Have Nursing Home Compare quality measure scores changed over time in response to competition? (65/189)

BACKGROUND: Currently, the Centers for Medicare and Medicaid Services report on 15 Quality Measures (QMs) on the Nursing Home Compare (NHC) website. It is assumed that nursing homes are able to make improvements on these QMs, and in doing so they will attract more residents. In this investigation, we examine changes in QM scores, and whether competition and/or excess demand have influenced these change scores over a period of 1 year. METHODS: Data come from NHC and the On-line Survey Certification And Recording (OSCAR) system. QM change scores are calculated using values from January 2003 to January 2004. A series of regression analyses are used to examine the association of competition and excess demand on QM scores. RESULTS: Eight QMs show an average decrease in scores (ie, better quality) and six QMs show an average increase in scores (ie, worse quality). However, for 13 of the 14 QMs these average changes averaged less than 1%. The regression analyses show an association between higher competition and improving QM scores and an association between lower occupancy and improving QM scores. CONCLUSION: As would be predicted based on the market-driven mechanism underlying quality improvements using report cards, we show that it is in the most competitive markets and those with the lowest average occupancy rates that improvements in the QM scores are more likely.  (+info)

Carrot and sticks? The Community Care Act (2003) and the effect of financial incentives on delays in discharge from hospitals in England. (66/189)

BACKGROUND: The belief that many delays in discharge from hospital were caused by social service departments (SSDs) led to the Community Care Act 2003 giving NHS hospitals in England the power to charge SSDs. METHODS: We surveyed 150 SSDs in England about the implementation of the Act and used routine data to analyse trends in the number of delayed discharge patients; the number and cause of delayed discharge bed days by sector; and the proportion of inpatient bed days that consisted of delayed discharges. FINDINGS: Most hospitals opted not to charge SSDs for delays. Almost two thirds of SSDs (62%) made no payment of any kind to an acute hospital in 2004/05 and 2005/06, preferring to work collaboratively. The fall in number of 'delayed discharge patients' is a long term trend which precedes the implementation of the 2003 Act. Delayed discharge bed days accounted for 1.58% of all inpatient bed days in 2004/05. Contrary to popular opinion, the NHS accounted for two thirds (67%) of bed day delays, lack of suitable alternative NHS provision and services is a key factor. Patients are being discharged in greater numbers and earlier in their post-acute recovery phase. There are however questions about the quality and safety of early discharge. For example, emergency hospital readmissions rates have risen from 5.4% in 2002/03 to 6.7% in 2005/06, and patient dissatisfaction is significant. CONCLUSION: Although delays in discharge from acute hospital beds have fallen, the quality of discharge and the capacity of Primary Care Trusts (PCTs) and SSDs to ensure appropriate and adequate post-discharge care is not as it should be. Contrary to popular perception, social services delays are of less significance than delays attributable to the NHS. There is no evidence to support government policy of charging SSDs for delay. Other factors, including NHS provision, are important, and a comprehensive overview of health and social care is vital.  (+info)

Influence of prolonged hospitalization on overall bed occupancy: a five-year single-centre study. (67/189)

BACKGROUND: Effective bed use is crucial to an efficient NHS. Current targets suggest a decrease in mean occupancy as the most appropriate method of improving overall efficiency. The elderly and those suffering from complex medical problems are thought to account for a high proportion of overall bed occupancy. AIM: To assess the effect of prolonged hospital stay (>100 days) on overall bed occupancy in a modern teaching hospital. DESIGN: Retrospective analysis. METHODS: Analysis of all admission episodes (n = 117,178) over a five-year period in a large teaching hospital in a single UK region, serving a population of approximately 200,000. A logistic regression multi-factorial model was used to assess the effect of demographic and diagnostic variables on duration of stay. RESULTS: A prolonged stay (>100 days) was seen in 648 admission episodes (0.6%). These accounted for 11% of the overall bed occupancy over the 5-year period. Excluding all prolonged admission episodes from our analysis made no difference to the overall median length of stay. DISCUSSION: Prolonged hospitalizations have a significant impact on bed occupancy. Targeting these very long (>100 days) hospital stays may better improve overall efficiency, compared to targeting mean or median length of stay.  (+info)

Capacity management of nursing staff as a vehicle for organizational improvement. (68/189)

BACKGROUND: Capacity management systems create insight into required resources like staff and equipment. For inpatient hospital care, capacity management requires information on beds and nursing staff capacity, on a daily as well as annual basis. This paper presents a comprehensive capacity model that gives insight into required nursing staff capacity and opportunities to improve capacity utilization on a ward level. METHODS: A capacity model was developed to calculate required nursing staff capacity. The model used historical bed utilization, nurse-patient ratios, and parameters concerning contract hours to calculate beds and nursing staff needed per shift and the number of nurses needed on an annual basis in a ward. The model was applied to three different capacity management problems on three separate groups of hospital wards. The problems entailed operational, tactical, and strategic management issues: optimizing working processes on pediatric wards, predicting the consequences of reducing length of stay on nursing staff required on a cardiology ward, and calculating the nursing staff consequences of merging two internal medicine wards. RESULTS: It was possible to build a model based on easily available data that calculate the nursing staff capacity needed daily and annually and that accommodate organizational improvements. Organizational improvement processes were initiated in three different groups of wards. For two pediatric wards, the most important improvements were found to be improving working processes so that the agreed nurse-patient ratios could be attained. In the second case, for a cardiology ward, what-if analyses with the model showed that workload could be substantially lowered by reducing length of stay. The third case demonstrated the possible savings in capacity that could be achieved by merging two small internal medicine wards. CONCLUSION: A comprehensive capacity model was developed and successfully applied to support capacity decisions on operational, tactical, and strategic levels. It appeared to be a useful tool for supporting discussions between wards and hospital management by giving objective and quantitative insight into staff and bed requirements. Moreover, the model was applied to initiate organizational improvements, which resulted in more efficient capacity utilization.  (+info)

Bed utilization indices at a tertiary care hospital in Goa: an eight year trend analysis. (69/189)

A retrospective data analysis of records from medical records department of Goa Medical College Hospital was done to analyse the trends of various bed utilisation indices from 1999 - 2006. Average length of stay, bed occupancy rate, turnover interval and bed turnover ratio were the indices calculated. During the eight year period from 1999 to 2006, the average length of stay for the entire hospital registered a small decline from 6.23 to 5.51 days, the overall bed occupancy rate increased from 72.13% to 83.12% and the bed turnover interval declined from 2.41 days to 1.12 days. The Orthopaedics ward had the highest increase in bed occupancy and also fastest decline in turnover interval in 2006. Bed utilization indices are an objective measure of the efficiency of the hospital management system.  (+info)

Measuring and explaining mortality in Dutch hospitals; the hospital standardized mortality rate between 2003 and 2005. (70/189)

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Small-scale testing of RFID in a hospital setting: RFID as bed trigger. (71/189)

RFID technology shows significant potential for transforming healthcare, yet few studies assess this potential. Our study measured the effectiveness of using RFID as a bed trigger: a tool to accelerate identification of empty beds. We made a small alteration in the discharge process to associate RFID tags with patients and created an RFID-based system that automatically determined discharge time. For each patient, we evaluated the difference in the discharge times recorded manually by the current process and the RFID-based system. The study was conducted on 86 patients over 2 months in 2 physically separate multi-specialty units. Compared to the preexisting process, the RFID-based system identified empty beds >20 minutes earlier 67% of the time with an average of 25 minutes and median of 9 minutes earlier. Hospital leadership defined an improvement of approximately 10 minutes as significant. With minimal investment, our small-scale study lead hospital leadership to begin planning RFID deployment.  (+info)

A tool for improving patient discharge process and hospital communication practices: the "Patient Tracker". (72/189)

Hospital bed demands sometimes exceed capacity, leading to delays in patient admissions, transfers and cancellations of surgical procedures. Effective strategies must be in place for an efficient use of existing beds. Establishing such strategies at academic hospitals poses serious challenges. We developed and implemented a web-based software application called "Patient Tracker" to manage the discharge process, minimize delays in admission and reduce surgical procedure cancellations. We also tested the effectiveness of the software on the work flow by comparing outcomes between the pre-implementation control group (2002-2003) and the post-implementation experimental group (2003-2006). Following the implementation of the software, the number of cancelled surgical procedures decreased (120 vs. 12, p<0.01). During the same period, the average number of inpatient admissions increased (5725 vs. 6120), and the median emergency department LOS decreased (247 vs. 232, p<0.01).  (+info)