Quality of care of hospitalized internal medicine patients bedspaced to non-internal medicine inpatient units

Outlier or handover: outcomes for General Medicine inpatients

Objective Patients admitted from the emergency department may be co-located on the treating team's 'home ward'. If no bed is available, patients may be sent to another ward, where they may remain under the admitting team as an 'outlier'. Conversely, care may be handed over to the team on whose home ward they are located. We conducted a retrospective analysis to understand the impact of outlier status and handovers of care on outcomes for General Medicine inpatients. Methods General Medicine admissions at the Royal Adelaide Hospital between September 2020 and November 2021 were analysed. We examined the rate of hospital-acquired complications, inpatient mortality rate, mortality within 48 h of admission, Relative Stay Index, time of discharge from hospital and rate of adverse events within 28 days of discharge. Results A total of 3109 admissions were analysed. Handovers within 24 h of admission were associated with a longer length of stay. There was a trend towards higher rates of adverse events within 28 days of discharge with handovers of care. Outlier status did not affect any outcome measures. Conclusions Handovers within the first 24 h of admission are associated with longer than expected length of stay.

Bedspacing and clinical outcomes in general internal medicine: A retrospective, multicenter cohort study

BACKGROUND

Admitting hospitalized patients to off-service wards ("bedspacing") is common and may affect quality of care and patient outcomes.

OBJECTIVE

To compare in-hospital mortality, 30-day readmission to general internal medicine (GIM), and hospital length-of-stay among GIM patients admitted to GIM wards or bedspaced to off-service wards.

DESIGN, PARTICIPANTS, AND MEASURES

Retrospective cohort study including all emergency department admissions to GIM between 2015 and 2017 at six hospitals in Ontario, Canada. We compared patients admitted to GIM wards with those who were bedspaced, using multivariable regression models and propensity score matching to control for patient and situational factors.

KEY RESULTS

Among 40,440 GIM admissions, 10,745 (26.6%) were bedspaced to non-GIM wards and 29,695 (73.4%) were assigned to GIM wards. After multivariable adjustment, bedspacing was associated with no significant difference in mortality (adjusted hazard ratio 0.95, 95% confidence interval [CI]: 0.86-1.05, p = .304), slightly shorter median hospital length-of-stay (-0.10 days, 95% CI:-0.20 to -0.001, p = .047) and lower 30-day readmission to GIM (adjusted OR 0.89, 95% CI: 0.83-0.95, p = .001). Results were consistent when examining each hospital individually and outcomes did not significantly differ between medical or surgical off-service wards. Sensitivity analyses focused on the highest risk patients did not exclude the possibility of harm associated with bedspacing, although adverse outcomes were not significantly greater.

CONCLUSIONS

Overall, bedspacing was associated with no significant difference in mortality, slightly shorter hospital length-of-stay, and fewer 30-day readmissions to GIM, although potential harms in high-risk patients remain uncertain. Given that hospital capacity issues are likely to persist, future research should aim to understand how bedspacing can be achieved safely at all hospitals, perhaps by strengthening the selection of low-risk patients.

Quality of care and outcomes in internal medicine patients bedspaced to noninternal medicine units

Hospital overcrowding has led to a practice known as bedspacing (in which admitted patients are placed on a different specialty's inpatient ward), yet little is known about the impact of this practice on healthcare quality.We investigated whether hospital outcome measures differ between bedspaced general internal medicine (GIM) patients vs nonbedspaced patients.Our retrospective study included patients admitted to GIM wards at 2 academic hospitals (2012-2014), comparing bedspaced to nonbedspaced patients, and identifying adverse events from the hospital's Electronic Patient Record.We compared these groups with respect to actual length of stay vs the expected length of stay (% ELOS), which is defined as length of stay (LOS) divided by expected length of stay (ELOS), 30-day readmission, adverse events (falls, medication-related incidents, equipment-related incidents, first treatment related incidents, laboratory-related incidents, and operative/invasive events), and in-hospital mortality.There were 22,519 patients analyzed with 15,985 (71%) discharged from a medical ward and 6534 (29%) discharged from a non-medical ward. Bedspaced patients had shorter lengths of stay (4.1 vs 6.2 days, P < .001) and expected lengths of stay (ELOS) (6.1 vs 6.4 days, P < .001). Bedspaced patients had a lower percentage of ELOS (% ELOS) than nonbedspaced patients (70% vs 91%, P < .001), similar readmission rates (9.8 vs 10.3 events per 100 patients, P = .24), lower in-hospital mortality rates (2.6 vs 3.3 events per 100 patients, P = .003) and fewer adverse events (0.20 vs 0.60 events per 100 patient days, P < .01).Bedspacing of patients is common. Patients who are bedspaced to off-service wards have better outcomes. This may relate to preferential allocation practices.

Influence of bedspacing on outcomes of hospitalised medicine service patients: a retrospective cohort study

BACKGROUND

Specialty wards cohort hospitalised patients to improve outcomes and lower costs. When demand exceeds capacity, patients overflow and are "bedspaced" to alternate wards. Some studies have demonstrated that bedspacing among medicine service patients is associated with adverse patient-centred outcomes, however, results have been inconsistent and have primarily been performed within national health systems. The objective of this study was to assess the association of bedspacing with patient-centred outcomes among United States patients admitted to general medicine services.

METHODS

We performed a retrospective cohort study of internal medicine, family medicine and geriatric service patients who were bedspaced vs cohorted for the entirety of their hospital stay within three large, urban United States hospitals (quaternary referral centre, tertiary referral centre and community hospital, with different patient demographics and case-mixes) in 2014 and 2015. We performed quantile regression to determine differences in length of stay (LOS) between bedspaced vs cohorted patients and logistic regression for in-hospital mortality and discharge to home.

RESULTS

Among 18 802 patients in 33 wards, 6119 (33%) patients were bedspaced. Bedspaced patients had significantly longer LOS compared with cohorted patients at the 25^th (0.1 days, 95% CI: 0.05 to 0.2, p=0.001), 50^th (0.2 days, 95% CI: 0.1 to 0.3, p=0.003) and 75^th (0.3 days, 95% CI: 0.2 to 0.5, p<0.001) percentiles; and no statistically significant differences in odds of mortality (OR=0.9, 95% CI: 0.6 to 1.3, p=0.5) or discharge to home (OR=0.9, 95% CI: 0.9 to 1.0, p=0.06) in adjusted analyses.

CONCLUSION

Bedspacing is associated with adverse patient-centred outcomes. Future work is needed to confirm these findings, understand mechanisms contributing to adverse outcomes and identify factors that mitigate these adverse effects in order to provide high-value, patient-centred care to hospitalised patients.

Mortality of hospitalised internal medicine patients bedspaced to non-internal medicine inpatient units: retrospective cohort study

OBJECTIVE

To compare inhospital mortality of general internal medicine (GIM) patients bedspaced to off-service wards with GIM inpatients admitted to assigned GIM wards.

METHOD

A retrospective cohort study of consecutive GIM admissions between 1 January 2015 and 1 January 2016 was conducted at a large tertiary care hospital in Canada.Inhospital mortality was compared between patients admitted to off-service wards (bedspaced) and assigned GIM wards using a Cox proportional hazards model and a competing risk model. Sensitivity analyses included propensity score and pair matching based on GIM service team, workload, demographics, time of admission, reasons for admission and comorbidities.

RESULTS

Among 3243 consecutive GIM admissions, more than a third (1125, 35%) were bedspaced to off-service wards with the rest (2118, 65%) admitted to assigned GIM wards. In hospital, 176 (5%) patients died: 88/1125 (8%) bedspaced patients and 88/2118 (4%) assigned GIM ward patients. Compared with assigned GIM wards patients, bedspaced patients had an HR of 3.42 (95% CI 2.23 to 5.26; P<0.0001) for inhospital mortality at admission, which then decreased by HR of 0.97 (95% CI 0.94 to 0.99; P=0.0133) per day in hospital. Competing risk models and sensitivity analyses using propensity scores and pair matching yielded similar results.

CONCLUSIONS

Bedspaced patients had significantly higher inhospital mortality than patients admitted to assigned GIM wards. The risk was highest at admission and subsequently declined. The results of this single centre study may not be generalisable to other hospitals and may be influenced by residual confounding. Despite these limitations, the relationship between bedspacing and patient outcomes requires investigation at other institutions to determine if this common practice represents a modifiable patient safety indicator.

The Association Between Hospital Capacity Strain and Inpatient Outcomes in Highly Developed Countries: A Systematic Review

BACKGROUND

Increases in patient needs can strain hospital resources, which may worsen care quality and outcomes. This systematic literature review sought to understand whether hospital capacity strain is associated with worse health outcomes for hospitalized patients and to evaluate benefits and harms of health system interventions to improve care quality during times of hospital capacity strain.

METHODS

Parallel searches were conducted in MEDLINE, CINAHL, the Cochrane Library, and reference lists from 1999-2015. Two reviewers assessed study eligibility. We included English-language studies describing the association between capacity strain (high census, acuity, turnover, or an indirect measure of strain such as delayed admission) and health outcomes or intermediate outcomes for children and adults hospitalized in highly developed countries. We also included studies of health system interventions to improve care during times of capacity strain. Two reviewers extracted data and assessed risk of bias using the Newcastle-Ottawa Score for observational studies and the Cochrane Collaboration Risk of Bias Assessment Tool for experimental studies.

RESULTS

Of 5,702 potentially relevant studies, we included 44 observational and 8 experimental studies. There was marked heterogeneity in the metrics used to define capacity strain, hospital settings, and overall study quality. Mortality increased during times of capacity strain in 18 of 30 studies and in 9 of 12 studies in intensive care unit settings. No experimental studies were randomized, and none demonstrated an improvement in health outcomes after implementing the intervention. The pediatric literature is very limited; only six observational studies included children. There was insufficient study homogeneity to perform meta-analyses.

DISCUSSION

In highly developed countries, hospital capacity strain is associated with increased mortality and worsened health outcomes. Evidence-based solutions to improve outcomes during times of capacity strain are needed.

Factors affecting the overcrowding in outpatient healthcare

BACKGROUND

The expansion of outpatient services and the desire to provide more outpatient care than inpatient care create some problems such as the overcrowding in the outpatient clinics. Given the importance of overcrowding in the outpatient clinics, this qualitative study aimed to determine the factors influencing the overcrowding in the specialty and subspecialty clinic of a teaching hospital.

MATERIALS AND METHODS

This was a qualitative study conducted in the specialty and subspecialty clinic of a hospital using content analysis method in the period of January to March 2014. The study population was all managers and heads of the outpatient wards. The studied sample consisted of 22 managers of the clinic wards who were selected using the purposive sampling method. The required data was collected using semi-structured interviews. The collected data was analyzed using conventional content analysis and the MAXQDA 10.0 software.

RESULTS

Three themes were identified as the main factors affecting the overcrowding including the internal positive factors, internal negative factors, and external factors.

CONCLUSIONS

Despite the efforts made to eliminate overcrowding, and reduce waiting times and increase access to the services for patients, the problem of overcrowding still has remained unresolved. In addition, the use of some strategies such as clarifying the working processes of the clinic for staff and patients and the relationships between the clinic and other wards especially emergency department, as well as using a simple triage system on the patients' arrival at the clinic are recommended.

Ability of nurse clinicians to predict unplanned returns to hospital within thirty days of discharge

OBJECTIVE

To evaluate the ability of nurse clinician discharge flow coordinators (DFCs) to identify medical patients at risk of unplanned return to the hospital emergency department within 30 days of discharge and whether a higher predicted risk of return was correlated with preventability.

DESIGN

Prospective cohort study of patients discharged from medical wards at 2 hospital sites of the McGill University Health Center between September 1, 2011, and January 1, 2012.

METHODS

Univariate and multivariate analyses of factors including the ability of DFCs to predict 30-day unplanned returns to the hospital. Assessment of the preventability of returns to the hospital was performed by chart review using prespecified criteria. The ability of DFCs to predict returns was compared to the LACE criteria (length of stay, acute admission through the emergency department, comorbidities, and emergency department visits in the past 6 months).

RESULTS

We found that 25.0% (95% CI, 21.3-28.5) of our patients returned to the emergency department within 30 days. The DFC predictions were found to be significant in both univariate and multivariate analysis. Patient age, sex, and length of stay were not significant predictors in univariate or multivariate analysis; 13.9% (95% CI, 8.2-19.6) of returns were preventable and a further 25.8% (95% CI, 18.1-33.5) were potentially preventable with added services in the community. There was a trend toward more preventable or potentially preventable returns with higher predicted probability of return. In contrast the LACE criteria did not have a good predictive capacity in our patient population.

CONCLUSION

In a large urban center, experienced nurse clinician DFCs were able to predict 30-day emergency department returns with reasonable accuracy. They were also able to identify the returns to the hospital that were most likely to be preventable. Our data suggests that DFCs can be used to target patients identified as having an increased probability of return with interventions that may be able to reduce the burden of return to hospital.