A Retrospective, Observational Analysis Using Real Time Location Systems (RTLS) Technology to Explore the Effects of Clinical Staffing on Hospital Productivity and Patient Health Outcomes
Conditions: Inpatients, Staff
Sponsor: The Royal Wolverhampton Hospitals NHS Trust

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A retrospective, observational 
analysis using Real Time 
Location Systems (RTLS) 
technology to explore the 
effects of clinical staffing on 
hospital productivity and 
patient health outcomes 
 
 
 
 
 
 
 
 
 
SPONSOR: The Royal Wolverhampton NHS Trust  
 
 
 
 
 
 
Version 8.0, August 30th 2019 
 
 
 
 
 
 
 
 
 
 

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Study Management Group 
 
Chief Investigator / Principal Investigator:  
Professor Ann-Marie Cannaby, PhD, MA, PGDip, BA, DN, RGN 
Chief Nurse, The Royal Wolverhampton NHS Trust, United Kingdom 
Professor of Nursing Sciences at Birmingham City University 
Honorary Fellow at La Trobe University Melbourne Australia ann-marie.cannaby@nhs.net 
 
 
Co-investigators: 
 
                       Professor Richard Gray, Professor of Clinical Nursing Practice, La Trobe  
                             
University, Melbourne Australia      
 
 
 
 
Assistant Professor Thomas Hoe  
                            
Cornell University, United States of America 
   
 
 
 
Mr Stephenson Strobel  
Cornell University, United States of America 
 
Mrs Rose Baker  
Associate Chief Nurse, The Royal Wolverhampton NHS Trust, United Kingdom 
 
Mrs Vanda Carter 
Trust Practice Education Facilitator for Research, The Royal Wolverhampton 
NHS Trust, United Kingdom  
 
Mrs Clare Nash 
Senior Nurse, Clinical Procurement and Productivity Programmes, The Royal 
Wolverhampton NHS Trust, United Kingdom 
 
 
 
 
 
 
 
Study Co-ordination: Mrs Vanda Carter vanda.carter@nhs.net 
 
 
 
Sponsor 
 
The Royal Wolverhampton NHS Trust is the research sponsor for this study.  For further information 
regarding the sponsorship conditions, please contact R&D Department, The Royal Wolverhampton 
Hospitals NHS Trust – Tel: 01902 695065 or email sarah.glover7@nhs.net lorraine.jacques@nhs.net 
or lucystelfox@nhs.net 
 
 
Acknowledgements 
To all staff within The Governance, H.R, Information, IT and Library Teams who helped and advised 
during this project 

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Every care has been taken throughout the protocol drafting stage to be inclusive and explicit, however 
amendments may be necessary as the study activities commence and the project evolves.  All 
amendments will follow the appropriate governance notification process and standard operating 
procedures advocated by the Trusts Research and Development Directorate. 
Problems relating to this study should be referred, in the first instance to the Chief Investigator or study 
co-ordinator. This study will adhere to the principles outlined in the UK Policy Framework for Health 
and Social Care Research ver. 3.0 (2017). 
It will be conducted in compliance with the protocol, the Data Protection Act and other regulatory 
requirements as appropriate to the principles of Good Clinical Practice. This is to ensure that the 
rights, safety and well-being of the study participants are protected and the research data is purposeful 
and of high quality. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Table of Contents 
 
1.0 
INTRODUCTION 
  
  6 
1.1 
LITERATURE REVIEW / BACKGROUND 
 
  7 
2.0 
STUDY OBJECTIVES 
 
  9 
3.0 
STUDY DESIGN 
 
   
3.1 
STUDY OUTCOME MEASURES 
           12 
 
4.0 
PARTICIPANT ENTRY 
 
12 
4.1 
INCLUSION CRITERIA 
 
12 
4.2 
EXCLUSION CRITERIA 
 
12 
4.3 
WITHDRAWAL CRITERIA 
 
12 
5.0 
ADVERSE EVENTS 
 
12 
6.0 
ASSESMENTS AND FOLLOW-UP 
 
13 
7.0 
STATISTICS AND DATA ANAYLSIS 
   
13 
8.0 
REGULATORY ISSUES 
   
14 
8.1 
REGULATORY APPROVAL 
 
14 
8.2 
CONSENT 
  
14 
8.3 
CONFIDENTIALITY 
 
 
 
 
 
 
 
14 
8.4 
IDEMNITY 
 
 
 
 
 
 
 
 
16 
8.5 
SPONSOR 
 
 
 
 
 
 
 
 
16 
8.6 
FUNDING  
 
 
 
 
 
 
 
 
16 
8.7 
AUDITS AND INSPECTION 
 
 
 
 
 
 
16 
9.0 
STUDY MANAGEMENT 
 
 
 
 
 
 
16 
10 
PUBLICATION POLICY 
 
 
 
 
 
 
16 
11 
    ARCHIVING 
 
 
 
 
 
 
 
 
17    
12  
    REFERENCES  
 
 
 
 
 
 
 
17 
 
 
 
 
 
 
 
 

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GLOSSARY OF ABBREVIATIONS AND TECHNICAL TERMS 
AE 
Adverse Event- any untoward medical occurrence in a patient or clinical study 
subject 
CI 
Chief Investigator 
Ci 
Co- Investigator 
DATIX 
Reporting governance system 
GCP 
Good Clinical Practice 
HRA 
Health Research Authority 
HPPD 
Hours per patient day 
HR 
Human Resource 
ICU 
Intensive Care Unit 
IFS 
Institute of Fiscal Studies 
NHPPD 
The total nursing hours divided by the number of hospitalised patients in one 
day Fike & Smith-Stoner (2016) 
Non – c-timp 
Non-clinical trial involving an investigational medicinal product 
PAS 
Patient Administration system 
PI 
Principal Investigator 
PIS 
Patient Information Sheet 
Pseudo-  
A masking technique that allows part of the data to be hidden with random 
characters or other data 
R and D 
Research and Development 
RTLS 
Real Time Location system 
RWT 
Royal Wolverhampton NHS Trust 
SOP 
Standard operating procedure 
UK 
United Kingdom 
UTI 
Urinary tract infection 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
KEYWORDS 
Real time location system, quality improvement, contact time, peer effects.  

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STUDY SUMMARY 
 
            TITLE 
A retrospective, observational analysis using Real Time Location Systems 
(RTLS) technology to explore the effects of clinical staffing on hospital 
productivity and patient health outcomes 
 
            DESIGN 
Retrospective, observational study 
AIMS and        
            OBJECTIVES 
• 
To measure the amount of time that nurses and other professional 
groups spend with patients (e.g. at the bedside) 
• 
To measure the amount of time that nurses and other professional 
groups spend in co-working spaces (e.g. nurse stations) 
• 
To explore if the amount of time nurses and other professional groups 
spend in direct patient care affects patient outcomes 
• 
To examine if the amount of professional to professional time in ward 
areas has an impact on patient outcomes 
• 
To explore if a reduction in rostered nurses impacts patient outcomes 
• 
To investigate if patient movement from original/speciality base ward has 
an impact on patient outcomes 
               POPULATION 
All admitted inpatients between 1 April 2016 and 31 March 2019, and all staff 
members with RTLS badges, at The Royal Wolverhampton NHS Trust (RWT), 
Wolverhampton, UK.  
                 ELIGIBILITY 
All inpatient and staff covered by the RTLS system 
                 DURATION 
Study duration to last up to 3 years from the date of the protocol approval. 
Retrospective data to cover the period 1 April 2016 to 31 March 2019. 
 
1.0 
INTRODUCTION  
This study will examine the productivity of the hospital workforce utilizing a unique dataset that is 
stored at the Royal Wolverhampton Trust (RWT). The data is recorded by a Real Time Location 
System (RTLS) that tracks the second-by-second physical location of patients, staff, and medical 
equipment. Using this data, we plan to measure the amount of time that clinical staff spend with 
patients and with other clinical staff, and then explore how these measures of contact time influence 
patient health outcomes. We will also be able to use the data to measure the location and movement 
of patients during their hospital stay, and test the impact of moving patients between wards on their 
health outcomes.  
We first describe the RTLS system in more detail, and then provide a review of the relevant literature 
and a rationale for our study and its contribution. The Royal Wolverhampton NHS Trust partnered 
with a US company (Tele-Tracking) in 2013 to develop a real-time patient flow and tracking solution 
to improve patient access, in-patient flow, discharge planning and quality and safety of care 
oversight (Nash 2014, Stephenson 2015). 
Transitioning from an environment of disjointed activity to patient throughput functions which are now  
Co-ordinated through a centralised command centre, supports staff to deliver efficient, quality driven  
care by providing real-time location and status visibility to all inpatients, beds, staff and equipment.  
The Trust’s patient flow solution is enabled by a Real Time Locating System (RTLS) that uses a 
combination of infra-red and radiofrequency technology to feed data into the patient flow and tracking 
software.  The Trust has to date, badged 1,500 assets (Trust equipment i.e. bladder scanners), 4,000 
staff, and all inpatients, making it the world’s largest deployment of RTLS technology in a healthcare 
setting and the first of its kind in the NHS. This technology, coupled with process redesign, has 
allowed the Trust to: 
• 
automate portering and bed cleaning functions; 
• 
utilize the real-time visibility of bed availability and patient status to allow for precision 
patient placement from all admission areas; 
• 
automate workflows and provides patient status at a glance information for staff, with the 
aim to reduce the time spent on administrative tasks; 
• 
improve patient throughput, access, discharge planning, and quality and safety; 

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• 
redesign processes and functions of the previous Patient Flow Team for greater efficiency 
through 24*7 access to a live bed state; 
• 
provide staff with real-time alerts to patient safety risks, isolation alarms, and patient walk 
out alarms and a system for escalation of staff safety concerns; 
• monitor all staff-patient-asset (hardware/ equipment diagnostic) interactions for incident 
investigations including infection prevention tracing and root cause analysis of falls and 
pressure ulcer acquisitions. 
 
1.1 
LITERATURE REVIEW / BACKGROUND 
A wide body of research across the medical and economics literature documents the critical 
role that hospitals’ workforces play in determining patient outcomes. This ranges from the 
role of management, nurses, doctors, and decisions over patient flow. We review the 
existing studies from these literatures here.  
 
Nursing care 
The numbers, skill mix and education of nurses to optimally care for patients continues to be 
debated against a landscape of increased patient demand and a changing demographic of 
the nursing workforce (Griffiths 2016a). An increasing number of research studies have 
shown that low nursing levels are associated with poor patient outcomes and an increase in 
mortality (Griffiths 2016b). Authors have reported that an increase of registered nurses 
caring for patients, known as a nurse to patient ratio (Kane et al 2007) or nursing hours per 
patient day (NHPPD) (Twigg 2011) are associated with lower mortality and a decrease in 
nurse sensitive outcomes, for example urinary tract infections and length of stay (Kane et al 
2007,Twigg et al 2011 and Chang et al 2017).  
Higher levels of educational preparation of registered nurses and a higher registered nurse skill mix, 
or ratio, have been associated with lower hospital mortality (Gkantaras et al 2016, Akien et al 2016, 
Griffiths et al 2016b). The strength of association varies across studies. According to Griffiths 
et al (2016b) nurses looking after six, or fewer, patients in medical wards in a recent study 
showed a 20% lower mortality rate compared to nurses on medical wards that cared for ten 
(10) or more patients. Authors in Australia and USA report policies mandating minimum 
nurse patient ratios (Twigg et al 2011, Spetz 2005), but with an ageing populations globally, 
the complexity of care, economic pressures and the shortage of nursing staff, have led 
many countries to consider the numbers and skill mix of their nursing workforce (Splibury et 
al 2009).  
Two systematic reviews (Lankshear et al 2005 and Kane et al 2007) were found in a 
literature review plus two further meta-analysis (Numata et al 2006,Tourangeau et al 2006) 
and sixteen (16) primary research papers (Needleman et al 2011, Griffiths et al 2016b, 
Junttila et al 2016, Akien et al 2016, Chang et al 2017, Glance et al 2012, Person et al 
2004, Rafferty et al 2007, Sochalski et al 2008, Twigg et al 2011, Cho et al 2009, West et al 
2014, Estabrooke et al 2005, Sales et al 2008, Blegen et al  2011, Tourangeau et al 2006), 
exploring the association between registered nurse numbers, ratios or skill mix and patient 
mortality.  All primary research papers found an association between ‘adequate’ or an 
increased level of RN nurses and a reduction in patient mortality, with the exceptions of two 
research studies one by Sales et al (2008) which found no significant difference in the 
nursing numbers and patient mortality in ICUs but found significance in non-ICU 
environments and Sochalski et al (2008), where significant effects were not found in fixed 
effects analysis. Two studies also explored the multi professional team’s (nurse, doctors 
and healthcare assistants) association with mortality rates, but do not yet present a consistent 
picture (West et al 2014, Griffiths et al 2016b).  
Nursing skill mix and staffing ratios are only a proxy measure of direct or bedside care a patient may 
receive from registered nurses. There is an underlying assumption that patient outcomes and 
experience will improve the more time that nurses spend providing care at the bedside (and not 
engaged in administration). There are of course two main key confounders, firstly patient acuity and 
dependency need to be measured as the sickest patients are likely to be receiving more nursing 
care, and secondly the diverse roles of nurses need to be considered, including time for 

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administration, training, sickness and annual leave, all which may or may not be factored into all 
staffing calculations. High level nursing numbers provide an indication or an overview of the amount 
of care that is available to patients, rather than actual care. Keogh, (2013) suggests that nursing 
workforce data and the number of nurses on the ground may not always correlate, however. 
The methodologies for calculating the available care hours that a patient may receive may be 
expressed as a nurse to patient ratio or Nursing Hours per Patient Day (NHPPD), calculated using 
direct patient hours or total hours, (a combination of direct and non-direct patient hours). Nurses or 
managers may collect the NHPPD calculation once or twice a day or on an ongoing basis throughout 
a 24-hour period. The calculation can be based on average times per activity, or from an acuity and 
dependency alternative nursing reporting system.  
These calculations can assume average care requirements of patients with the same diagnostic 
related group, presenting in the same ward on the same date as receiving the same amount of care, 
which may be flawed. Furthermore, when the acuity or dependency of each patient is not considered, 
the use of NHPPD might be misleading, as this provides an average number of available hours, 
rather than the actual hours required by an individual patient.  These reporting figures again 
provide a proxy measure for actual care, by calculating the amount of nursing time available for 
each patient, not the amount of direct or bedside care a patient receives. 
Relative to these existing studies, the RTLS data available to this project will allow us to calculate the 
actual contact time between staff and patients. We will be able to compare contact time calculations 
across the distribution of patients (e.g. by severity, location) and staff (e.g. nurses and doctors).  
Other members of the workforce and workforce interactions 
The interaction between staff members and the importance of teams has been shown to 
influence productivity in numerous settings including hospital care. Economists often 
prescribe these effects to so-called ‘peer effects’ or ‘free-riding’. Peer effects describe a 
range of social phenomenon between team members that lead to one individual influencing 
the productivity or behaviour of another. For example, team members may directly care 
about the productivity of their peers, communicate well with certain groups, or simply enjoy 
working with certain team members. As a result of these interactions the group may be 
more or less productive (Mas and Moretti, 2009; Bandiera, Barankay and Rasul, 2010). 
Free riding describes a similar concept which has potential negative impacts on productivity. 
For example, when working with a high productivity team, a health professional may choose 
to reduce their own productivity when it is difficult for mangers to observe this behaviour. 
Similarly, it may be that the work environment values the social interactions between their 
peers and do so at the expensive of their productivity (Bandiera, Barankay, and Rasul, 
2009).  
Recent research has shown that these types of interactions are present in health care 
settings. Health care teams lose productivity when nurses with significant skill and 
experience leave, and replacing these staff with a similarly experienced or skilled nurse 
does not make up the loss in productivity (Bartel, Beaulieu, Phibbs and Stone, 2014). Peer 
effects also extend to physician interactions. Allowing emergency physicians to monitor one 
another’s workload has been shown to improve productivity and reduce free riding (Chan, 
2016). Similarly, the pace of work of emergency doctors has been shown to directly 
influence the pace of work their peers (Silver, 2016), and the shift pattern assigned to 
doctors also influences the work-rate of doctors towards the end of their shifts (Chan, 2018). 
Finally, the role of management has also been demonstrated, where UK hospitals with 
higher rated managers being associated with improved hospital performance as measured 
by patient mortality (Bloom, Propper, Seiler and Van Reenen, 2015). 
Patient flow 
Patient outcomes may not only be affected by the interaction of peer health care providers 
but also by the flows of patients into or within hospitals. High patient flows can influence the 
use of resources that might have otherwise been provided to other patients. This is 
documented in several reviews of associational evidence where ‘bed strain’ or occupancy 
(Eriksson et al. 2017) and emergency department crowding (Hoot and Aronsky, 2008) is 
correlated with increased mortality. Economic studies have shown that these correlations 

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can arise because hospitals run short of available beds, and as a result must allocate scare 
bed resources amongst patients (Freedman, 2016) or new patient arrivals cause hospitals 
to discharge existing patients early (Hoe, 2018).  
These cases suggest that reductions in the physical space and resources devoted to 
patients, or the management of these resources, can impact patient care and outcomes. 
 
1.2 
RATIONALE FOR CURRENT STUDY  
The economic and demographic challenges facing the National Health Service are intensely felt by 
the healthcare workforce delivering services on the front line. Given that approximately 70% of 
recurring NHS provider costs relates to staffing, and that the NHS is one of the world’s largest 
employers, it is vital that the service invests in making the best use of staff to ensure they can deliver 
the care required by patients into the future (Addicott et al 2015).  
A key feature of the existing research on the workforce is that, to our knowledge, no prior 
study has been able to precisely measure the physical location of staff and patients. The 
RTLS system in place at RWT enables us to do this accurately and will be the novel feature 
of our work. We will be able to study productivity – in terms of staff-to-patient contact time 
and professional-to-professional interactions – in a detailed and accurate manner that goes 
far beyond the capability of previous datasets.  
 
2.0 STUDY OBJECTIVES 
There are six main objectives of the study, each relying primarily on the unique data recorded by the 
RTLS at RWT: 
• 
To measure the amount of time that nurses and other professional groups spend with patients 
(e.g. at the bedside) 
• 
To measure the amount of time that nurses and other professional groups spend in co-working 
spaces (e.g. nurse stations) 
• 
To explore if the amount of time nurses and other professional groups spend in direct patient 
care affects patient outcomes.  
• 
To examine if the amount of professional to professional time in ward areas has an impact on 
patient outcomes 
• 
To explore if a reduction in rostered nurses or other staff, or a change in the skill mix of nursing 
or other staff, impacts patient outcomes 
• 
To investigate if patient movement from original/speciality base ward, or other aspects of 
patient flow (e.g. congestion, crowding, capacity strain), have an impact on patient outcomes 
 
3.0 STUDY DESIGN 
The protocol relates to a series of studies that will be based on retrospective observation data 
containing patients admitted to or discharged from RWT over the period 1 April 2016 to 31 March 
2019. The most substantial task to produce these studies is to first extract a series of datasets from 
RWT and link these together such that the RTLS information.  This can then be studied alongside 
information on the characteristics of patients and staff, as well as the health outcomes of patients.  
Data to be extracted 
Data will be extracted from the following databases held at RWT.  
1. The real-time location service part of the TT system (‘RTLS data’). The RTLS data contains 
information on the second-by-second location of each patient and staff member at RWT. The 
locations cover all areas of the internal hospital buildings at RWT, with each building being 
classified according to a number of discrete ‘zones’ (e.g. wards, bed spaces, corridors).  
2. The RWT in-patient database (‘PAS data’). The PAS data contains detailed information on 
each inpatient visit to RWT. From the PAS data we will extract information for all patients 
admitted or discharged at RWT during the sample period. We plan to extract the following 

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information for each patient-visit: home location (first 3 or 4 digits of postcode), sex, ethnicity, 
age, administrative category (e.g. overseas vs. regular), admission date, admission method, 
number of days on waiting list, admission source, discharge date, discharge method, 
discharge destination, patient classification (e.g. inpatient vs. day case), ward type, 
specialties treated under, pseudo-anonymised consultant ID, indicators for neonatal or 
psychiatric care, recorded diagnoses, operation codes and dates, days in critical care, and 
HRG payment codes. 
3. HR records on job roles and absences from work (‘HR data’). The HR data contains 
information on the characteristics of RWT staff (e.g. age, gender, ethnicity, tenure at RWT) 
and their length of time in role (e.g. position on pay scale), as well as periods of absence from 
work. 
4. The RWT incident reports (‘Datix’). The Datix system contains data of untoward / adverse 
incidents. It is submitted by RWT staff relating to staff, patient and visitors on the trust 
property or whilst conducting trust business. We will extract the following fields which relate to 
each incident: date, time, location, patient number, result, harm, type, category, and sub-
category. The scope of incidents will only include those relating to patient care.  
Extraction, pseudo-anonymization and linkage of data 
Figure 1 (p.11) summarises the four data extracts, how they will be linked together, and the 
responsible teams at RWT for producing the extracts. The text below gives more specific 
details of the steps that will need to be taken to complete the process. 
The following process has been designed such that Thomas Hoe, the lead analyst, will not 
be directly involved in producing any of the data extracts which contain identifiable patient or 
staff information. All data extracted will also be done on site by RWT personnel. A final 
check / assurances will also be carried out by the Chief Investigator before data (Extracts 1-
4) are taken off site for analysis. 
For the PAS data, we will use the datasets created by RWT for submission to NHS England 
which already remove any patient-identifiable information. We will take the following 
processing steps:  
• S.R (Information Dept.) to replace the medical record numbers in the PAS extracts 
with pseudo-anonymised patient IDs. 
• S.R (Information Dept.) to provide the pseudo-anonymised PAS extracts (“EXTRACT 
1”) to Thomas Hoe. 
For the HR data, we will use the standard reports generated by the HR team. We will take 
the following processing steps:  
• A.R (HR) to provide Thomas Hoe with a list of staff names, job roles, and HR staff 
IDs from all of the monthly standard reports over the sample period. 
• Thomas Hoe to manually match the HR staff IDs, using the name and job role 
information, to the staff IDs contained in the RTLS dataset, and to create a common, 
anonymised staff ID across both datasets.  
• Thomas Hoe to provide the HR team with the common anonymised staff IDs. 
• A.R (HR) to add the common anonymised staff ID to the HR standard reports and 
then removing all identifiable information from the HR datasets.  
• A.R (HR) to provide the anonymised HR standard reports (“EXTRACT 2”) to Thomas 
Hoe. 

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Figure1.0 . Summary of data extracts and linkage process 
 

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For the RTLS data, we will take the following processing steps:  
• S.R (Information) to produce a lookup file between RTLS tag IDs and pseudo-
anonymised patient IDs. 
• Thomas Hoe to prepare bespoke data extracts, as these datasets do not contain any 
identifiable information (“EXTRACT 3”). For the Datix data, we will use custom 
reports generated by S K We will take the following processing steps:  
• S.K (Governance) to produce custom Datix reports as specified above in paragraph 
(iv). 
• S.K (Governance) to provide S. R. (Information) with the custom reports. 
• S.R (Information) to replace the medical record numbers in the customer reports with 
pseudo-anonymised patient IDs. 
• S.R (Information) to provide Thomas Hoe with the pseudo-anonymised Datix data 
(“EXTRACT 4”). 
Once Thomas Hoe has received EXTRACTS 1 through 4, the extracts will be transported to 
the Institute for Fiscal Studies as described in Section 8.3. 
 
3.1 
STUDY OUTCOME MEASURES 
We plan to study the following outcomes: 
 
• 
30-day in-hospital mortality (RWT specific) 
• 
30-day readmission (RWT  
• 
Length of stay 
• 
Nurse sensitive indicators (e.g. Falls, medication errors, pressure ulcers and  UTIs,  
• 
Adverse incidents affecting patient care as recorded by the Datix information (e.g. falls, 
medication errors) 
 
4.0 
PARTICIPANT ENTRY   
 
4.1 
INCLUSION CRITERIA 
Patients are only eligible for inclusion if they satisfy all of the following criteria: 
• 
Patients admitted to inpatient departments at RWT and are present at RWT during the period 
of 1 April 2016 to 31 March 2019 inclusive. 
 
4.2 
EXCLUSION CRITERIA 
The exclusion criteria are: 
• 
Patients admitted to outpatient wards at RWT; 
• 
Patients that were not admitted at or discharged from RWT between 1 April 2016 to 31 March 
2019 inclusive. 
 
4.3 
WITHDRAWAL CRITERIA 
No withdrawal criteria, is considered.  All information extracted from Trust databases will be pseudo- 
anonymised by RWT personnel.  All care and consideration will be maintained by the researchers 
involved in the study for the duration of the study. Patients care will not be affected by this study.  
 
5.0 
ADVERSE EVENTS 
No patient care related adverse events will occur as this is an observational retrospective study.  
 
There are no known or expected risks or specified “Adverse Events” to the patient as this protocol is 
designed to collect information rather than be designed for any interventional pathway and 
comparative analysis.   
 

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Advice has been sought and consideration has been made with regard to data confidentiality and 
participant’s data protection in the design and the conduct of this study. All data will be anonymised on 
case report forms/databases and coded appropriately. 
The Datix system within RWT will enable the researchers to establish if occurrences such as patient 
falls or pressure sores are related to staffing or patient flow and will form part of the main dataset.  
 
6. 
ASSESSMENT AND FOLLOW-UP  
Data extraction will not incur any extra visits or patient/staff involvement and therefore no 
assessments or follow-ups are required for the study.  
 
7. 
STATISTICS AND DATA ANALYSIS 
All processing of the pseudo-anonymised data extracts will be conducted by Thomas Hoe 
and Stephenson Strobel. The pseudo-anonymised data extracts will remain at the Institute 
for Fiscal Studies and the processing will be done using a remote access computing 
environment. No data will move from the secure location during any of the processing. All of 
the research results that are extracted after the data has been processed will be non-
confidential and any results for small groups of individuals (below 30) will be suppressed.  
In line with the research topics outlined earlier in this document, the major processing tasks 
will be: 
1. Computing the amount of contact time that staff have with patients (i.e. the number of 
seconds spent in the same RTLS location); 
2. Computing the amount of contact time that staff have with other staff (i.e. the number 
of seconds spent in the same RTLS location); 
3. Computing the amount of time that staff and patients spend in different hospital 
locations (e.g. the proportion of time that staff or patients spend in the bed spaces). 
Each of these measures will be computed using the pseudo-anonymised data extracts with 
the aid of statistical computer software (Microsoft Excel, Stata, and Microsoft SQL).  
These computing tasks will primarily rely on the RTLS data. The other data extracts, such 
as the HR data and the PAS data which will be linked to the RTLS data using the unique 
anonymised patient and staff identifiers, will then be used to assess how these measures 
vary with the type of patient (e.g. severity, age, location) and staff (e.g. seniority, planned 
and unplanned absences, team composition), and how they correlate with patient outcomes 
(e.g. readmission, mortality). 
The primary statistical methods that will be used are descriptive statistics, correlation and 
regression analysis, and tests of statistical significance.  
To give a more detailed sense of how this analysis will proceed, consider our study of 
contact time and patient health outcomes. We will first compute the contact time measure. 
We plan to begin by focusing on a definition of contact time that is time spent with nurses 
when in a bed space. This will simplify some of the issues that arise when patients move 
between locations and receive contact time in busy corridors with multiple staff and patients. 
We will therefore compute, for each patient, the total time in any bed space during their 
hospital stay using the RTLS data. With the same data, we can make the equivalent 
computation for the total amount of time spent by nurses with that patient in those bed 
spaces. From this we can then compute the total amount of contact time in the bed spaces 
for every patient in our dataset. This can be done for each individual nurse and then 
aggregated up to types of nurses (e.g. all nurse contact time, all registered nurse contact 
time, or all HCA contact time). 
These measures of contact time, based on the RTLS data, will be themselves a novel 
contribution to the literature. We will use standard statistical tools to describe and illustrate 
the extent to which different types of patient (e.g. based on their diagnosis and ward) 

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receive contact time, and the mix of contact time across different nurse categories (e.g. by 
qualifications and seniority). 
We will then compute the health outcome measures. These will primarily be based on the 
PAS data, where we will use the discharge information to identify hospital deaths, the 
pseudo-anonymised patient ID to identify readmission episodes, and the medical record 
information to identify length of stay and any nurse-sensitive conditions. We will also use the 
Datix data to identify any adverse medical events associated with that patient’s hospital stay 
(e.g. patient falls). 
Finally, we will make comparisons between the contact time and health outcome measures. 
A potential concern when doing this is that sicker patients may tend to receive more contact 
time from staff (who are consciously providing more care to those patients with complex 
medical conditions) and, irrespective of contact time, these patients may tend to have worse 
health outcomes (as a result of the complex medical conditions). This could generate a 
negative correlation between contact time and health outcomes.  
To deal with this issue, one approach we will take is to risk-adjust the health outcomes 
using the diagnosis information contacted in the PAS data. This will mean we compare the 
relationship between contact time and health outcomes for patients that have comparable 
health conditions (i.e. primary diagnosis and comorbidities). The statistical analysis, which 
will rely on regression models, will therefore implicitly compare similar patients that receive 
different levels of contact time. We expect this hypothetical type of comparison to be 
plausible and to exist in our data, which will contain large volume of patients that have 
received care in different wards, bays, and time periods. As a simple example, some 
patients may be exposed to time periods when wards are understaffed because of a leave 
of absence (leading to less contact time) and we can compare these patients to otherwise-
similar patients that were not exposed to these leaves of absence (more contact time).  
We will use similar methods to analyse the role of professional-to-professional contact time, 
medical outliers and patient flow.  
 
8. 
REGULATORY ISSUES 
 
8.1 
REGULATORY APPROVAL 
The Chief Investigator will obtain the appropriate approvals from an approved Research Ethics 
Committee and the Health Research Authority (as required).  The protocol will be submitted 
appropriately and will undergo internal governance and scrutiny (peer review), seeking confirmation of 
RWT Research and Development and appropriate Capacity and Capability approval . The data will not 
be used outside of the protocol of this study. 
 
8.2 
CONSENT  
Research data will be collected by the research team who are part of the clinical care team of patients 
and have access to the data through their day-to-day roles within RWT. They also have access to the 
staff data as part of their roles. No participants or staff will be affected by the data collection and will 
not require any intervention; therefore consent is not required in order to collect the data. Patient and 
staff data will be pseudo-anonymised by the research team who have access to this data as standard 
practice. Other members of the research team who do not normally have access to the data will only 
have access to anonymised data and will not be able to link the data to any personal identifiers.  
 
8.3 
CONFIDENTIALITY 
All data will be kept confidential and protected at all times. Patient and staff data will be pseudo-
anonymised so that privacy is protected at all times (see section 3 for details) The preparation of the 
data extracts, including the pseudo-anonymization process, will be conducted by RWT staff on-site at 
RWT using the business-as-usual RWT systems. This will ensure that data security and confidentiality 
provisions are unaffected while preparing the data extracts. 
No consent forms will be required for this project.  
 

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Arrangements for securely storing RWT data offsite 
The primary analysis for the research will be conducted by Thomas Hoe and his research 
assistant (Stephenson Strobel). Thomas and Stephenson are employed by Cornell 
University in the United States. Thomas spends around half of his time at Cornell and the 
other half working remotely in London at the Institute for Fiscal Studies, while Stephenson is 
permanently based at Cornell University.  
To enable efficient access to the data it is important that it is stored in a location that can be 
accessed remotely. This will enable Thomas and Stephenson to regularly work on the data, 
and it will allow Thomas to illustrate the working results to the CI during future visits to RWT.  
The pseudo-anonymised data extracts will be stored at the secure data facilities at the 
Institute for Fiscal Studies in London. These facilities are routinely used by leading 
economic researchers in the UK to store confidential, sensitive and, in some cases, 
personally identifiable data. The following security provisions are in place: 
• The IFS information security management system is ISO27001 compliant (certificate 
available on request) and there is an Information Classification and Handling Policy 
which sets out a comprehensive set of guidelines for handling all types of data and 
information (including highly confidential information). IFS researchers will follow 
strict procedures in this policy and adhere to the IFS Information Security Policy 
when using or collecting data. Any staff involved in the analysis of sensitive data, 
have been subject to a Baseline Personnel Security Standard check. 
• All data are stored on a secure network. Network access is restricted by strong 
individual passwords for all staff. Network passwords are changed every 91 days 
unless staffs are accessing specific types of information where there is a requirement 
for the password to be changed more often. An enforced PC screen lock is in place 
after a defined period of time. The network is monitored by the IT Manager to check 
for security breaches. All members of IFS staff also have a responsibility to report 
security incidents. 
• Access to data via the network is only ever given to named individuals who have 
obtained the correct permission to use them. Permissions are controlled by the IT 
Department and Data Managers. 
• The Local Area Network is protected by a firewall for the web and mail-servers that is 
Common Criteria EAL4 compliant. This ensures that there can be absolutely no 
external access to anything on the common network drives. Internet access is 
through JANET. Security is continually monitored internally using log files. 
• The data on the servers are backed up to an off-site machine, stored securely with 
IDNet in Docklands. Machines are stored in a locked cabinet in a locked room which 
is physically restricted to named IFS technical support staff and IDnet staff.  
• Network access is restricted to named IFS technical support staff. Back-ups are run 
overnight onto disks and then onto tape. Automated weekly full back-ups to tapes are 
made, with daily incremental update 
• Where data need to be removed from back-ups, long term back up is not carried out. 
This makes it easier to securely erase data from back-ups. 
• Data on the network is not currently encrypted as stand but the systems would allow 
to do this when required 
The IFS offices are not accessible to the general public and are locked when unattended. 
Access is restricted through the use of door entry codes and there is an out of hours alarm 
system. 
 
 
 

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Transferring data from RWT to the Institute for Fiscal Studies 
To transfer the data to this location, we take the following steps: 
1. Extract the relevant datasets from RWT’s systems (as described above, including the 
process of pseudo-anonymization); 
2. Transfer the data to a physical encrypted storage device (e.g. an external hard drive); 
3. Courier the physical storage device to the Institute for Fiscal Studies 
4. The IT team at the Institute for Fiscal Studies uploads the data to the secure data 
facility; 
5. The physical storage device will then be stored in a secure location either at the 
Institute for Fiscal Studies or, if required, delivered back to RWT for storage.  
Thomas Hoe in conjunction with the IT team at RWT will be responsible for steps 1 to 3. At no point 
in the transfer process will the data be left unsecured. Once the transfer has taken place, all of the 
data will remain in a single place which will have restricted access by only Thomas Hoe and 
Stephenson Strobel.  
Data use agreement 
Details of the above arrangement will be formalized in a Data Use Agreement (DUA) element of a 
Collaboration Agreement between the Sponsor and other parties that specifies all the parameters of 
how the data will be transferred, stored and processed.  
 
8.4 
INDEMNITY 
Standard NHS Indemnity will apply for the research elements of the study conducted on NHS 
premises. 
 
The Institute for Fiscal Studies, where the pseudo-anonymised data will be stored, has professional 
indemnity insurance (£2m limit). The Institute for Fiscal Studies also has insurance coverage for: (i) 
claims and investigations arising from [...] the misuse of data, including a breach of any data protection 
legislation (limit of £2m: Corporate legal liability insurance); and (ii) the costs of notifying anyone 
whose personal data may have been accessed following a data breach or violation of computer 
system by an employee (limit of £250,000: Crime Insurance). 
 
8.5 
SPONSOR 
The Royal Wolverhampton NHS Trust will act as Sponsor. 
 
8.6 
FUNDING 
No funding will be received externally to support this project. 
 
8.7 
AUDITS AND INSPECTIONS  
The Royal Wolverhampton NHS Trust through the R&D Directorate will support and ensure 
governance of the project throughout its duration.  A monitoring plan for this study will discussed, 
agreed and utilized.  The level of requirement will be structured on their SOPs for monitoring.   
 
9.0       STUDY MANAGEMENT 
The study will be co-ordinated by Mrs Vanda Carter – Trust Practice Education Facilitator for 
Research alongside the Chief/Principle Investigator, Trust Chief Nurse : Professor Ann-Marie 
Cannaby. 
 
10.       PUBLICATION POLICY 
The study and its results will be submitted/ published in a large impact, peer-reviewed journal. All data 
used for the study belongs to RWT and publications will be subject to review from senior RWT 
executes. The provider of the RTLS system (Tele-Tracking) will be kept updated regarding any 
publications. 
 
 

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11.     ARCHIVING  
The study will adhere to RWT Research and Development SOP requirements with regard to archiving 
S11. for a non c-timp trial. The duration of time study documents will  be archived /stored  will  be 
documented following standards recommended by the UK Policy Framework for Health and Social 
Care Research ver. 3.0 (2017). 
  
 
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