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Direct hospital costs for patients with inflammatory bowel disease in a Canadian Tertiary Care University Hospital

2000· article· en· W2010664148 on OpenAlexaffabout
Çharles N. Bernstein, Noreen Papineau, Patricia Rawsthorne, Gisele Okrusko, James Blanchard

Bibliographic record

VenueThe American Journal of Gastroenterology · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsManitoba HealthUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsMedicineUlcerative colitisInflammatory bowel diseaseMedical diagnosisDiseaseInpatient careCrohn's diseaseConfidence intervalEmergency medicineInternal medicinePediatricsHealth care

Abstract

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OBJECTIVE: We set out to determine the direct costs of hospitalizations of patients with Crohn's disease and ulcerative colitis admitted to a university-affiliated tertiary care hospital and to contrast the costs of medical versus surgical inpatient care, Crohn's disease versus ulcerative colitis, and to identify dominant components of inpatient costs. METHODS: We used a patient-specific case costing system at Saint Boniface General Hospital, Winnipeg, Manitoba, for fiscal years 1994 and 1995. We extracted all inpatients whose hospital discharge abstracts included ICD-9-CM codes 555 (Crohn's disease) and 556 (ulcerative colitis) among the top eight discharge diagnoses, and performed a chart review on all cases to ensure that the hospitalization was for inflammatory bowel disease and the diagnoses were accurate. We analyzed cases based on their disease diagnosis, primary mode of therapy associated with the hospitalization (medical vs surgical), and their major diagnosis-related group (DRG). This study evaluated direct patient care costs only and costs are expressed in Canadian dollars. RESULTS: Of 362 hospital admissions, 325 were eligible and of these admissions 275 belonged to the digestive system DRGs. Seventy-one (37%) were admitted more than once during the 2 yr of the study, accounting for 202 (62%) of the total number of admissions. The mean cost per admission of all cases of Crohn's disease was $3,149 (95% confidence interval [CI], $2,665-$3,634) and for ulcerative colitis was $3,726 (95% CI $3,008-$4,445). Surgical therapy cases accounted for 49.8% of all admissions, 57.8% of all hospital days, and 60.5% of all costs. Patients treated surgically had more costly hospitalizations than those treated medically, particularly when analyzing only nontotal parenteral nutrition (TPN) cases. Surgical treatment admissions were significantly more costly for ulcerative colitis digestive DRG admissions than Crohn's disease. The nondigestive DRG admissions were more costly than the digestive DRGs in all categories although this was only statistically different among medically treated Crohn's disease. Patients treated medically were similarly costly whether they had Crohn's disease or ulcerative colitis. There was no significant difference in cost per admission among cases admitted multiple times, compared with those admitted only once. TPN cases accounted for 9.5% of cases but 27.1% of costs. TPN-associated hospitalizations were more costly than non-TPN-use hospitalizations but these costs were primarily driven by duration of stay rather than TPN use itself. For all cases, the top five cost categories in descending order were nursing unit bed-days, drugs and pharmacy, diagnostic lab tests, operating room, and diagnostic imaging and endoscopy. CONCLUSIONS: Using our system we could determine direct costs for inpatients with inflammatory bowel disease and the factors that determined increased costs. Medical therapy admissions were similarly costly between Crohn's disease and ulcerative colitis; however, surgical therapy admissions were costlier among ulcerative colitis patients. Admissions for nondigestive DRGs were more costly than those for digestive DRGs. TPN use identified a sicker group of patients who remained in the hospital longer than nonusers and, not surprisingly, these were the costliest patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.001
GPT teacher head0.167
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations103
Published2000
Admission routes2
Has abstractyes

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