Enhanced, Personalized and Integrated Care for Infection Management at the Point-Of-Care
Conditions: Antibiotic Resistant Infection, Infection
Sponsor: Imperial College London

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EPIC IMPOC 
 
 
 
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Study protocol 
 
 
 
 
 
EPIC IMPOC 
 
Enhanced, Personalized and Integrated Care for Infection Management at Point of 
Care 
 
V9.0   25/10/2016 
 
 
 
 
 
 
 
 
 
 
 
SPONSOR: Joint Research Compliance Office, Imperial College London, Room 215, Level 
2, Medial School Building, Norfolk Place, W12 1PG 
T: 0207 594 1872  
DOCUMAS Ref: 16HH3526 
 
 
FUNDER: National Institute for Health Research Invention for Innovation Product Development Award 
 
STUDY COORDINATION CENTRE: National Institute for Health Research Health Protection Research  
Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London. 
 
NRES REF:  
IRAS No: 204949 
 
 
 
Protocol authorised by: 
 
Name & Role 
Date 
Signature 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Principal Investigator:  
Prof Alison Holmes 
 
Co-investigators:  
Dr Pantelis Georgiou,  
Dr Luke Moore,  
Ms Esmita Charani,  
Dr Pau Herrero-Vinas,  
Dr Hugo Donaldson,  
Prof Chris Toumazou. 
 
Researchers: 
Mr Bernard Hernandez-Perez (PhD Student) 
 
Dr Timothy M Rawson (PhD Student) 
 
 
Study Management:  
National Institute for Health Research Health Protection Research Unit in Healthcare 
Associated Infection and Antimicrobial Resistance, Imperial College London (Hammersmith 
Campus), Du Cane Road. London. W12 0NN. 
 
Study Coordination Centre  
For general queries, supply of study documentation, and collection of data, please contact: 
 
National Institute for Health Research Health Protection Research Unit in Healthcare 
Associated Infection and Antimicrobial Resistance, Imperial College London (Hammersmith 
Campus), Du Cane Road. London. W12 0NN 
 
 
 
 
 
 
Tel: 020 33132732  
 
 
 
 
 
 
 
Fax:  020 8383 3394  
Email: head.ops@imperial.ac.uk 
 
 
 
 
 
 
 
Clinical Queries 
Clinical queries should be directed to Dr Luke Moore (l.moore@imperial.ac.uk; 
02033132732) who will direct the query to the appropriate person 
 
Sponsor 
Imperial College London is the main research Sponsor for this study.  For further information 
regarding the sponsorship conditions, please contact the Head of Regulatory Compliance at: 
 
 
 
Joint Research Compliance Office, Imperial College London, Room 215, Level 2, Medical 
School Building, Norfolk Place, W12 1PG 
Tel: 0207 594 1872 
 
Funder 
National Institute for Health Research Invention for Innovation Product Development Award 
 
This protocol describes the EPIC IMPOC study and provides information about procedures for 
entering participants.  Every care was taken in its drafting, but corrections or amendments may be 
necessary. These will be circulated to investigators in the study. Problems relating to this study should 
be referred, in the first instance, to the Chief Investigator. This study will adhere to the principles 
outlined in the NHS Research Governance Framework for Health and Social Care (2nd edition). It will 
be conducted in compliance with the protocol, the Data Protection Act and other regulatory 
requirements as appropriate.  

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Table of Contents 
1.    INTRODUCTION 
6 
1.1 
BACKGROUND 
6 
1.2 
RATIONALE FOR CURRENT STUDY 
6 
2.   STUDY OBJECTIVES 
7 
3.   STUDY DESIGN 
8 
3.1 
STUDY OUTCOME MEASURES 
8 
3.2 
STUDY DESIGN 
9 
4.   PARTICIPANT ENTRY 
14 
4.1 
PRE-REGISTRATION EVALUATIONS 
14 
4.2 
INCLUSION CRITERIA 
14 
4.3 
EXCLUSION CRITERIA 
15 
4.4 
WITHDRAWAL CRITERIA 
15 
5.   ADVERSE EVENTS 
16 
5.1 
DEFINITIONS 
16 
5.2 
REPORTING PROCEDURES 
16 
6.    ASSESSMENT AND FOLLOW-UP 
18 
7.   STATISTICS AND DATA ANALYSIS 
18 
8.    REGULATORY ISSUES 
19 
8.1 
ETHICS APPROVAL 
19 
8.2 
CONSENT 
19 
8.3 
CONFIDENTIALITY 
19 
8.4 
INDEMNITY 
19 
8.5 
SPONSOR 
19 
8.6 
FUNDING 
19 
8.7 
AUDITS 
19 
9.    STUDY MANAGEMENT 
20 
10.  PUBLICATION POLICY 
20 
11.  REFERENCES 
21 
12. APPENDICES 
ERROR! BOOKMARK NOT DEFINED. 
12.1  
ORGANOGRAM 
ERROR! BOOKMARK NOT DEFINED. 
12.2  
PROJECT GANTT CHART 
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12.3  
HEALTHCARE PROFESSIONAL PIL & CONSENT FORM FOR DEVELOPMENT PHASE
 
ERROR! BOOKMARK NOT DEFINED. 
12.4  
PATIENT & PUBLIC REPRESENTATIVE PIL & CONSENT FORM FOR DEVELOPMENT 
PHASE 
ERROR! BOOKMARK NOT DEFINED.6 
12.5  
HEALTHCARE PROFESSIONAL PIL & CONSENT FORM FOR EVALUATION PHASE ERROR! 
BOOKMARK NOT DEFINED.9 
12.6  
PATIENT & PUBLIC REPRESENTATIVE PIL & CONSENT FORM FOR EVALUATION PHASE 
 
 
ERROR! BOOKMARK NOT DEFINED.2 

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GLOSSARY OF ABBREVIATIONS 
POC 
Point of care 
DSS 
Decision support system 
AMR 
Antimicrobial resistance 
AMS 
Antimicrobial stewardship 
PCA 
Principle component analysis 
PPI 
Patient and public involvement 
AE 
Adverse event 
SAE 
Serious adverse event 
PIL 
Participant involvement leaflet 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
KEYWORDS 
Antimicrobial resistance 
Decision support system 
eHealth 
mHealth 
 
 
 

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STUDY SUMMARY 
 
TITLE 
Enhanced, Personalized and Integrated Care for Infection 
Management at Point of Care (EPIC IMPOC). 
DESIGN 
Development, and prospective mixed methods evaluation, of a 
medical (software) device 
AIMS 
Development, validation and ascertainment of the effectiveness 
of a point-of-care decision support system (POC DSS; i.e. a 
piece of advisory software accessible by clinicians on mobile 
devices) for infection management (i.e. antimicrobial prescribing) 
for secondary care inpatients. 
OUTCOME MEASURES 1)  Development of the POC DSS. 
2) Improved understanding of decision making in antimicrobial 
prescribing in secondary care. 
3) Development of a pharmacokinetic/ dynamic and therapeutic 
drug monitoring (TDM) software module. 
4)  Development of a patient engagement module. 
5) Evaluation of the implementation of the product and its impact 
on antimicrobial prescribing behaviour. 
6) Evaluation of the impact of the product on antimicrobial 
prescribing practice and antimicrobial resistance. 
POPULATION 
(i) Healthcare professionals,  
(ii) Patient and public representatives, and  
(iii) Patients with infections,  
at Imperial College Healthcare NHS Trust 
DURATION 
3 years 6 months 
 
 

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1. 
INTRODUCTION  
 
1.1 
BACKGROUND 
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 we 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. 
 
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. We propose to adapt the prototype to 
meet these needs. This system should improve patient safety and help preserve the 
effectiveness of existing antimicrobials. 
 
1.2 
RATIONALE FOR CURRENT STUDY 
Antimicrobial resistance is a major UK public health threat and optimising antimicrobial 
prescribing is a high impact intervention to combat this. Policies and guidelines play a central 
role in such optimising of antimicrobial prescribing [Charani et al, Infect Dis Clin North 
Am.2014;28(2):169176]. Strategies to control antimicrobial resistance to date have focused 
on the interrelated elements of ‘prudent’ antimicrobial use, surveillance, and infection control. 
In pursuit of ‘prudent’ antimicrobial use, a balance in antimicrobial spectra advocated in 
policies must be achieved to ensure care for the patient, but not exert unnecessary pressure 
toward generation of resistance. However, an implementation gap exists between policy and 
practice, and the social elements inherent in antimicrobial prescribing create a need for 
innovative methods to influence antimicrobial prescribing at point of care. An additional 
factor contributing to broad spectrum antimicrobial use is uncertainty around the 
antimicrobial resistance of the causative organism in any given patient, particularly during 
the early phase of infection management. Advances in rapid diagnostics may, in the near 
future, decrease this window of uncertainty, but the mechanism for feeding back these 
results to point of care needs clarification. We have successfully developed a prototype 
point-of-care decision support system (POC DSS) which is about to be deployed for use at 
the bedside in critical care which addresses these issues. The prototype runs on a mobile 
tablet, drawing patient data from NHS servers and utilizes case based reasoning (CBR) 
artificial intelligence to aid clinician decision making when prescribing antimicrobials. This 
POCDSS may play a key role in integrating systems and results such that antimicrobial 
policies, stratified to individual patients with timely microbiology results, can realise ‘prudent’ 
antimicrobial use. 
 
This novel product builds on: 1) our innovative work integrating antimicrobial policies into 
patient care pathways through mobile platforms [Charani et al, J Antimicrob Chemother. 
2013;68(4):960967]; 2) our seminal work on understanding prescribing etiquette [Charani et 
al, Clin Infect Dis. 2013;57(2):18896]; and 3) our extensive experience in bioinspired circuits 

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and systems [Herrero et al, J Diabetes Sci Technol. 2012;6(3):60616; Herrero et al, J 
Diabetes Sci Technol. 2012;6(2):462465(A64)].  
 
However, whilst critical care has a particularly high level of antimicrobial resistance, levels in 
other areas of secondary care are still significantly higher than in the community [Moore et 
al, J Antimicrob Chemother. 2014;69(12):340922]. There is therefore a need to develop the 
prototype, adapting it for use for clinicians prescribing antimicrobials for other secondary 
care inpatients. Furthermore there is a key need to engage patients with how decisions are 
made around antimicrobial prescribing. We propose to adapt the prototype to meet these 
needs. This system should improve patient safety and help preserve the effectiveness of 
existing antimicrobials.  
 
Inherent in development of this decision support system is the need to evaluate its utility. 
This will require a mixed methods analysis, incorporating qualitative methods looking at 
implementation and impact on prescribing behaviour, and also quantitative methods looking 
at impact on prescribing practice, patient outcomes and antimicrobial resistance rates.  
 
2.  STUDY OBJECTIVES 
 
1) Development of the POC DSS to support clinicians in providing personalised, optimised, 
antimicrobial management for secondary care patients outside of critical care. 
 
2) Improved understanding of the decision making processes around antimicrobial 
prescribing in secondary care patient cohorts. 
 
3) Personalisation of antimicrobial dosing to optimise therapy and minimise toxicity through a 
pharmacokinetic/dynamic and therapeutic drug monitoring (TDM) software module. 
 
4) Engagement with patients in decision making around antimicrobial prescribing through a 
module for use within the POC DSS. 
 
5) Evaluation of the implementation of the product and the impact on antimicrobial 
prescribing behaviour. 
 
6) Evaluation of the impact of the product on antimicrobial prescribing practice and 
antimicrobial resistance. 
 
 

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3.  STUDY DESIGN 
 
This study relates to the development, and prospective mixed methods evaluation, of a 
medical device. The prototype POC-DSS has now been developed and is ready for in-
house, proof of concept testing in the hospital setting.  
 
 
3.1 
STUDY AIMS & OBJECTIVES 
The principle objective of this in-house proof-of-concept study is to develop, validate, and 
ascertain the effectiveness of a point-of-care decision support system (POC DSS); i.e. a 
piece of advisory software accessible by clinicians on mobile devices) for infection 
management (i.e. antimicrobial prescribing) for secondary care inpatients. 
This builds on an already developed prototype targeted at infection management for patients 
in critical care. It targets improvement among clinicians in three areas: 1) personalisation of 
infection management through real-time adaptation of evidence based guidelines to cohort 
level antimicrobial resistance data and patient/drug/organism specific variables; 2) continuity 
through support for interpersonal communication; and 3) education during interactions 
between clinicians and infection specialists, and between doctors and patients. 
The prototype has been modified so the software is usable for clinicians caring for other 
secondary care patient cohorts (i.e. specialties outside of intensive care) and two software 
modules have been added to; (i) further personalise antimicrobial dosing and,(ii) to enable 
clinicians to engage with patients regarding their antimicrobial therapy. 
The secondary research objectives include:  
1) Development of the POC DSS to support clinicians in providing personalised, optimised, 
antimicrobial management for secondary care patients outside of critical care. 
2) Improved understanding of the decision making processes around antimicrobial 
prescribing in secondary care patient cohorts. 
3) Personalisation of antimicrobial dosing to optimise therapy and minimise toxicity through a 
pharmacokinetic/dynamic and therapeutic drug monitoring (TDM) software module. 
4) Engagement with patients in decision making around antimicrobial prescribing through an 
interactive, personalised information document for use within the POC-DSS. This will aim to 
facilitate improvements in doctor-patient communication during infection management. 
5) Evaluation of the implementation of the product and the impact on antimicrobial 
prescribing behaviour. 
6) Evaluation of the impact of the product on antimicrobial prescribing practice and 
antimicrobial resistance. 
 
 
 

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3.2 
STUDY DESIGN 
Phase 1: Infection specialist feasibility study 
  This initial pilot will follow a previously validated format, used to test the prototype POC-
DSS in the intensive care setting. The primary aim of this phase is to ensure that the POC-
DSS provides appropriate decision support advice in line with expert practice. Furthermore, 
each of the three core modules within the POC-DSS will be individually tested over a three-
month period beginning in January 2017. This phase will be a mixed methods assessment 
involving both qualitative and quantitative methods. Participants will be identified based on 
their roles within the infectious disease team (level and areas of the hospital covered) and 
will be directly approached by the study PI / lead infectious diseases clinician and provided 
with the participant information documents if they are happy to consider participating with 
this phase of the project. Physicians will then be consented to participate by a member of the 
research team at least 24 hours after being provided with a participant information leaflet. 
They will be consented to participate in using the application during infection management 
rounds to support their decisions for antimicrobial prescribing in a range of clinical settings. 
They will also be consented to participate in providing feedback on the POC-DSS to help 
with iterative development and identification of any technical issues that arise. They will 
undergo a short 10 minute training session on the background of the study and the device.  
  Technical evaluation & review of the Case-Based Reasoning alorithm:  Written and verbal 
feedback will be collected through regular micro-feedback physician interviews during the 
intervention period as well as researcher observation of the POC-DSS being used in clinical 
practice. This will initially occur at approximately 1-2 x per week with diminishing frequencies 
over time (as issues with the system are dealt with). Furthermore, evaluation of reported use 
and physician engagement will be compared to automated audit logs within the application, 
which allow us to track time spent using different sections of the device. This will facilitate 
identification of issues with the POC-DSS workflow, usability, and data acquisition compared 
to usual practice with normal hospital systems. Concordance of the suggested antimicrobial 
regime suggested by the CBR algorithm against expert opinion will also be assessed during 
this period using a recording feature within the device, based on a 5 point Likert scale as 
well as comparison with microbiology data, where available fpr comparison. They will be 
responsible for selecting patients that they deem as appropriate for use of the POC-DSS  to 
manage infections. This will be assessed through: 
  Precision dosing / TDM module: A prospective pilot interventional study to demonstrate 
proof-of-concept and assess the potential impact that an iterative learning control (ILC) 
algorithm would have on the management of patients receiving vancomycin and amikacin 
therapy. This will be undertaken by members of the research team. 
  i. 
20 patients receiving vancomycin & 20 patients receiving amikacin across secondary 
care for documented infections captured within the POC-DSS will have their data 
prospectively input into the ILC algorithm. These patients will be identified by infection 
clinicians during their usual practice as they use the POC-DSS. Consent will not be required 
as all data used by researchers will be anonymised before inclusion in this aspect of the 
study. If additional information not collected by the POC-DSS is required for this phase, a 
member of the patients clinical team will be responsible for collecting and anonymising this 
data before passing it onto the research team.  

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  ii. 
Simulation will be performed to assess the potential impact of use of the ILC 
algorithm in the management of these patients. Dosing recommendations from the ILC 
algorithm will be compared to dosing in clinical practice to assess the potential impact on 
optimisation of therapy for these patients. This will then be tested under strict infection 
specialist supervision on the 40 patients identified in point i.  
  iii. 
Quality control checks will be undertaken during this period to assess the safe limits 
of operation of the ILC algorithm. This will ensure that all safety limits placed on the 
controller appear correct and that it recommends dosing within acceptable limits.  
  Patient engagement module evaluation: A pre-post interventional study aiming to assess 
the impact of the patient engagement module. This will use a mixed methods assessment 
taking place on the infectious diseases ward at Hammersmith Hospital comparing the 
engagement module to usual care. The aims are to assess the potential impact of the 
module on patient engagement with infection management, improve patient – physician 
communication about infections and their management.  
  i. 
Patients being managed for infections will be identified by their treating clinician and 
consented to be approached by a member of the research team to discuss the study in detail 
within 5 days of commencing antimicrobial therapy. 
   a. 
Their treating clinician will identify them as appropriate for inclusion in the study 
based on overall inclusion/exclusion criteria for the study and make the initial approach for 
potential participation in the study. If the participant is happy to by approached a member of 
the research team will then provide potential participants with detailed information leaflet and 
give them 24 hours to consider involvement in the study before consent is sought.    
  ii. 
Participants upon inclusion in the trial will be asked to complete a 15 minute pre-
intervention questionnaire documenting understanding about their infection, its management 
and their perception of the information they have received regarding this. Questionnaires 
have been developed deductively based on patient and public involvement sessions, where 
the intervention was developed previously [Rawson et al., BMJopen, 2016]. 
  iii. 
Following completion of the questionnaire, a researcher will go through the patient 
module with the patient as it would be used in clinical practice. The participant will be 
provided with the personalised health information sheet based on their clinical situation. A 
follow up session will be offered 24 hours after the information is provided to the patient. 
  iv. 
At the follow up session the participant will be asked to complete a post intervention 
questionnaire designed to quantify the immediate impact that the intervention has had.   
  v. 
Participants will also be able to opt to take part in a follow up face-to-face / 
telephone/ electronic / postal survey approximately 5-30 days after completing the post-
intervention survey that will test their retention of knowledge surrounding the engagement 
module and usefulness of the intervention post discharge. The timing of this intervention will 
be based on iterative review of emerging themes / retention rates as well as convenience for 
the participant.    
 
 

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Data analysis and evaluation 
  During this phase, data analysis & evaluation will be iterative to allow constant refinement & 
re-testing of the POC-DSS. Furthermore, this will allow rapid identification of potential 
technical or safety issues allowing them to be dealt with urgently. All data will be reviewed 
monthly during this period at working group meetings and oversight will be provided by the 
group steering committee during this period.  
  Before progression onto phase 2 of the study clearance will need to be provided by the 
project steering committee, following demonstration of device safety in phase 1. If there are 
concerns about any modules within the POC-DSS, the steering committee may opt to 
remove them from further roll-out of the overall device, allowing the remaining modules to be 
trialled in phase 2. The Caldicott approval will also be reviewed at this stage if further 
portable mobile devices are required to be registered for use in the trial (as per Ref.726505)  
Phase 2: Testing on selected specialty wards 
  This phase will pilot a quasi-experimental format on several selected specialty wards 
across Imperial College Healthcare NHS Trust. The intervention phase is planned to 
commence in April 2017 (with 3 months of data collection during usual care occurring prior to 
intervention). Pre-intervention data collection will be anonymously extracted in the same way 
that it will be collected by the POC-DSS during the intervention period. Any data beyond that 
which can be collected by this system will be extracted by a member of the clinical research 
team before being anonymised and passed on to the research team.  
  Participants (physicians) will be identified based on the wards that they work, as in phase I, 
in and will be approached by members of the research team to discuss whether they would 
be happy to participate in the trial. If they are interested a member of the research team will 
provide a participant information leaflet and organise a time to go through the details of the 
study in detail and obtain consent. This will be at least 24 hours after provision of the patient 
information leaflet.  
  Physicians will be consented to use the POC-DSS as part of their routine clinical practice, 
including the patient engagement module, to support their decision making for infection 
management (used at their discretion). They will also be asked whether they would consent 
to participating in face-to-face interviews exploring their experience of using the application 
as described below.   
  In April 2017, doctors for each of the 5 wards will be given a 10 minute introduction and 
instructions on the background of the study. They will also be provided with information 
sheets and asked whether they would be willing to participate in a stakeholder interview 
towards the end of their rotation.  
  A two-week implementation period will then follow to allow acclimatisation to using the 
device and any technical issues to be addressed. Three months of data collection will then 
ensue with the device being trailed in clinical practice. 
i. 
Before the end of this period a number of participant semi-structured interviews will 
be conducted as well as a team focus group for each ward, to evaluate the POC-
DSS. 

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  ii. 
Quantitative data analysis will be performed as described above  
  iii. 
Patients who use the patient engagement module during routine consultations with 
their treating clinician may be identified by their treating physician and approached to take 
part in a 15-30 minute semi-structured interview to assess the impact of this module on their 
engagement with their infection and its management. 
  Data will regularly be reviewed on a monthly basis by the working group committee to 
highlight any technical or safety issues that may arise during the study period..  
Data analysis 
  The research team have considerable experience in large dataset analysis of antimicrobial 
usage and antimicrobial resistance patterns and have established recourse to continual data 
for these two parameters from the Academic Health Sciences Centre. Near completion 
projects have developed surveillance tools to analyse these microbiology and pharmacy 
data sets for detailed geographical and temporal trends. Use of these data, combined with 
the database generated from the CBR algorithm, will be used to triangulate a quantitative 
analysis of the impact of the POC-DSS on prescribing patterns, patient outcomes and 
antimicrobial resistance trends. 
  Note: This phase is planned to run for 7 months (3 months pre-intervention, 1 month 
implementation and 3 months post implementation) in total. However, this may be extended 
to 12 - 18 months if it is deemed that further data collection is required to complete this 
proof-of-concept, in house study. 
PLEASE NOTE:  
  EMAILS 
MAY 
BE 
USED 
TO 
RECRUIT 
PARTICIPANTS 
FOR 
QUALITATIVE 
EVALUATION OF THE APPLICATION (IF DETAILS AND CONSENT HAVE PREVIOUSLY 
BEEN PROVIDED DURING FACE-TO-FACE CONSENT AT RECRUITMENT). COPIES OF 
THE EMAILS THAT WILL BE USED FOR BOTH PATIENTS AND PRESCRIBERS CAN BE 
FOUND IN STUDY PROTOCOL V8.0. ALL IDENTIFIABLE CONTACT INFORMATION 
WILL BE KEPT ON AN ENCRYPTED SPREADSHEET WITHIN THE TRUSTS FIREWALL. 
  THE DIVISIONAL MANAGERS AND HEADS OF DEPARTMENT FOR SPECIALTIES 
WHERE PARTICIPANTS (PRESCRIBERS OR PATIENTS) MAY BE RECRUITED FROM 
WILL BE CONTACTED IN ADVANCE OF THESE PHASES BEING IMPLEMENTED TO 
ENSURE THAT THEY ARE HAPPY FOR THE STUDY TO TAKE PLACE WITHIN THEIR 
DEPARTMENTS/DIVISIONS.   
 
Study outcome measures:  
Derived from a systematic review of clinical decision support literature and using Delphi 
method with a multi-professional panel of experts. The feasibility of the device will be tested 
internally within Imperial College Healthcare NHS Trust in 2 stages.  
 

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Principle objective 
The principle objective development and proof-of-concept of a point-of-care decision support 
system (POC-DSS) for enhanced, personalised infection management across secondary 
care. 
 
Phase 1:  
 
o Primary outcome measures: 
 
Appropriateness 
of 
antimicrobial 
recommendations 
compared 
to 
microbiology confirmation/expert opinion. 
 
Safe recommendations for dose optimisation of vancomycin and amikacin 
by the iterative-learning control (ILC) algorithm and improvements in 
therapeutic drug monitoring of these agents. 
 
Evidence of improved patient engagement with infection management 
following use of the patient engagement module. 
 
o Secondary outcome measures:  
 
Predicted rate reduction in interventions by the antimicrobial stewardship 
team if POC-DSS recommendations were followed. 
 
Rate of IV-PO switching and appropriate de-escalation of therapy. 
 
Economic evaluation of the POC-DSS versus usual care 
 
Level of physician engagement with the POC-DSS 
 
 
Phase 2:  
 
o Primary outcome: 
 
Appropriateness 
of 
antimicrobial 
recommendations 
compared 
to 
microbiology confirmation/expert opinion. 
 
Rates 
of 
broad 
spectrum 
antimicrobial 
use 
(e.g. 
3rd/4th 
Gen 
Cephalosporins. Piperacillin/Tazobactam, meropenem) 
 
Rate of interventions required by antimicrobial stewardship team during 
control and intervention period 
 
Economic analysis of the intervention 
 
o Secondary outcomes: 
 
Enhanced patient engagement with infection management  
 
Assessment of trends in AMR across all care pathways (including ICU) 
 
Impact on length of treatment  
 
Impact on 30 day mortality  
 
Prescriber and non-prescriber engagement with the application  
 
Number of medication adverse events 
 
Assessment for unintended consequences of application use (quantitative 
and qualitative)  
 
 
 
 
 
 
 

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4. 
PARTICIPANT ENTRY  
 
4.1 
RECRUITMENT 
 
Phase I & II - Patient recruitment: 
 
To meet the evaluation component of patient engagement module, patients may be asked 
for permission to be approached by a member of the research team who will provide them 
with a participant information leaflet to participate in the qualitative evaluation of the the 
patient module in phases 1/2. The researcher will allow 24 hours for the patient to consider 
the PIL before discussing consent for participation in the study. During the consent phase 
the researcher will also offer the participant the opportunity to provide a contact email 
address to be contacted 5-30 days after completing use of the patient engagement module 
to undertake a further evaluation of the module by either telephone, survey (paper / electric), 
or face-to-face. 
 
For patients for whom their clinician chooses to use the decision support system when 
making antimicrobial prescribing decisions, no consent is needed, in the same way as 
consent is not needed for when a clinician chooses to use any other resource when making 
a decision (reading journal articles, text books etc).Therefore patients who are deemed 
suitable will be selected by their treating physician.  
 
For prescriber recruitment to use the application the following methods of identifying 
participants will be undertaken: 
 
Phase 1: Participants will be identified based on their roles within the infectious disease 
team (level and areas of the hospital covered) and will be directly approached by the study 
PI / clinical infection lead to request their support with this phase of the project. They will be 
provided with a PIL and then approached after 24 hours by a member of the research team 
for consent in using the POC-DSS 
 
Phase 2: Participants (physicians) will be identified based on the wards that they work in. 
The PI/Clinical lead will make an initial approach to provide participants with the PIL. A 
member of the research team will approach the potential participants after leaving 24 hours 
for consideration to discuss whether they would be happy to participate in participating in the 
trial. If they are interested the researcher will go through the details of the study in detail and 
obtain consent. Before this approach is made the project will be fully discussed and cleared 
with the heads of department and senior team members responsible for the wards in 
question. They will responsible for suggesting suitable prescribers from the ward selected for 
inclusion. 
 
 
4.2 
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 
 

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4.3 
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) 
 
4.4 
WITHDRAWAL CRITERIA 
If evidence of the unintended consequences noted below becomes apparent during 
the course of the study, particularly when interval analysis is reviewed at the 
oversight committee meetings, then consideration will be given by the oversight 
committee as whether the research must be suspended, modified or stopped 
prematurely. Furthermore the application may be withdrawn upon Identification of 
recurring technical faults, which have the potential to affect patient outcomes. 
 
Scoping exercises conducted for the prototype theorise a range of unintended 
consequences including 
(i) 
the potential for reduced diversity of antimicrobial prescribing,  
(ii) 
the potential for data input error 
(iii) 
the potential for erroneous artificial intelligence learning.  
 

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5. 
ADVERSE EVENTS 
 
5.1 
DEFINITIONS   
Adverse Event (AE): any untoward medical occurrence in a patient or clinical study subject.   
 
Serious Adverse Event (SAE): any untoward and unexpected medical occurrence or effect 
that: 
 
Results in death 
 
Is life-threatening – refers to an event in which the subject was at risk of death 
at the time of the event; it does not refer to an event which hypothetically might 
have caused death if it were more severe 
 
Requires 
hospitalisation, 
or 
prolongation 
of 
existing 
inpatients’ 
hospitalisation 
 
Results in persistent or significant disability or incapacity 
 
Is a congenital anomaly or birth defect 
 
Medical judgement should be exercised in deciding whether an AE is serious in other 
situations.  Important AEs that are not immediately life-threatening or do not result in death 
or hospitalisation but may jeopardise the subject or may require intervention to prevent one 
of the other outcomes listed in the definition above, should also be considered serious. 
 
5.2 
REPORTING PROCEDURES 
All adverse events should be reported. Depending on the nature of the event the reporting 
procedures below should be followed. Any questions concerning adverse event reporting 
should be directed to the Chief Investigator in the first instance.   
 
If any cause for concern over an individual’s clinical practice are identified during the course 
of data collection / analysis, the involved parties will be informed of this and information will 
be escalated to their line managers for further review of the incidents. This is outlined in the 
PIL document during the consent to participate procedure.  
 
5.2.1 Non serious AEs 
All such events, whether expected or not, should be recorded.   
 
5.2.2 Serious AEs 
An SAE form should be completed and faxed to the Chief Investigator within 24 hours.  
However, relapse and death due to non-infective consitions and hospitalisations for elective 
treatment of a pre-existing condition do not need reporting as SAEs. 
 
All SAEs should be reported to the <name of REC> where in the opinion of the Chief 
Investigator, the event was: 
 
‘related’, ie resulted from the administration of any of the research procedures; 
and 
 
‘unexpected’, ie an event that is not listed in the protocol as an expected 
occurrence 
 
Reports of related and unexpected SAEs should be submitted within 15 days of the Chief 
Investigator becoming aware of the event, using the NRES SAE form for non-IMP studies.  
The Chief Investigator must also notify the Sponsor of all SAEs. 
 
Local investigators should report any SAEs as required by their Local Research Ethics 
Committee, Sponsor and/or Research & Development Office. 
 
Contact details for reporting SAEs 
Fax: 0208383394,  

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Urgent attention Prof A Holmes 
Tel: 02033132732 (Mon to Fri 09.00 – 17.00) 

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6. 
ASSESSMENT AND FOLLOW-UP  
 
As described, quantitative and qualitative assessment of the device will be undertaken, 
which will incorporate prescribing data, antimicrobial resistance data, and patient outcome 
data. Patients will not be followed up beyond the last use of the POC DSS by the clinician 
using the device as a resource for prescribing. The exception to this will be when 
participants consent to undergo a follow-up interview between 5 and 30 days after being 
engaged with the patient engagement module of the device. 
 
7. 
STATISTICS AND DATA ANALYSIS 
 
For qualitative evaluation: Face-to-face interviews and surveys of clinical users of the POC-
DSS will provide qualitative evaluation of the impact of CBR on antimicrobial choice. Data 
will be interrogated using thematic analysis and a conceptual framework developed to inform 
evolution of this POC-DSS, and to contribute to the wider debate on healthcare software 
development and integration into clinical practice. The multi-professional background and 
qualitative analytic skill set within the research group will facilitate a robust evaluation of 
impact. Furthermore, the research group has the considerable experience in technology roll-
out to healthcare settings [Kyratsis et al, Implementation Science.2012;7:22, Charani et al, J 
Antimicrob Chemother.2013;68(4):960-7] and analysis of the implementation mechanism will 
benefit future translation of the product to the wider NHS. 
 
For quantitative evaluation: 
A phased implementation of the POC-DSS will be implemented to ensure that decision 
support provided is deemed safe and appropriate (outlines below in 7.1).  
Phase 1: An initial pilot implementation of the POC-DSS will be performed via the Infection 
teams within the Trust. These teams are made up of infection specialists, who review 
between 30-50 patients a day across the hospital for infection related matters. This will allow 
use of the POC-DSS under supervision of expert prescribers to allow for expert feedback on 
the devices performance. This follows a model successfully trialled during the pilot of the 
POC-DSS in the intensive care setting. This phase is expected to run over approximately 3 
months 
Phase 2: The POC-DSS will be tested within specific subgroups of core specialties within the 
hospital Trust (e.g. general medicine, haematology, renal medicine, general surgery and 
care of the elderly) on selected wards. This will utilise a quasi-experimental interrupted time 
series design to evaluate the impact of the product on antimicrobial prescribing, patient 
outcomes and antimicrobial resistance trends over a 3 month period. 
The research team have considerable experience in large dataset analysis of antimicrobial 
usage and antimicrobial resistance patterns and have established recourse to continual data 
for these two parameters from the Academic Health Sciences Centre. Near completion 
projects have developed surveillance tools to analyse these microbiology and pharmacy 
data sets for detailed geographical and temporal trends. Use of these data, combined with 
the database generated from the CBR algorithm, will be used to triangulate a quantitative 
analysis of the impact of the POC-DSS on prescribing patterns, patient outcomes and 
antimicrobial resistance trends. 
 
Storage: Data and all appropriate documentation will be stored for a minimum of 10 years 
after the completion of the study, including the follow-up period. Participant consent forms 
will be kept in a locked cabinet within a locked room on the 7th floor of the commonwealth 
building. Only researchers and those with clearance to perform regulatory audits of research 
procedures will have access to this. Participant audio recordings (anonymous) will also be 
stored in this manner until transcribed when they will then be erased.   
Anonymised data will be stored on an Imperial College networked computer within the 
firewall. This will be accessible to researchers only. 
 

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8. 
REGULATORY ISSUES 
 
8.1 
ETHICS APPROVAL 
The Chief Investigator is obtaining approval from the Research Ethics Committee and HRA.  
The study will be conducted in accordance with the recommendations for physicians 
involved in research on human subjects adopted by the 18th World Medical Assembly, 
Helsinki 1964 and later revisions. 
 
8.2 
CONSENT  
Consent to enter the study must be sought from each participant only after a full explanation 
has been given, an information leaflet offered and at least 24 hours has been allowed to 
decide upon participation. Signed participant consent will be obtained.  The right of the 
participant to refuse to participate without giving reasons will be respected.  After the 
participant has entered the study the clinician remains free to give alternative treatment to 
that specified in the protocol at any stage if he/she feels it is in the participant’s best interest, 
but the reasons for doing so should be recorded.  In these cases the participants remain 
within the study for the purposes of follow-up and data analysis.  All participants are free to 
withdraw at any time from the protocol treatment without giving reasons and without 
prejudicing further treatment. 
 
8.3 
CONFIDENTIALITY 
Patient Identifiable Data from clinical and laboratory databases will only be accessed within 
the host NHS Trust firewall after the clinical team user has passed their Lightweight 
Directory Access Protocol (LDAP) authentication. Anonymised data will be analysed on an 
Imperial College London networked computer. Any data used for analysis outside of the 
firewall will be fully anonymised using the SHA256(SHA2 family) one way hash algorithm. 
Interviews will be transcribed either by researchers in the department or by a clinical 
transcription company (the transcription agency). During audio recordings participants are 
asked to not use any identifiable information or names with all recordings checked and any 
identifiable information removed before being sent out for transcription. At the transcription 
agency confidentiality is also maintained through: All employees are subject to Baseline 
Security checks and have provided documentation to confirm their identity, nationality and 
immigration status where applicable. All staff have signed non disclosure/confidentiality 
agreements and are willing to do so for individual clients if required. Confidentiality clauses 
are written into all contracts.  The Transcription Agency has an Information Security Policy 
and it covers the requirements for information security, the scope of the Information Security 
Management System, including business functions, areas and sites covered and the general 
philosophy towards information security. All electronic media is deleted after completion of 
each project and receipt of transcripts has been confirmed. Anonymised data will be 
analysed within Imperial College using the college network. This will be undertaken by lead 
researchers for the project, with support from Imperial College Statistical Advisory Services. 
 
8.4 
INDEMNITY 
Imperial College London holds negligent harm and non-negligent harm insurance policies 
which apply to this study. 
 
8.5 
SPONSOR 
Imperial College London will act as the main Sponsor for this study.  Delegated 
responsibilities will be assigned to the NHS trusts taking part in this study.   
 
8.6 
FUNDING 
The National Institute for Health Research is funding this study through an Invention for 
Innovation Product Development Award of £687,000 over 3 years. 
 
8.7 
AUDITS  
The study may be subject to inspection and audit by Imperial College London under their 
remit as sponsor and other regulatory bodies to ensure adherence to GCP and the NHS 
Research Governance Framework for Health and Social Care (2nd edition).  
 

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9. 
STUDY MANAGEMENT 
 
The principal investigator will be responsible for overall study oversight. They will chair a 
formal project oversight meeting once a quarter. The meeting will act to iteratively monitor 
project progress against the key deliverables and outcome measures, plan all future steps 
and identify any potential issues including the creation of intellectual property and the best 
methods for dissemination and communication of research and product outcomes.  
 
On a day to day basis the project will benefit from management by the Head of Operations 
within HPRU who is experienced in integrating the workflows of the engineers and the multi-
professional healthcare team from the two collaborating Centres. The research team 
engaged in developing the prototype POC-DSS will continue to be involved in meeting the 
post-prototype broader objectives described. 
 
The study PI (AH) has an established collaboration with parallel managed teams in the fields 
of healthcare and bio-technology. The project will be subject to the usual management 
practices within the groups, meaning that updates of progress will be required at work-
stream meetings within HPRU and wider work-inprogress meetings at Imperial College 
London.  
 
The organogram for the oversight committee and the operational management can be found 
in the appendix. 
 
10. 
PUBLICATION POLICY 
 
The methods and results from development of the POC-DSS will be presented at national 
and international conferences to both infection and patient safety specialists who are 
researchers and decision makers. Targeting of these specific groups in disseminating the 
research findings will allow not only engaged and productive feedback, but is also likely to 
generate further questions and raise awareness of the product as a resource for wider 
adoption. A key emphasis will be placed on highlighting both the barriers and the facilitators 
towards project completion, and on the impact of the product on antimicrobial prescribing, 
patient outcomes and antimicrobial resistance. The impact on patient outcomes will focus on 
both individual level outcomes, but also look at the wider perspective of the impact of the 
product on the patient pathway. 
 
High impact peer reviewed publications will arise from this research evaluation, enabling 
wider engagement of researchers and decision makers. The mixed methods evaluations 
proposed for the product, utilising both qualitative and quantitative analyses, will enable a 
truly robust evaluation of the product. Open-access fees have been included in the research 
costs requested, against four high impact journals to enable the widest possible 
dissemination of the findings. 
 

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11. 
REFERENCES 
Charani E, Castro-Sánchez E, Holmes AH. The Role of Behavior Change in Antimicrobial 
Stewardship. Infectious Disease Clinics of North America. 2014;[In Print]. 
 
Charani E, Castro-Sánchez E, Moore LSP, Holmes AH. Do smartphone applications in 
healthcare require a governance and legal framework ? It depends on the application ! BMC 
Medicine. 2014;12:29. 
 
Charani E, Castro-Sánchez E, Sevdalis N, Kyratsis Y, Drumright L, Shah N, Holmes AH. 
Understanding the determinants of antimicrobial prescribing within hospitals: the role of 
"prescribing etiquette". Clin Infect Dis. 2013;57(2):188-96. 
 
Charani E, Holmes AH. Antimicrobial stewardship programmes: the need for wider 
engagement. BMJ Qual Saf. 2013;22(11):885-7. 
 
Charani E, Kyratsis Y, Lawson W, Wickens H, Brannigan ET, Moore LSP, Holmes AH. An 
analysis of the development and implementation of a smartphone application for the delivery 
of antimicrobial prescribing policy: lessons learnt. J Antimicrob Chemother. 2013;68(4):960-
7. 
 
Desai M, Dean Franklin B, Holmes AH, Trust S, Richards M, Jacklin A, Bamford KB. A new 
approach to treatment of resistant gram-positive infections: potential impact of targeted IV to 
oral switch on length of stay. BMC Infectious Diseases. 2006;6:94. 
 
Farkas A, Daroczi G, Saunders-Hao P. Individually Designed Optimum Dosing Strategies 
(ID-ODS), a multi-model based online application to individualize antibiotic dosing in critically 
ill patients. 53rd ICAAC (Abstr.) Denver, USA. 2013;A1054. 
 
Herrero P, Georgiou P, Oliver N, Johnston DG, Toumazou C. A bio-inspired glucose 
controller based on pancreatic β-cell physiology. J Diabetes Sci Technol. 2012;6(3):606-16. 
 
Herrero P, Georgiou P, Oliver N, El Sharkawy M, Pesl P, Johnston D, Toumazou C. In Silico 
Validation of a Bio-Inspired Glucose Controller. J Diabetes Sci Technol. 2012;6(2):462-
465(A63.) 
 
Kaki R, Elligsen M, Walker S, Simor A, Palmay L, Daneman N. Impact of antimicrobial 
stewardship in critical care: a systematic review. J Antimicrob Chemother. 2011;66(6):1223-
30. 
 
Kyratsis Y, Ahmad R, Holmes AH. Making sense of evidence in management decisions: the 
role of research-based knowledge on innovation adoption and implementation in healthcare. 
study 
protocol. 
Implementation 
Science. 
2012;7:22.

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