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PubMed · 7632049

Using a computerized scheduling system to predict procedure lengths.

B Bross·B B Gamblin·S L Holtzclaw·S E Johnston
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Abstract

To determine the effectiveness of our computerized OR scheduling system and database, we compared scheduled surgical procedure lengths to actual procedure lengths for a large, multispecialty sample (1,103 procedures in 14 surgical subspecialties). We were able to accurately predict procedure lengths, within 15 minutes, 65% of the time. The estimated procedure lengths were more accurate in some surgical subspecialties than others, and four specific factors accounted for a large percentage (ie, 84%) of all delayed start times.

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AlabamaAppointments and SchedulesForecastingHumansOperating Room Information SystemsPersonnel Staffing and Scheduling Information SystemsSurgical Procedures, OperativeTime and Motion StudiesComparative StudyJournal Article

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B Bross, B B Gamblin, S L Holtzclaw, S E Johnston. 1995. Using a computerized scheduling system to predict procedure lengths.. https://doi.org/10.1016/s0001-2092(06)63809-x

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Comparison of inpatient charges between academic and nonacademic services in a children's hospital.

OBJECTIVE: To compare inpatient hospital charges generated within a children's hospital by academic and nonacademic pediatric services for common medical diagnoses. METHODS: Hospital admissions to a free-standing children's hospital between 9/1/90 and 8/30/94 were selected for patients who were hospitalized 1 to 14 days, with one of six selected diagnoses, and with discharge attending of record either a private pediatrician or an academic subspecialist. Discharge diagnoses, based on ICD-9 codes, included asthma (n = 1983), bronchiolitis (n = 692), gastroenteritis (n = 733), rule out sepsis (n = 1065), urinary tract infection (n = 516), and viral meningitis (n = 288). Charges associated with patient records were dichotomized as above or below the median charge for each diagnostic category. Each category was analyzed separately using a logistic regression model where the dichotomous-dependent variable was charges above the median charge for each diagnosis. Independent variables included physician type, payor status, patient residence, ICD-9 code as primary or secondary diagnosis, patient age, and presence of complicating conditions. RESULTS: By univariate comparison, academic physicians cared for a higher percentage of underinsured patients, and their care was more expensive. Complicated claims were associated with higher charges than uncomplicated claims for all diagnostic categories. Academic and nonacademic physicians were equally likely to generate above-median charges for five of the six diagnostic categories when controlling for confounding factors. A linear regression model in which charge was the dependent variable generated similar results. CONCLUSIONS: Within the same pediatric health care facility, no consistent difference was found between charges incurred on academic vs private inpatient services.

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