Determinants of Hospital Use: A Cross-Diagnostic Analysis

Goldfarb, Marsha G.; Hornbrook, Mark C.; Higgins, Craig S.

doi:10.1097/00005650-198301000-00004

Cited by 24 publications

(19 citation statements)

References 17 publications

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“…Con¯icting research results also surround teaching activity and length of stay (Goldfarb et al, 1983;Lave and Frank, 1990;Roos et al, 1986). It may reduce (Rosenthal et al, 1997), increase (Schultz et al, 1998) or have no effect (Burns and Wholey, 1991;Cannoodt and Knickman, 1984) on length of stay.…”

Section: Control Variablesmentioning

confidence: 98%

“…Hospital administration, policies and practices may also affect physician decision making and length of stay (Becker et al, 1980;Cannoodt and Knickman, 1984;Lee et al, 1988;Marrie et al, 2000). Finally, diagnostic and therapeutic spending may be positively associated with length of stay (Goldfarb et al, 1983). Although more funding to these spending areas may reduce length of stay, within a ®xed budget, increased funding to one area decreases funding to another.…”

Section: Resource Allocation and Ef®ciencymentioning

confidence: 99%

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A province-wide study of the association between hospital resource allocation and length of stay

Anderson

Pink

McKillop

et al. 2003

Health Serv Manage Res

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The relationship between hospital resource allocation and clinical efficiency is poorly understood. Within the single-payer healthcare system in Ontario, Canada, the association between hospital spending patterns and length of stay was studied using data from 1117090 patient discharges in 1997/8 at 162 of 171 acute care hospitals. A weighted regression model was created using an overall hospital length of stay index (actual length of stay divided by predicted length of stay) as the dependent variable. Control variables included: hospital size, teaching activity, occupancy rate, rural location and geographic region. Four independent spending variables were defined as a percentage of total hospital spending: nursing, ambulatory care, administration and support, and diagnostics and therapeutics. The reduced regression model had an r-squared of 0.45. Across all spending variables, hospitals spending relatively too little or too much had significantly longer length of stay. Hospitals' overall pattern of resource allocation was also significantly associated with length of stay. Thus, measurable clinical effects can be seen with resource allocation decisions made by hospital management, supporting the need for rigorous decision-making processes. Future research should focus on exploring the nature of this relationship and the potential interdependencies among hospital services that cause this effect.

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Section: Control Variablesmentioning

confidence: 98%

Section: Resource Allocation and Ef®ciencymentioning

confidence: 99%

A province-wide study of the association between hospital resource allocation and length of stay

Anderson

Pink

McKillop

et al. 2003

Health Serv Manage Res

View full text Add to dashboard Cite

show abstract

“…Several studies (Reynolds, Chitnis, & Roland, 1991;Grol, Whitfield, De Maeseneer, & Mokkink, 1990;Armstrong, Fry, & Armstrong, 1991) show the referral behavior of doctors is driven primarily by their clinical skills, willingness to take risks, and pressure from patients. Additional research (McMahon & Newbold, 198 6;Goldfarb, Hornbrook, & Higgins, 1983) indicates that it is physician specific characteristics that account for variation in referral rates and each PCM will have a different approach to initiating a referral based upon their experience level.…”

Section: Variationmentioning

confidence: 99%

Tmproving the Consult Management Process at David Grant Medical Center

Edwards¹

1998

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Public reporting burden for this collection of infermotion it ostimoted to overage 1 hour per response, including the time for reviewing instructions, saerching existing data sources, gathering and mahteming the data needed, and completing and reviewing the collection of mformotion Send comments regarding thlt burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to

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“…Based on a literature review Martin and Smith (1996) concluded that patient demographics and hospital characteristics were the two major factors that determine patient LOS. Among demographic characteristics, studies have reported that LOS varied according to age and disease group (Goldfarb et al, 1983;McMullan et al, 2004), whereas among hospital characteristics, LOS has been reported to vary by region, hospital size, and health care service (Health Technology Case Study 24, 1983;Xiao, 1997;Clarke, 2002). LOS can be terminated by cure, transfer or death discharge.…”

Section: Introductionmentioning

confidence: 99%

Methods for Analyzing Hospital Length of Stay with Application to Inpatients Dying in Southern Thailand

Lim¹,

Tongkumchum²

2009

GJHS

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This study investigated length of stay (LOS) for patients who died in hospital in Southern Thailand from 2000 to 2003 with respect to principal diagnosis and demographic, geographic and hospital size factors. The computerized data of 40,498 mortality cases were obtained from the Ministry of Public Health from 167 hospitals in 14 provinces of Southern Thailand between October 2000 and September 2003 with information on age, gender, principal diagnosis, province and hospital size. Logistic and linear regression with log-transformed LOS was used to analyze the data. Patients with injuries as principal diagnosis had shortest LOS, whereas cancer patients had the longest LOS. Older patients, particularly females, had higher LOS for all diagnoses. LOS increased with hospital size except in the North and North West. Small hospitals in the South West region had the lowest LOS whereas large hospitals in the North West had the highest. The highest proportion of bed days (11.2%) occurred in males aged less than 60 diagnosed with infectious diseases. Males aged less than 60 diagnosed with injuries and digestive diseases, and aged at least 60 diagnosed with COPD, and aged less than 60 diagnosed with infectious diseases, accounted for more than double those for female patients in the same disease groups. Both logistic regressions with LOS at least 1 week as the outcome and linear regression on appropriately log transformed LOS gave consistent results. Providing suitable palliative care or allowing patients to select the place for spending their final time of life, especially for patients with chronic diseases, which can reduce hospital resource utilization.

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Determinants of Hospital Use: A Cross-Diagnostic Analysis

Cited by 24 publications

References 17 publications

A province-wide study of the association between hospital resource allocation and length of stay

A province-wide study of the association between hospital resource allocation and length of stay

Tmproving the Consult Management Process at David Grant Medical Center

Methods for Analyzing Hospital Length of Stay with Application to Inpatients Dying in Southern Thailand

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