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Journal of Research in Nursing logoLink to Journal of Research in Nursing
. 2021 Nov 15;27(5):421–433. doi: 10.1177/17449871211016169

The feasibility and validity of using a real time location system (RTLS) to measure bedside contact time

Ann-Marie Cannaby 1,9, Vanda Carter 2,9,✉, Thomas Hoe 3,9, Stephenson Strobel 4,9, Elena Ashtari Tafti 5,9, Rephna Baker 6,9, Clare Nash 7,9, Vanessa Whatley 8,9, Richard Gray 9
PMCID: PMC9483226  PMID: 36131691

Abstract

Background

The association between the nurse-to-patient ratio and patient outcomes has been extensively investigated. Real time location systems have the potential capability of measuring the actual amount of bedside contact patients receive.

Aims

This study aimed to determine the feasibility and accuracy of real time location systems as a measure of the amount of contact time that nurses spent in the patients’ bed space.

Methods

An exploratory, observational, feasibility study was designed to compare the accuracy of data collection between manual observation performed by a researcher and real time location systems data capture capability. Four nurses participated in the study, which took place in 2019 on two hospital wards. They were observed by a researcher while carrying out their work activities for a total of 230 minutes. The amount of time the nurses spent in the patients’ bed space was recorded in 10-minute blocks of time and the real time location systems data were extracted for the same nurse at the time of observation. Data were then analysed for the level of agreement between the observed and the real time location systems measured data, descriptively and graphically using a kernel density and a scatter plot.

Results

The difference (in minutes) between researcher observed and real time location systems measured data for the 23, 10-minute observation blocks ranged from zero (complete agreement) to 5 minutes. The mean difference between the researcher observed and real time location systems time in the patients’ bed space was one minute (10% of the time). On average, real time location systems measured time in the bed space was longer than the researcher observed time.

Conclusions

There were good levels of agreement between researcher observation and real time location systems data of the time nurses spend at the bedside. This study confirms that it is feasible to use real time location systems as an accurate measure of the amount of time nurses spend at the patients’ bedside.

Keywords: direct patient contact, healthcare resource management, nurse staffing requirements, nursing time, real time location system

Introduction

Background

Nurses are the largest group of clinicians within the National Health Service (NHS) and represent approximately a quarter of all employees within the service (NAO, 2020). Nurses are essential in the delivery of complex care and vital services to patients and provide 24 hours of care, 7 days a week. Not surprisingly, there has been considerable interest in nurse staffing levels, training, skill mix and the impact they have on patient outcomes.

The availability and the quality of care nurses provide impact patient health outcomes and experiences in numerous ways. These include having oversight in reducing patient harm, facilitation of the administration and monitoring of treatment/s and through the emotional support nurses provide to patients during their hospital stay (NAO, 2020). Many authors have studied the relationship between the number of patients a nurse cares for or has responsibility for during a hospital shift (the nurse-to-patient ratio) and the risk of a patient dying in hospital (Aiken et al., 2011; Arkin et al., 2014; Ball et al., 2018; Cannaby et al., 2017; Cho et al., 2015; Lee et al., 2017).

Aiken et al. (2011), for example, examined the relationship between the skilled graduate nurse-to-patient ratio, the perceived quality of environment and the 30-day inpatient mortality rates in 1,262,120 surgical patients and 39,038 nurses in the USA, utilising patient datasets and self-reporting postal surveys (Aiken et al., 2011). Aiken et al. (2014) repeated this methodology in the RN4CAST study with 422,730 patients and 26,515 nurses from hospitals throughout Europe. Both studies provided marked evidence that an increase in the skilled, graduate nurse workforce-to-patient ratio and the quality of the clinical environment was associated with a reduction in the odds of the patient dying in hospital. Similar associations have been noted in other observational studies (Aiken et al., 2017; Arkin et al., 2014; Ball et al., 2018; Cho et al., 2015; Jansson et al., 2019; Van den Heede et al., 2009) providing further evidence that nursing numbers and skill mix affect patient outcomes.

However, studies that rely on self-reporting survey data can suffer from significant limitations as participants may misremember or misrepresent their responses and bias the estimates of these studies (Safdar et al., 2016). Response rates among nurses in these studies are often relatively low; for example, 39% and 62% of nurses responding, respectively, in Aiken and colleagues (2011, 2014) studies. Poor response rates have been defended as being ‘typical’ in health services research; nevertheless, they do introduce an important source of bias into the measures. Low response rates raise concerns of sample selection and limit the extent to which empirical results can potentially inform policy makers.

Other methodologies have been explored by researchers to investigate the relationship between nursing care and patient health outcomes (Cannaby et al., 2017). Some authors, such as Williams et al. (2020), have directly observed nurses in their work environment or asked nurses to record how they spend their day. Studies continue to add value and build on the findings of previous work (Aiken and colleagues, 2011, 2014, 2017; Arkin et al., 2014; Ball et al., 2018; Cho et al., 2015; Jansson et al., 2019; Van den Heede et al., 2009) but they too suffer from methodological limitations. Observed nurses may change their behaviour in order to look better for the purposes of ‘time and motion’ research, and when asked to record their behaviour, nurses are likely to inflate the amount of bedside contact that they provide as a result of social desirability bias (Berney and Needleman, 2006; Robert et al., 2020).

A real time locating system (RTLS) has the potential to measure the amount of bedside contact nurses provide to patients that is accurate and free from bias. RTLS technology has been used for decades in many industries to tag and track the location of goods or assets in the form of equipment and people (workforce and patients) in real time or as close as physically possible (Boulous and Berry, 2012; Elmer et al., 2019; Rainham et al., 2008). RTLS is a technology that has been applied in a wide range of logistics and has become a technology enabling the real time visibility of people or items in a defined geographical location that includes the hospital environment (Gladysz and Santarek, 2017). There are several types of RTLS locating systems: infrared, radiofrequency, ultrasound and hybrid solutions that combine infrared and ultrasound, Wi-Fi and bluetooth technologies.

Typically, RTLSs require four main components which are illustrated in Figure 1: (a) a locating node (i.e. multiple boxes on the celling covering clinical areas); (b) a location server (the computer that receives all the data); (c) user application (the software that interprets the data); (d) the tag (typically a badge donned by members of staff, patients and equipment).

Figure 1.

Figure 1.

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How the system works: the real time locating system (RTLS) hardware and staff/patient badges/equipment tags. ©The Royal Wolverhampton NHS Trust 23/01/20, reproduced with permission.

Clinician RTLS badges/tags can be worn by staff – on a lanyard or clip – and for patients, attached to a wrist or ankle via a bracelet. The technology transmits continuous location data which is unique to the tagged person or item in real or near real time (Boulas and Berry, 2012).

There are several hospitals that currently use RTLSs primarily as a bed management system; the system allows them to know precisely when a patient has been discharged and when a bed becomes vacant. Specialist equipment can also be tagged and located in real time, potentially maximising the use of equipment and allowing clinician time to be used more efficiently (i.e. nurses are not phoning different wards to track down a piece of equipment when needed). Studies have used RTLS tracking systems as a research measure (Elmer et al., 2019; Fisher and Monahan, 2012) (Figure 1).

The technology has the potential to generate data on the amount of time clinicians (physicians, nurses, allied health professionals) spend at the patient bedside, the quantity of inter-professional interaction and skill mix (unqualified to qualified care) of nurses providing contact.

However, as far as the authors can determine, there have been no studies that have established the accuracy of the RTLS system in measuring the amount of bedside contact that patients receive. It is important to establish the validity of the system prior to using the system to test further if there are associations between the amount of bedside contact patients receive and clinical outcomes.

Aims and objectives

The aim of this feasibility study was to determine the accuracy of RTLS as a measure of the amount of time that nurses spend in a patient’s bed space (as a proxy measure for bedside care). This was to ascertain the reliability and accuracy of the data from the RTLS in preparatory work for a greater, data-driven observational study.

Design

An exploratory, observational, feasibility study was designed to compare the accuracy of data collection between manual observations performed by a researcher (one) and RTLS data capture available within an NHS Trust. Each of the participants was wearing employee identification and RTLS badges as part of the Trust uniform policy.

Setting

The Royal Wolverhampton NHS Trust is an integrated (acute and community care) Trust in the UK which partnered with a US company in 2013 to implement a real time patient flow and tracking solution to improve infection prevention adherence, patient access, inpatient flow, discharge planning and quality and safety of care oversight (Nash, 2014). Transitioning from an environment of disjointed activity to patient throughput functions which are coordinated through a centralised command centre supports staff to deliver efficient, quality-driven care by providing real time information in clinical areas;.these include location, status visibility of all inpatients, beds, staff and some equipment. The Trust's patient flow solution is enabled by a RTLS that uses a combination of infrared and radiofrequency technology to feed data into the patient flow and tracking software.

Methods

Four nurses were invited on two (previously agreed) clinical wards to volunteer and participate in the study. They were given an explanation as to the nature of the initiative by the researcher. All four nurses were approached at random (convenience sample) and subsequently agreed to volunteer and be observed during their working day for a number of observation windows. Each participant nurse understood the reasons why the work was being carried out for the purposes of testing the accuracy of the RTLS. However, they were not provided with the specific information regarding what activities would be measured and recorded as it was perceived that this may affect their behaviours. The researcher was confident that all nurses were adequately informed. They each provided their informed verbal consent prior to being observed. All nurses were wearing their allocated identification and RTLS badges, and batteries were checked and reported as being operational. The researcher (observer) was an economist and academic by profession and was part of a small multidisciplinary research team formed by the principal author at the Royal Wolverhampton NHS Trust.

Each nurse observation lasted up to an hour in time. Observations took place on 3 May, 1 July and 2 July 2019 on two wards: a 28-bed renal ward and a 34-bed stroke speciality ward. Nurses were then observed at various periods of the daytime (between 9 am and 5 pm) without prior notice. The nurses went about their usual work routines during each observation window. The researcher was not a clinician and did not disturb or discuss the nature of the observation with the nurses during the observation windows. During every 10-minute observation window the researcher documented the cumulative amount of minutes the nurse spent in patient bed spaces. This was defined by the physical boundaries of the RTLS zones, which are approximately 1 m to the side of each bedside or the walls of the patient’s room if they were in a side room. This was done by starting and stopping a timer when the nurse was either within (approximately) 1 m of the edge of a patient’s bed (this RTLS zone around each bed; see Figure 1). For example, if the nurse spent the entire first 10 minutes at the nurses’ station, this would be recorded as one observation of zero time in the bed space. If the nurse then spent 5 of the subsequent 10 minutes caring for a patient in their bed, this would be recorded as one observation of 5 minutes in the bed space. The process therefore led to a series of data points, each representing the time that a nurse spent in bed spaces during a 10-minute interval and, equivalently, the proportion of the interval spent in bed spaces.

In some instances, nurses spent significant periods of time in positions that were difficult to classify because they were on the border of a bed space. This was especially common during drug rounds, for example, or when nurses were talking with patients from the end of their bed. When these situations persisted for over 30 s during a 10-minute observation period, the researcher coded that interval as ‘subjective’. The subjective time represented a period of time in which the nurse was perhaps not dealing directly with a patient at the bedside or may not have even been related to the patient where the observed was at the edge of a bed space. An example of this is when a nurse may have parked the drug trolley while preparing medication to dispense. They may not directly be providing face-to-face care but are indirectly providing care as prescribed. We consider these observations subject to human error in the recording.

Data analysis

There are two sets of data that were used in this analysis. The first set of data is the observational data which consists of the time a nurse is in contact with a patient and was measured directly by the researcher. The major variable of interest in these data is the observed contact time. The second set of data is the RTLS data which is contact time as measured by the tracking system, installed in the Royal Wolverhampton NHS Trust. The major variable of interest in these data is the measured contact time when patients and nurses are in the same location. The data are therefore analysed in two ways.

First, descriptive measures are provided for each dataset. For each 10-minute block examined, the mean amount of time that the nurses spent in direct contact with their patients in minutes and as a percentage of the total amount of observation time is described. This includes the standard deviation (SD) (mean) of the contact time measured in minutes, enabling a comparison to be made of the difference in contact time between the same observations in the RTLS and observational dataset highlighting how different these observations are.

Second, comparisons of the datasets are presented, graphically, in two forms. The first will be presented as a kernel density plot chart of the observational and RTLS datasets; kernel density plots are a smoothed variation of a histogram which plots the density of a result (Li and Racine, 2007). Second, a comparison was made of the distribution of the measured contact time in the RTLS data and the observed contact time in the observational data, in which higher values on the y-axis mean a higher probability of a result occurring. The goal of this was to provide suggestive evidence on whether the observer over or under-measured contact time relative to the RTLS system. If the density of the RTLS data is more skewed to the right as compared to the observational data, this suggests that the observer is underestimating contact time.

An examination of the correlation between the measured contact time and the observed contact time using a scatter plot will be the second graphic presented. The data points therefore are classified in the observed data as subjective or non-subjective. The former are observations when nurses were on the edge of two areas, and the latter are when nurses were unmistakably in a zone such as being by the patient’s bed. A correlation of the amount of contact time for each 10-minute block between the measured contact time and the observed contact time were calculated to produce the square of the Pearson correlation coefficient or R-squared (Hung et al., 2017). Higher correlations suggest that the RTLS system is producing similar estimates of contact time to the observed contact time. A calculation of the R-squared for both subjective and non-subjective observations is also provided.

RTLS coding procedures

RTLS data were extracted from SQL (domain specific language) databases for the participating nurses (using their ID badge number) and for the time periods for which they were observed. Each second in the data was coded as either in a bed space (defined as within a bed zone or a side room, see Figure 1.) or not (i.e. in any other RTLS zone). For each 10-minute period the RTLS data were utilised to compute the proportion of time recorded in specific bed spaces.

Results

Descriptive measures

The four nurses were tracked for a total of 230 minutes, generating 23 data points, each representing one nurse over a 10-minute time interval: nurse 1 (10 data points), nurse 2 (1 data points), nurse 3 (9 data points), nurse 4 (3 data points). In total, the nurses were observed spending 26% of their time (60.4 minutes) providing direct contact. Per 10-minute observation block the average time at the bedside was 2.63 minutes (SD 3.01).

RTLS data were extracted in seconds and aggregated into minutes to allow comparison. RTLS data were extracted for each nurse for all the same observation periods. There were no missing data; that is, periods of time when the system was not able to locate the nurse for any reason (e.g. because the tag was in a nurse’s pocket or the nurse was in a place with no beacons present, staff room/rest room). RTLS data indicated that all participating nurses were in their designated clinical area. Of the 230 minutes of RTLS positional data, participating nurses spent 83.4 (36%) minutes in the patients’ bed space. Of the 23 (10-minute) blocks where nurses were observed they spent between zero and 10 minutes in the patients’ bed space, an average of 3.6 (SD 3.45) minutes per block.

For each 10-minute block the difference between researcher observed and RTLS data was calculated. The differences (in minutes) between observational data and RTLS data for the 23 10-minute observation blocks ranged from zero (complete agreement) to 5 minutes. The mean difference between RTLS and observational time in the patient’s bed space was one minute (10% of the interval time). RTLS data appear therefore to suggest that nurses spent slightly more time in the patient’s bed space than the researcher observed.

Graphical comparisons between observational data and RTLS data

In Figure 2, the kernel density compares plots of both the RTLS data and observational data. The distribution of RTLS measurements displays a thicker right tail relative to observational measurements. This indicates that the RTLS system registers more observations to be on a higher level of bedside time than what the researcher had observed. Consequently, there is also a lower density of observations in the observed data at lower estimates of contact time. The discrepancy in distributions therefore relates to whether the observation is classified as subjective or non-subjective (not shown); the distribution of non-subjective observations in the observational data mimics the distribution of the RTLS data almost exactly. This suggests that the observer was classifying many of these subjective observations as ‘low’ contact time when the RTLS system was classifying them as ‘high’ contact time.

Figure 2.

Figure 2.

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Kernal density plot concordantly illustrates that the distribution of real time locating system (RTLS) measurements is more positively skewed relative to observed measurements.

Figure 3 displays the scatter plot and correlation between the researcher observed and RTLS measured bedside time for subjective and non-subjective observations. There are 17 non-subjective and six subjective observations. The 45 degree line indicates a perfect correlation or an R-squared of one between observer and RTLS. The calculated R-squared for the non-subjective observations is 0.99 indicating that in these cases the observed and measured contact time share in the 10-minute block was essentially the same. For subjective observations, this R-squared is 0.83 indicating that the system when the nurse is in between areas is more likely to detect contact time than an actual observed one would. A linear regression and t-test suggest that the relationship between the measured contact time and the observed contact time is statistically significant (P < 0.01).

Figure 3.

Figure 3.

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Correlations between observer and real time locating system (RTLS) measured contact time as a percentage.

In summary, these results suggest good correlation between observer estimated patient contact time and the RTLS system estimated contact time. When there are discrepancies, these are attributable to how the observer estimated contact time for subjective observations. These differences are because the RTLS overestimates contact when patient care does not actually occur, such as when a staff member is simply nearby rather than caring for a patient. These subjective observations consist of a minority of the observations, with the remaining observations having extremely high accuracy; moreover, even when including the subjective observations in the sample there remains high correlation between observed contact time and RTLS measured contact time.

Discussion

The aim of this study was to establish the feasibility of using RTLS to measure the amount of time nurses spend at the bedside. There was a high level of agreement between the RTLS and researcher observations. This suggests that RTLS can be successfully used as a measure of the amount of bedside contact health professionals actually provide in hospital. The technology appears to be well suited for measuring bedside contact between patients and nurses and could potentially be used in studies of the association between direct nursing care and patient outcomes. The implicit assumption in previous studies is that the more nurses there are working on a ward the better the patient outcomes, because of the perceived higher levels of nursing care. However, this theory has not been explicitly validated because accurate measures are not available.

RTLS provides a precise and accurate estimate of the amount of bedside care that patients receive and could aid data limitations found in these previous studies. In this study there were no missing RTLS data, the four participating nurses were wearing their RTLS tags. It is likely that in a large-scale study involving many hundreds of nurses there would be a degree of non-compliance and consequently some missing data.

It would be informative to estimate in a sample of nurses the compliance rate of nurses wearing RTLS tags, although within this setting this has been standard practice for some period of time. Concern has been expressed that in clinical practice, nurses might put their RTLS badges in their pockets – because the badge and a lanyard might interfere with patient care – potentially disrupting the RTLS signal (Boulos and Berry, 2012). This did not happen in our study and we had no missing data. It will be important to investigate this in larger cohorts of nurses. The RTLS system for staff is unique in its application and this tracking does raise a number of interesting ethical and compliance questions (Fisher and Monahan, 2008; Norten, 2011; Winston, 2016). The use of this tagging system is new in the NHS, its use tactically and strategically still requires careful ethical consideration, debate and transparency.

Other public sector workers; for example, the police and ambulance personal, are used to either car/ambulance trackers or personal location wearables that help protect and ensure their safety, as yet this is not widespread across the healthcare sector. Clinicians who join the trust are given a RTLS tag to wear and it is part of the Trust uniform policy. Information is provided through staff induction when they join the Trust. While staff are aware that they should always wear the RTLS badge while working clinically this does not necessarily translate into 100% compliance. Patients are made aware of the system on admission by way of patient literature and information posters displayed throughout the trust in clinical environments.

Limitations

There are a number of important limitations to this study. First, beyond observing if nurses were in the bed space, we did not code activity data and so this current study does not capture the type or quality of care that the nurse/s provide. Nurses may have been providing personal nursing care or nurses could have been at the patient’s bedside writing patient notes or any number of important and relevant patient-related activities. Second, the observation of the nurse requires a relatively intensive effort which means that the study had a limited sample size of four nurses. Similar to previous studies (Elmer et al., 2019) direct behavioural observation studies can overcome problems of individualised biases but can suffer from typically small sample sizes with short observational periods. The observations were carried out during a typical day shift. For completeness and comparison it is acknowledged that observation could have been carried out during a night shift when expectations and care requirements may be very different depending on the clinical environment and medical intervention required. However, the authors felt that as the aim of the study was regarding accuracy rather than the actual activity or perceived quality of care being provided, this was not required in this instance. It is also acknowledged that the transferability and generalisability of the study will be limited as only a limited amount of healthcare providers to date have invested in RTLS technology within their organisations.

Conclusions

This study has provided evidence that RTLS data can generate accurate information to measure the amount of time that nurses spend providing direct bedside contact (as a proxy measure of bedside care). This technology offers a useful, practical and more precise way of examining how to measure and evaluate accurately nursing staffing levels and skill mix within healthcare organisations. This work has also provided validation and an evidence base for other exploratory observational work being developed by the research team at the Royal Wolverhampton NHS Trust.

Key points for policy, practice and/or research

  • The association between the nurse-to-patient ratio, nurses skill mix, time spent with patients and patient outcomes has been extensively debated, investigated and remains problematic in healthcare management.

  • RTLS is a technology which can be utilised in an efficient way to measure and evaluate accurately without bias the numbers of nurses needed to assess and plan in order to deliver safe and effective nursing care as required to optimise patient outcomes and experience.

  • Further research, exploring the association between nurse staffing and patient outcomes using RTLS as a measure is warranted.

Acknowledgements

The author(s) would like to thank the Royal Wolverhampton NHS Trust, Clinical Illustration Department, Information, Human Resources. IT teams and John Hudson, Whise Library, for their continuous support and assistance.

Biography

Ann-Marie Cannaby is the Chief Nurse of an integrated NHS Trust and a Professor of Nursing Sciences at Birmingham City University, and a visiting Fellow at Staffordshire University, UK, and La Trobe University, Australia.

Vanda Carter is currently Practice Education Facilitator for Research. She has many years’ experience working in the NHS as a research nurse. Her interests include health management, health policy, education, safety quality and innovation.

Thomas Hoe holds an honorary contract with the Royal Wolverhampton NHS Trust and is an Economist with a wealth of experience in the healthcare sector.

Stephenson Strobel is a PhD candidate of policy analysis and management and is a part time locum physician. His interests include health economics, health financing, poverty and health applied econometrics.

Elena Ashtari Tafti is a PhD in economics candidate and holds an honorary contract with the Royal Wolverhampton NHS Trust. Her interests include health economics and teaching.

Rephna Baker is the Associate Chief Nurse of an integrated NHS Trust. Her interest is nurse workforce and in particular its efficient and effective optimisation to benefit patient care.

Clare Nash is a Senior Nurse and Clinical Programme Lead for SafeHands. Her interests are quality, safety and sustainable healthcare.

Vanessa Whatley is Deputy Chief Nursing Officer of an integrated NHS Trust. Her interests include infection prevention, end-of-life care, health and wellbeing and quality systems.

Richard Gray is a Professor of Clinical Nursing Practice, School of Nursing and Midwifery La Trobe University, Melbourne, Australia. He has interests in mental health.

Footnotes

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics: As per advice from the UK Policy Framework for Health and Social Care Research (Health Research Authority, 2017) and local research and development experts, the study was deemed as a service evaluation. Formal ethical approvals were therefore not required for this work but the principles of good clinical practice were adhered to throughout all the activities performed within the study.

Funding: The author(s) received no financial support for the research authorship, and/or publication of this article.

Contributor Information

Ann-Marie Cannaby, Chief Nurse, The Royal Wolverhampton NHS Trust, New Cross Hospital, Wolverhampton, UK.

Vanda Carter, Practice Education Facilitator for Research, The Royal Wolverhampton NHS Trust, New Cross Hospital, Wolverhampton, UK.

Thomas Hoe, Economist Researcher/ Consultant, The Royal Wolverhampton NHS Trust, New Cross Hospital, Wolverhampton, UK.

Stephenson Strobel, Economics Candidate, Cornell University Department of Policy Analysis and Management, Ithaca, NY.

Elena Ashtari Tafti, PhD in Economics Candidate, University College London, London, UK; The Royal Wolverhampton NHS Trust, New Cross Hospital, Wolverhampton, UK.

Rephna Baker, Associate Chief Nurse,The Royal Wolverhampton NHS Trust, New Cross Hospital, Wolverhampton, UK.

Clare Nash, Senior Procurement Nurse, The Royal Wolverhampton NHS Trust, New Cross Hospital, Wolverhampton, UK.

Vanessa Whatley, Deputy CNO, The Royal Wolverhampton NHS Trust, New Cross Hospital, Wolverhampton, UK.

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