Occupancy rate and adverse patient outcomes in general hospitals in Thailand: A predictive study.

Patient outcomes are important indicators of the quality of care. Occupancy rate is one factor that significantly affects adverse patient outcomes. The aim of the present study was to determine factors associated with adverse patient outcomes in Thailand. A retrospective study was conducted with 146 inpatient units from 16 general hospitals. Hospital characteristics and adverse patient outcomes were recorded, and data were analyzed by using frequency, percentage, and binomial logistic regression. The results revealed that the average number of beds per hospital was 430.5 (standard deviation [SD] = 108.6), the average number of beds per unit was 27.9 (SD = 8.9), and the average occupancy rate was 81.1% (SD = 20.6, range = 28.8-133.1%). Data were adjusted for hospital size, unit type, and number of beds in each unit; a 1% increase in occupancy rate increased the likelihood of pressure ulcers by 4.3% (P = 0.001), of hospital-acquired pneumonia by 2.4% (P = 0.032), and of hospital-acquired urinary tract infections by 2.1% (P = 0.033). The findings suggest that a higher level of occupancy rates predicted a greater likelihood of adverse patient outcomes.

Patient and family description of receiving care in acuity adaptable care model.

AIM: To explore patient and family perspectives of hospital care in an acuity adaptable care model implemented in an urban, public safety-net hospital. BACKGROUND: Specialty care units result in reactionary bed management. Changes in acuity generate costly, disruptive, intra-hospital patient transfers, which negatively affect clinical outcomes while increasing nurse workload. The acuity adaptable care model is a universal bed model structured to support patients in one room while providing staff, equipment and other resources across varying levels of acuity. METHOD: Qualitative descriptive methods were used to analyse the narratives of a purposive sample of patients and family members about receiving care in an acuity adaptable care delivery model. RESULTS: Three content areas emerged from the narratives and were categorized as feeling safe, perceiving continuity of care and valuing family, which culminated in a sense of comfort and healing while in the hospital. CONCLUSION: By bringing care services to the patient instead of taking the patient to the services, the acuity adaptable care model facilitated a perception of a healing environment for patients and family members. IMPLICATIONS FOR NURSING MANAGEMENT: The acuity adaptable care model should be considered when hospital facilities are undergoing major renovation or replacement.

Tracking the take - Using patient flow data to improve AMU performance and safety.

AIMS: To create a system to co-ordinate the medical take, bed management and track patient flow. To use the system to continuously audit against Society for Acute Medicine Quality Indicators. To use the data to model patient flow and optimise working patterns to improve waiting times. METHOD: An online whiteboard and underlying database system were designed, tested and implemented. Data from this system were used to audit against SAM Quality Indicators and then analysed to optimise both trainee and consultant working patterns. RESULTS: The online whiteboard proved effective and popular as a working tool. Data collection improved using the electronic system. Optimising junior doctor working patterns to match demand led to a reduction of average waiting time to see a doctor from 190 minutes to 71 minutes (p < 0.0001), and a reduction in the proportion of patients waiting over 4 hours from 40% to 10% (p > 0.0001). Optimising consultant working patterns did not produced significant changes in waiting times. CONCLUSIONS: The online whiteboard improved day-to-day working and data collection, when compared to the previous paper-based system. Better data facilitated analysis of working patterns leading to a significant improvement in patient waiting times.

A multifaceted feedback strategy alone does not improve the adherence to organizational guideline-based standards: a cluster randomized trial in intensive care.

BACKGROUND: Organizational data such as bed occupancy rate and nurse-to-patient ratio are related to clinical outcomes and to the efficient use of intensive care unit (ICU) resources. Standards for these performance indicators are provided in guidelines. We studied the effects of a multifaceted feedback strategy to improve the adherence to these standards. METHODS: In a cluster randomized controlled study design the intervention ICUs received extensive monthly feedback reports, they received outreach visits and initiated a quality improvement team. The control ICUs received limited quarterly feedback reports only. We collected primary data prospectively within the setting of a Dutch national ICU registry over a 14-month study period. The target indicators were bed occupancy rate (aiming at 80 % or below) and nurse-to-patient ratio (aiming at 0.5 or higher). Data were collected per 8-h nursing shift. Logistic regression analysis was performed. For both study end points, the odds ratios (OR) for improvements at follow-up versus at baseline were calculated separately for control and intervention ICUs. RESULTS: We analyzed data on 67,237 nursing shifts. The bed occupancy rate did not improve in the intervention group compared to baseline (adjusted OR 0.88; 95 % confidence interval (CI), 0.62-1.27) or compared to control group (OR 0.67; 95 % CI 0.39-1.15). The nurse-to-patient ratio did not improve (OR 0.72; 95 % CI 0.41-1.26 compared to baseline and OR 0.65; 95 % CI 0.35-1.19 compared to control group). CONCLUSIONS: A multifaceted feedback intervention did not improve the adherence to guideline-based standards on the organizational issues bed occupancy rate and nurse-to-patient ratio in the ICU. The reasons may be a limited confidence in data quality, the lack of practical tools for improvement, and the relatively short follow-up. ISRCTN: ISRCTN50542146.

The number of patients simultaneously present at the emergency department as an indicator of unsafe waiting times: a receiver operated curve-based evaluation.

BACKGROUND: Emergency department (ED) crowding and prolonged waiting times have been associated with adverse consequences towards quality and patient safety. OBJECTIVE: This study investigates whether the number of patients simultaneously present at the ED might be an indicator of unsafe waiting and at what threshold hospital-wide measures to improve patient outflow could be justified. METHODS: Data were retrospectively collected during a 1-year period; all ED patients aged ≥16 years, and triaged as ESI-1 or ESI-2 were eligible for inclusion. The number of patients simultaneously present was used as occupancy rate. Waiting time was considered unsafe if it was longer than 10 min for ESI-1 patients, or longer than 30 min for ESI-2 patients. Differences in waiting time and occupancy between patients with safe and unsafe waiting times were analysed using the Mann-Whitney U test. The ability of the occupancy rate to discriminate unsafe waiting times was analysed using a receiver operating characteristic curve. RESULTS: The overall median waiting time was 5 min (IQR=4-8) for ESI-1, and 12 min (IQR=6-24) for ESI-2 patients. Unsafe waiting times occurred in 16.0% of ESI-1 patients (median waiting time=17 min, IQR=13-23), and in 18.9% of ESI-2 patients (median waiting time=48 min, IQR=37-68). The occupancy rate was a weak indicator for unsafe waiting times in ESI-1 patients (AUC=0.625, 95%CI 0.537-0.713) but a fair indicator for unsafe waiting times in ESI-2 patients (AUC=0.740, 95%CI 0.727-0.753) for which the threshold to predict unsafe waiting times with 90% sensitivity was 51 patients. CONCLUSION: The number of patients simultaneously present is a moderate indicator of unsafe waiting times. Future initiatives to improve safe waiting times should not focus solely on occupancy, and expand their focus towards other factors affecting waiting time.

Using quality improvement to optimise paediatric discharge efficiency.

BACKGROUND: Bed capacity management is a critical issue facing hospital administrators, and inefficient discharges impact patient flow throughout the hospital. National recommendations include a focus on providing care that is timely and efficient, but a lack of standardised discharge criteria at our institution contributed to unpredictable discharge timing and lengthy delays. Our objective was to increase the percentage of Hospital Medicine patients discharged within 2 h of meeting criteria from 42% to 80%. METHODS: A multidisciplinary team collaborated to develop medically appropriate discharge criteria for 11 common inpatient diagnoses. Discharge criteria were embedded into electronic medical record (EMR) order sets at admission and could be modified throughout a patient's stay. Nurses placed an EMR time-stamp to signal when patients met all discharge goals. Strategies to improve discharge timeliness emphasised completion of discharge tasks prior to meeting criteria. Interventions focused on buy-in from key team members, pharmacy process redesign, subspecialty consult timeliness and feedback to frontline staff. A P statistical process control chart assessed the impact of interventions over time. Length of stay (LOS) and readmission rates before and after implementation of process measures were compared using the Wilcoxon rank-sum test. RESULTS: The percentage of patients discharged within 2 h significantly improved from 42% to 80% within 18 months. Patients studied had a decrease in median overall LOS (from 1.56 to 1.44 days; p=0.01), without an increase in readmission rates (4.60% to 4.21%; p=0.24). The 12-month rolling average census for the study units increased from 36.4 to 42.9, representing an 18% increase in occupancy. CONCLUSIONS: Through standardising discharge goals and implementation of high-reliability interventions, we reduced LOS without increasing readmission rates.

Midnight census revisited: Reliability of patient day measurements in US hospital units.

BACKGROUND: Patient days are widely used in nurse staffing research and for nursing quality measurement. Nursing hours per patient day (NHPPD) and fall rates incorporate patient days in the denominator and are endorsed by the US National Quality Forum (NQF) as nursing sensitive consensus measures. Measurement error introduced by patient days would affect the accuracy of these nursing quality indicators. OBJECTIVES: The aim of this study was to assess the reliability of five patient day reporting methods accepted by the National Database of Nursing Quality Indicators (NDNQI). The specific aims were (1) to investigate the agreement of five patient day measurements with a defined quasi-gold standard, (2) to explore method bias by investigating the association of potential confounding variables with the differences between the routine measurements and the quasi-gold standard, and (3) to extrapolate the potential effect of bias of the patient day methods on nursing quality indicators. DESIGN: A multiple census study with a national convenience sample of hospital units in the US was conducted. SETTING: 260 out of 282 units (92%) from 54 hospitals sent bi-hourly patient census data for seven randomly selected days in September 2008. METHODS: The multiple census data comprised the quasi-gold standard and was compared with data routinely submitted to the database. Intraclass correlations were calculated for an agreement analysis. A Bayesian regression analysis was conducted to explore the impact of different data collection methods and the degree of short stay patients. RESULTS: Overall agreement between routine data and the quasi-gold standard was excellent (ICC [95% CI]: 0.967 [0.958-0.974]). A Bayesian regression analysis identified that two methods underestimated patient days and an interaction between the degrees of short stay patients and one of the data collection methods also affected patient day measurement by up to 7.6%.

Hospital bed occupancy: more than queuing for a bed.

Timely access to safe hospital care remains a major concern. Target bed-occupancy rates have been proposed as a measure of the ability of a hospital to function safely and effectively. High bed-occupancy rates have been shown to be associated with greater risks of hospital-associated infection and access block and to have a negative impact on staff health. Clinical observational data have suggested that bed occupancies above 85% could adversely affect safe, effective hospital function. Using this figure, at least initially, would be of value in the planning and operational management of public hospital beds in Australia. There is an urgent need to develop meaningful outcome measures of patient care that could replace the process measures currently in use.

Myths of ideal hospital size.

Current methods used to calculate the required size of hospitals are underestimating the true capacity needed for operational efficiency. Trends in occupied bed-days (rather than admissions and length of stay) give better estimates of future bed requirements. Hospital occupancy rates depend on volatility in demand, not efficiency. Larger bed pools and hospitals can operate at higher average occupancy. Cost efficiency should be focusing on staffing based on the patients in the beds and not on the available beds. Hospitals require supporting climatalogical forecasts to allow for seasonal and other climate-related changes in admissions, if flexible staff deployment is to become a reality.

Hospital beds: a primer for counting and comparing.

There is considerable public, political and professional debate about the need for additional hospital beds in Australia. However, there is no clarity in regard to the definition, meaning and significance of hospital bed counts. Relative to population, there has been a total decline in bed availability in Australia over the past 15 years of 14.6% (22.9% for public hospital beds). This decline is partly offset by reductions in length of stay and changes to models of care; however, the net effect is increased bed occupancy which has in turn resulted in system-wide congestion. Future bed capability needs to be better planned to meet growing demands while at the same time continuing trends for more efficient use. Future planning should be based in part on weighted bed capability matched to need.

[Aorn A.Cardarelli Hospital: the re-entry plan in the Region Campania]. / Aorn Cardarelli: applicazione del piano di rientro della Regione Campania.

In the article are shown in the interventions application for re-entry plan in the Region Campania.

Overcrowding and understaffing in modern health-care systems: key determinants in meticillin-resistant Staphylococcus aureus transmission.

Recent decades have seen the global emergence of meticillin-resistant Staphylococcus aureus (MRSA), causing substantial health and economic burdens on patients and health-care systems. This epidemic has occurred at the same time that policies promoting higher patient throughput in hospitals have led to many services operating at, or near, full capacity. A result has been limited ability to scale services according to fluctuations in patient admissions and available staff, and hospital overcrowding and understaffing. Overcrowding and understaffing lead to failure of MRSA control programmes via decreased health-care worker hand-hygiene compliance, increased movement of patients and staff between hospital wards, decreased levels of cohorting, and overburdening of screening and isolation facilities. In turn, a high MRSA incidence leads to increased inpatient length of stay and bed blocking, exacerbating overcrowding and leading to a vicious cycle characterised by further infection control failure. Future decision making should use epidemiological and economic evidence to evaluate the effect of systems changes on the incidence of MRSA infection and other adverse events.

[More rapid transfer to an additional-care facility]. / Sneller doorplaatsen naar een vervolgvoorziening.

Elderly patients admitted to a hospital ward often need additional care after discharge. Waiting for this additional care often leads to blocked beds. The administrative procedures for transferring patients to additional-care facilities require about 10 days. The main reason for this delay is the time needed by the regional indicating office (RIO) to do their assessment and to give approval. There are various possible ways to make the discharge procedure more efficient. Should the available capacity within the facilities be the only delaying factor, then this would constitute a task for the regional care office.