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At least 19 recordsLinked to original sources
PubMed2003

Cyclic fluctuations in hospital bed occupancy in Roma (Italy): supply or demand driven?

The objective of this study was to assess hospital bed occupancy both by planned and unplanned cases, and to assess how supply and demand affect bed occupancy. Data was obtained from the Lazio Hospital Information System (HIS) dataset on all hospital discharges from July 1998 to June 2001. Using Diagnosis Related Groups (DRG) as the reason for hospital stay, admissions were classified into four categories: 'planned stay', 'presumed planned stay', 'presumed unplanned stay', and 'unplanned stay'. Time series analysis of daily bed occupancy by category of stay was performed. Generalized Additive Models (GAMs) were used to asses the effect of weekdays and holidays on bed occupancy. Fluctuations in daily occupancy were observed in all categories of stay-in general, bed occupancy decreased over weekends, on national holidays, and during the major holiday season of August. In comparison with unplanned stays, the largest fluctuations were observed for planned stays while presumed planned and unplanned stays showed lesser fluctuations. It is possible to distinguish planned and unplanned hospital stays by using DRG grouping. Cyclic rigidities in the supply of services rather than the availability of beds or demand for beds seem to dictate hospital use in Roma so that restrictions in services hamper any reallocation of beds for 'planned stay' when demand for 'unplanned stay' beds declines.

Bed Occupancy↗
PubMed2006

Total and excess bed occupancy by age, specialty and insulin use for nearly one million diabetes patients discharged from all English Acute Hospitals.

To investigate total diabetes bed occupancy and prolonged inpatient length of stay (LOS) in all English Acute Hospitals, we analysed hospital episode statistics (HES) discharge data for all English Acute Hospitals over 4 years for ICD10 discharge codes of E10 ('insulin-dependent diabetes') or E11 ('non-insulin dependent diabetes') by age-band (18-60, 61-75 and >75 years) and specialties. We matched these data to control discharges without these codes. There were 943,613 diabetes discharges (6,508,668 bed days) and 10,724,414 matched controls. Mean diabetes LOS increased with age for each specialty and both E10 and E11 codes, but excess diabetes LOS decreased with age. Excess diabetes LOS was <1.0 days in most groups and highest (1.2 days) in insulin-dependent surgical patients under 60 years old, where 19.7% of bed days were excess. A similar pattern was seen for 76,570 diabetes inpatients with key cardiac or surgical conditions. Excess bed occupancy due to prolonged mean LOS accounted for 325,033 bed days under general medical and surgical codes. There were 25,525 discharges with diabetic ketoacidosis (126,495 bed days) in these 4 years. Excess diabetes LOS is concentrated in younger age groups. Excess bed occupancy due to prolonged LOS in medical and surgical inpatients is three times greater than bed occupancy due to diabetic ketoacidosis. Strategies to reduce excess diabetes bed occupancy should emphasize reducing inpatient LOS in younger inpatients.

Adolescent↗
PubMed2001

Bed occupancy. Don't take it lying down.

There is no standard optimum occupancy rate for hospitals. Bed occupancy rates must be considered in relation to the number of beds in a hospital. In general, a large hospital operating the same occupancy rate as a small one will have to turn away fewer patients. The bed needs of each trust need to be analysed in greater detail than has previously been the case.

Bed Occupancy↗
PubMed2006

Trends in bed occupancy for inpatients with diabetes before and after the introduction of a diabetes inpatient specialist nurse service.

AIMS: To compare diabetes bed occupancy and inpatient length of stay, before and after the introduction of a dedicated diabetes inpatient specialist nurse (DISN) service in a large UK Hospital. METHODS: We analysed bed occupancy data for medical or surgical inpatients for 6 years (1998-2004 inclusive), with a DISN service in the final 2 years. Excess bed days per diabetes patient were derived from age band, specialty, and seasonally matched data for all inpatients without diabetes. We also analysed the number of inpatients with known diabetes who did not have diabetes recorded as a discharge diagnosis. RESULTS: There were 14,722 patients with diabetes (9.7% of all inpatients) who accounted for 101 564 occupied bed days (12.4% of total). Of these, 18 161 days (17.8%) were excess compared with matched patients without diabetes, and were concentrated in those < 75 years old. Mean excess bed days per diabetes inpatient under 60 years of age was estimated to be 1.9 days before the DISN appointment, and this was reduced to 1.2 bed days after the appointment (P = 0.03). This is equivalent to 700 bed days saved per year per 1000 inpatients with diabetes under 60 years old, with an identical saving for those aged 61-75 years (P = 0.008), a saving of 1330 diabetes bed days per year by one DISN. Excess diabetes bed occupancy was 167 excess bed days per year per 1000 patients with diabetes in the local population after the DISN appointment. One quarter of the known Type 2 diabetes population were admitted annually, but one quarter of patients had no diagnostic code for diabetes. CONCLUSIONS: Diabetes excess bed occupancy was concentrated in patients < 75 years old, and this was reduced notably following the introduction of a DISN service.

Adult↗
PubMedDate not supplied

A decision support system for bed-occupancy management and planning hospitals.

The planning of services within a hospital is a complex task which relies on the availability of accurate data. Such data on patterns of bed occupancy enable us to develop tools which assess performance measures based on activity within a hospital and its beds, and hence they improve the efficiency of bed management and they facilitate the more effective use of resources. We report the development of a bed-occupancy-modelling package that uses a mathematical model to separate the pattern of bed occupancy in hospitals into acute, rehabilitative, and long-stay components. The bed-occupancy management and planning system analyses data downloaded from the patient administrative system. A query-by-example function is then used to separate the data into meaningful subgroups, such as age groups, or the various specialties. The underlying model is a mixed exponential where the number of terms in the mixture corresponds to the number of stages in the hospital spell, typically, acute, rehabilitative, and long stay. The software has been used to analyse data from a number of different specialties and services. A 'what-if' capability [correction of capacility] allows the planner to assess the effect of changes prior to their introduction.

Bed Occupancy↗
PubMed1998

An audit of acute psychiatric admission bed occupancy in Northern Ireland.

The Northern Ireland Section (Irish Division) of the Royal College of Psychiatrists were requested to investigate apparent increasing pressures on acute psychiatric beds. Information on bed occupancy and associated service activity was collected by clinicians on site in every psychiatric unit in Northern Ireland over the past eight years. Three separate years (1987, 1991 and 1995) were studied. Bed occupancy rose across these three years. There was an associated reduction in the number of acute psychiatric beds, reduction in adult continuing care beds, increased recorded referrals to psychiatric units and evidence of considerable numbers of new long-stay patients and difficulties with community placements. Acute bed occupancy in Northern Ireland is high, frequently over 100% and rising. Occupancy rose between each of the years studied. The problem is not confined to urban areas and several associated service factors may be contributing. Without change, acute bed provision will inevitably fail to match mental health needs.

Adult↗
PubMed1994

Low bed occupancy rates in Uganda's peripheral health units: is it a policy problem?

A survey of 71 government and non-governmental health units ranging from hospitals to dispensaries was made to determine the utilisation of available beds in these units. In addition, an assessment of consumer practices on the use of beds was made using qualitative methods. The major finding of the study was the very low bed occupancy rates at the primary health care (PHC) level, ranging from 0.2-42%; compared to very high bed occupancy rates at the hospitals, ranging from 54-153%. Most patients referred themselves directly to the hospitals, travelling very long distances of up to 80 km. The reason for low bed occupancy rates at the primary health care level are multifactorial, including lack of medically trained personnel at this level, sporadic supply of drugs and other medical supplies and a complete breakdown in the transfer and referral system. In order to implement the policy of PHC which government has adopted, there is need to redirect resources to the PHC level and revive the referral system.

Bed Occupancy↗
PubMedDate not supplied

Bed occupancy, turnover intervals and MRSA rates in English hospitals.

This article (a follow on from an article concentrating on Northern Ireland) examines the relationship between percentage bed occupancy (PO), turnover interval (TI) and methicillin-resistant Staphylococcus aureus (MRSA) rates in the acute beds of specialist English hospital trusts and describes the TI and levels of bed occupancy. The data were collected from publicly available data: MRSA rates of blood-borne infection per 1000 bed days from the Department of Health; average length of stay from Hospital Episode Statistics; and percentage occupancy from the Department of Health Hospital Activity statistics were used. Pearson's Correlation coefficients were used as basis for inferential analysis. The mean TI for all trusts was as 0.94 days, median 0.95 days. Twenty percent of trusts had TIs, on average, of less than 0.58 days (13.9 hours) and 10% had a TI less than 0.32 days (7.6 hours). The mean PO was 84.98% and the median was 84.76%. Seventy percent of the trusts exceeded the recommended 82% bed occupancy. The inference from this study is that there is a relationship between TI and PO and rate of MRSA infection in specialist English hospitals and that PO rates are at a level which may interfere with good infection control procedures.

Acute Disease↗
PubMed2006

Influenza outbreaks and hospital bed occupancy in Rome (Italy): current management does not accommodate for seasonal variations in demand.

OBJECTIVE: Our goal was to assess how different hospital wards react to influenza epidemics, and whether related specialties cooperate in coping with winter bed crises. STUDY DESIGN: The Lazio Hospital Information System (HIS) dataset from July 1998 to June 2001 was used for the study. The HIS collects data on all hospital discharges. We considered diagnosis-related groups (DRG) as the reason for hospital stay and used DRG to classify admissions as influenza related or influenza unrelated. Time series analysis of daily bed occupancy in different specialty areas by influenza-related and influenza-unrelated cases was performed. Generalized additive models (GAMs) were used to take the effect of short-term and seasonal bed occupancy into account on influenza-related occupancy. RESULTS: Influenza-related bed occupancy ranges from 770 patients/day during the influenza season to 525 patients/day during the rest of the year. Daily occupancy by influenza-related cases represents 2.8% of total hospital occupancy and 7% of general medicine occupancy during the influenza season. When comparing the influenza season with the rest of the year, general medicine occupancy by influenza-related cases increases by 51% versus the 25-32% increase in other specialty wards. Little change in daily occupancy by influenza-unrelated cases was observed in all specialties when comparing the influenza season with the rest of the year. CONCLUSIONS: Hospital specialty wards react poorly and single handedly to a minor and predictable burden. Any winter bed crisis in the Lazio region is probably the result of defective management of available beds more than excess in demand.

Disease Outbreaks↗
PubMed1990

[Bed occupancy in emergency hospitalization of patients].

When planning the average number of bed occupancy days per year at a hospital providing emergency hospitalization one should take into account the demurrage of reserve beds which are needed for urgent hospitalization of patients. The influence of emergency demurrage of reserve beds on occupancy rate is not determined by the absolute number of these beds and their share in the structure of hospital bed fund. The number of reserve beds depends on the number of emergency patients hospitalized and the average length of hospital stay.

Bed Occupancy↗
PubMed2003

Bed occupancy and overcrowding as determinant factors in the incidence of MRSA infections within general ward settings.

Methicillin-resistant Staphylococcus aureus (MRSA) incidence and bed occupancy rates at St Luke's Hospital, Malta, were assessed over a 24-month period. A significant positive correlation was established (r=0.463; P<0.05) between new cases of MRSA infections and overall levels of bed occupancy. This would imply that overcrowding may be a relevant factor in MRSA spread within hospitals, even in non-intensive care settings.

Bed Occupancy↗
PubMed2005

The use of cusum analysis in the early detection and management of hospital bed occupancy crises.

OBJECTIVE: To assess the value of cusum analysis in hospital bed management. DESIGN: Comparative analysis of medical patient flows, bed occupancy, and emergency department admission rates and access block over 2 years. SETTING: Internal Medicine Services and Emergency Department in a teaching hospital. INTERVENTIONS: Improvements in bed use and changes in the level of available beds. MAIN OUTCOME MEASURES: Average length of stay; percentage occupancy of available beds; number of patients waiting more than 8 hours for admission (access block); number of medical patients occupying beds in non-medical wards; and number of elective surgical admissions. RESULTS: Cusum analysis provided a simple means of revealing important trends in patient flows that were not obvious in conventional time-series data. This prompted improvements in bed use that resulted in a decrease of 9500 occupied bed-days over a year. Unfortunately and unexpectedly, after some initial improvement, the levels of access block, medical ward congestion and elective surgical admissions all then deteriorated significantly. This was probably caused by excessive bed closures in response to the initial improvement in bed use. CONCLUSION: Cusum analysis is a useful technique for the early detection of significant changes in patient flows and bed use, and in determining the appropriate number of beds required for a given rate of patient flow.

Bed Occupancy↗
PubMed1993

The periodicities in and biometeorological relationships with bed occupancy of an acute psychiatric ward in Antwerp, Belgium.

Recently, some investigators have established a seasonal pattern in normal human psychology, physiology and behaviour, and in the incidence of psychiatric psychopathology. In an attempt to elucidate the chronopsy and meteotropism in the latter, we have examined the chronograms of, and the biometeorological relationships to bed occupancy of the psychiatric ward of the Antwerp University Hospital during three consecutive calendar years (1987-1989). Weather data for the vicinity were provided by a local meteorological station and comprise mean atmospheric pressure, air temperature, relative humidity, wind speed and minutes of sunlight and precipitation/day. The number of psychiatric beds occupied during the study period exhibited a significant seasonal variation. Peaks in bed occupancy were observed in March and November, with lows in August. An important part of the variability in the number of beds occupied could be explained by the composite effects of weather variables of the preceding weeks. Our results suggest that short-term fluctuations in atmospheric activity may dictate some of the periodicities in psychiatric psychopathology.

Bed Occupancy↗
PubMed1997

AIDS and hospital bed occupancy: an overview.

In several countries of sub-Saharan Africa more than 10% of the adult population are infected with HIV, while in large towns such as Kampala, Lusaka, Blantyre, Kigali and Harare this proportion exceeds 25%. One of the most obvious consequences is the increased occupancy of hospital beds by patients with HIV infection, perhaps to the exclusion of patients with other ailments. This paper gives an overview of several hospital occupancy studies.

Acquired Immunodeficiency Syndrome↗
PubMed1987

A unique urban state hospital: partial-hospital programs reduce full-time bed occupancy.

This paper describes a complex treatment system which serves a large urban population with a very small number of inpatient beds. From its very outset, the South Beach Psychiatric Center, a state facility in New York City, was determined to treat its patients with minimal use of inpatient bed occupancy. As a result, it started with a large outpatient program, in which day hospitals played a critical role. The aim was to treat patients on their home turf and, thus, to limit inpatient bed use to the period when the patient was out of control and a danger to self or others. Partial-hospitalization (PH) literature is replete with articles about utilization and underutilization [see Kennedy, L. L., A Bibliography on Partial Hospitalization, American Association for Partial Hospitalization (AAPH), Washington, D.C., 1986, pp. 1-70; Proceedings of the Annual Conference on Partial Hospitalization, Washington, D.C., 1976-1986; Wilner, M. (ed.), AAPH Newsletter, AAPH, Washington, D.C.] There are many discussions about the appropriate use of the PH program, how to get the program reimbursed, and how to get referrals to the program. Often, a pragmatic, although inefficient and sometimes very wrong, decision is made that, in the absence of a proper choice of treatment modalities, one judges the best referral to be the one that is available. South Beach therefore presents a useful study because it has a very wide range of treatment modalities: inpatient acute, intermediate, and chronic beds, admitting services, clinics, day hospitals (adolescent, adult, geriatric, acute, chronic), varieties of residences including quarter-way housing and supervised and unsupervised apartments, etc.(ABSTRACT TRUNCATED AT 250 WORDS)

Bed Occupancy↗
PubMedDate not supplied

Bed occupancy, turnover interval and MRSA rates in Northern Ireland.

The data describe bed turnover intervals (TI), bed percentage occupancy (PO) and methicillin-resistant Staphylococcus aureus (MRSA) rates per 1000 bed days of patient episodes. It was collected from annual hospital statistics in Northern Ireland (NI) and from the Communicable Diseases Surveillance Centre (CDSC) NI. The descriptive data show 6 of the general 11 surgical Trusts, out of a total of 12 Trusts examined, had PO greater than 85%; and all 11 medical facilities in these Trusts had occupancy rates greater than 85%. A significant correlation was established between turnover interval and MRSA per 1000 bed days of patient episodes in acute services beds. The correlation of PO with MRSA rates was 0.49 (ns). The conclusions drawn from the study are that in many Trusts the rates of bed occupancy for general surgery and general medicine is in excess of national guidelines and rapid turnover of patients is related to rates of MRSA infection. The implications for nurses and managers are discussed.

Bed Occupancy↗