Inter-hospital Transfer Decision-making During the COVID-19 Pandemic: a Qualitative Study

BACKGROUND

Inter-hospital patient transfers to hospitals with greater resource availability and expertise may improve clinical outcomes. However, there is little guidance regarding how patient transfer requests should be prioritized when hospital resources become scarce.

OBJECTIVE

To understand the experiences of healthcare workers involved in the process of accepting inter-hospital patient transfers during a pandemic surge and determine factors impacting inter-hospital patient transfer decision-making.

DESIGN

We conducted a qualitative study consisting of semi-structured interviews between October 2021 and February 2022.

PARTICIPANTS

Eligible participants were physicians, nurses, and non-clinician administrators involved in the process of accepting inter-hospital patient transfers. Participants were recruited using maximum variation sampling.

APPROACH

Semi-structured interviews were conducted with healthcare workers across Michigan.

KEY RESULTS

Twenty-one participants from 15 hospitals were interviewed (45.5% of eligible hospitals). About half (52.4%) of participants were physicians, 38.1% were nurses, and 9.5% were non-clinician administrators. Three domains of themes impacting patient transfer decision-making emerged: decision-maker, patient, and environmental factors. Decision-makers described a lack of guidance for transfer decision-making. Patient factors included severity of illness, predicted chance of survival, need for specialized care, and patient preferences for medical care. Decision-making occurred within the context of environmental factors including scarce resources at accepting and requesting hospitals, organizational changes to transfer processes, and alternatives to patient transfer including use of virtual care. Participants described substantial moral distress related to transfer triaging.

CONCLUSIONS

A lack of guidance in transfer processes may result in considerable variation in the patients who are accepted for inter-hospital transfer and in substantial moral distress among decision-makers involved in the transfer process. Our findings identify potential organizational changes to improve the inter-hospital transfer process and alleviate the moral distress experienced by decision-makers.

Among-Hospital Variation in Intensive Care Unit Admission Practices and Associated Outcomes for Patients with Acute Respiratory Failure

Rationale: We have previously shown that hospital strain is associated with intensive care unit (ICU) admission and that ICU admission, compared with ward admission, may benefit certain patients with acute respiratory failure (ARF). Objectives: To understand how strain-process-outcomes relationships in patients with ARF may vary among hospitals and what hospital practice differences may account for such variation. Methods: We examined high-acuity patients with ARF who did not require mechanical ventilation or vasopressors in the emergency department (ED) and were admitted to 27 U.S. hospitals from 2013 to 2018. Stratifying by hospital, we compared hospital strain-ICU admission relationships and hospital length of stay (LOS) and mortality among patients initially admitted to the ICU versus the ward using hospital strain as a previously validated instrumental variable. We also surveyed hospital practices and, in exploratory analyses, evaluated their associations with the above processes and outcomes. Results: There was significant among-hospital variation in ICU admission rates, in hospital strain-ICU admission relationships, and in the association of ICU admission with hospital LOS and hospital mortality. Overall, ED patients with ARF (n = 45,339) experienced a 0.82-day shorter median hospital LOS if admitted initially to the ICU compared with the ward, but among the 27 hospitals (n = 224-3,324), this effect varied from 5.85 days shorter (95% confidence interval [CI], -8.84 to -2.86; P < 0.001) to 4.38 days longer (95% CI, 1.86-6.90; P = 0.001). Corresponding ranges for in-hospital mortality with ICU compared with ward admission revealed odds ratios from 0.08 (95% CI, 0.01-0.56; P < 0.007) to 8.89 (95% CI, 1.60-79.85; P = 0.016) among patients with ARF (pooled odds ratio, 0.75). In exploratory analyses, only a small number of measured hospital practices-the presence of a sepsis ED disposition guideline and maximum ED patient capacity-were potentially associated with hospital strain-ICU admission relationships. Conclusions: Hospitals vary considerably in ICU admission rates, the sensitivity of those rates to hospital capacity strain, and the benefits of ICU admission for patients with ARF not requiring life support therapies in the ED. Future work is needed to more fully identify hospital-level factors contributing to these relationships.

Hospital Strain and Variation in Sepsis ICU Admission Practices and Associated Outcomes

To understand how strain-process-outcome relationships in patients with sepsis may vary among hospitals.

DESIGN

Retrospective cohort study using a validated hospital capacity strain index as a within-hospital instrumental variable governing ICU versus ward admission, stratified by hospital.

SETTING

Twenty-seven U.S. hospitals from 2013 to 2018.

PATIENTS

High-acuity emergency department patients with sepsis who do not require life support therapies.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The mean predicted probability of ICU admission across strain deciles ranged from 4.9% (lowest ICU-utilizing hospital for sepsis without life support) to 61.2% (highest ICU-utilizing hospital for sepsis without life support). The difference in the predicted probabilities of ICU admission between the lowest and highest strain deciles ranged from 9.0% (least strain-sensitive hospital) to 45.2% (most strain-sensitive hospital). In pooled analyses, emergency department patients with sepsis (n = 90,150) experienced a 1.3-day longer median hospital length of stay (LOS) if admitted initially to the ICU compared with the ward, but across the 27 study hospitals (n = 517-6,564), this effect varied from 9.0 days shorter (95% CI, -10.8 to -7.2; p < 0.001) to 19.0 days longer (95% CI, 16.7-21.3; p < 0.001). Corresponding ranges for inhospital mortality with ICU compared with ward admission revealed odds ratios (ORs) from 0.16 (95% CI, 0.03-0.99; p = 0.04) to 4.62 (95% CI, 1.16-18.22; p = 0.02) among patients with sepsis (pooled OR = 1.48).

CONCLUSIONS

There is significant among-hospital variation in ICU admission rates for patients with sepsis not requiring life support therapies, how sensitive those ICU admission decisions are to hospital capacity strain, and the association of ICU admission with hospital LOS and hospital mortality. Hospital-level heterogeneity should be considered alongside patient-level heterogeneity in critical and acute care study design and interpretation.

Exploratory analysis of novel electronic health record variables for quantification of healthcare delivery strain, prediction of mortality, and prediction of imminent discharge

OBJECTIVE

To explore the relationship between novel, time-varying predictors for healthcare delivery strain (eg, counts of patient orders per hour) and imminent discharge and in-hospital mortality.

MATERIALS AND METHODS

We conducted a retrospective cohort study using data from adults hospitalized at 21 Kaiser Permanente Northern California hospitals between November 1, 2015 and October 31, 2020 and the nurses caring for them. Patient data extracted included demographics, diagnoses, severity measures, occupancy metrics, and process of care metrics (eg, counts of intravenous drip orders per hour). We linked these data to individual registered nurse records and created multiple dynamic, time-varying predictors (eg, mean acute severity of illness for all patients cared for by a nurse during a given hour). All analyses were stratified by patients' initial hospital unit (ward, stepdown unit, or intensive care unit). We used discrete-time hazard regression to assess the association between each novel time-varying predictor and the outcomes of discharge and mortality, separately.

RESULTS

Our dataset consisted of 84 162 161 hourly records from 954 477 hospitalizations. Many novel time-varying predictors had strong associations with the 2 study outcomes. However, most of the predictors did not merely track patients' severity of illness; instead, many of them only had weak correlations with severity, often with complex relationships over time.

DISCUSSION

Increasing availability of process of care data from automated electronic health records will permit better quantification of healthcare delivery strain. This could result in enhanced prediction of adverse outcomes and service delays.

CONCLUSION

New conceptual models will be needed to use these new data elements.