Abstract
When the hospital census is high, perioperative medical directors or operating room (OR) managers sometimes need to review with surgical departments as to which surgical cases scheduled to be performed within the next three days may need to be postponed. Although distributions of hospital length of stay (LOS) are highly skewed, a surprisingly effective summary measure is the percentage of patients previously undergoing the same category of procedure as that scheduled whose LOS was zero or one day. We evaluated how to forecast each hospital's percentage of cases with LOS of <2 days, segmented by category of surgical procedure. The large teaching hospital studied included several inpatient adult surgical suites, an ambulatory surgery center, and a pediatric surgical suite.
We included 98,540 cases in a training dataset to predict 24,338 cases in a test dataset. For each category of procedure, we calculated the cumulative count of cases among quarters, from the most recent quarter, second most recent quarter, and so forth up to the quarter resulting in at least 800 cases. If every quarter combined had fewer than 800 cases for a given category of procedure, we included all cases for that category. For each combination of category and quarter, we used the cumulative counts of cases and cases with LOS of <2 days, excluding the current quarter. Then, for each category of procedure, and for each of the preceding quarters included for the category, we used the cumulative counts to calculate the asymptotic standard error (SE) for the proportion of cases with LOS of <2 days. If all preceding quarters combined provided a sample size such that the estimated SE for the proportion exceeded 1.25%, we included all preceding quarters. The observed absolute percentage error was 0.76% (SE: 0.12%). This error was nearly 100-fold smaller than the percentage of cases to which it would be used (i.e., 0.76% versus 73.1% with LOS of <2 days). The principal weakness of the forecasting methodology was a small bias caused by a progressive reduction in the overall LOS over time. However, this bias is unlikely to be important for predicting cases’ LOS when the hospital census is high. When performing these time series calculations quarterly, a reasonable approach is to perform calculations of both case counts and SEs for each category of procedure. We recommend using the fewest historical quarters, starting with the most recent quarter, either with at least 800 cases or an estimated asymptotic SE for the estimated percentage no greater than 1.25%. Applying our methodology with local LOS data will allow OR managers to estimate the number of patients on the elective OR schedule each day who will be hospitalized for longer than overnight, facilitating communication and decision-making with surgical departments when census considerations constrain the ability to run a full surgical schedule.
