Beyond order-based nursing workload: A retrospective cohort study in intensive care units.

INTRODUCTION: In order to be positioned to address the increasing strain of burnout and worsening nurse shortage, a better understanding of factors that contribute to nursing workload is required. This study aims to examine the difference between order-based and clinically perceived nursing workloads and to quantify factors that contribute to a higher clinically perceived workload. DESIGN: A retrospective cohort study was used on an observational dataset. METHODS: We combined patient flow, nurse staffing and assignment, and workload intensity data and used multivariate linear regression to analyze how various shift, patient, and nurse-level factors, beyond order-based workload, affect nurses' clinically perceived workload. RESULTS: Among 53% of our samples, the clinically perceived workload is higher than the order-based workload. Factors associated with a higher clinically perceived workload include weekend or night shifts, shifts with a higher census, patients within the first 24 h of admission, and male patients. CONCLUSIONS: The order-based workload measures tended to underestimate nurses' clinically perceived workload. We identified and quantified factors that contribute to a higher clinically perceived workload, discussed the potential mechanisms as to how these factors affect the clinically perceived workload, and proposed targeted interventions to better manage nursing workload. CLINICAL RELEVANCE: By identifying factors associated with a high clinically perceived workload, the nurse manager can provide appropriate interventions to lighten nursing workload, which may further reduce the risk of nurse burnout and shortage.

Telemedicine is associated with reduced socioeconomic disparities in outpatient clinic no-show rates.

INTRODUCTION: The global pandemic caused by coronavirus (COVID-19) sped up the adoption of telemedicine. We aimed to assess whether factors associated with no-show differed between in-person and telemedicine visits. The focus is on understanding how social economic factors affect patient no-show for the two modalities of visits. METHODS: We utilized electronic health records data for outpatient internal medicine visits at a large urban academic medical center, from February 1, 2020 to December 31, 2020. A mixed-effect logistic regression was used. We performed stratified analysis for each modality of visit and a combined analysis with interaction terms between exposure variables and visit modality. RESULTS: A total of 111,725 visits for 72,603 patients were identified. Patient demographics (age, gender, race, income, partner), lead days, and primary insurance were significantly different between the two visit modalities. Our multivariable regression analyses showed that the impact of sociodemographic factors, such as Medicaid insurance (OR 1.23, p < 0.01 for in-person; OR 1.03, p = 0.57 for telemedicine; p < 0.01 for interaction), Medicare insurance (OR 1.11, p = 0.04 for in-person; OR 0.95, p = 0.32 for telemedicine; p = 0.03 for interaction) and Black race (OR 1.36, p < 0.01 for in-person; OR 1.20, p < 0.01 for telemedicine; p = 0.03 for interaction), on increased odds of no-show was less for telemedicine visits than for in-person visits. In addition, inclement weather and younger age had less impact on no-show for telemedicine visits. DISCUSSION: Our findings indicated that if adopted successfully, telemedicine had the potential to reduce no-show rate for vulnerable patient groups and reduce the disparity between patients from different socioeconomic backgrounds.

Use of Real-Time Information to Predict Future Arrivals in the Emergency Department.

STUDY OBJECTIVE: We aimed to build prediction models for shift-level emergency department (ED) patient volume that could be used to facilitate prediction-driven staffing. We sought to evaluate the predictive power of rich real-time information and understand 1) which real-time information had predictive power and 2) what prediction techniques were appropriate for forecasting ED demand. METHODS: We conducted a retrospective study in an ED site in a large academic hospital in New York City. We examined various prediction techniques, including linear regression, regression trees, extreme gradient boosting, and time series models. By comparing models with and without real-time predictors, we assessed the potential gain in prediction accuracy from real-time information. RESULTS: Real-time predictors improved prediction accuracy on models without contemporary information from 5% to 11%. Among extensive real-time predictors examined, recent patient arrival counts, weather, Google trends, and concurrent patient comorbidity information had significant predictive power. Out of all the forecasting techniques explored, SARIMAX (Seasonal Autoregressive Integrated Moving Average with eXogenous factors) achieved the smallest out-of-sample the root mean square error (RMSE) of 14.656 and mean absolute prediction error (MAPE) of 8.703%. Linear regression was the second best, with out-of-sample RMSE and MAPE equal to 15.366 and 9.109%, respectively. CONCLUSION: Real-time information was effective in improving the prediction accuracy of ED demand. Practice and policy implications for designing staffing paradigms with real-time demand forecasts to reduce ED congestion were discussed.

Quantifying Utilitarian Outcomes to Inform Triage Ethics: Simulated Performance of a Ventilator Triage Protocol under Sars-CoV-2 Pandemic Surge Conditions.

BACKGROUND: Equitable protocols to triage life-saving resources must be specified prior to shortages in order to promote transparency, trust and consistency. How well proposed utilitarian protocols perform to maximize lives saved is unknown. We aimed to estimate the survival rates that would be associated with implementation of the New York State 2015 guidelines for ventilator triage, and to compare them to a first-come-first-served triage method. METHODS: We constructed a simulation model based on a modified version of the New York State 2015 guidelines compared to a first-come-first-served method under various hypothetical ventilator shortages. We included patients with SARs-CoV-2 infection admitted with respiratory failure requiring mechanical ventilation to three acute care hospitals in New York from 3/01/2020 and 5/27/2020. We estimated (1) survival rates, (2) number of excess deaths, (3) number of patients extubated early or not allocated a ventilator due to capacity constraints, (4) survival rates among patients not allocated a ventilator at triage or extubated early due to capacity constraints. RESULTS: 807 patients were included in the study. The simulation model based on a modified New York State policy did not decrease mortality, excess death or exclusion from ventilators compared to the first-come-first-served policy at every ventilator capacity we tested using COVID-19 surge cohort patients. Survival rates were similar at all the survival probabilities estimated. At the lowest ventilator capacity, the modified New York State policy has an estimated survival of 28.5% (CI: 28.4-28.6), compared to 28.1% (CI: 27.7-28.5) for the first-come-first-served policy. CONCLUSIONS: This simulation of a modified New York State guideline-based triage protocol revealed limitations in achieving the utilitarian goals these protocols are designed to fulfill. Quantifying these outcomes can inform a better balance among competing moral aims.

The Impact of High-Flow Nasal Cannula Use on Patient Mortality and the Availability of Mechanical Ventilators in COVID-19.

Rationale: How to provide advanced respiratory support for coronavirus disease (COVID-19) to maximize population-level survival while optimizing mechanical ventilator access is unknown.Objectives: To evaluate the use of high-flow nasal cannula for COVID-19 on population-level mortality and ventilator availability.Methods: We constructed dynamical (deterministic) simulation models of high-flow nasal cannula and mechanical ventilation use for COVID-19 in the United States. Model parameters were estimated through consensus based on published literature, local data, and experience. We had the following two outcomes: 1) cumulative number of deaths and 2) days without any available ventilators. We assessed the impact of various policies for the use of high-flow nasal cannula (with or without "early intubation") versus a scenario in which high-flow nasal cannula was unavailable.Results: The policy associated with the fewest deaths and the least time without available ventilators combined the use of high-flow nasal cannula for patients not urgently needing ventilators with the use of early mechanical ventilation for these patients when at least 10% of ventilator supply was not in use. At the national level, this strategy resulted in 10,000-40,000 fewer deaths than if high-flow nasal cannula were not available. In addition, with moderate national ventilator capacity (30,000-45,000 ventilators), this strategy led to up to 25 (11.8%) fewer days without available ventilators. For a 250-bed hospital with 100 mechanical ventilators, the availability of 13, 20, or 33 high-flow nasal cannulas prevented 81, 102, and 130 deaths, respectively.Conclusions: The use of high-flow nasal cannula coupled with early mechanical ventilation when supply is sufficient results in fewer deaths and greater ventilator availability.

The Impact of Step-Down Unit Care on Patient Outcomes After ICU Discharge.

OBJECTIVES: To examine whether and how step-down unit admission after ICU discharge affects patient outcomes. DESIGN: Retrospective study using an instrumental variable approach to remove potential biases from unobserved differences in illness severity for patients admitted to the step-down unit after ICU discharge. SETTING: Ten hospitals in an integrated healthcare delivery system in Northern California. PATIENTS: Eleven-thousand fifty-eight episodes involving patients who were admitted via emergency departments to a medical service from July 2010 to June 2011, were admitted to the ICU at least once during their hospitalization, and were discharged from the ICU to the step-down unit or the ward. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Using congestion in the step-down unit as an instrumental variable, we quantified the impact of step-down unit care in terms of clinical and operational outcomes. On average, for ICU patients with lower illness severity, we found that availability of step-down unit care was associated with an absolute decrease in the likelihood of hospital readmission within 30 days of 3.9% (95% CI, 3.6-4.1%). We did not find statistically significant effects on other outcomes. For ICU patients with higher illness severity, we found that availability of step-down unit care was associated with an absolute decrease in in-hospital mortality of 2.5% (95% CI, 2.3-2.6%), a decrease in remaining hospital length-of-stay of 1.1 days (95% CI, 1.0-1.2 d), and a decrease in the likelihood of ICU readmission within 5 days of 3.6% (95% CI, 3.3-3.8%). CONCLUSIONS: This study shows that there exists a subset of patients discharged from the ICU who may benefit from care in an step-down unit relative to that in the ward. We found that step-down unit care was associated with statistically significant improvements in patient outcomes especially for high-risk patients. Our results suggest that step-down units can provide effective transitional care for ICU patients.

The Impact of Opening a Medical Step-Down Unit on Medically Critically Ill Patient Outcomes and Throughput: A Difference-in-Differences Analysis.

OBJECTIVE: To understand the impact of adding a medical step-down unit (SDU) on patient outcomes and throughput in a medical intensive care unit (ICU). DESIGN: Retrospective cohort study. SETTING: Two academic tertiary care hospitals within the same health-care system. PATIENTS: Adults admitted to the medical ICU at either the control or intervention hospital from October 2013 to March 2014 (preintervention) and October 2014 to March 2015 (postintervention). INTERVENTIONS: Opening a 4-bed medical SDU at the intervention hospital on April 1, 2014. MEASUREMENTS AND MAIN RESULTS: Using standard summary statistics, we compared patients across hospitals. Using a difference-in-differences approach, we quantified the association of opening an SDU and outcomes (hospital mortality, hospital and ICU length of stay [LOS], and time to transfer to the ICU) after adjustment for secular trends in patient case-mix and patient-level covariates which might impact outcome. We analyzed 500 (245 pre- and 255 postintervention) patients in the intervention hospital and 678 (323 pre- and 355 postintervention) in the control hospital. Patients at the control hospital were younger (60.5-60.6 vs 64.0-65.4 years, P < .001) with a higher severity of acute illness at the time of evaluation for ICU admission (Sequential Organ Failure Assessment score: 4.9-4.0 vs 3.9-3.9, P < .001). Using the difference-in-differences methodology, we identified no association of hospital mortality (odds ratio [95% confidence interval]: 0.81 [0.42 to 1.55], P = .52) or hospital LOS (% change [95% confidence interval]: -8.7% [-28.6% to 11.2%], P = .39) with admission to the intervention hospital after SDU opening. The ICU LOS overall was not associated with admission to the intervention hospital in the postintervention period (-23.7% [-47.9% to 0.5%], P = .06); ICU LOS among survivors was significantly reduced (-27.5% [-50.5% to -4.6%], P = .019). Time to transfer to ICU was also significantly reduced (-26.7% [-44.7% to -8.8%], P = .004). CONCLUSIONS: Opening our medical SDU improved medical ICU throughput but did not affect more patient-centered outcomes of hospital mortality and LOS.

Incorporating Longitudinal Comorbidity and Acute Physiology Data in Template Matching for Assessing Hospital Quality: An Exploratory Study in an Integrated Health Care Delivery System.

OBJECTIVE: We sought to build on the template-matching methodology by incorporating longitudinal comorbidities and acute physiology to audit hospital quality. STUDY SETTING: Patients admitted for sepsis and pneumonia, congestive heart failure, hip fracture, and cancer between January 2010 and November 2011 at 18 Kaiser Permanente Northern California hospitals. STUDY DESIGN: We generated a representative template of 250 patients in 4 diagnosis groups. We then matched between 1 and 5 patients at each hospital to this template using varying levels of patient information. DATA COLLECTION: Data were collected retrospectively from inpatient and outpatient electronic records. PRINCIPAL FINDINGS: Matching on both present-on-admission comorbidity history and physiological data significantly reduced the variation across hospitals in patient severity of illness levels compared with matching on administrative data only. After adjustment for longitudinal comorbidity and acute physiology, hospital rankings on 30-day mortality and estimates of length of stay were statistically different from rankings based on administrative data. CONCLUSIONS: Template matching-based approaches to hospital quality assessment can be enhanced using more granular electronic medical record data.

The Impact of Adding a Physician Assistant to a Critical Care Outreach Team.

RATIONALE: Hospitals are increasingly using critical care outreach teams (CCOTs) to respond to patients deteriorating outside intensive care units (ICUs). CCOT staffing is variable across hospitals and optimal team composition is unknown. OBJECTIVES: To assess whether adding a critical care medicine trained physician assistant (CCM-PA) to a critical care outreach team (CCOT) impacts clinical and process outcomes. METHODS: We performed a retrospective study of two cohorts-one with a CCM-PA added to the CCOT (intervention hospital) and one with no staffing change (control hospital)-at two facilities in the same system. All adults in the emergency department and hospital for whom CCOT consultation was requested from October 1, 2012-March 16, 2013 (pre-intervention) and January 5-March 31, 2014 (post-intervention) were included. We performed difference-in-differences analyses comparing pre- to post-intervention periods in the intervention versus control hospitals to assess the impact of adding the CCM-PA to the CCOT. MEASUREMENTS AND MAIN RESULTS: Our cohort consisted of 3,099 patients (control hospital: 792 pre- and 595 post-intervention; intervention hospital: 1114 pre- and 839 post-intervention). Intervention hospital patients tended to be younger, with fewer comorbidities, but with similar severity of acute illness. Across both periods, hospital mortality (p = 0.26) and hospital length of stay (p = 0.64) for the intervention vs control hospitals were similar, but time-to-transfer to the ICU was longer for the intervention hospital (13.3-17.0 vs 11.5-11.6 hours, p = 0.006). Using the difference-in-differences approach, we found a 19.2% reduction (95 confidence interval: 6.7%-31.6%, p = 0.002) in the time-to-transfer to the ICU associated with adding the CCM-PA to the CCOT; we found no difference in hospital mortality (p = 0.20) or length of stay (p = 0.52). CONCLUSIONS: Adding a CCM-PA to the CCOT was associated with a notable reduction in time-to-transfer to the ICU; hospital mortality and length of stay were not impacted.

Association Among ICU Congestion, ICU Admission Decision, and Patient Outcomes.

OBJECTIVES: To employ automated bed data to examine whether ICU occupancy influences ICU admission decisions and patient outcomes. DESIGN: Retrospective study using an instrumental variable to remove biases from unobserved differences in illness severity for patients admitted to ICU. SETTING: Fifteen hospitals in an integrated healthcare delivery system in California. PATIENTS: Seventy thousand one hundred thirty-three episodes involving patients admitted via emergency departments to a medical service over a 1-year period between 2008 and 2009. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A third of patients admitted via emergency department to a medical service were admitted under high ICU congestion (more than 90% of beds occupied). High ICU congestion was associated with a 9% lower likelihood of ICU admission for patients defined as eligible for ICU admission. We further found strong associations between ICU admission and patient outcomes, with a 32% lower likelihood of hospital readmission if the first inpatient unit was an ICU. Similarly, hospital length of stay decreased by 33% and likelihood of transfer to ICU from other units-including ICU readmission if the first unit was an ICU-decreased by 73%. CONCLUSIONS: High ICU congestion is associated with a lower likelihood of ICU admission, which has important operational implications and can affect patient outcomes. By taking advantage of our ability to identify a subset of patients whose ICU admission decisions are affected by congestion, we found that, if congestion were not a barrier and more eligible patients were admitted to ICU, this hospital system could save approximately 7.5 hospital readmissions and 253.8 hospital days per year. These findings could help inform future capacity planning and staffing decisions.