Recording Of Vital Signs Research Articles

How do nurses work in chronic management in the age of artificial intelligence? development and future prospects.

AI is undeniably revolutionizing medical research and patient care across diverse fields. Chronic disease nursing care, a pivotal aspect of clinical management, has significantly reaped the benefits of AI across numerous dimensions. Understanding the operational principles of artificial intelligence before implementation is crucial, avoiding indiscriminate replacement of all tasks with AI. Nurses serve as the primary force in symptom group research, expanding beyond diabetes to encompass various chronic diseases; their primary responsibility involves recording patients' daily symptoms and vital signs. However, a substantial portion of current AI research excludes nurses from the developmental phase, encompassing them solely in user and feedback populations. The comprehensive design of the symptom analysis and long-term management approach necessitates the guidance and oversight of nurses; however, their current insufficient involvement might stem from nursing staff's comparatively limited comprehension of AI and their ambiguous perception of their role's value in AI. Therefore, an imperative exploration of nurses' roles in symptom analysis and long-term management, leveraging the latest research in these areas, is vital to pinpoint breakthroughs in nurses' AI involvement in the future.

Perinatal Outcome in Patients of Antepartum Haemorrhage

Objectives: To determine the perinatal outcome in patients of antepartum haemorrhage at tertiary care hospital. Study design: descriptive, case series. Settings: Department of Gynecology & Obstetrics, Liaquat National Hospital Karachi. Study duration: 3rd November 2018 to 2nd May 2019. Materials & Methods: Total 132 patients with antepartum haemorrhage of 15-45 years of age were selected. Congenital anomalies detected on ultrasonography were excluded. A detailed history including patient’s age, parity, gestational age, and presenting complaints was taken. A general examination with recording of vital signs was performed followed by systemic examination. All parameters of maternal and fetal well-being were recorded. All cases with antepartum haemorrhage were evaluated and perinatal outcome of patients with antepartum haemorrhage was recorded including low birth weight, still birth and neonatal death. Results: Age range in this study was from 15 to 45 years with mean age of 28.94 ± 4.23 years. Majority of the patients 79 (59.85%) were between 15 to 30 years of age. Mean gestational age was 33.20 ± 3.92 weeks. Mean parity was 3.13 ± 0.89. Mean amount of bleeding per vagina was 845.33 ± 67.22 ml. In this study, frequency of perinatal outcome in patients of antepartum haemorrhage was as follows; low birth weight was found in 71 (53.79%), stillborn in 56 (42.42%) patients and neonatal death in 23 (17.42%) patients. Conclusion: This study concluded that a proper protocol should be designed in these high risk patients for antenatal monitoring and proper management plans in order to reduce the morbidity and mortality of the fetus. Keywords: Antepartum Haemorrhage, Low Birth Weight, Neonatal Death.

Comparison metrics for multi-step prediction of rare events in vital sign signals

Prediction of changes in biomedical signals, such as vital signs, is useful for many clinical applications. Several signal prediction (forecasting) tools were developed, but their evaluation and applicability to a specific clinical use is context dependent. In this work, we propose a novel method to tackle the problem of evaluation and comparison of vital sign predictors for intervention based clinical studies. The proposed prediction quality measures are particularly well-suited for forecasting rare events scenarios. Specifically, using the novel metrics, we measure the prediction statistics and compare nine deep learning and autoregressive forecasting models for multi-step prediction of rare bradycardia events in preterm infants, however the new concepts allow applications to other biomedical signals. We validated the novel metrics with experimental results on testing sets with several days of vital sign recordings. Our results show that simple statistical predictors could outperform state-of-the-art deep learning architectures for low-dimensional signals.

A prediction model for massive hemorrhage in trauma: a retrospective observational study

BackgroundMassive hemorrhage is the main cause of preventable death after trauma. This study aimed to establish prediction models for early diagnosis of massive hemorrhage in trauma.MethodsUsing the trauma database of Chinese PLA General Hospital, two logistic regression (LR) models were fit to predict the risk of massive hemorrhage in trauma. Sixty-two potential predictive variables, including clinical symptoms, vital signs, laboratory tests, and imaging results, were included in this study. Variable selection was done using the least absolute shrinkage and selection operator (LASSO) method. The first model was constructed based on LASSO feature selection results. The second model was constructed based on the first vital sign recordings of trauma patients after admission. Finally, a web calculator was developed for clinical use.ResultsA total of 2353 patients were included in this study. There were 377 (16.02%) patients with massive hemorrhage. The selected predictive variables were heart rate (OR: 1.01; 95% CI: 1.01–1.02; P<0.001), pulse pressure (OR: 0.99; 95% CI: 0.98–0.99; P = 0.004), base excess (OR: 0.90; 95% CI: 0.87–0.93; P<0.001), hemoglobin (OR: 0.95; 95% CI: 0.95–0.96; P<0.001), displaced pelvic fracture (OR: 2.13; 95% CI: 1.48–3.06; P<0.001), and a positive computed tomography scan or positive focused assessment with sonography for trauma (OR: 1.62; 95% CI: 1.21–2.18; P = 0.001). Model 1, which was developed based on LASSO feature selection results and LR, displayed excellent discrimination (AUC: 0.894; 95% CI: 0.875–0.912), good calibration (P = 0.405), and clinical utility. In addition, the predictive power of model 1 was better than that of model 2 (AUC: 0.718; 95% CI: 0.679–0.757). Model 1 was deployed as a public web tool (http://82.156.217.249:8080/).ConclusionsOur study developed and validated prediction models to assist medical staff in the early diagnosis of massive hemorrhage in trauma. An open web calculator was developed to facilitate the practical application of the research results.

The association between age and vital signs documentation of trauma patients in prehospital settings: analysis of a nationwide database in Japan

BackgroundEmergency medical service (EMS) providers are the first medical professionals to make contact with patients in an emergency. However, the frequency of care by EMS providers for severely injured children is limited. Vital signs are important factors in assessing critically ill or injured patients in the prehospital setting. However, it has been reported that documentation of pediatric vital signs is sometimes omitted, and little is known regarding the performance rate of vital sign documentation by EMS providers in Japan. Using a nationwide data base in Japan, this study aimed to evaluate the relationship between patients’ age and the documentation of vital signs in prehospital settings.MethodsThis study was a secondary data analysis of the Japan Trauma Data Bank. The inclusion criterion was patients with severe trauma, as defined by an Injury Severity Score ≥ 16. Our primary outcome was the rate of recording all four basic vital signs, namely blood pressure, heart rate, respiratory rate, and level of consciousness in the prehospital setting among different age groups. We also compared the prehospital vital sign completion rate, that is, the rate at which all four vital signs were recorded in a prehospital setting based on age groups. Multivariate analysis was performed to evaluate factors associated with the prehospital vital sign completion rate.ResultsWe analyzed 75,777 severely injured patients. Adults accounted for 94% (71400) of these severely injured patients, whereas only 6% of patients were children. The rate of prehospital recording of vital signs was lower in children ≤5 years than in adult patients for all four vital signs. When the adult group was used as a reference, the adjusted odds ratios of vital sign completion rate in infants (0 years), younger children (1–5 years), older children (6–11 years), and teenagers (12–17 years) were 0.09, 0.30, 0.78, and 0.87, respectively.ConclusionsAnalysis of the nationwide trauma registry showed that younger children tended to have a lower rate of vital sign documentation in prehospital settings.

Attempting cardiac arrest prediction using artificial intelligence on vital signs from Electronic Health Records

This work attempts to determine whether it is possible to predict the occurrence of in-hospital Cardiac Arrest (CA) using vital signs routinely recorded in a hospital's Electronic Health Record (EHR) system. The goal is to give caregivers advance notice of the possibility so they can intervene before its occurrence or prepare for immediate response in case it occurs. The work concentrates on three different aspects: which prediction model, which vital signs, and what time window produce the most accurate prediction. A number of experiments were conducted using six artificial intelligence algorithms and vital sign recordings ranging from 1 to 12 h. The work demonstrates that Random Forest produces the best prediction accuracy, reaching higher than 80%. Accuracy improves by more than 10% when prediction is done using vital sign data from the immediately preceding 60 min. Additionally, performance improves by only 3% when the observation range is increased from 1 h to 12 h.

Variations among Electronic Health Record and Physiologic Streaming Vital Signs for Use in Predictive Algorithms in Pediatric Severe Sepsis

AbstractObjective This study sought to describe the similarities and differences among physiologic streaming vital signs (PSVSs) and electronic health record (EHR)-documented vital signs (EVSs) in pediatric sepsis.Methods In this retrospective cohort study, we identified sepsis patients admitted to the pediatric intensive care unit. We compared PSVS and EVS measures of heart rate (HR), respiratory rate, oxyhemoglobin saturation, and blood pressure (BP) across domains of completeness, concordance, plausibility, and currency.Results We report 1,095 epochs comprising vital sign data from 541 unique patients. While counts of PSVS measurements per epoch were substantially higher, increased missingness was observed compared with EVS. Concordance was highest among HR and lowest among BP measurements, with bias present in all measures. Percent of time above or below defined plausibility cutoffs significantly differed by measure. All EVS measures demonstrated a mean delay from time recorded at the patient to EHR entry.Conclusion We measured differences between vital sign sources across all data domains. Bias direction differed by measure, possibly related to bedside monitor measurement artifact. Plausibility differences may reflect the more granular nature of PSVS which can be critical in illness detection. Delays in EVS measure currency may impact real-time decision support systems. Technical limitations increased missingness in PSVS measures and reflect the importance of systems monitoring for data continuity. Both PSVS and EVS have advantages and disadvantages that must be weighed when making use of vital signs in decision support systems or as covariates in retrospective analyses.

Temporal patterns in vital sign recording within and across general hospital wards

IntroductionThe use of mobile devices on hospital wards to record patient vital signs and Early Warning Scores provides opportunity for secondary analysis of the data collected. This research investigated how such analysis can contribute to the understanding of the complexities of managing clinical care in hospital environments. MethodsThe influence of ward type and the distribution of patient observation intervals was evaluated in relation to the timing of vital signs observation patterns in data collected from eight adult in-patient wards over a 12-month period. Actual and projected observation times were compared across patients with higher and lower National Early Warning Scores (NEWS). ResultsBoth ward type and the distribution of patient observation intervals were significant predictors of temporal observation patterns. Observation patterns showed evidence of grouping of observation recordings. This was, however, not found for observations of patients with higher NEWS scores (3 or more). ConclusionsSecondary analysis of vital signs observation data can reveal insights into how ward operate. The patterns of observation recordings within a ward are a reflection of ward type and the distribution of patient observation intervals. The grouping of observation recordings of patients with low NEWS (<3) result in late or early observations to fit activity peaks characteristic of the ward culture.

Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients

Continuous monitoring of high-risk patients and early prediction of severe outcomes is crucial to prevent avoidable deaths. Current clinical monitoring is primarily based on intermittent observation of vital signs and the early warning scores (EWS). The drawback is lack of time series dynamics and correlations among vital signs. This study presents an approach to real-time outcome prediction based on machine learning from continuous recording of vital signs. Systolic blood pressure, diastolic blood pressure, heart rate, pulse rate, respiration rate and peripheral blood oxygen saturation were continuously acquired by wearable devices from 292 post-operative high-risk patients. The outcomes from serious complications were evaluated based on review of patients’ medical record. The descriptive statistics of vital signs and patient demographic information were used as features. Four machine learning models K-Nearest-Neighbors (KNN), Decision Trees (DT), Random Forest (RF), and Boosted Ensemble (BE) were trained and tested. In static evaluation, all four models had comparable prediction performance to that of the state of the art. In dynamic evaluation, the models trained from the static evaluation were tested with continuous data. RF and BE obtained the lower false positive rate (FPR) of 0.073 and 0.055 on no-outcome patients respectively. The four models KNN, DT, RF and BE had area under receiver operating characteristic curve (AUROC) of 0.62, 0.64, 0.65 and 0.64 respectively on outcome patients. RF was found to be optimal model with lower FPR on no-outcome patients and a higher AUROC on outcome patients. These findings are encouraging and indicate that additional investigations must focus on validating performance in a clinical setting before deployment of the real-time outcome prediction.

Vital signs in pediatric oncology patients assessed by continuous recording with a wearable device, NCT04134429

Pediatric patients with cancer are at high risk for severe infections. Changes in vital signs, triggered by infections, may be detected earlier by continuous recording with a wearable device than with discrete measurements. This prospective, observational single-center feasibility study consecutively recruited pediatric patients undergoing chemotherapy for cancer. The WD Everion® was used for 14 days in each of the 20 patients on study to continuously record vital signs. Nine different vital signs and health indicators derived from them, plus six quality scores. This resulted in 274 study days (6576 hours) with 85’854 measuring points, which are a total of 772’686 measurements of vital signs and health indicators, plus 515’124 quality scores. Additionally, non-WD data like side effects, acceptability of the WD and effort for investigators were collected. In this manuscript, we present the methods of acquisition and explanations to the complete data set, which have been made publically available on open access and which can be used to study feasibility of continuous multi-parameter recording of vital signs by a WD.

Cardiorespiratory effect of Swedish back massage in hypertensive patients: a randomized clinical trial

Background: Chronic stress is one of the main risk factors for incident hypertension. Behavioral methodologies, such as unwinding and rub, have a dramatic effect on controlling the individuals’ reaction to stress, diminishing hypertension and heart rate, as well as changing temperature and respiration rate. Material and methods: 90 patients with primary hypertension were enrolled to this open-label study. The patients have been haphazardly classified into an affect and an intervention cluster. In the two groups, systolic and diastolic blood pressure, heart rate, body temperature and respiratory rate) were measured and recorded two times a week before and after a ten-min Swedish back massage and relaxation for six weeks. The records have been accrued using a questionnaire which includes demographic records, a checklist of vital signs (systolic and diastolic blood pressure, heart rate, temperature, and respiratory) record, a fixed manometer and thermometer. Results: Within the experimental group, systolic and diastolic blood pressure, heart rate, and respiratory levels reduced to 6.44 and 4.77 mm Hg, 2.9 bpm, 0.94 breaths per minute, respectively (p &lt; 0.001) and temperature increased to 0.08°C after the back massage (p = 0.004). Conclusion: The obtained results show the effectiveness of back massage in reducing blood pressure, heart rate, and respiratory as well as increased temperature in the study participants.

Machine Learning Model for Human Activity Analysis

Human Activity Recognition is an active subject of research and scientific progress in which several models have been presented for identifying and categorizing activities using Machine Learning utilizing various methodologies. The purpose of human activity recognition is to look at activities in video or still photos. Human activity recognition systems are motivated by this fact, and their goal is to appropriately classify input data into its underlying activity category. Human activities are classified as (a) gestures, (b) atomic actions, (c) human-to-object or human-to-human interactions, (d) collective actions, (e) behaviors, and (f) events, depending on their complexity. Today, health informatics is a critical field for improving healthcare efficiency by streamlining the collecting, storage, and retrieval of critical patient health data. In this paper, an intelligent smart healthcare system is provided that uses machine learning approaches to deliver ubiquitous human activity recognition (HAR) in an automated manner. The goal is to model and recognize activities of everyday living in a precise and efficient manner. Furthermore, for HAR purposes, we focus on a dataset collecting body motion and vital sign recordings from volunteers of various profiles while performing various physical activities. This research has demonstrated that identifying human activity from sensor data is extremely difficult, even with the availability of a number of machine learning approaches. When it comes to machine learning techniques, there is no one-size-fits-all approach.

Using an Early Warning Score for Nurse Shift Patient Handover: Before-and-after Study

PurposeThis study aimed to examine the impact of using an early warning score for shift patient handover on nurse and patient outcomes. MethodsA before-and-after study was conducted with nurses and patients in three general wards in a tertiary teaching hospital. A short-time nurse education on the National Early Warning Score 2 and the use of a checklist for score calculation were performed from June 4, 2019 to June 30, 2019. Outcomes of nurse response (safety competency, handover quality, teamwork, safety climate, and documentation of vital signs and clinical concerns), patient response (deterioration occurrence postadmission, hospitalization length, and discharge status), and adverse events (mortality, cardiopulmonary arrest, and unplanned intensive care unit admission) were measured using questionnaires and medical record reviews. Data from 89 nurses and 388 patients were analyzed. ResultsRegarding nurse outcomes, handover quality (p < .001), teamwork (p = .004), safety climate (p = .018), and recordings of vital signs (p = .047) and clinical concerns (p = .008) increased after early warning score use. However, no significant change in the safety competency scores was observed. Regarding patient outcomes, there were no significant changes in the occurrence of deterioration, hospitalization length, discharge status, and occurrence of adverse events between preintervention and postintervention. ConclusionDespite no significant changes in patient outcomes, using a simple, evidence-based early warning score for patient handover enhanced socio-cultural factors for patient safety, with improved patient monitoring. The findings provide evidence that supports the active implementation of an early warning score to improve patient safety.

Physiological deterioration in the Emergency Department: The SNAP40-ED study

Continuous novel ambulatory monitoring may detect deterioration in Emergency Department (ED) patients more rapidly, prompting treatment and preventing adverse events. Single-centre, open-label, prospective, observational cohort study recruiting high/medium acuity (Manchester triage category 2 and 3) participants, aged over 16 years, presenting to ED. Participants were fitted with a novel wearable monitoring device alongside standard clinical care (wired monitoring and/or manual clinical staff vital sign recording) and observed for up to 4 hours in the ED. Primary outcome was time to detection of deterioration. Two-hundred and fifty (250) patients were enrolled. In 82 patients (32.8%) with standard monitoring (wired monitoring and/or manual clinical staff vital sign recording), deterioration in at least one vital sign was noted during their four-hour ED stay. Overall, the novel device detected deterioration a median of 34 minutes earlier than wired monitoring (Q1, Q3 67,194; n=73, mean difference 39.48, p&lt;0.0001). The novel device detected deterioration a median of 24 minutes (Q1, Q3 2,43; n=42) earlier than wired monitoring and 65 minutes (Q1, Q3 28,114; n=31) earlier than manual vital signs. Deterioration in physiology was common in ED patients. ED staff spent a significant amount of time performing observations and responding to alarms, with many not escalated. The novel device detected deterioration significantly earlier than standard care.

Malnutrition and liver disease in a developing country.

Malnutrition is a highly prevalent and under recognized condition in developing countries of South Asia. The presence of malnutrition causes a severe impact on patients with liver cirrhosis. The etiology of cirrhosis differs in the South Asian region compared to the West, with hepatitis B and C still being the leading causes and the prevalence of nonalcoholic fatty liver disease increasing over time. Comorbid malnutrition worsens outcomes for cirrhosis patients. Urgent attention to address malnutrition is needed to improve patient outcomes. The etiology and pathophysiology of malnutrition in liver diseases is multifactorial, as reduction in liver function affects both macronutrients and micronutrients. A need for nutritional status assessment for liver disease patients exists in all parts of the world. There are many widely studied tools in use to perform a thorough nutritional assessment, of which some tools are low cost and do not require extensive training. These tools can be studied and evaluated for use in the resource limited setting of a country like Pakistan. Treatment guidelines for proper nutrition maintenance in chronic liver disease exist for all parts of the world, but the knowledge and practice of nutritional counseling in Pakistan is poor, both amongst patients and physicians. Emphasis on assessment for nutritional status at the initial visit with recording of vital signs is needed. Simultaneously, treating physicians need to be made aware of the misconceptions surrounding nutritional restrictions in cirrhosis so that patient education is done correctly based on proper scientific evidence.

The impact of web-based and face-to-face simulation education programs on nurses' response to patient deterioration: A multi-site interrupted time series study

BackgroundNurses' response to patient deterioration in acute hospital wards is a priority issue. Simulation education programs improve nurses' knowledge and confidence, but the translation into better care is largely unknown for both web based (WB) and face to face (F2F) simulation programs. AimTo measure the impact of simulation education on nurses' response to patient deterioration in acute medical ward settings, and to compare the impact of WB and F2F versions. DesignAn interrupted time series, non-randomised trial across four medical wards in Victoria, Australia. Wards were allocated to either web-based or face-to-face versions of the same simulation program, FIRST2ACT. Interrupted time series measurement for six fortnights both before and after the intervention were used to measure and compare responses to deterioration. Responses to patient deterioration were extracted from medical records and grouped into outcomes for escalation (e.g. initiation of clinical review), assessment and observation (e.g. increased recording of vital signs, conscious state and pain scores) and clinical interventions (e.g. oxygen administration). Results126 nurses (89%) participated across the four wards. 946 patient records (506 in the F2F; 440 in the WB group) were included in analyses.There were significant and sustained improvements between pre and post samples in outcomes for escalation (13.0% to 28.8%; p = 0.000) and assessment and observation (conscious state recorded increased from 91.1% to 100%; p = 0.000, and pain score recorded increased from 97.8% to 99.8%; p = 0.000).There were no differences between the web-based and face-to-face groups except in appropriate oxygen application which increased by 7.7% in the F2F group and decreased by 11.8% in the WB group (p = 0.046). ConclusionsThere was a significant improvement in nurses' response to patient deterioration following both versions of simulation, indicating that both have a role to play in supporting nurses' response to patient deterioration.

Health Support System for Diabetes Prevention Using IoT and Networked Health Monitoring Equipment

For participants of diabetes prevention programs, we consider that it is effective to record vital signs, such as weight and blood pressure, and to receive instruction based on such readings during the follow-up period in order to avoid rebound. Although the Internet is an effective medium for recording data and reporting everyday health status, health support systems that require the use of a PC can place a heavy burden on users, especially elderly users. In this research, we utilized a sensor information web service which is a home network based on ZigBee, and discuss effective implementation of a follow-up system. We explained and demonstrated the proposed system to participants who had completed a diabetes prevention program and administered a questionnaire about their impression of system use. From the questionnaire results, we clarify the considerations and requirements necessary for introducing such a home network system with the elderly. The ultimate purpose of this paper is providing to provide a standard knowledge base system toward a medical prescription capable of dealing with(inconsistencies) and exceptions cases, which will enhance individual healthcare and provide an appropriate prescription. This is accomplished by expanding the capabilities of description logic with defeasible rules, to achieve accurate prescription decision for any patient’s condition(s).

A computational model for adaptive recording of vital signs through context histories.

Wearable devices emerged from the advancement of communication technology and the miniaturization of electronic components. These devices periodically monitor the user’s vital signs and generally have a short battery life. This work introduces ODIN, a model for optimized vital signs collection based on adaptive rules. Analyzing vital sign values requires preciseness, so the adaption of these collected data allows a personalized analysis of the user’s health condition. The comparison with related works indicates that ODIN is the only model that presents context-aware-adaptive vital signs collection. The implementation of a prototype allowed to perform three evaluations of ODIN. The first evaluation used simulations in different scenarios, with the adaptive approach increasing battery life by 119% through the analysis of input data compared to data collection without adaptivity. The second evaluation applied the prototype to a database of real physiologic data, which allowed reduced data collection when the user has regular vital signs. This reduction optimized battery consumption by 66% compared to collection without adaptivity. Finally, the third evaluation applied ODIN through an Arduino and a heart rate monitor (Polar H7). The average power saved across mobile devices was 21%. Consequently, the adaptive strategy presented in this work allows the optimization of computational resources during the collection and analysis of vital signs. This optimization occurs because of the reduction in energy expenditure and the reduction in the amount of data that needs to be collected and stored.

How long do nursing staff take to measure and record patients’ vital signs observations in hospital? A time-and-motion study

IntroductionMonitoring vital signs in hospital is an important part of safe patient care. However, there are no robust estimates of the workload it generates for nursing staff. This makes it difficult to plan adequate staffing to ensure current monitoring protocols can be delivered. ObjectiveTo estimate the time taken to measure and record one set of patient's vital signs; and to identify factors associated with the time required to measure and record one set of patient's vital signs. MethodsWe undertook a time-and-motion study of 16 acute medical or surgical wards across four hospitals in England. Two trained observers followed a standard operating procedure to record the time taken to measure and record vital signs. We used mixed-effects models to estimate the mean time using whole vital signs rounds, which included equipment preparation, time spent taking vital signs at the bedside, vital signs documentation, and equipment storing. We tested whether our estimates were influenced by nurse, ward and hospital factors. ResultsAfter excluding non-vital signs related interruptions, dividing the length of a vital signs round by the number of vital signs assessments in that round yielded an estimated time per vital signs set of 5 min and 1 second (95% Confidence Interval (CI) = 4:39–5:24). If interruptions within the round were included, the estimated time was 6:26 (95% CI = 6:01–6:50). If only time taking each patient's vital signs at the bedside was considered, after excluding non-vital signs related interruptions, the estimated time was 3:45 (95% CI = 3:32–3:58). We found no substantial differences by hospital, ward or nurse characteristics, despite different systems for recording vital signs being used across the hospitals. DiscussionThe time taken to observe and record a patient's vital signs is considerable, so changes to recommended assessment frequency could have major workload implications. Variation in estimates derived from previous studies may, in part, arise from a lack of clarity about what was included in the reported times. We found no evidence that nurses save time when using electronic vital signs recording, or that the grade of staff measuring the vital signs influenced the time taken. ConclusionsMeasuring and recording vital signs is time consuming and the impact of interruptions and preparation away from the bedside is considerable. When considering the nursing workload around vital signs assessment, no assumption of relative efficiency should be made if different technologies or staff groups are deployed.

The FAST-M complex intervention for the detection and management of maternal sepsis in low-resource settings: a multi-site evaluation.

To evaluate whether the implementation of the FAST-M complex intervention was feasible and improved the recognition and management of maternal sepsis in a low-resource setting. A before-and-after design. Fifteen government healthcare facilities in Malawi. Women suspected of having maternal sepsis. The FAST-M complex intervention consisted of the following components: the FAST-M maternal sepsis treatment bundle and the FAST-M implementation programme. Performance of selected process outcomes was compared between a 2-month baseline phase and 6-month intervention phase with compliance used as a proxy measure of feasibility. Compliance with vital sign recording and use of the FAST-M maternal sepsis bundle. Following implementation of the FAST-M intervention, women were more likely to have a complete set of vital signs taken on admission to the wards (0/163 [0%] versus 169/252 [67.1%], P<0.001). Recognition of suspected maternal sepsis improved with more cases identified following the intervention (12/106 [11.3%] versus 107/166 [64.5%], P<0.001). Sepsis management improved, with women more likely to receive all components of the FAST-M treatment bundle within 1hour of recognition (0/12 [0%] versus 21/107 [19.6%], P=0.091). In particular, women were more likely to receive antibiotics (3/12 [25.0%] versus 72/107 [67.3%], P=0.004) within 1hour of recognition of suspected sepsis. Implementation of the FAST-M complex intervention was feasible and led to the improved recognition and management of suspected maternal sepsis in a low-resource setting such as Malawi. Implementation of a sepsis care bundle for low-resources improved recognition & management of maternal sepsis.