NATIONAL EARLY WARNING SCORES (NEWS):

A QUALITY IMPROVEMENT PROJECT

SREEDEVI S. WARRIER, DNP, RN, MA, CNS

MARGARET BRADY, PhD, RN, CHAIRJOY R. GOEBEL, PhD, RN, COMMITTEE MEMBER,

SAVITRI SINGH-CARLSON, PhD, APHN-BC, COMMITTEE MEMBER

NEWSBackground

Research suggests that patients transferred from medical/surgical wards to intensive care units experience higher mortality rates when compared to patients admitted from emergency or operating rooms

Patients suffering from a cardiac and/or respiratory arrest usually exhibit physiological deviations, such as changes in vital signs and/or mental status, at least eight hours prior to their need for more intensive care

Several studies suggest the use of early warning scores (EWS) in different patient care settings within acute care hospitals are effective in the identification and initiation of early intervention for patients who present with or develop acute illness

(Stenhouse, Coates, Tivey, Allsop, & Parker, 2000 & Royal College of Physicians, 2015)

NEWSBackground

Several bedside tools are available for nurses to identify patients experiencing clinical deterioration

National Early Warning Scores (NEWS) is an EWS prediction tool

NEWS focuses on a simple scoring system in which physiological measurements are assigned a predetermined score, which is then applied to a patient’s physiological measurements (vital signs)

Six simple physiological parameters form the basis of the scoring system: respiratory rate, oxygen saturation, temperature, systolic blood pressure, heart rate, and level of consciousness.

A specific NEWS value is designated to each physiological measurement and the aggregated score from all six parameters and the use of oxygen are used to predict the magnitude of variation from the norm

(Royal College of Physicians, 2015)

NEWSBackground

An initial pilot study was conducted at the project site in one medical/surgical unit to evaluate the effectiveness of the NEWS and the results of early interventions if needed

The outcome of interest in this NEWS pilot study was the number of Rapid Response (RRT) and Code Blue activations from the pilot unit vs. other medical/surgical units

The study was conducted over 26-days in one 50-bed unit (February 2014) 40-patients were identified who meet NEWS alert criteria 14-patients were transferred to a higher level of care and 26-patients

stayed in the unit with some type of bedside intervention The decision was made to implement a Phase 2 NEWS study in 6

medical/surgical unit

NEWSComparison of 2014 Pilot Study

NEWSScoring Criteria

Scoring Guidelines and Clinical Risk Assessment

NEWSReview of Literature

Evidence based articles were used to examine the efficacy, specificity, sensitivity, validity and reliability of using a NEWS tool in different clinical settings

Multiple search methods were used to access the following databasesCumulative Index to Nursing and Allied Health (CINAHL)Oxford Journals PubMedMedical Subject Heading (MeSH)MedlineCochrane LibraryGoogle Scholar

NEWSFramework

Plan-Do-Study-Act (PDSA)

Focuses on real-time changes

Action orientedDeveloped by Best

and Newhauser (2006)

PDSA can guide a process change with measurable outcome

Adapted from NHS-Institute for innovation and Improvement. Retrieved from www.ihi.org/resources/pages/Healthimprove/default.aspx

NEWSPurpose

Early identification of clinical deterioration in patients, so that nurses and providers could initiate an immediate intervention(s) ensuring appropriate use of RRT and/or Code Blue activations and preventing further clinical decline

NEWSAims

1) Compare the number of RRT/Code Blue calls in six medical/surgical units before and after NEWS implementation (November/December 2014 vs. November/December 2015)

2) Explore the number of RRT/Code Blue activations based on NEWS and nursing judgment

3) Identify demographic and/or clinical factors associated with RRT/Code Blue activation

NEWSAims

4) Describe the characteristics of patients who were transferred to a higher level of care with patients who remained in the unit with a reportable NEWS. What was the patient’s subsequent clinical condition after receiving an intervention?

5) Describe the clinical condition(s) of patients who received a NEWS of 5 or greater or 3 in any single parameter

6) Identify the number of patients who did not receive interventions despite a NEWS scores of 5 or greater or 3 in any single parameter

NEWSMethods

Six adult non-monitored medical surgical units in a Level 1 trauma center in a Southern California Academic Medical Center Inclusion criteria

Age 18 or olderHospitalized in one of the six designated pilot unitAny medical or surgical diagnosis

Exclusion criteriaUnder 18 years of agePregnancyRepeated, unchanged NEWS Receiving active palliative/comfort care

NEWSProcedures

Conducted a pilot test of the NEWS using the Electronic Health Record (EHR)

Pilot testing helped to identify the issues with EHR Approval from nursing and medical administration Institutional Review Board (IRB) approval from CSULB and the pilot

hospital Developed NEWS manual documentation form PowerPoint presentation was developed and reviewed by the team In-service provided for over 500 nursing staff and 250 providers

NEWSData Collection

Started November 10, 2015 to December 9, 2015 Information includes:

Date Patient identification code Demographic information (age, race, and gender) Diagnosis Routine vital signs and NEWS scores (every 4-hours = 6 times in a

24 hour period) Level of care/intervention(s)

NEWSResults

Data cleaning Total number of patients in the initial data set was 3,154 Anticipated data collection points were 113,536 Available data points were 102,473 9.7% of the data were missing from the total data set (90.3%

complete) Late admissionDischargeOff unit (procedure/surgery)Refusal

NEWSData Distribution

Respiratory

Rate

Oxygen

Saturation

Oxygen

SupplyTemperature

Systolic Blood

PressureHeart Rate

Level of

Consciousness

Data

Available 17096 (90.3%) 17073 (90.2%) 17124 (90.5%) 17058 (90.1%) 17056 (90.1%) 17066 (90.2%) 17180 (90.8%)

Unavailable 1826 (9.7%) 1847 (9.8%) 1799 (9.5%) 1866 (9.9%) 1867 (9.9%) 1858 (9.8%) 1780 (9.2%)

Reason Unavailable

Missing 1417 (7.5%) 1418 (7.5%) 1422 (7.5%) 1430 (7.6%) 1424 (7.5%) 1426 (7.5%) 1416 (7.5%)

*D/C (555) 167 (0.9%) 167 (0.9%) 167 (0.9%) 167 (0.9%) 167 (0.9%) 165 (0.9%) 164 (0.9%)

*No V/S (666) 7 (0.0%) 7 (0.0%) 8 (0.0%) 5 (0.0%) 14 (0.1%) 5 (0.0%) 0 (0.0%)

Refused (777) 133 (0.7%) 153 (0.8%) 99 (0.5%) 162 (0.9%) 160 (0.8%) 160 (0.8%) 56 (0.3%)

*OR (888) 12 (0.1%) 12 (0.1%) 12 (0.1%) 12 (0.1%) 12 (0.1%) 12 (0.1%) 11 (0.1%)

Off Unit (999) 90 (0.5%) 90 (0.5%) 91 (0.5%) 90 (0.5%) 90 (0.5%) 90 (0.5%) 90 (0.5%)

Summary of Missing Data Across Six Measures by Cause, Aggregate Data from Six Time-Points (N = 3,154 patients)

NEWSComparison of RRT and Code Blue

Aim # 1: Comparison of RRT, and RRT/Code Blue activations before and after NEWS implementation (Pre-NEWS – Nov/Dec 2014 and NEWS Nov/Dec 2015)

NEWSComparison of RRT and Code Blue

Results revealed that the number of RRT, and RRT/Code Blue calls decreased (x1, 000 patient days): November 2014/2015 = 11/5 December 2014/2015 = 9/7 Values are not statistically significant (p =>0.05)Clinically significant Small numbers can affect statistical testing sensitivity

NEWSRRT/Code Blue Calls

Aim # 2: RRT/Code Blue calls based on NEWS vs. nursing judgment

Total number of RRT calls during the pilot period was 91 17 from the pilot unit

16 RRT were activated by using NEWS (94.7%)

1 alert was called because of nursing clinical judgment (5.3%)

2 Code Blue Activations Nursing judgment (100%)

NEWSDemographic Data

AIM # 3: Demographic and clinical factors associated with RRT/Code Blue activations during this project period Total number of patients 3,154 patients

After data cleaning selected 2,667 patients (met all 6 time points with a complete NEWS value)

1,611 males and 1,056 females

Race and ethnicity Hispanic 48.4%

Black/African American 23.4%

White/Caucasians 18.2%

Asians 9.2%

NEWSDemographic Data Distribution

NEWS Score

Overall 0-4

5-6

(or 3 in any parameter) 7+ p*

N 2667 2634 32 1

Mean (SD)

Age, in years 52.89 (15.27) 52.83 (15.28) 57.53 (14.14) 73.00 (n/a) .08

Frequency (valid %)

Gender .40

Male 1611 (60.4) 1594 (60.5) 17 (53.1) 0 (0)

Female 1056 (39.6) 1040 (39.5) 15 (46.9) 1 (100)

Race .05

Asian 245 (9.2) 240 (9.1) 5 (15.6) 0 (0)

Black/African American 625 (23.4) 618 (23.5) 7 (21.9) 0 (0)

Hawaiian/Pacific Islander 5 (0.2) 5 (0.2) 0 (0) 0 (0)

Hispanic

1292 (48.4)

1279 (48.6) 13 (40.6) 0 (0)

Native American 6 (0.2) 5 (0.2) 1 (3.1) 0 (0)

White 486 (18.2) 479 (18.2) 6 (18.8) 1 (100)

Other 4 (0.1) 4 (0.2) 0 (0) 0 (0)

Unknown 4 (0.1) 4 (0.2) 0 (0) 0 (0)

* Due to the limited sample size of NEWS Scores of 7+ formal comparisons via hypothesis testing included only the first two groups* Presented for Independent Samples t-tests and Chi-Square tests of Independence

NEWSScore 5-6 or 3 in Any Single Criteria

AIM # 4: Compare the number of patients who had a high NEWS and transferred to a higher level of care with patients with similar scores who remained on the unit Total of 122 patients received a score of 5-6 or 3 in any single

parameter 48 patients missed interventions or were not timely identified 74 patients received some type of intervention(s) 63 remained in the unit after an intervention 11 patients transferred to a higher level of care

NEWSScore 7 or Greater

16 patients received a score of 7 or greater 5 patients remained in the unit 11 patients transferred either with NEWS score or by initiation

of RRT 2 patients received a higher NEWS that was not reported due

to unchanged NEWS (benign hypertension; COPD; asymptomatic atrial fibrillation)

2 patient placed on comfort/palliative care 1 patient with a high NEWS score was acknowledged by the

provider and specific parameters written for RRT activation (Dx: s/p lobectomy; small cell lung cancer; interstitial lung disease)

NEWSIntervention(s)Distribution Table

Intervention Employed

Stayed in Unit

Score Time N n (valid %) 0-4 1 9 7 (77.8)

2 5 5 (100) 3 9 9 (100) 4 5 4 (80.0) 5 20 17 (85.0) 6 16 10 (62.5)

5-6, or 3 in any single

parameter

1 18 16 (88.9) 2 11 10 (90.9) 3 10 8 (80.0) 4 8 6 (75.0) 5 8 7 (87.5) 6 19 16 (84.2)

7+ 1 1 0 (0)

2 2 1 (50.0) 3 4 2 (50.0) 4 1 1 (100) 5 4 0 (0) 6 4 1 (25.0)

Number of Patients Who remained in Unit After Receiving Intervention

NEWSPost-Intervention Distribution

Efficacy of the tool The efficacy of interventions for patients who remained in the

unit was analyzed using a repeated measures of ANOVA that tracked scores across 24 hours

Results showed a statistically significant NEWS over time Statistically significant reductions were seen between baseline

and 8-24 hours For patients who stayed in the unit with a high NEWS score, their

subsequent scores decreased significantly after receiving an intervention

NEWSPost-Intervention Distribution

Mean NEWS Scores at Post-Intervention Timeframes for Patients Remaining in Unit

NEWSClinical conditions & Missed Interventions

Aim # 5: Clinical condition of patients who received a high NEWS score

Data retrieval included information about the clinical diagnosis of all patients

Multiple comorbidities along with the admitting diagnosis made analysis inconclusive. The project investigator was unable to extract a specific diagnosis

Aim # 6: Number or patients with missed intervention(s)

48 patients with a NEWS of 5 or greater or 3 in any single parameter experienced missed intervention

Majority of patients missed intervention during the 0200 vital sign time period followed by the time periods of 0600, 1000, 2200

NEWSIntervention(s) Distribution Table

Intervention Rate by NEWS Score Category n (valid %) Score Time Total

Sample Intervention Employed*

Intervention Missed

0-4 1 2634 9 (0.3)

n/a

2 2672 5 (0.2) 3 2785 9 (0.3) 4 2861 5 (0.2) 5 2980 20 (0.7) 6 2990 16 (0.5)

5-6, or 3 in any single parameter

1 32 18 (56.3) 14 (43.8) 2 19 11 (57.9) 8 (42.1) 3 18 10 (55.6) 8 (44.4) 4 14 8 (57.1) 6 (42.9) 5 13 8 (61.5) 5 (38.5) 6 26 19 (73.1) 7 (26.9)

7+ 1 1 1 (100) 0

2 2 2 (100) 0 3 5 4 (80.0) 1 (20.0) 4 1 1 (100) 0 5 5 4 (80.0) 1 (20.0) 6 4 4 (100) 0

*

NEWSSummary of Results

The results did not show statistically significant differences related to the use of NEWS; nevertheless, the trend analysis is encouraging

Sensitivity of statistical testing can be affected with small subject numbers

In dealing with issues related to a patient’s clinical condition, any downward trend showing a decrease in the number of missed opportunities to intervene in situations of clinical deterioration is clinically significant for the patient, family and providers

NEWSDiscussion

Appropriate level of admission in medical/surgical units 2% clinical deterioration and mortality rate Number of RRT, and RRT/Code Blue pre-NEWS and with

NEWS Use of clinical judgment and high NEWS values Demographic data (age and race has clinical

significance)

NEWSDiscussion

Delay in implementing the NEWS tool in other units after the pilot study in 2014

Missed interventions especially at 0200Other reasons for missing data Timely documentation and acceptance of the call by

the provider Provider and nursing education

NEWSLimitations

Missing data from the total data set Unavailability of the NEWS tool in EHR Provider education (Nursing 95% complete)Motivation to use the tool

NEWSConclusion

NEWS helps to identify clinical deterioration and outcome improvement

Value in using PDSA framework, allowing real time feedback Scores are readily available for nurses and providers that will

enhance the initiation of an intervention NEWS provides a realistic tool for clinical decision-making

because the score includes a single physiological measure of extreme value in addition to aggregate scores that activate interventions

Plans to implement the NEWS tool in all DHS hospitals Initiating pilot study in one step-down unit

Acknowledgements

References

Agency for Healthcare and Research Quality (2013). Plan-Do-Study-Act (PDSA) cycle. Retrieved from https://innovations.ahrq.gov/qualitytools/plan-do-study-act-pdsa-cycle

Agency for Healthcare and Research Quality Patient Safety Network (2013). Failure to Rescue. Retrieved from https://psnet.ahrq.gov/popup_glossery.aspx?name=failuretorescue

Best, B & Neuhauser, D. (2006). Walter A Shewhart, 1924, and the Hawthorne factory. Quality and Safety in Healthcare, 15(20, 142-143. doi: 10.1136/qshc.2006-018093

Burch, V. C., Tarr, G., & Morroni, C. (2008). Modified early warning score predicts the need for hospital admission and inhospital mortality. Emergency Medical Journal, 25, 674-678. doi: 10.1136/emj.2007.057661

Capan, M., Ivy, J. S., Rohleder, T., Hickman, J., & Huddleston, J. M. (2015). Individualizing and optimizing the use of ear ly warning scores in acute medical care for deteriorating hospitalized patients. Resuscitation. doi: 10.1016/j.resuscitation.2014.12.0032

Capan, M., Ivy, J. S., Wilson, J. R., & Huddleston, J. M. (2015). A stochastic model of acute -care decisions based on patient and provider heterogeneity. Health Care Management Science. doi: 10.1007/s10729-015-9347-x

De Meester, K., Das, T., Hellemans, K., Verbrugghe, W., Jorens, P. G., Verpooten, G. A., & Van Bogaert, P. (2013). Impact of a standardized nurse observation protocol including MEWS after intensive care unit discharge. Resuscitation, 84,184-188.

Ebrahamian, A., Seydin, H., Jamshidi-Orak, R., & Masoumi, G. (2014). Physiological-social scores in predicting outcomes of prehospital internal patients. Emergency Medicine International. doi: 10.1155/2014/312189

References

Fullerton, J. N., Price, C. L., Silvey, N. E., Brace, S. J., & Perkins, G. D. (2012). Is the modified early warning score ( MEWS) superior to clinician judgment in detecting critical illness in the pre-hospital environment? Resuscitation, 83, 557-562.

Gardner-Thorpe, J., Love, N., Wrightson, J., Walsh, S., & Keeling, N. (2006). The value of Modified Early Warning Score (MEWS) in surgical in-patients: a prospective observational study. Annals of Royal College of Surgeons England, 88, 571-575. doi: 10.1308/003588406x130615

Hammond, N. E., Spooner, A. J., Barnett, A. G., Corley, A., Brown, P., & Fraser, J. F. (2013). The effect of implementing a modified early warning scoring (MEWS) system on the adequacy of vital sign documentation. Australian Critical Care, 26, 18-22.

Institute for Healthcare Improvement (2015). How to improve. Science of improvement: How to improve, 1-3. Retrieved from http://www.ihi.org/resources/Pages/HowtoImprove/ScienceofImprovementHowtoImprove.aspx

Institute for Healthcare Improvement (2015). How to improve. Science of improvement: Setting Aims, 1-3. Retrieved from http://www.ihi.org/resources/Pages/HowtoImprove/ScienceofImprovementSettingAims.aspx

Jarvis, S., Kovacs, C., Briggs, J., Meredith, P., Schmidt, P. E. et al. (2015). Aggregate national early warning scores (NE WS) values are more important than high scores for a single vital signs parameter for discriminating the risk of adverse outcomes. Resuscitation, 87, 75-80. Retrieved from http://dx.doi.org/10.1016/j.resuscitation.2014.11.014

Kolic, I., Crane, S., McCartney, S., Perkins, Z., & Taylor, A. (2015). Factors affecting response to National Early WarningScores (NEWS). Resuscitation, 90, 85-90. Retrieved from http://dx.doi.org/10.1016/j.resuscitation.2015.02.009Ludikehuize, J., Smorenburg, S. M., De Rooji, S. E., & De Jonge, E. (2012). Identification of deteriorating patients on general ward; measurement of vial parameters and potential effectiveness of the Modified Early Warning Score. Journal of Critical Care, 27, 424.e7-424.e.13. doi: 10.1016/j.jcrc.2012.01.003

Moen, R., & Norman, C. (2010). Circling back clearing up myths about the Deming cycle and seeing how it keeps evolving, 23-28. Retrieved from apiweb.org/circling-back, pdf

Moen, R., & Norman, C. (2013). Evolution of PDCA Cycle. Retrieved from pkpinc.com/files/NAOIMoenNorman-fullpaper.pdf

References

Neigsch, M., Labritius, M. L., & Anhoj, J. (2013). Imperfect implementation of an early warning scoring system in a Danish teaching hospital: A cross-sectional study. PLOS One, 8 (7), 1-6.

NHS Institute for Innovation and Improvement (2015). Plan, Do, Study, Act (PDSA). Retrieved from http://www.institute.nhs.uk/quality_and_service_improvement_tools/quality_and_service_improvement_tools/plan_do_study_act.html

Nursing times (2011). Can failure to rescue be a key indicator of patient safety? Nursing times, 107(48), 14-15.

Romero-Brufau, S. et. al (2014). Widely used track and trigger scores: Are they ready for automation in practice? Resuscitation, 85, 549-552. doi: 10.1016/j_resuscitation.2013.12.017

Stenhouse, C., Coates, S., Tivey, M., Allsop, P., & Parker, T. (2000). Prospective evaluation of a modified early warning sc oreto aid earlier detection of patients developing critical illness on a general surgical ward. British Journal of Anesthesia, 84(5), 663. doi: 10.1093/bja/84.5.663

Taylor, M. J., MCNicholas, C., Nicolay, C., Darzi, A., Bell, D., & Reed, J. (2013). Systematic review of the plan -do-study-act method to improve quality in healthcare. BMJ Quality & Safety, 1-9. doi: 10.1136/bmjqs-2013-001862

U. S. Department of Health and Human Services Health Resources and Service Administration. (2011). Testing for Improvement, 1-18. Retrieved from http://www.hrsa.gov/quality/toolbox/508pdfs/testingforimprovement.pdf

Yu, S., Leung, S., Heo, M., Soto, G. J., Shah, R. T., Gunda, S., & Gong, M. N. (2014). Comparison of risk prediction scoringsystems for ward patients: a retrospective nested case-control study. Critical Care, 18:R132

NEWS(National Early Warning Scores)

Questions?