Capacity Pooling in Hospitals: The Hidden Consequences of Off-Service Placement

Abstract

Hospital managers struggle with the day-to-day variability in patient admissions to different clinical services, each of which typically has a fixed allocation of hospital beds. In response, many hospitals engage in capacity pooling by assigning patients from a service whose designated beds are fully occupied to an available bed in a unit designated for a different service. This “off-service placement” occurs frequently, yet its impact on patient and operational measures has not been rigorously quantified. This is, in part, because of the challenge of properly accounting for the endogenous selection of off-service patients. We use an instrumental variable approach to quantify the causal effects of off-service placement of hospitalized medical/surgical patients, having accounted for the endogeneity issues. Using data from a large academic medical center with 19.6% of medical/surgical patients placed off service on average, we find that off-service placement is associated with a 22.8% increase in remaining hospital length of stay (LOS) and a 13.1% increase in the likelihood of hospital readmission within 30 days. We find no significant effect on in-hospital mortality or clinical trigger (rapid response) activation. We identify longer distances to the service’s home unit as a key mechanism that drives the effect on LOS. In contrast, a mismatch in nursing specialization does not seem to explain this effect. By quantifying the effects of off-service placement on patient and operational outcomes, we enable clinicians and hospital managers to make better-informed short-term decisions about off-service placement and longer-term decisions about capacity allocation. This paper was accepted by Stefan Scholtes, healthcare management.

The Frontline Nurse’s Experience of Nursing Outlier Patients

“…While the delayed nursing care and missed care remain prominent in acute care hospitals internationally [ 60 , 61 ], nursing outlier patients can be a potential stressor worsening the current situation. Increases in remaining hospital length of stay (22.8%) and in hospital readmission within 30 days (13.1%) for outlier patients have been reported when comparing to specialty-appropriate patients [ 62 ]. The compromised patient’s outcome indicates a need to investigate the association between malpractice liability and appropriate level of care in nursing the outlier patients.…”

“…However, the occurrence of outlier patients and the studied phenomenon persists. Alternatively, measures to eliminate the occurrence of outlier patients, such as “dedicated configuration” that transfers patients to another hospital when the waiting time threshold for the matching hospital beds has been reached, have been implemented [ 62 ]. The feasibility of solely employing “dedicated configuration” is questionable given the global hospital beds shortage and the high hospital bed occupancy rate [ 16 , 17 , 18 , 19 ].…”

“…The history of specialization in medicine and of the hospitals designed to deliver the best patient care within those specializations, would suggest not. While literature claimed that outlier patients will be admitted to the overflow ward staffed with “multi-skilled nursing teams capable of caring for all specialties” [ 62 ], the inquiry concerns how to assure to the public that nurses in practice possess continuing competence, in other words, the ability to practice safe and quality nursing in the overflow ward [ 67 ]. Barriers for establishing the continuing competence, such as the lack of personal insight to reflect on or assess the nursing performance, the failure to seek continuing professional development opportunities and to recognize unsafe practice, ought to be considered carefully in the restructuring of hospital wards or bed management planning [ 68 ].…”

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

“…For all analyses, we included hospital fixed effects to account for hospital-specific factors. Covariates for multivariable model adjustment were developed based on existing literature1–3 10 and included age, gender, race, ethnicity, insurance type, Elixhauser comorbidity scores, any time spent in an ICU, the Centers for Medicare and Medicaid Services (CMS) four-level severity risk adjustment (based on admitting diagnosis, demographics and comorbidities),14 15 admission diagnosis category (from International Classification of Disease-9 [ICD-9] and ICD-10 codes), daily mean number of medicine service patients by hospital during the hospitalisation, hospital admission source, season and if a hospital admission occurred during a weekend (table 1).…”

“…Bedspaced ward patients, similar to ICU patients, may be cared for by the physicians from the home specialty ward and the nurses, therapists and case managers from the bedspaced ward. This asynchronous care team may experience lapses in communication from unfamiliarity and physical separation and the bedspaced ward staff may lack the expertise and experience required to best care for the bedspaced patients, leading to adverse patient-centred outcomes 8–10…”