Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration

Abstract

Background: Hospitalized adults whose condition deteriorates while they are in wards (outside the intensive care unit [ICU]) have considerable morbidity and mortality. Early identification of patients at risk for clinical deterioration has relied on manually calculated scores. Outcomes after an automated detection of impending clinical deterioration have not been widely reported.

Methods: On the basis of a validated model that uses information from electronic medical records to identify hospitalized patients at high risk for clinical deterioration (which permits automated, real-time risk-score calculation), we developed an intervention program involving remote monitoring by nurses who reviewed records of patients who had been identified as being at high risk; results of this monitoring were then communicated to rapid-response teams at hospitals. We compared outcomes (including the primary outcome, mortality within 30 days after an alert) among hospitalized patients (excluding those in the ICU) whose condition reached the alert threshold at hospitals where the system was operational (intervention sites, where alerts led to a clinical response) with outcomes among patients at hospitals where the system had not yet been deployed (comparison sites, where a patient's condition would have triggered a clinical response after an alert had the system been operational). Multivariate analyses adjusted for demographic characteristics, severity of illness, and burden of coexisting conditions.

Results: The program was deployed in a staggered fashion at 19 hospitals between August 1, 2016, and February 28, 2019. We identified 548,838 non-ICU hospitalizations involving 326,816 patients. A total of 43,949 hospitalizations (involving 35,669 patients) involved a patient whose condition reached the alert threshold; 15,487 hospitalizations were included in the intervention cohort, and 28,462 hospitalizations in the comparison cohort. Mortality within 30 days after an alert was lower in the intervention cohort than in the comparison cohort (adjusted relative risk, 0.84, 95% confidence interval, 0.78 to 0.90; P<0.001).

Conclusions: The use of an automated predictive model to identify high-risk patients for whom interventions by rapid-response teams could be implemented was associated with decreased mortality. (Funded by the Gordon and Betty Moore Foundation and others.).

Figure 1.. Study Cohorts.

Eligible patients were those whose initial hospital unit after admission was the ward or step-down unit and those who had initially been admitted to a surgical area and whose first postsurgical unit was the ward or step-down unit. Patients with direct admission to the intensive care unit (ICU) were not eligible. (Information about patients who had been admitted to the ICU is provided in Section S3.) Eligible hospitalizations were assigned to the target or nontarget population on the basis of the study definitions. The target population consisted of patients who had a hospitalization in which the patient’s condition reached the alert threshold of the Advance Alert Monitor program. If the alert occurred at a hospital where the program was active, the hospitalization was included in the intervention cohort; if the program was not active at the hospital, the hospitalization was included in the comparison cohort.

Figure 2.. Staggered Deployment of the Advance Alert Monitor Program.

The left column shows the deployment sequence number of each hospital. The next columns show the numbers of hospitalizations in which the patient reached the alert threshold (target hospitalizations) before and after the activation of the program; the numbers in parentheses show mortality within 30 days after an alert that occurred in a given hospitalization. The bar graph shows the dates on which the program became active at each hospital. The study period began on August, 1, 2015, which was 1 year before the deployment of the program at the first hospital.