Discuss about the Monitoring in Detection of Clinical Deterioration.
In this chapter, an overview of previous research on CCRT, Modified early warning score, clinical deterioration will be discussed .It introduces the framework for the studies that comprises the main focus of the research described in this study. The literature search methodology will be explained and discussed. The publications year of all the articles will be adjusted accordingly to ensure enough data is collected to analyze this topic. Research articles on the advantages and limitations will be explored to get more information. The most relevant and up to date articles will be reviewed.
The search was initiated through The National Centre for Biotechnology Information (NCBI) using PubMed, Medscape, Science Direct, Cochrane and Research Gate, Google Scholar search, CINAHL Nursing Journal Databases and Springer. This chapter will explain how important of early identification of deteriorating patients with proper treatment according to patient condition that may help patient in their survival. The key words that used to search article for this study is Modified early warning score and Rapid Response team which discovered 18,900 articles.
Besides that, searching was done by using specific words which all related to this study such as clinical deterioration, unplanned admission to Intensive care Unit, code blue and mortality shows 4,450 articles.
During the searching of the articles, the researcher found that some of the article that were published was not a full texted articles but only abstracts. The searching of Articles for this study was from the year of 1990 till 2018.
Most of the studies on Modified early warning score teaching were linked to international settings because very limited studies focus from Middle East. This studies more focus on how to reduce patient’s clinical deterioration, unplanned admission to Intensive care Unit, code blue and mortality.
The past decade of research finding revealed that, there is increased focus on identifying and act according to patient deteriorating condition in hospitalized patient (Massey et al, 2017, Australian Commission on Safety and Quality in Health Care (ACSQHC) 2010, National Institute for Health and Clinical Excellence (NICE), 2007). Patient deterioration can be identified by simple procedure which calls vital sign monitoring. Monitoring vital signs is essential to the entire hospitalized patient in acute care hospitals (Mirela et al, 2016). Traditionally, Vital signs are measurements of the body’s most basic functions and this signs are universally used to monitor patients’ progress. The five main vital signs are routinely been monitored by healthcare workers especially nurses for all the patients in ward. Those five vital signs are blood pressure (BP), pulse/heart rate (HR), Respiratory Rate (RR), oxygen saturation (SpO 2) and temperature .Any changes in their patient’s vital sign trends, it can indicate clinical deterioration or Adverse Events (AEs).However, Elliott and Coventry (2012) have opined that monitoring of the five vital signs are not comprehensive for the assessment of the clinical deterioration. According to Elliott and Coventry (2012), proper documentation of the clinical deterioration can be achieved via monitoring of five vital signs along with periodic assessment of pain, the degree of consciousness (Glassglow Coma Scale or GCS score) and amount of urine output. The study undertaken by Khan et al. (2016), highlighted that at present, monitoring of the vital signs are done via the use of automated wearable human vital sensors. This automated sensor has special sensing mechanisms, sensor fabrication and other data processing requirements which help to side pass the human errors and thereby helping the healthcare professionals to fetch accurate results.
According to Massey et al, (2016), ‘’a systematic review of eight studies from the US, Canada, the UK, Australia and New Zealand, highlights that the median overall incidence of adverse events was 9.2% and almost half of these events were regarded as preventable “on the other hand, in 2013, Jha et al conducted a observational studies and the burden of AEs worldwide was estimated, approximately 43 million AEs occur every year around the world .This AEs lead to ‘”23 million associated disability?adjusted life years, increasing hospital length of stay, poor quality of life and increasing morbidity and mortality “(Massey et al, 2016).
Almost all the hospitals are facing the same situation where suboptimal care leading to patient mortality and it is recognized worldwide as a major problem requiring special attention. A good and timely care for these patients may reduce the dangers they encounter. Therefore, the ability nurses in early identification of deteriorating patient and response to it timely manner are very essential in patient’s outcome. (Purling & King 2012, Massey et al, 2016). According to Mok, Wang and Liaw (2015) vital signs monitoring are an important nursing assessment. However, the nursing professionals seem to be performing it as a part of routine regime and thus frequently overlooking the significance of the parameters of the vital signs to detect patient deterioration. The study conducted by Hands et al. (2013) highlighted that there is only partial adherence of the vital signs monitoring protocol. Critically ill patients appear more likely to have vital signs monitored overnight but their analysis is not followed by periodic repeated assessment. Mok, Wang and Liaw (2015) have opined that proper clinical knowledge of the nurses about the vital signs along with increase in awareness about the responsibility of reporting vital signs will help to prevent adverse events and clinical deterioration in the ward.
AEs is unsafe situation in an inpatient hospital setting (Jha et al, 2013). Studies have been proved that, most of the ICU admission case are results of poor quality care in wards (Jeddian et al, 2016) and how acutely unwell patients exhibit abnormal vital signs which are either not recognized or are treated inappropriately can contribute to physiological deterioration of patients with major consequences on morbidity, mortality and requirement for intensive care. According to Massey et al, 2016, there is four way of recognizing or identify patient is deteriorating which is by assessing the patient by vital sign monitoring , knowing the patient through ‘gut feelings or a sixth sense’ and identified this as intuition, The level of education of the nurses who had graduated from a 4?year university educational program identified patient deterioration significantly faster than nurses who had graduated from a 2?year educational program (Pantazopoulos et al. 2012) and the last one is equipment, where the nurses are Unfamiliarity with equipment which cause the nurses fail to patient who are at high risk of deterioration. In 2016 , Mirela with other researcher conducted a structured surveys and it shows that ,Nurses were not confident about the accuracy of current equipment used to measure vital signs and about 52.7% nurses from neurosurgery department believe that automated observation machine did not give a proper reading. Nurses knowledge and practice is vital in identify patient who are deteriorating, their Clinical decision making is fundamental to the daily practice as well but in some condition they are reluctant to escalate care due to concerns they might be criticized if the patient is not that unwell and about 23.9% of the nurses agreed that due to time constraints most of vital sign and the accurate of the vital sign monitoring been neglected (Wenqi Mok et al. 2015). The study conducted by Yoder et al. (2013) reported that nurses also failed to understand the urgency of monitoring of the vital signs. Their study highlighted that overnight vital signs are collected frequently among the patients of the wards irrespective of their risk of clinical deterioration. Yoder et al. (2013) suggested that nighttime frequency of vital signs monitoring for the low risk inpatients must be reduced and such reduction is bound to bring dramatic benefits to the seep of the low risk patients and thereby reducing their length in the hospital stay. According to Mok et al. (2015), there is a need for continuous professional development in order to improve the attitudes of the ward nurses towards vital signs monitoring via prioritizing the workload planning.
As part of IHI’s 100,000 Lives Campaign some 1,500 hospitals are now actively using and/or implementing RRS. The rate of Cardiac arrest, mortality, and lengths of stay in the ICU are reducing compare to before, and hospitals with Rapid Response System(RRS) are moving their cultures toward a team-based approach to clinically challenging situations” (IHI, 2017).
According to Joint positon statement , 2016 on RRS “A hospital wide structure providing a safety net for patients potentially becoming critically ill who have a mismatch between their clinical needs and the local resources to manage them”. Implementation of RRS in hospital can reduce hospital mortality. According to Al-Qahtani S,et al,(2012) studied ,the impact of actualizing RRS on cardiopulmonary arrest and hospital mortality, they found that after the RRS was actualized, non ICU cardiopulmonary captures reduced from 1.4 to 0.9 for each 100 healing center confirmations in one of tertiary care academic center in Saudi Arabia. Besides that, in-hospital mortality rates decreased after RRS had been implemented in 6 of 10 tertiary care hospitals. (Al-Qahtani S,et al,2012) . In addition to that , a retrospective chart review indicated that before implementation of the RRS, the percentage of cardiopulmonary arrests was 83% or 1.84 per 1000 discharges and mortality was 1.42 per 1000 discharges whereas after RRT implementation, cardiopulmonary arrests decreased to 12.7% or 1.7 per 1000 discharges and hospital mortality decreased to 1.25 per 1000 discharges (Chen J,2014). Bagshaw SM et al, 2010 mention that, 84.2% believed that the RRT could prevent cardiopulmonary arrest in acutely ill patients, and 94% believed that the RRT allowed them to seek help for patients they were worried about. It is wide approach in improving the early identification and management of deteriorating patients.
The success of RRS relies on willingness and awareness of the staff member to initiate the code. Studies have shown that nurses are aware about the RRS in their hospital, yet many still hesitate to activate it when patient is showing sign and symptom of deterioration; Fifteen percent indicated reluctance to activate the RRS because of fear of criticism, but only 2.2% considered the RRT overused. (Sean M,et al,2010)
Studies revealed that the relationship between nurses and physicians can be a barrier, because many nurses believe that the physician should be contacted before an RRS (Leach LS, 2013). Most nurses (75.9%) would call the responsible physician before activating the RRS (Bagshaw SM et al, 2010). Hence, the RRS alone is in essence more reactive than preventive in nature.
In 1997, Morgan, Williams, and Wright introduced the Early Warning System (EWS). The EWS is one of the parts of the RRS. The EWS help to identify the patient who are at the risk level for the clinical deterioration. The EWS is trigger, which is known as the Medical Emergency team. This was begun in 2004. The result of the rapid response of the team evolution starts the effectiveness of EWS in the education system (Williams et al. 2012).
EWS helps to measure different physiological parameters of the patient’s vital signs which is heart rate, respiratory rate, systolic blood pressure, temperature, and consciousness level (Kyriacos, Jelsma and Jordan 2011). EWS help in predict outcomes and also to serve as a track and trigger system to recognize early signs of deterioration (Mathukia et al, 2015). In the United Kingdom, EWS was modified to Modified Early Warning System (MEWS) (Mathukia et al, 2015).The National Institute for Health and Clinical Excellence (NICE), (2007) recommended that MEWS should implemented in all adult patients who hospitalized in acute care settings so that the health care provides able to identify deteriorating patients and to ensure a timely escalation of care.
MEWS help in monitoring the patients for their safety to reduce the risk factors including life-threatening situation. It is used for the early detection in clinical deterioration of every hospitalized adult patient. Zayts and Sarangi (2013) stated that MEWS helps to determine the occurrence of critical illness. However, the MEWS does not replace the genetic scoring system like the disease specific system and Glasgow Coma Scale (GCS).
Failure of the compliance can create risks of the patient safety and can be the cause of the patient mortality. The MEWS can improve the patient safety with the clinical outcomes in the academic community hospital (Danesh, 2015).
According to Eggins and Slade (2015), in the hospitals the patient safety plays the key role for the development of the reputation. The poor condition of patient safety can increase the risk rate as well as can create the life threatening condition. Therefore, it is necessary to monitor the patients for their safety to reduce the risk factors including life-threatening situation (Jarvis et al. 2015). It is the responsibility of the nursing staffs to interpret the signs and symptoms of the patient adequately (Eggins and Slade 2015). For this purpose, the nursing staffs need proper training and professional approach to modify their practices and imply proper technique to improve the patient outcomes and clinical outcome. The outcome can measure the specific interest associated with the early deviation in the vital parameters and the severe deterioration (Bunkenborg 2014). Before implying the study intervention, the nursing monitoring practice needs to be influenced by the levels of the professionalism. This is characterized by the reflection, inter-professional collaboration and knowledge.
As opined by Kyriacos, Jelsma and Jordan (2014), these risks are high in the patients who are transferred to the general bed from the theatre recovery (Anzanpour et al. 2015). The MEWS provide the guidelines for the post-operative patients who have high risks of death and vital signs after seen. The MEWS provide useful scoring system to interpret the clinical deterioration and guide the intervention (Bellomo 2012). The remit of the group develops the MEWS system to provide the standardized track and the trigger system (De Meesteret al. 2013).
A prospective observational study was conducted by Kolic et al in 2015 at London District Medical Hospital, a total number of 370 adult patients were admitted in hospital. The study showed that the correct use of MEWS able to improve patient outcomes on patient’s clinical deterioration condition by providing early interventions to them.
Besides that ,A study was conducted by Van Galen et al. 2016 with 1053 patients and with 3673 vital sign measurement, Patients with critical scores had significant higher rates of unplanned ICU admissions [7.0% vs 1.3%, p < 0.001], in-hospital mortality [6.0% vs 0.8%, p < 0.001], 30-day readmission rates [18.6% vs 10.8%, p < 0.05], and a longer length of stay [15.65 (SD: 15.7 days) vs 6.09 (SD: 6.9), p < 0.001]. Specificity of MEWS related to composite adverse events was 83% with a negative predicting value of 98.1%.
Another study shows that, since the implementation of MEWS, “the number of RRT has increased from 0.24 per 100PD in 2011 to 0.38 per 100PD in 2013, and 0.48 per 100PD in 2014. The percentage of RRTs that progressed to Code Blue, an indicator of poor outcome of RRT, has been decreasing. In contrast, the numbers of Code Blue in non-ICU floors has been progressively decreasing from 0.05 per 100PD in 2011 to 0.02 per 100PD in 2013 and 2014. These improved clinical outcomes are associated with a decline of overall inpatient mortality rate from 2.3% in 2011 to 1.5% in 2013 and 1.2% in 2014”. (Mathukia et al. 2015).
Naeem and Montenegro (2005) said that that MEWS score introduction helped in the increase of patients with the rhythm as VF/VT, 8.5% vs. 23.7%. Introduction of MEWS resulted in a better survival to hospital discharge and it was statistically significant (5.2% vs. 16.8%). Introduction of MEWS helped in reducing the percentage of in-hospital cardiac arrest by 16% and death had reduced by 11.6%. Early introduction of MEWS might decrease the occurrence of cardiac arrests, mortality and will increase the survival of patients who are admitted in the HDUs, wards and ICUs. Early detection of the physiological deterioration and the imminent cardiac arrest can allow the help to arrive early or on time which may indirectly prevent the event of cardiac arrest.
The study conducted by Liu et al. (2012), highlighted that early unplanned ICU transfers occurring within 8 hours of hospitalization are often associated with increased rate of mortality. Their study revealed that 5% of patients admitted through emergency department experienced unplanned ICU transfer and this incidence rate are higher within first 24hours of hospital admission. The main reason highlighted by Liu et al. (2012) is improper monitoring of the vital signs. The research conducted by Mapp, Davis and Krowchuk (2013) stated that patients exhibit physiological changes up to 8 hours before an arrest event. The systematic review conducted by Mapp, Davis and Krowchuk (2013) concluded that early warning scoring system that interface with the electronic medical records and are accordingly supplemented with proper decision aids (algorithms) and clinical support system generates an effective screening framework for early identification of the critically ill or deteriorating patients. Mapp, Davis and Krowchuk (2013) highlighted this as a multifaceted approach that help to decrease unplanned ICU admissions and hospital related mortality. The meta-analysis conducted by Mapp, Davis and Krowchuk (2013) also provided similar results. According to them, ED patients admitted who are admitted with the respiratory complications are at modestly increased risk of unplanned ICU transfer and thus can be benefitted from closer monitoring.
The study conducted by Collins et al. (2013) showed that the monitoring of the vital signs help in the reduction of the morality rate among the patients with cardiac complications. Collins et al. (2013) further opined that the monitoring of the vital signs along and then comparing patient’s valuable information from the electronic medical records help in reducing the rate of mortality. The effectiveness of monitoring of the vital signs is highlighted in different aspects via Fridkin et al. (2014). According to them, effective monitoring of the vital signs helps in the improvement of the antibiotic usage among the hospitalized patients and thereby helping to reduce the rate drug related mortality. Systematic review conducted by Centers for Disease Control and Prevention (2012) further stated that monitoring of the vital signs help in the reduction of the unintentional injury or dath and thereby helping to reduce the mortality. They study is mainly based on the systematic review of the papers published between 2000 to 2009 and the statistical analysis of the reviewed papers showed 29% of the statistical significance and thereby indicating 15% decrease in the mortality rate.
The study conducted by Somanchi et al. (2015) showed that code blue is an emergency code that is used under the hospital settings in order to detect high risk patients for cardiac arrest or who are in an urgent need for resuscitation. Somanchi et al. (2015) further reported that when code blue is used in accordance with the MEWS help to yield better results for the detection of the risk of the patients with cardiac emergency. Qureshi et al. (2012) reported that there is no specific time for the employment of code blue among the cardiac patients. Their study conducted on 1692 patients showed that there is no significant difference in the overall rate of the survival of the patients or detection of the early vital signs of the patients in respect to the time of application of code blue. The study conducted by Churpek et al. (2012) showed that CART (cardiac arrest risk triage) score is simpler and can accurately detect CA (cardiac arrest) along with ICU transfer in comparison to MEWS. The conducted retrospective cohorts study among the hospitalized patients. The significance rate of CART score (blue code analysis) in comparison to MEWS showed to be 89.9% specific (p<0.001).
The study conducted by De Meester et al. (2013) showed that in-corporation of the critical care response team (CCRT) for the thorough analysis of the MEWS help in reduction of the serious adverse events under the hospital setting. () has further opined that CCRT working under the special nursing observation protocol help in the proper assessment and the implementation of the MEWS under color graphics observation chart. They mainly calculated patient observation frequency per nursing shift (POFPNS) for a 5-days period under the ICU settings and showed that the rate of discharge under the CCRT increased by 95% with decrease in the rate of the post discharge intervention by 95% (p =.005) (De Meester et al. 2013). The reports published by De Meester et al. (2013) go in sync with the reports published by Kyriacos Jelsma and Jordan (2011). According to them, monitoring of the vital signs via employing MEWS is found to provide better results when done under the active supervision of the CCRT. They mainly conducted a systematic review based on 534 papers framed over MEWS for adult inpatients.
MEWS is regarded as a better tool for others because Kyriacos et al. (2014) stated that modified version of MEWS also include psychological assessment along with the monitoring of the 4 vital signs. Mathukia et al. (2015) further opined that MEWS helps in the improvement of the patient safety via effective monitoring of the patient vital parameters under the setup of academic community hospital. The comparative study conducted by Finlay, Rothman and Smith (2014) on Modified Early Warning System (MEWS) and Rothman index (RI) showed that general acuity metric (RI) computed employing data routinely entered into an electronic medical records outperforms MEWS in the domain of identifying hospitalized patients who have susceptibility of high mortality rate within 24 hours. Their study highlighted that RI is successful in formulating more significant likelihood ratio (LR+) in comparison to MEWS. Finlay, Rothman and Smith (2014) further opined that MEWS is mainly based on 4 vital signs however, RI also includes neurologic assessment along with vital signs which helps in prompt detection of the early signs of the clinical deterioration. Moreover, RI also include through nursing assessment of the vital signs and thereby helping to side-pass the chances of receiving false positive results which is common MEWS threshold breach tools.
|
Source |
Objective |
Source of data |
Types of data |
Techniques of analysis |
Result |
Analysis |
|
Collins, S.A., Cato, K., Albers, D., Scott, K., Stetson, P.D., Bakken, S. and Vawdrey, D.K., 2013. Relationship between nursing documentation and patients’ mortality. American Journal of Critical Care, 22(4), pp.306-313 |
How monitoring of vital signs help to reduced patient’s mortality rate |
Data mining |
Raw data collected via analyzing 15 000 acute care patients |
Primary data analysis |
Vital signs monitoring helps to reduce the mortality rate |
Quantitative analysis |
|
Fridkin, S., Baggs, J., Fagan, R., Magill, S., Pollack, L.A., Malpiedi, P., Slayton, R., Khader, K., Rubin, M.A., Jones, M. and Samore, M.H., 2014. Vital signs: improving antibiotic use among hospitalized patients. Morbidity and Mortality Weekly Report, 63(9), pp.194-200. |
Monitoring of the vital signs for the improvement of the proper antibiotic administration |
National administrative database (MarketScan Hospital Drug Database) and CDC’s Emerging Infections Program (EIP) data were analyzed |
Secondary data |
Secondary data analysis |
Effective monitoring of the vital signs helps in the improvement of the antibiotic usage among the hospitalized patients and thereby helping to reduce the rate drug related mortality. |
Quantitative analysis |
|
Churpek, M.M., Yuen, T.C., Park, S.Y., Meltzer, D.O., Hall, J.B. and Edelson, D.P., 2012. Derivation of a cardiac arrest prediction model using ward vital signs. Critical care medicine, 40(7), p.2102. |
CART score to predict CA in comparison to MEWS |
Hospitalized patient of academic medical center of US |
Primary data |
Statistical analysis |
The study showed that CART (cardiac arrest risk triage) score is more simple and can accurately detect CA (cardiac arrest) along with ICU transfer in comparison to MEWS. The significance rate of CART score (blue code analysis) in comparison to MEWS showed to be 89.9% specific (p<0.001). |
Retrospective cohort study |
|
De Meester, K., Das, T., Hellemans, K., Verbrugghe, W., Jorens, P.G., Verpooten, G.A. and Van Bogaert, P., 2013. Impact of a standardized nurse observation protocol including MEWS after Intensive Care Unit discharge. Resuscitation, 84(2), pp.184-188. |
To examine the impact of a standard nurse observation protocol over the implementation of the Modified Early Warning Score (MEWS) under color graphic observation chart. |
ICU discharge to 14 medical and surgical wards (n before = 530 and n after is 509) |
Primary data |
Statistical analysis |
has further opined that CCRT working under the special nursing observation protocol help in the proper assessment and the implementation of the MEWS under color graphics observation chart. |
Pre- and post-intervention study via quantitative analysis |
|
Elliott, M. and Coventry, A., 2012. Critical care: the eight vital signs of patient monitoring. British Journal of Nursing, 21(10), pp.621-625. |
The aim of the paper, is to generate an overview of the crucial knowledge that are important for accurate assess of the vital signs. |
Secondary |
Secondary data analysis |
Secondary data analysis via using a qualitative approach |
Monitoring of the five vital signs are not comprehensive for the assessment of the clinical deterioration. Proper documentation of the clinical deterioration can be achieved via monitoring of five vital signs along with periodic assessment of pain, the degree of consciousness (Glassglow Coma Scale or GCS score) and amount of urine output |
Qualitative |
|
Mok, W.Q., Wang, W. and Liaw, S.Y., 2015. Vital signs monitoring to detect patient deterioration: An integrative literature review. International journal of nursing practice, 21(S2), pp.91-98. |
The aim of this paper is to detect the factors surrounding nursing practice of monitoring of the vital signs for detecting and reporting deterioration signs and symptoms among the patients |
Secondary data (1990 to 2012) |
Qualitative data analysis |
Integrative literature review |
Vital signs monitoring are an important nursing assessment. However, the nursing professionals seem to be performing it as a part of routine regime and thus frequently overlooking the significance of the parameters of the vital signs to detect patient deterioration. |
Qualitative data analysis via thematic review |
|
Yoder, J.C., Yuen, T.C., Churpek, M.M., Arora, V.M. and Edelson, D.P., 2013. A prospective study of nighttime vital sign monitoring frequency and risk of clinical deterioration. JAMA internal medicine, 173(16), pp.1554-1555. |
The impact of the nighttime vital signs monitoring among the low risk patients |
Primary data from adult inpatients between the tenure of November 4, 2008, and August 31, 2011 in the critical care units |
Quantitative analysis |
Prospective cohort study |
Nighttime frequency of vital signs monitoring for the low risk inpatients must be reduced and such reduction is bound to bring dramatic benefits to the seep of the low risk patients and thereby reducing their length in the hospital stay. |
Quantitative data analysis of primary data |
|
Liu, V., Kipnis, P., Rizk, N.W. and Escobar, G.J., 2012. Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. Journal of hospital medicine, 7(3), pp.224-230. |
Analysis of the timing of the unplanned ICU admission and hospital |
6369 patients’ data within 24 hours of hospital admission. The Cohort was matched with mortality, gender, age and diagnosis |
Primary data |
Prospective cohort study |
Early unplanned ICU transfers occurring within 8 hours of hospitalization are often associated with increased rate of mortality. Their study revealed that 5% of patients admitted through emergency department experienced unplanned ICU transfer and this incidence rate are higher within first 24hours of hospital admission. The main reason highlighted by Liu et al. (2012) is improper monitoring of the vital signs. |
Quantitative data with statistical analysis Analytical Framework |
References
Centers for Disease Control and Prevention (CDC, 2012. Vital signs: Unintentional injury deaths among persons aged 0-19 years-United States, 2000-2009. MMWR. Morbidity and mortality weekly report, 61, p.270.
Churpek, M.M., Yuen, T.C., Park, S.Y., Meltzer, D.O., Hall, J.B. and Edelson, D.P., 2012. Derivation of a cardiac arrest prediction model using ward vital signs. Critical care medicine, 40(7), p.2102.
Collins, S.A., Cato, K., Albers, D., Scott, K., Stetson, P.D., Bakken, S. and Vawdrey, D.K., 2013. Relationship between nursing documentation and patients’ mortality. American Journal of Critical Care, 22(4), pp.306-313.
De Meester, K., Das, T., Hellemans, K., Verbrugghe, W., Jorens, P.G., Verpooten, G.A. and Van Bogaert, P., 2013. Impact of a standardized nurse observation protocol including MEWS after Intensive Care Unit discharge. Resuscitation, 84(2), pp.184-188.
Elliott, M. and Coventry, A., 2012. Critical care: the eight vital signs of patient monitoring. British Journal of Nursing, 21(10), pp.621-625.
Finlay, G.D., Rothman, M.J. and Smith, R.A., 2014. Measuring the modified early warning score and the Rothman index: advantages of utilizing the electronic medical record in an early warning system. Journal of hospital medicine, 9(2), pp.116-119.
Fridkin, S., Baggs, J., Fagan, R., Magill, S., Pollack, L.A., Malpiedi, P., Slayton, R., Khader, K., Rubin, M.A., Jones, M. and Samore, M.H., 2014. Vital signs: improving antibiotic use among hospitalized patients. Morbidity and Mortality Weekly Report, 63(9), pp.194-200.
Hands, C., Reid, E., Meredith, P., Smith, G.B., Prytherch, D.R., Schmidt, P.E. and Featherstone, P.I., 2013. Patterns in the recording of vital signs and early warning scores: compliance with a clinical escalation protocol. BMJ Qual Saf, pp.bmjqs-2013.
Khan, Y., Ostfeld, A.E., Lochner, C.M., Pierre, A. and Arias, A.C., 2016. Monitoring of vital signs with flexible and wearable medical devices. Advanced Materials, 28(22), pp.4373-4395.
Kyriacos, U., Jelsma, J. and Jordan, S., 2011. Monitoring vital signs using early warning scoring systems: a review of the literature. Journal of nursing management, 19(3), pp.311-330.
Kyriacos, U., Jelsma, J., James, M. and Jordan, S., 2014. Monitoring vital signs: development of a modified early warning scoring (MEWS) system for general wards in a developing country. PloS one, 9(1), p.e87073.
Liu, V., Kipnis, P., Rizk, N.W. and Escobar, G.J., 2012. Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. Journal of hospital medicine, 7(3), pp.224-230.
Mapp, I.D., Davis, L.L. and Krowchuk, H., 2013. Prevention of unplanned intensive care unit admissions and hospital mortality by early warning systems. Dimensions of Critical Care Nursing, 32(6), pp.300-309.
Mathukia, C., Fan, W., Vadyak, K., Biege, C. and Krishnamurthy, M., 2015. Modified Early Warning System improves patient safety and clinical outcomes in an academic community hospital. Journal of community hospital internal medicine perspectives, 5(2), p.26716.
Mok, W., Wang, W., Cooper, S., Ang, E.N.K. and Liaw, S.Y., 2015. Attitudes towards vital signs monitoring in the detection of clinical deterioration: scale development and survey of ward nurses. International Journal for Quality in Health Care, 27(3), pp.207-213.
Mok, W.Q., Wang, W. and Liaw, S.Y., 2015. Vital signs monitoring to detect patient deterioration: An integrative literature review. International journal of nursing practice, 21(S2), pp.91-98.
Qureshi, S.A., Ahern, T., O’Shea, R., Hatch, L. and Henderson, S.O., 2012. A standardized code blue team eliminates variable survival from in-hospital cardiac arrest. Journal of Emergency Medicine, 42(1), pp.74-78.
Somanchi, S., Adhikari, S., Lin, A., Eneva, E. and Ghani, R., 2015, August. Early prediction of cardiac arrest (code blue) using electronic medical records. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2119-2126). ACM.
Yoder, J.C., Yuen, T.C., Churpek, M.M., Arora, V.M. and Edelson, D.P., 2013. A prospective study of nighttime vital sign monitoring frequency and risk of clinical deterioration. JAMA internal medicine, 173(16), pp.1554-1555.
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