The Effect of Real Time Analytics on Adverse Events Among Hospitalized Patients

This study will examine the effect of providing nurses with continuous, remote, real-time monitoring of their patient's vital signs and MEWS scores using the BAS on the occurrence of adverse events, admissions to the ICU, hospital length of stay and activation of the rapid response team among patients on non-intensive care hospital units. A longitudinal study will measure the outcome variables among an estimated 60 patients per month during 6 month intervals when the BAS is not and is available to the nursing staff.

Study Overview

• Status: Recruiting
• Conditions: Hospital Acquired Condition
• Intervention / Treatment: Device: The Beat Analytics System (BAS)

Detailed Description

Adverse events (AEs) among hospitalized patients are defined as "Unintended injuries or complications that result in disability at discharge, death or prolonged hospital stay and are caused by events other than the patient's underlying disease." Annually, approximately 1.1% of all hospital admissions, or 400,000 patients, die due to AEs while costing the US economy roughly 17.1 billion dollars. The Canadian Adverse Events Study reported AEs among 7.5% of hospital admissions, with 37% of these events deemed preventable. Common AEs, including infections and sepsis, cardiac and respiratory failure and deaths have been reported to be proceeded by a change in the patient's vital signs recognizable up to 48 hours prior to the AE being diagnosed. In many cases, subtle changes in a patient's vital signs have been recognized retrospectively as precursors of AEs that can lead to unplanned admissions to ICUs or death.

A number of early warning systems have been developed to assist nursing staff in identifying changes in vital signs as precursors to AEs. The commonly used Modified Early Warning Score (MEWS) attempts to identify acute clinical deterioration based on the patient's vital signs and level of consciousness. The higher the MEWS score, the greater risk of an AE. However, the efficacy of the MEWS score is contingent upon the frequency of both the score being recorded and being assessed by the nursing staff. Although continuous monitoring of vital signs takes place within and outside of ICUs these data are rarely provided to the nurse when they are outside of the patient's room in real time. Further, the vital signs and MEWS scores are commonly recorded in the patient record at scheduled intervals during a 24-hour period (e.g. every 4, 6, 8 or 12 hours). If a patient's physiological condition deteriorates between these scheduled intervals and the nurse is not continually with the patient, the opportunity is lost for early recognition of this deterioration that may lead to an AE. The importance of monitoring vital signs in clinical practice is indisputable, but how to best monitor and interpret them and how frequently they should be measured in order to minimize AEs remains unclear.

In order to address this gap in the literature, the project team has developed an innovative technology. The Beat Analytics System (BAS) provides nurses with both real-time monitoring of the patient's vital signs and continuous calculation of their patient's MEWS scores through an app on their cell phone. This information can be presented both numerically (with boundary conditions for alerts) and graphically, in order to observe change in the MEWS score over time. The purpose of this study is to examine the effect of providing nurses with remote, continuous, real-time monitoring of their patients vital signs and MEWS scores using the BAS on the occurrence of AEs, admissions to the ICU, hospital length of stay, and activation of the rapid response team among patients on non-intensive care hospital units. This purpose will be addressed through a longitudinal sequential study design in which the outcome variables (AEs, admissions to the ICU hospital length of stay and activation of the rapid response team) will be monitored on two 20-bed non-intensive care units monthly for 6 months without the BAS. The 6-month baseline data collection period will be followed by a month of training the nursing staff on the targeted unit to using the BAS. Following this orientation month, the outcome variables will again be measured during a 6-month intervention phase in which the BAS will be available to the nurses caring for patients on the targeted unit where the patient's vital signs will be continually recorded.

• Study Type: Observational
• Enrollment (Anticipated): 1000

Contacts and Locations

• Study Contact: Name: Robert Topp, PhD; Phone Number: 4193835852; Email: robert.topp@utoledo.edu

Study Locations

• United States
  • Ohio
    • Toledo, Ohio, United States, 43614
      • Recruiting
      • The University of Toledo Medical Center
      • Contact: Robert Topp; Phone Number: 419-383-5852; Email: robert.topp@utoledo.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Subjects for this study will include patients who are admitted to one of the two non-intensive care targeted units within the UTMC hospital.

Description

Inclusion Criteria:

• Admitted to one of two non-intensive care units within the University of Toledo Medical Center (UTMC) hospital.

Exclusion Criteria:

• unable to provide informed consent

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Usual Care; Every patient who is admitted or transferred to the target unit during both the baseline and intervention phases of the study will be approached by a member of the research staff to be a subject in the study. The patient will be informed of the overall study objectives and be requested to provide informed consent to participate. The patient's involvement in the study will include having the research staff access and extract relevant outcome variables collected from their electronic health record (EHR) (AEs, admissions to the ICU, hospital length of stay and activation of the rapid response team) as a result of their hospital stay.
Intervention; If the patient provides consent during the intervention phase, the BAS technology will passively monitor their vital signs generated by the Philips vital sign monitor by relaying their deidentified vital signs data to the CLU, proprietary Cloud server, and subsequently Lumori® on a study-issued cell phone of the RN who is primarily responsible for the patient's care. Patient's admitted or transferred to the targeted unit will NOT be excluded from being approached to participate in the study.	Device: The Beat Analytics System (BAS); There are three subsystems to the BAS; data aggregation, data analysis and data presentation.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Adverse events	Development of an infection or sepsis, cardiac or respiratory failure, and death	during hospital admission averaging 7 days
Length of stay in the hospital	Duration of days during which a subject was admitted to the hospital	during hospital admission averaging 7 days
Transfer to the ICU	Transfer of the patient to the intensive care unit	during hospital admission averaging 7 days
RRT activation	Activation of the hospital's rapid response team to support the care of the patient	during hospital admission averaging 7 days

Collaborators and Investigators

• Sponsor: Bob Topp
• Investigators: Principal Investigator: Robert Topp, PhD, College of Nursing

Publications and helpful links

General Publications

• van Galen LS, Dijkstra CC, Ludikhuize J, Kramer MH, Nanayakkara PW. A Protocolised Once a Day Modified Early Warning Score (MEWS) Measurement Is an Appropriate Screening Tool for Major Adverse Events in a General Hospital Population. PLoS One. 2016 Aug 5;11(8):e0160811. doi: 10.1371/journal.pone.0160811. eCollection 2016.
• Unbeck M, Schildmeijer K, Henriksson P, Jurgensen U, Muren O, Nilsson L, Pukk Harenstam K. Is detection of adverse events affected by record review methodology? an evaluation of the "Harvard Medical Practice Study" method and the "Global Trigger Tool". Patient Saf Surg. 2013 Apr 15;7(1):10. doi: 10.1186/1754-9493-7-10.
• Van Den Bos J, Rustagi K, Gray T, Halford M, Ziemkiewicz E, Shreve J. The $17.1 billion problem: the annual cost of measurable medical errors. Health Aff (Millwood). 2011 Apr;30(4):596-603. doi: 10.1377/hlthaff.2011.0084.
• Lapointe-Shaw L, Bell CM. Measuring the cost of adverse events in hospital. CMAJ. 2019 Aug 12;191(32):E877-E878. doi: 10.1503/cmaj.190912. No abstract available. Erratum In: CMAJ. 2019 Dec 2;191(48):E1340.
• Islam MM, Nasrin T, Walther BA, Wu CC, Yang HC, Li YC. Prediction of sepsis patients using machine learning approach: A meta-analysis. Comput Methods Programs Biomed. 2019 Mar;170:1-9. doi: 10.1016/j.cmpb.2018.12.027. Epub 2018 Dec 26.
• Kim J, Chae M, Chang HJ, Kim YA, Park E. Predicting Cardiac Arrest and Respiratory Failure Using Feasible Artificial Intelligence with Simple Trajectories of Patient Data. J Clin Med. 2019 Aug 29;8(9):1336. doi: 10.3390/jcm8091336.
• Jayasundera R, Neilly M, Smith TO, Myint PK. Are Early Warning Scores Useful Predictors for Mortality and Morbidity in Hospitalised Acutely Unwell Older Patients? A Systematic Review. J Clin Med. 2018 Sep 28;7(10):309. doi: 10.3390/jcm7100309.
• Kause J, Smith G, Prytherch D, Parr M, Flabouris A, Hillman K; Intensive Care Society (UK); Australian and New Zealand Intensive Care Society Clinical Trials Group. A comparison of antecedents to cardiac arrests, deaths and emergency intensive care admissions in Australia and New Zealand, and the United Kingdom--the ACADEMIA study. Resuscitation. 2004 Sep;62(3):275-82. doi: 10.1016/j.resuscitation.2004.05.016.
• Downey CL, Tahir W, Randell R, Brown JM, Jayne DG. Strengths and limitations of early warning scores: A systematic review and narrative synthesis. Int J Nurs Stud. 2017 Nov;76:106-119. doi: 10.1016/j.ijnurstu.2017.09.003. Epub 2017 Sep 13.
• Ludikhuize J, Smorenburg SM, de Rooij SE, de Jonge E. Identification of deteriorating patients on general wards; measurement of vital parameters and potential effectiveness of the Modified Early Warning Score. J Crit Care. 2012 Aug;27(4):424.e7-13. doi: 10.1016/j.jcrc.2012.01.003. Epub 2012 Feb 14.
• Kim WY, Shin YJ, Lee JM, Huh JW, Koh Y, Lim CM, Hong SB. Modified Early Warning Score Changes Prior to Cardiac Arrest in General Wards. PLoS One. 2015 Jun 22;10(6):e0130523. doi: 10.1371/journal.pone.0130523. eCollection 2015.
• Wang AY, Fang CC, Chen SC, Tsai SH, Kao WF. Periarrest Modified Early Warning Score (MEWS) predicts the outcome of in-hospital cardiac arrest. J Formos Med Assoc. 2016 Feb;115(2):76-82. doi: 10.1016/j.jfma.2015.10.016. Epub 2015 Dec 24.
• Brekke IJ, Puntervoll LH, Pedersen PB, Kellett J, Brabrand M. The value of vital sign trends in predicting and monitoring clinical deterioration: A systematic review. PLoS One. 2019 Jan 15;14(1):e0210875. doi: 10.1371/journal.pone.0210875. eCollection 2019.
• Smith GB, Recio-Saucedo A, Griffiths P. The measurement frequency and completeness of vital signs in general hospital wards: An evidence free zone? Int J Nurs Stud. 2017 Sep;74:A1-A4. doi: 10.1016/j.ijnurstu.2017.07.001. Epub 2017 Jul 4. No abstract available.
• Yoder JC, Yuen TC, Churpek MM, Arora VM, Edelson DP. A prospective study of nighttime vital sign monitoring frequency and risk of clinical deterioration. JAMA Intern Med. 2013 Sep 9;173(16):1554-5. doi: 10.1001/jamainternmed.2013.7791. No abstract available.

Study record dates

Study Major Dates

• Study Start (Actual): April 1, 2021
• Primary Completion (Anticipated): January 30, 2022
• Study Completion (Anticipated): January 30, 2022

Study Registration Dates

• First Submitted: December 4, 2020
• First Submitted That Met QC Criteria: December 12, 2020
• First Posted (Actual): December 19, 2020

Study Record Updates

• Last Update Posted (Actual): May 3, 2021
• Last Update Submitted That Met QC Criteria: April 27, 2021
• Last Verified: April 1, 2021

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Iatrogenic Disease
• Other Study ID Numbers: 300854-UT

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: No Plan Has been developed

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No