Antibiotic Resistant Infection, Infection
Conditions
Keywords
Antimicrobial Resistance, Antibiotic prescribing, Clinical Decision Support Systems
Brief summary
Antimicrobials (drugs that kill or stop the growth of microorganisms including bacteria, thereby treating infections) commonly used to treat patients with infections are becoming less effective over time as bacteria develop resistance to them. Antimicrobial usage itself can lead to development and spread of antimicrobial resistance. Antimicrobial resistance is now a major threat to patient safety. To conserve the effectiveness of antimicrobials the investigator need to develop ways to use them more sensibly healthcare professionals who diagnose and treat infections must be able to access antimicrobial guidelines and test results at the patient bedside. This needs to be provided rapidly and with support to make sure that the decisions on prescribing antimicrobials are the best that can be made.
Detailed description
Prototype software to achieve this has been developed through collaboration between healthcare professionals and biomedical engineers. This prototype software (run on a mobile device) retrieves patient results from various laboratory and clinical databases (securely within the Trust firewall) and displays this to the clinician making the prescribing decision. Furthermore a machine learning algorithm is applied to the data, and similar anonymised historical cases (and the antimicrobials prescribed and the clinical outcomes) are also displayed to the clinician to further inform their decision making. The prototype has been designed for use in intensive care, where the risk of infection is high, but through the research project detailed here, the software will be developed and validated across other areas of hospital patient care. Furthermore there is a key need to engage patients with how decisions are made around antimicrobial prescribing. The investigator propose to adapt the prototype to meet these needs. This system should improve patient safety and help preserve the effectiveness of existing antimicrobials
Interventions
DEVICEEPIC IMPOC
Clinical Decision Support System for antibiotic prescribing.
Sponsors
Imperial College London
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
(i) healthcare professionals for evaluation phases: Have read the PIL and consent to participate in the study (ii) patients for whom the clinician chooses to use the POC DSS as a resource when prescribing antimicrobials: Adult patients \> 18 years old Being managed for infection outside of the critical care setting in Imperial College Healthcare NHS Trust Deemed appropriate for management with POC DSS by attending physician Prescribed antimicrobial agents outside of the critical care setting in last 5 days (iii) Prescriber / healthcare professional for using POC DSS: Trained Healthcare Professional Working within wards under assessment Deemed suitable for recruitment by senior member of their team
Exclusion criteria
(i) healthcare professionals: Do not wish to participate in the study Working across wards which is acting as a control ward Deemed no suitable for recruitment by a senior member of their team Non-permanent member of the Trust Information governance training not up-to-date (ii) patients recruits Critical care patients Paediatric patients \< 18 years old Deemed not suitable for management using POC DSS by attending physician On palliative care, end of life pathway Prisoners / young offenders in custody of HM Prison Service Involved in current research or have recently been involved in any research prior to recruitment (last 3 months)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Percentage of Appropriate Antimicrobial Prescriptions Recommended | Single prescription at the time of antimicrobial prescribing assessment (e.g. at the time antibiotics were prescribed) | This will be measured by assessing the appropriateness of prescriptions recommended by the system compared to current clinical practice. Appropriateness is determined by evaluating prescribing against current clinical guidelines or infection expert opinion on best practice and is expressed as a proportion of the total number of antibiotic prescriptions made. Each individual patient has a single antibiotic prescription evaluated. |
| Evaluation of Effectiveness Assessed by User Acceptance of the Device | Single time point before and after use of the device in the study | This was assessment was a single time point at baseline (Pre-intervention) and single time point after use of the device in the study. Scores were pre-determined based on anticipated answers provided by participants pre- and post- intervention using a bespoke mark scheme (https://aricjournal.biomedcentral.com/articles/10.1186/s13756-018-0333-1). Participants could score between 0 (lowest) and 13 (highest) marks based on their responses to questions assessing knowledge and understanding. |
Countries
United Kingdom
Participant flow
Recruitment details
Professionals recruited = 15 Patients recruited for prospective study of engagement tool = 18
Pre-assignment details
15 healthcare professionals were recruited who would be using the tool to make antibiotic prescribing decisions. 18 patients were enrolled in the pre- post- assessment of the impact of a patient-communication tool within the system on knowledge and understanding of their infection and its management.
Participants by arm
| Arm | Count |
|---|---|
| Patients and Public Exploration of patient and public engagement with antibiotic decision making in secondary care. Qualitative evaluation and co-design of interventions. Prospective evaluation of intervention.
EPIC IMPOC: Clinical Decision Support System for antibiotic prescribing. | 18 |
| Prescribers Quantitative and qualitative evaluation of the impact of using a clinical decision support system to support antibiotic decision making.
EPIC IMPOC: Clinical Decision Support System for antibiotic prescribing. | 15 |
| Total | 33 |
Baseline characteristics
| Characteristic | Patients and Public | Total | Prescribers |
|---|---|---|---|
| Age, Continuous | 66 Years | 66 Years | — |
| Race and Ethnicity Not Collected | — | 0 Participants | — |
| Sex: Female, Male Female | 8 Participants | 15 Participants | 7 Participants |
| Sex: Female, Male Male | 10 Participants | 18 Participants | 8 Participants |
Adverse events
| Event type | EG000 affected / at risk |
|---|---|
| deaths Total, all-cause mortality | 0 / 0 |
| other Total, other adverse events | 0 / 0 |
| serious Total, serious adverse events | 0 / 0 |
Outcome results
Primary
Evaluation of Effectiveness Assessed by User Acceptance of the Device
This was assessment was a single time point at baseline (Pre-intervention) and single time point after use of the device in the study. Scores were pre-determined based on anticipated answers provided by participants pre- and post- intervention using a bespoke mark scheme (https://aricjournal.biomedcentral.com/articles/10.1186/s13756-018-0333-1). Participants could score between 0 (lowest) and 13 (highest) marks based on their responses to questions assessing knowledge and understanding.
Time frame: Single time point before and after use of the device in the study
Population: Patients in hospital.
| Arm | Measure | Group | Value (MEDIAN) |
|---|---|---|---|
| Appropriateness of Antibiotic Prescribing | Evaluation of Effectiveness Assessed by User Acceptance of the Device | Post-intervention | 10 score on a scale |
| Appropriateness of Antibiotic Prescribing | Evaluation of Effectiveness Assessed by User Acceptance of the Device | Pre-intervention | 3 score on a scale |
Primary
Percentage of Appropriate Antimicrobial Prescriptions Recommended
This will be measured by assessing the appropriateness of prescriptions recommended by the system compared to current clinical practice. Appropriateness is determined by evaluating prescribing against current clinical guidelines or infection expert opinion on best practice and is expressed as a proportion of the total number of antibiotic prescriptions made. Each individual patient has a single antibiotic prescription evaluated.
Time frame: Single prescription at the time of antimicrobial prescribing assessment (e.g. at the time antibiotics were prescribed)
Population: 15 prescribers made 224 antimicrobial prescriptions during the study period which were analysed for the outcome measure.
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| Appropriateness of Antibiotic Prescribing | Percentage of Appropriate Antimicrobial Prescriptions Recommended | 90 percentage of prescriptions appropriate |