Anthony Youn. Why doctors fall asleep while treating you. Suddenly, you're jolted awake. Intense, blinding lights glare into your face. You find yourself gagging on a thick plastic tube stuck down your throat. The lights turn away.
Your eyes begin to adjust, and you see four faces you've never seen before. That was the finding of a Duke University study published in the journal Quality and Safety in Healthcare. Researchers found operations starting between 3 and 4 p. These circadian rhythms dip between 3 and 5 p. The Spaniards apparently discovered this long ago when they instituted the afternoon siesta.
And George Costanza of "Seinfeld" fame isn't the only one who naps on the job. Nike and Google have reportedly instituted official sleeping rooms so their employees can deal with the afternoon circadian dip. A potential problem is the reproducibility of this estimate, considering it is an opinion and not an objective factor. Further studies should be carried out to identify causes and find new solutions, considering the operating room is present in most — if not all — Brazilian hospitals.
This study identified duration patterns in surgeries by several specialties and their variations in the stages: operative time, anesthetic time, and operating room stay. The indicators offer a tool and are an opportunity to improve the efficiency of operating room time management and scheduling.
National Center for Biotechnology Information , U. Journal List Einstein Sao Paulo v. Einstein Sao Paulo. Altair da Silva Costa, Jr 1 , 2. Author information Article notes Copyright and License information Disclaimer. Corresponding author: Altair da Silva Costa Jr. E-mail: moc. Received Oct 20; Accepted Jan Copyright notice. This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article has been cited by other articles in PMC. ABSTRACT Objective To evaluate the indicators duration of anesthesia, operative time and time patients stay in the operating rooms of different surgical specialties at a public university hospital. Methods It was done by a descriptive cross-sectional study based on the operating room database.
Results The study measured 8, operations of 12 surgical specialties performed within one year. Conclusion This study identified patterns in the duration of surgery stages. Resultados O estudo incluiu 8. METHODS This was a descriptive cross-sectional study carried out from databases of the information technology system of the operating room of a teaching hospital from a federal university.
Table 1 Number of procedures analyzed by specialty, with the respective percentage, mean and standard deviation in minutes. Open in a separate window. Figure 1. Table 2 Mean, 80th percentile and variation between them, in the stages anesthesia, operative time and operating room time in minutes.
P 80 th percentile. Figure 2. Figure 3. Table 3 Duration times of operating room stages. Operating room stages Mean P80 1 Operating room time OR: operating room. Figure 4. Size and distribution of the global volume of surgery in Bull World Health Organ.
Most frequent operating room procedures performed in U. Hospitals, statistical Brief If multiple times are found, select the earliest time among the third priority synonyms.
Note: Priority order applies to items in inclusion table, not to source document. Also, please note the synonyms in the lists are alphabetized, not prioritized. If multiple times are found, use earliest time among the highest priority. Procedure times are important variables that often are included in studies of quality and efficiency.
However, due to the need for costly chart review, most studies are limited to single-institution analyses. In this article, the authors describe how well the anesthesia claim from Medicare can estimate chart times. The authors abstracted information on time of induction and entrance to the recovery room "anesthesia chart time" from the charts of 1, patients who underwent general and orthopedic surgical procedures in Pennsylvania. The authors then merged the associated bills from claims data supplied from Medicare Part B data that included a variable denoting the time in minutes for the anesthesia service.
The authors also investigated the time from incision to closure "surgical chart time" on a subset of 1, patients. When predicting chart time from Medicare bills, variables reflecting procedure type, comorbidities, and hospital type did not significantly improve the prediction, suggesting that errors in predicting the chart time from the anesthesia bill time are not related to these factors; however, the individual hospital did have some influence on these estimates.
Anesthesia chart time can be well estimated using Medicare claims, thereby facilitating studies with vastly larger sample sizes and much lower costs of data collection. THE surgical and anesthesia literature commonly reports procedure time. The length of a procedure also has an impact on cost, both with respect to the opportunity cost of the operative suite 11,12 and the cost of labor and anesthetic agents.
However, because obtaining procedure time usually requires chart review, the variable is often analyzed from single-institution studies or studies including a small number of cooperating institutions in which such information is more easily abstracted. We did this by measuring both the chart times and the claim times for 1, Medicare patients throughout Pennsylvania.
If procedure time could be well estimated using Medicare anesthesia bills, the study of the length of surgical procedures could be expanded to a much larger Medicare population, enabling the study of a vast array of research questions more appropriate for the more general Medicare population.
Medicare data for patients 65 yr and older is the most representative healthcare data for the elderly in the United States because Medicare is an entitlement program. The only significant group of elderly citizens not represented in the Medicare claims is those who opted out of the Medicare fee-for-service arrangement and joined a Medicare-approved prepaid health maintenance organization.
The Research Data Assistance Center is staffed by a consortium of epidemiologists, public health specialists, health services researchers, biostatisticians, and health informatics specialists from the University of Minnesota.
A patient's hospital bill from the Inpatient Standard Analytical file can be linked to the bills submitted by the physicians and other providers who took care of the patient during the hospital stay. For this study, we selected the principal procedure as determined in retrospect by Medicare. There is an anesthesia time unit, defined as a min interval, associated with each anesthesia provider claim. In the documentation received with the electronic claims file, time units are reported as integers but should be interpreted as having one decimal.
For example, a time unit value of 25 implies 2. Both the time and base units are used in determining payment from Medicare. We obtained detailed chart abstraction data in a subset of patients from the same pool of patients for which we had claims data. The charts were abstracted as part of the Surgical Outcomes Study. Four landmark times were abstracted from each available chart: induction, incision, closure, and entrance to recovery room. Our landmarks were chosen because they reflect how reimbursement from Medicare is determined and because the abstractors for Medicare had used these landmarks in previous work auditing Medicare charts.
We excluded patients from the anesthesia time analyses if either induction or recovery room time was missing; we also excluded patients from the surgical time analysis if either incision or closure time was missing. Each bill for anesthesia services for a patient for a surgical procedure date may include none, one, or more than one claim from the Medicare Part B files, an associated Current Procedural Terminology or HCPCS code, and a variable identifying the specialty of the anesthesia provider.
We tested numerous definitions for variable construction concerning the total claim minutes used in the predictive model. We defined the claim time to equal the longest time billed by any anesthesia provider on the day of the surgery. If two anesthesia bills were found for the same date as the principal procedure, the longer bill was used in the modeling.
We considered several other possible definitions of the claim time, but they worked poorly. For instance, using a summation of the bills, or modeling two bills as two variables, did not improve the simple rule of using the longest anesthesia provider bill. When we included nurse anesthetist bills separately, by adding a second variable to the modeling process, we found almost no change in fit R 2 , and any coefficients on the second variable that were significant were extremely small, producing no appreciable difference in our estimates.
Hence, we used a simple rule for defining claim time—the longest bill by an anesthesia provider on the same day as the principal procedure in question. For these two patients, the bill and the chart are said to be concordant if the patient with the longer chart time also had the longer bill time.
We used the claim time to predict the chart time because researchers with data from Medicare will have the claim times and our algorithm for predicting the chart times from the claim times. The question then is how well that formula will perform. This prevents one or two peculiar claims from increasing or decreasing the R 2.
There were 2, abstracted charts available for review from the Surgical Outcomes Study. The Surgical Outcome Study was a case-control study two controls for each case of mortality after surgery, so one third of the patients had died within 2 months of surgery. Of these, 2, have an anesthesia bill from Medicare Part B data. We excluded 30 charts with missing chart anesthesia time or negative chart anesthesia time and 40 charts with surgery time longer than anesthesia time.
We excluded five charts for operations that were less than or equal to 10 min in length according to the anesthesia claim; we also excluded one operation with claim anesthesia bill time greater than min. Of the 1, charts remaining, all included both induction and recovery room times, and 1, charts included incision and closure times as well as induction and recovery room times.
For each analysis in table 1 , we let the anesthesia claim time represent the independent variable and predict anesthesia chart time as abstracted as part of the Surgical Outcome Study. We present five models: Model I uses only the claim time to predict chart time. Model I is estimated in two ways.
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