Predicting the cost of hospital stay for stroke patients: the use of diagnosis related groups

Evers, Silvia M. A. A.; Voss, Gemma; Nieman, Fred; Ament, André; Groot, T.L.C.M.; Lodder, J.; Boreas, Anita; Blaauw, G.

doi:10.1016/s0168-8510(01)00219-6

Cited by 34 publications

(38 citation statements)

References 39 publications

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“…The per cents of the variance explained by the linear regression models in each clinic were in the range 36.0% [Clinical medicine] to 55.0% [Thoracic surgery], results in agreement with other studies in the area, e.g., (Scwartz et al, 1996;Evers et al, 2002;Polverejan et al, 2003). Overall, the most important cost predictors were the risk adjustment index, the complexity of the performed procedures, and the clinic occupation rates.…”

Section: Discussionsupporting

confidence: 87%

“…Most commonly, hospital cost estimations are concerned with specific procedures or patient conditions (e.g., Evers et al, 2002;Polverejan et al, 2003;Shah et al, 2004;Polinder et al, 2005). That hinders the direct comparison with the costs estimated here, which were not procedure-specific, but, rather, represented averages according to clinics.…”

Section: Discussionmentioning

confidence: 97%

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Hospital costs estimation and prediction as a function of patient and admission characteristics

Ramiarina

Almeida

Pereira

2007

Health Planning & Management

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SUMMARYThe present work analyzed the association between hospital costs and patient admission characteristics in a general public hospital in the city of Rio de Janeiro, Brazil. The unit costs method was used to estimate inpatient day costs associated to specific hospital clinics. With this aim, three ''cost centers'' were defined in order to group direct and indirect expenses pertaining to the clinics. After the costs were estimated, a standard linear regression model was developed for correlating cost units and their putative predictors (the patients gender and age, the admission type (urgency/elective), ICU admission (yes/no), blood transfusion (yes/no), the admission outcome (death/no death), the complexity of the medical procedures performed, and a risk-adjustment index). Data were collected for 3100 patients, January 2001-January 2003. Average inpatient costs across clinics ranged from (US$) 1135 [Orthopedics] to 3101 [Cardiology]. Costs increased according to increases in the risk-adjustment index in all clinics, and the index was statistically significant in all clinics except Urology, General surgery, and Clinical medicine. The occupation rate was inversely correlated to costs, and age had no association with costs. The (adjusted) per cent of explained variance varied between 36.3% [Clinical medicine] and 55.1% [Thoracic surgery clinic]. The estimates are an important step towards the standardization of hospital costs calculation, especially for countries that lack formal hospital accounting systems.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 97%

Hospital costs estimation and prediction as a function of patient and admission characteristics

Ramiarina

Almeida

Pereira

2007

Health Planning & Management

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“…Policies regarding introduction of the diagnosis related group-based payment system might carefully consider the role of disease severity. 4 An observational hospital-based study of this kind may be subjected to observation bias and practice differences. We also recognize the limitations of using hospital charges to conduct this study.…”

Section: Discussionmentioning

confidence: 99%

Costs of Acute Care of First-Ever Ischemic Stroke in Taiwan

Chang

Tseng

2003

Stroke

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Background and Purpose-We sought to investigate the direct costs of acute hospitalization for patients with first-ever ischemic stroke in Taiwan. Methods-Data were prospectively collected from 360 first-ever ischemic stroke patients. Hospital charges were used for analysis. Multiple linear regression analysis was used to identify the main factors influencing costs. Results-Mean age was 64.9 years (median, 67.0 years), and 58% were male. Mean National Institutes of Health Stroke Scale (NIHSS) score at admission was 9.4 (median, 6.0

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“…Most patients with stroke are hospitalised for more than 3 days, with LOS and costs dependent on additional variables, such as stroke severity and level of functioning after the stroke, comorbidity and complications. [30][31] With multiple care systems in place in modern hospitals, matching the needs of individual patients to services best able to meet these needs is important. On admission to the hospital, clinicians are asked to estimate the date of discharge of their patients.…”

Section: Models Of Carementioning

confidence: 99%

Admission variables predicting short lengths of stay of acutely unwell older patients: relevance to emergency and medical short-stay units

Basic¹,

Khoo²

2009

Aust. Health Review

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Objective: To help develop criteria to identify older patients suitable for admission to medical short-stay units, by determining predictors of length of stay (LOS) of 3 days or less. Methods: The data were prospectively collected from consecutive older patients admitted from the emergency department of a university hospital to an acute geriatric medicine service. Data included active medical diagnoses, the Modified Barthel Index (MBI), the Timed Up and Go (TUG) test, and demographic information. Logistic regression was used to model the probability of LOS of 3 days or less (short LOS). Results: Among 2036 patients discharged alive from hospital (mean age, 82 years; median LOS, 7 days), 398 had a short LOS (median, 2 days), while 1638 had a long LOS (median, 9 days). In logistic regression analysis, the main independent predictors of short LOS were an MBI score > 15/20 (OR, 2.98; 95% CI, 1.97-4.49), ability to perform the TUG test (OR, 2.08; 95% CI, 1.34-3.24) and absence of delirium (OR, 2.66; 95% CI, 1.56-4.54). Patients without infection, anaemia, gastrointestinal disorder and stroke were also more likely to have a short LOS in multivariate analysis (all P < 0.05). Conclusion: Preserved function, measured using the MBI and TUG, and the absence of delirium are strong predictors of short LOS. In conjunction with early, skilled clinical evaluation, these criteria could be used to select older patients presenting to the emergency depart- ments for admission to short-stay units.THE INCREASING NUMBER and proportion of older persons is having profound consequences and far-reaching implications, especially for health care systems. Older persons visit the emergency department and are admitted to hospital in greater numbers than their proportion in the total population.1 Due to escalating demands on a limited health workforce and budget, pressures on emergency departments and hospitals are growing, 2 leading to increasing scrutiny and evaluation of systems of care.

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Predicting the cost of hospital stay for stroke patients: the use of diagnosis related groups

Cited by 34 publications

References 39 publications

Hospital costs estimation and prediction as a function of patient and admission characteristics

Hospital costs estimation and prediction as a function of patient and admission characteristics

Costs of Acute Care of First-Ever Ischemic Stroke in Taiwan

Admission variables predicting short lengths of stay of acutely unwell older patients: relevance to emergency and medical short-stay units

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