Vital Signs Are Still Vital: Instability on Discharge and the Risk of Post-Discharge Adverse Outcomes

Nguyen, Oanh Kieu; Makam, Anil N.; Clark, Christopher; Zhang, Song; Xie, Bin; Velasco, Ferdinand; Amarasingham, Ruben; Halm, Ethan A.

doi:10.1007/s11606-016-3826-8

Cited by 49 publications

(41 citation statements)

References 34 publications

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“…Others have established that different types of clinicians have different attitudes toward each type of long‐term care and that medicine residents have limited knowledge about SNFs . The pressure to discharge patients quickly is well described and may result in patients being discharged from the hospital “quicker and sicker.”…”

Section: Discussionmentioning

confidence: 99%

“…The intent of the Continuity Assessment Record and Evaluation tool is to standardize evaluation; it has not been validated for predicting the optimal postdischarge care for an individual . The timing of evaluation could be delayed until the patient has met specific medical criteria (e.g., normal vital signs, recovery of baseline mental status) to best assess functional ability . Furthermore, the roles of clinicians in evaluation and decision‐making must be made explicit, including the processes each uses to evaluate patients for postacute care.…”

Section: Discussionmentioning

confidence: 99%

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How Hospital Clinicians Select Patients for Skilled Nursing Facilities

Burke

Lawrence

Ladebue

et al. 2017

J American Geriatrics Society

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Background/Objective Hospitalized older adults are frequently transferred to skilled nursing facilities (SNFs) for post-discharge care. Hospitals and SNFs are increasingly held jointly accountable for costs and outcomes of this care. However, it is unclear how clinicians select patients for SNF care; we sought to understand how hospital-based clinicians evaluate older adults in the hospital and decide who will be transferred to a SNF for post-acute care. Design Semi-structured interviews paired with a qualitative analytic approach informed by Social Constructivist theory. Setting Inpatient care units in three hospitals. We used purposive sampling to maximize variability in hospitals, units within hospitals, and staff on those units. Participants Twenty-five clinicians involved in evaluation and decision-making regarding post-acute care, including hospitalists, nurses, therapists, social workers, and case managers. Measurements Central themes related to clinician evaluation and discharge decision-making. Results Clinicians described pressure to expedite evaluation and discharge decisions, resulting in the use of SNF as a “safety net” for older adults being discharged from the hospital. The lack of hospital-based clinician knowledge of SNF care practices, quality, or patient outcomes resulted in the lack of a standardized evaluation process or a clear primary decision-maker. Conclusion Hospital clinician evaluation and decision-making about post-acute care in a SNF may be characterized as rushed, without a clear system or framework for making decisions, and uninformed by knowledge of SNF or patient outcomes in those discharged to SNF. This leads to SNF being used as a “safety net” for many older adults. As hospitals and SNFs are increasingly held jointly accountable for outcomes of patients transitioning between hospital and SNF, novel solutions for improving evaluation and decision-making are urgently needed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

How Hospital Clinicians Select Patients for Skilled Nursing Facilities

Burke

Lawrence

Ladebue

et al. 2017

J American Geriatrics Society

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“…Studies on readiness for discharge have revealed that it affects patient outcomes such as readmission (Greysen et al., ; Nguyen et al., ; Nylor et al., ; Weiss, Yakusheva, & Bobay, ; Weiss et al., ), mortality (Halm et al., ; Lau et al., ) and emergency department (ED) visits (Bobay, Jerofke, Weiss, & Yakusheva, ; Jack et al., ). Readiness for discharge can be influenced by many factors such as age, sex, socioeconomic status, comorbidity and income (Bobay et al., ; Coffey & McCarthy, ; Howard‐Anderson, Busuttil, Lonowski, Vangala, & Afsar‐Manesh, ; Lau et al., ; Schmocker et al., ; Weiss, Costa, Yakusheva, & Bobay, ; Weiss & Piacentine, ; Weiss et al., ).…”

Section: Introductionmentioning

confidence: 99%

Patients’ readiness for discharge: Predictors and effects on unplanned readmissions, emergency department visits and death

et al. 2018

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“…All hospitals used the Epic EHR (Epic Systems Corporation, Verona, WI). Details of this cohort have been previously published 11, 14, 15, 16, 17. We used data from 2009 to 2010, before hospital‐based readmission interventions became widespread, to ensure that AMI cohorts across all 6 hospitals were comparable.…”

Section: Methodsmentioning

confidence: 99%

Predicting 30‐Day Hospital Readmissions in Acute Myocardial Infarction: The AMI “READMITS” (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus , Nonmale Sex , Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score

Nguyen

Makam

Clark

et al. 2018

JAHA

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BackgroundReadmissions after hospitalization for acute myocardial infarction (AMI) are common. However, the few currently available AMI readmission risk prediction models have poor‐to‐modest predictive ability and are not readily actionable in real time. We sought to develop an actionable and accurate AMI readmission risk prediction model to identify high‐risk patients as early as possible during hospitalization.Methods and ResultsWe used electronic health record data from consecutive AMI hospitalizations from 6 hospitals in north Texas from 2009 to 2010 to derive and validate models predicting all‐cause nonelective 30‐day readmissions, using stepwise backward selection and 5‐fold cross‐validation. Of 826 patients hospitalized with AMI, 13% had a 30‐day readmission. The first‐day AMI model (the AMI “READMITS” score) included 7 predictors: renal function, elevated brain natriuretic peptide, age, diabetes mellitus, nonmale sex, intervention with timely percutaneous coronary intervention, and low systolic blood pressure, had an optimism‐corrected C‐statistic of 0.73 (95% confidence interval, 0.71–0.74) and was well calibrated. The full‐stay AMI model, which included 3 additional predictors (use of intravenous diuretics, anemia on discharge, and discharge to postacute care), had an optimism‐corrected C‐statistic of 0.75 (95% confidence interval, 0.74–0.76) with minimally improved net reclassification and calibration. Both AMI models outperformed corresponding multicondition readmission models.ConclusionsThe parsimonious AMI READMITS score enables early prospective identification of high‐risk AMI patients for targeted readmissions reduction interventions within the first 24 hours of hospitalization. A full‐stay AMI readmission model only modestly outperformed the AMI READMITS score in terms of discrimination, but surprisingly did not meaningfully improve reclassification.

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Vital Signs Are Still Vital: Instability on Discharge and the Risk of Post-Discharge Adverse Outcomes

Cited by 49 publications

References 34 publications

How Hospital Clinicians Select Patients for Skilled Nursing Facilities

How Hospital Clinicians Select Patients for Skilled Nursing Facilities

Patients’ readiness for discharge: Predictors and effects on unplanned readmissions, emergency department visits and death

Predicting 30‐Day Hospital Readmissions in Acute Myocardial Infarction: The AMI “READMITS” (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus , Nonmale Sex , Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score

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