Improving real-time vital signs documentation

Safer and more efficient vital signs monitoring protocols to identify the deteriorating patients in the general hospital ward: an observational study

Background

The frequency at which patients should have their vital signs (e.g. blood pressure, pulse, oxygen saturation) measured on hospital wards is currently unknown. Current National Health Service monitoring protocols are based on expert opinion but supported by little empirical evidence. The challenge is finding the balance between insufficient monitoring (risking missing early signs of deterioration and delays in treatment) and over-observation of stable patients (wasting resources needed in other aspects of care).

Objective

Provide an evidence-based approach to creating monitoring protocols based on a patient's risk of deterioration and link these to nursing workload and economic impact.

Design

Our study consisted of two parts: (1) an observational study of nursing staff to ascertain the time to perform vital sign observations; and (2) a retrospective study of historic data on patient admissions exploring the relationships between National Early Warning Score and risk of outcome over time. These were underpinned by opinions and experiences from stakeholders.

Setting and participants

Observational study: observed nursing staff on 16 randomly selected adult general wards at four acute National Health Service hospitals. Retrospective study: extracted, linked and analysed routinely collected data from two large National Health Service acute trusts; data from over 400,000 patient admissions and 9,000,000 vital sign observations.

Results

Observational study found a variety of practices, with two hospitals having registered nurses take the majority of vital sign observations and two favouring healthcare assistants or student nurses. However, whoever took the observations spent roughly the same length of time. The average was 5:01 minutes per observation over a 'round', including time to locate and prepare the equipment and travel to the patient area. Retrospective study created survival models predicting the risk of outcomes over time since the patient was last observed. For low-risk patients, there was little difference in risk between 4 hours and 24 hours post observation.

Conclusions

We explored several different scenarios with our stakeholders (clinicians and patients), based on how 'risk' could be managed in different ways. Vital sign observations are often done more frequently than necessary from a bald assessment of the patient's risk, and we show that a maximum threshold of risk could theoretically be achieved with less resource. Existing resources could therefore be redeployed within a changed protocol to achieve better outcomes for some patients without compromising the safety of the rest. Our work supports the approach of the current monitoring protocol, whereby patients' National Early Warning Score 2 guides observation frequency. Existing practice is to observe higher-risk patients more frequently and our findings have shown that this is objectively justified. It is worth noting that important nurse-patient interactions take place during vital sign monitoring and should not be eliminated under new monitoring processes. Our study contributes to the existing evidence on how vital sign observations should be scheduled. However, ultimately, it is for the relevant professionals to decide how our work should be used.

Study registration

This study is registered as ISRCTN10863045.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 17/05/03) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 6. See the NIHR Funding and Awards website for further award information.

Implementation evaluation of a rapid response system in a regional emergency department: a dual-methods study using the behaviour change wheel

BACKGROUND

Failure to recognise and respond to clinical deterioration is a major cause of high mortality events in emergency department (ED) patients. Whilst there is substantial evidence that rapid response teams reduce hospital mortality, unplanned intensive care admissions, and cardiac arrests on in-patient settings, the use of rapid response teams in the ED is variable with poor integration of care between emergency and specialty/intensive care teams.

OBJECTIVES

The aim of this study was to evaluate uptake and impact of a rapid response system on recognising and responding to deteriorating patients in the ED and identify implementation factors and strategies to optimise future implementation success.

METHODS

A dual-methods design was used to evaluate an ED Clinical Emergency Response System (EDCERS) protocol implemented at a regional Australian ED in June 2019. A documentation audit was conducted on patients eligible for the EDCERS during the first 3 months of implementation. Quantitative data from documentation audit were used to measure uptake and impact of the protocol on escalation and response to patient deterioration. Facilitators and barriers to the EDCERS uptake were identified via key stakeholder engagement and consultation. An implementation plan was developed using the Behaviour Change Wheel for future implementation.

RESULTS

The EDCERS was activated in 42 (53.1%) of 79 eligible patients. The specialty care team were more likely to respond when the EDCERS was activated than when there was no activation ([n = 40, 50.6%] v [n = 26, 32.9%], p = 0.01). Six facilitators and nine barriers to protocol uptake were identified. Twenty behaviour change techniques were selected and informed the development of a theory-informed implementation plan.

CONCLUSION

Implementation of the EDCERS protocol resulted in high response rates from specialty and intensive care staff. However, overall uptake of the protocol by emergency staff was poor. This study highlights the importance of understanding facilitators and barriers to uptake prior to implementing a new intervention.

Workload involved in vital signs-based monitoring & responding to deteriorating patients: A single site experience from a regional New Zealand hospital

Objective

This study aimed to quantify the workload involved in patient monitoring by vital signs and early warning scores (EWS), and the time spent by a rapid response team locally known as the Patient-at-Risk (PaR) team in responding to deteriorating patients.

Methods

The workload involved in the measurement and the documentation of vital signs and EWS was quantified by time and motion study using electronic stopwatch application in 167 complete sets of vital signs observations taken by nursing staff on general hospital wards at Taranaki Base Hospital, New Plymouth, New Zealand. The workload involved in responding to deteriorating patients was measured by the PaR team in real-time and recorded in an electronic logbook specifically designed for this purpose. Dependent variables were studied using analysis of variance (ANOVA), post hoc Tukey, Kruskal Wallis test, Mann-Whitney test and correlation tests.

Results

The mean time to measure and record a complete set of vital signs including interruptions was 4:18 (95% CI: 4:07–4:28) minutes. After excluding interruptions, the mean time taken to measure and record a set of vital signs was 3:24 (95% CI: 3:15–3:33) minutes. We found no statistical difference between the observer, location of the patient, staff characteristics or experience and patient characteristics. PaR nurses' mean time to provide rapid response was 47:36 (95% CI: 44:57–50:15) minutes. Significantly more time was spent on patients having severe degrees of deterioration (higher EWS) < 0.001. No statistical difference was observed between ward specialty, and nursing shifts.

Conclusions

Patient monitoring and response to deterioration consumed considerable time. Time spent in monitoring was not affected by independent and random factors studied; however, time spent on the response was greater when patients had higher degrees of deterioration.

Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications

With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed.

Manchester Triage System: assessment in an emergency hospital service

OBJECTIVES

to analyze demographic data, clinical profile and outcomes of patients in emergency services according to Manchester Triage System's priority level.

METHODS

a cross-sectional, analytical study, carried out with 3,624 medical records. For statistical analysis, the Chi-Square Test was used.

RESULTS

white individuals were more advanced in age. In the red and white categories, there was a higher percentage of men when compared to women (p=0.0018) and higher prevalence of personal history. Yellow priority patients had higher percentage of pain (p<0.0001). Those in red category had a higher frequency of altered vital signs, external causes, and death outcome. There was a higher percentage of exams performed and hospitalization in the orange category. Blue priority patients had a higher percentage of non-specific complaints and dismissal after risk stratification.

CONCLUSIONS

a higher percentage of altered vital signs, number of tests performed, hospitalization and death were evidenced in Manchester protocol's high priority categories.

Application of AI and IoT in Clinical Medicine: Summary and Challenges

The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.

What is the nursing time and workload involved in taking and recording patients' vital signs? A systematic review

AIMS AND OBJECTIVES

To synthesise evidence regarding the time nurses take to monitor and record vital signs observations and to calculate early warning scores.

BACKGROUND

While the importance of vital signs' monitoring is increasingly highlighted as a fundamental means of maintaining patient safety and avoiding patient deterioration, the time and associated workload involved in vital signs activities for nurses are currently unknown.

DESIGN

Systematic review.

METHODS

A literature search was performed up to 17 December 2019 in CINAHL, Medline, EMBASE and the Cochrane Library using the following terms: vital signs; monitoring; surveillance; observation; recording; early warning scores; workload; time; and nursing. We included studies performed in secondary or tertiary ward settings, where vital signs activities were performed by nurses, and we excluded qualitative studies and any research conducted exclusively in paediatric or maternity settings. The study methods were compliant with the PRISMA checklist.

RESULTS

Of 1,277 articles, we included 16 papers. Studies described taking vital signs observations as the time to measure/collect vital signs and time to record/document vital signs. As well as mean times being variable between studies, there was considerable variation in the time taken within some studies as standard deviations were high. Documenting vital signs observations electronically at the bedside was faster than documenting vital signs away from the bed.

CONCLUSIONS

Variation in the method(s) of vital signs measurement, the timing of entry into the patient record, the method of recording and the calculation of early warning scores values across the literature make direct comparisons of their influence on total time taken difficult or impossible.

RELEVANCE TO CLINICAL PRACTICE

There is a very limited body of research that might inform workload planning around vital signs observations. This uncertainty means the resource implications of any recommendation to change the frequency of observations associated with early warning scores are unknown.