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

Bob Topp

Status: Withdrawn

Conditions: Hospital Acquired Condition

Treatments: Device: The Beat Analytics System (BAS)

Study type: Observational

Funder types: Other

Identifiers

NCT04674098

300854-UT

Details and patient eligibility

About

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.

Full 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.

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

0 participants in 2 patient groups

Usual Care

Description:

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

Description:

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.

Treatment:

Device: The Beat Analytics System (BAS)