiological and/or infection management advice. All required antimicrobial-related information. Also, most supported the use of electronic resources of information, including apps, by their doctors. While a minority of people currently use health apps, many feel that apps could be used to provide additional support/information related to infections and appropriate use of antimicrobials. In addition, we found that there is a need for health care professionals to engage with patients and help address common misconceptions around the generation of antimicrobial resistance.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".