Improving care quality through nurse-to-nurse consults and early warning system technology

Goellner, Yvonne; Tipton, Eydie; Verzino, Tammie; Weigand, Laura

doi:10.1097/01.numa.0000795580.57332.fa

“…Matching covariates included features which could in uence either return to ICU or the primary and secondary outcomes, speci cally: patient age, gender, and admission type, medical/surgical classi cation for hospitalization using MS-DRG grouping, the rst RI score during the visit, and the Charlson Comorbidity Index. The Charlson Comorbidity Index uses patient comorbidities to predict long-term mortality and is a commonly used algorithm to assess chronic conditions in hospitalized patients [23]. Logistic regression techniques were used to identify the cumulative probability of a return to ICU using the matching covariates.…”

Section: Discussionmentioning

confidence: 99%

The Rothman Index predicts unplanned readmissions to intensive care associated with increased mortality and hospital length of stay: a propensity-matched cohort study

Stahel,

Belk,

McInnis

et al. 2023

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Background Patients with unplanned readmissions to the intensive care unit (ICU) are at high risk of preventable adverse events. The Rothman Index represents an objective real-time grading system of a patient’s clinical condition and a predictive tool of clinical deterioration over time. This study was designed to test the hypothesis that the Rothman Index represents a sensitive predictor of unanticipated ICU readmissions. Methods A retrospective propensity-matched cohort study was performed at a tertiary referral academic medical center in the United States from January 1, 2022, to December 31, 2022. Inclusion criteria were adult patients admitted to an ICU and readmitted within seven days of transfer to a lower level of care. The control group consisted of patients who were downgraded from ICU without a subsequent readmission. The primary outcome measure was in-hospital mortality or discharge to hospice for end-of-life care. Secondary outcome measures were overall hospital length of stay, ICU length of stay, and 30-day readmission rates. Propensity matching was used to control for differences between the study cohorts. Regression analyses were performed to determine independent risk factors of an unplanned readmission to ICU. Results A total of 5,261 ICU patients met the inclusion criteria, of which 212 patients (4%) had an unanticipated readmission to the ICU within 7 days. The study cohort and control group were stratified by propensity matching into equal group sizes of n = 181. Lower Rothman Index scores (reflecting higher physiologic acuity) at the time of downgrade from the ICU were significantly associated with an unplanned readmission to the ICU (p < 0.0001). Patients readmitted to ICU had a lower mean Rothman Index score (p < 0.0001) and significantly increased rates of mortality (19.3% vs. 2.2%, p < 0.0001) and discharge to hospice (14.4% vs. 6.1%, p = 0.0073) compared to the control group of patients without ICU readmission. The overall length of ICU stay (mean 8.0 vs. 2.2 days, p < 0.0001) and total length of hospital stay (mean 15.8 vs. 7.3 days, p < 0.0001) were significantly increased in patients readmitted to ICU, compared to the control group. Conclusion The Rothman Index represents a sensitive predictor of unanticipated readmissions to ICU, associated with a significantly increased mortality and overall ICU and hospital length of stay. The Rothman Index should be considered as a real-time objective measure for prediction of a safe downgrade from ICU to a lower level of care.

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“…1 One area of focus to reduce avoidable mortality is timely identi cation of, and response to, clinical deterioration. 2,3 In support of these efforts, hospitals are increasingly investing in the implementation of early warning score (EWS) systems, which commonly report performance for mortality risk prediction [4][5][6] and which have been successfully incorporated into clinical processes to reduce mortality, [7][8][9][10][11] although consistent bene t from EWS deployments remains unclear. 12 However, operationalization of EWS technologies and associated care delivery processes entails signi cant expense; these include information technology and software technology costs, technical and informatics resources to set-up and maintain EWS systems, clinical care redesign efforts, end-user training and support, and the cost of time taken by end-users to review and interpret the EWS output.…”

Section: Introductionmentioning

confidence: 99%

The potential for in-patient mortality reductions to drive cost savings through decreases in hospital length of stay and intensive care unit utilization: a propensity matched cohort analysis

IV,

McInnis,

Belk

2024

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Background The economics of healthcare increasingly compels hospitals to accompany quality initiatives with a financial business case. Quality programs are frequently implemented with the aim of identifying clinical deterioration and reducing avoidable mortality. However, low rates of inpatient mortality with a diverse etiology make quantifying cost savings from avoidable mortality reduction challenging. To establish a relationship between mortality and length of stay, a commonly accepted indicator of hospital cost, we evaluated total hospital and intensive care unit length of stay for inpatients who expired compared to similar patients who did not expire during their hospital stay. Methods We conducted a retrospective propensity-matched cohort analysis of hospital inpatients who did and did not expire using de-identified data from 24 hospitals representing six health systems across the United States. In addition to demographic measures, the Rothman Index patient condition score was used to ensure cohorts were matched in physiologic acuity. Multivariable regression models were used to estimate the impact of mortality on the primary outcomes of total and ICU length of stay. Results Matched cohorts of patients who did and did not expire each comprised 6,129 patients. Patients who expired had longer mean overall hospital length of stay (LOS) compared to those who did not (13.3 vs 8.3 days, p < 0.0001) as well as longer mean ICU LOS (7.2 vs 5.7 days, p < 0.0265). Multivariable general linear models identified a 51% increase in predicted LOS and 33% increase in ICU LOS for patients who expired in the hospital. Conclusion As length of stay measures are routinely quantified in financial terms by hospitals, this work suggests it may be possible to translate mortality reductions into length of stay reductions as an inferential step in deriving a financial return on investment for mortality-focused quality of care initiatives.

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The Rothman Index predicts unplanned readmissions to intensive care associated with increased mortality and hospital length of stay: a propensity-matched cohort study

Stahel,

Belk,

McInnis

et al. 2024

Patient Saf Surg

4

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Background Patients with unplanned readmissions to the intensive care unit (ICU) are at high risk of preventable adverse events. The Rothman Index represents an objective real-time grading system of a patient’s clinical condition and a predictive tool of clinical deterioration over time. This study was designed to test the hypothesis that the Rothman Index represents a sensitive predictor of unanticipated ICU readmissions. Methods A retrospective propensity-matched cohort study was performed at a tertiary referral academic medical center in the United States from January 1, 2022, to December 31, 2022. Inclusion criteria were adult patients admitted to an ICU and readmitted within seven days of transfer to a lower level of care. The control group consisted of patients who were downgraded from ICU without a subsequent readmission. The primary outcome measure was in-hospital mortality or discharge to hospice for end-of-life care. Secondary outcome measures were overall hospital length of stay, ICU length of stay, and 30-day readmission rates. Propensity matching was used to control for differences between the study cohorts. Regression analyses were performed to determine independent risk factors of an unplanned readmission to ICU. Results A total of 5,261 ICU patients met the inclusion criteria, of which 212 patients (4%) had an unanticipated readmission to the ICU within 7 days. The study cohort and control group were stratified by propensity matching into equal group sizes of n = 181. Lower Rothman Index scores (reflecting higher physiologic acuity) at the time of downgrade from the ICU were significantly associated with an unplanned readmission to the ICU (p < 0.0001). Patients readmitted to ICU had a lower mean Rothman Index score (p < 0.0001) and significantly increased rates of mortality (19.3% vs. 2.2%, p < 0.0001) and discharge to hospice (14.4% vs. 6.1%, p = 0.0073) compared to the control group of patients without ICU readmission. The overall length of ICU stay (mean 8.0 vs. 2.2 days, p < 0.0001) and total length of hospital stay (mean 15.8 vs. 7.3 days, p < 0.0001) were significantly increased in patients readmitted to ICU, compared to the control group. Conclusion The Rothman Index represents a sensitive predictor of unanticipated readmissions to ICU, associated with a significantly increased mortality and overall ICU and hospital length of stay. The Rothman Index should be considered as a real-time objective measure for prediction of a safe downgrade from ICU to a lower level of care.

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Improving care quality through nurse-to-nurse consults and early warning system technology

Cited by 3 publications

References 10 publications

The Rothman Index predicts unplanned readmissions to intensive care associated with increased mortality and hospital length of stay: a propensity-matched cohort study

The Rothman Index predicts unplanned readmissions to intensive care associated with increased mortality and hospital length of stay: a propensity-matched cohort study

The potential for in-patient mortality reductions to drive cost savings through decreases in hospital length of stay and intensive care unit utilization: a propensity matched cohort analysis

The Rothman Index predicts unplanned readmissions to intensive care associated with increased mortality and hospital length of stay: a propensity-matched cohort study

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