Biomedical subjects

Richard H Epstein

Publications and source records attributed to Richard H Epstein.

7 recordsLinked to original sources

PubMed2003

Optimizing second shift OR staffing.

In surgical suites when ORs sometimes run late, nurse anesthetists or perioperative nurses may be scheduled to work a second shift to cover procedures. Nurse anesthetists' OR workload in the afternoons can differ from that of perioperative nurses. At the end of long procedures, times to transport and stabilize patients can be considerable. This article shows that optimal second-shift OR staffing is the same for nurse anesthetists and perioperative nurses when assessed using anesthesia billing data and OR information systems data respectively. Managers do not need hospital information systems staff members to provide data from both anesthesia billing and OR information systems to make second-shift staffing decisions. One or the other is adequate.

Hospital Costs↗

PubMed2003

Scheduling of cases in an ambulatory center.

Perhaps the most important thing for an anesthesiologist and OR manager to understand is that there are different systems for OR allocation and case scheduling. We referred to them as Fixed Hours, Any Workday, and Reasonable Time. This understanding makes the OR management literature clear and applicable to all staff members. Most ambulatory centers handle cases on the workday chosen by the patient and surgeon but strive to do the work each day as efficiently as possible. Precisely how to make OR allocation and case scheduling decisions to achieve these objectives have been worked out. Studies show that case scheduling decisions to enhance OR efficiency are practiced in many facilities. In contrast, OR allocation decisions tend to be different than what OR managers do in practice. This means that it is important to apply the statistical methods for allocating OR time.

Ambulatory Care Facilities↗

PubMed2003

Labor costs incurred by anesthesiology groups because of operating rooms not being allocated and cases not being scheduled to maximize operating room efficiency.

UNLABELLED: Determination of operating room (OR) block allocation and case scheduling is often not based on maximizing OR efficiency, but rather on tradition and surgeon convenience. As a result, anesthesiology groups often incur additional labor costs. When negotiating financial support, heads of anesthesiology departments are often challenged to justify the subsidy necessary to offset these additional labor costs. In this study, we describe a method for calculating a statistically sound estimate of the excess labor costs incurred by an anesthesiology group because of inefficient OR allocation and case scheduling. OR information system and anesthesia staffing data for 1 yr were obtained from two university hospitals. Optimal OR allocation for each surgical service was determined by maximizing the efficiency of use of the OR staff. Hourly costs were converted to dollar amounts by using the nationwide median compensation for academic and private-practice anesthesia providers. Differences between actual costs and the optimal OR allocation were determined. For Hospital A, estimated annual excess labor costs were $1.6 million (95% confidence interval, $1.5-$1.7 million) and $2.0 million ($1.89-$2.05 million) when academic and private-practice compensation, respectively, was calculated. For Hospital B, excess labor costs were $1.0 million ($1.08-$1.17 million) and $1.4 million ($1.32-1.43 million) for academic and private-practice compensation, respectively. This study demonstrates a methodology for an anesthesiology group to estimate its excess labor costs. The group can then use these estimates when negotiating for subsidies with its hospital, medical school, or multispecialty medical group. IMPLICATIONS: We describe a new application for a previously reported statistical method to calculate operating room (OR) allocations to maximize OR efficiency. When optimal OR allocations and case scheduling are not implemented, the resulting increase in labor costs can be used in negotiations as a statistically sound estimate for the increased labor cost to the anesthesiology department.

Anesthesiology↗

PubMed2002

Statistical power analysis to estimate how many months of data are required to identify PACU staffing to minimize delays in admission from ORs.

When each nurse in the Phase I setting is caring for the maximum number of patients allowed by hospital staffing standards (typically 2 per ASPAN standards), patients may have to be held in the OR until a PACU nurse becomes available. Previously, the authors described a statistical method to determine the process of scheduling existing nurses without increasing staffing hours (Dexter et al. Anesth Analg. 92:947-949, 2001). The end result was to minimize the percentage of future workdays during which at least one patient would wait in his or her OR for Phase I PACU admission. In this study, the authors performed a statistical power analysis to determine how many months of PACU workload data are needed to optimize PACU staffing by using this "set covering" algorithm. One year (232 workdays) of data was available from a PACU employing up to 10 nurses working a total of 72 clinical hours a day. The data were divided into 2 subsets. Using the first subset, which varied in size between 20 and 140 days of data, the authors identified the optimal staffing solutions. These solutions were tested on the second subset of data. This process then was repeated thousands of times. There was a marked improvement in the performance of the staffing solutions at preventing "PACU hold" by increasing from 20 to 80 historical workdays of data, a slight but statistically significant improvement between 80 and 100 workdays, but no significant improvement in further increasing the number of workdays of data. PACU nurse managers should use at least 4 months of data when choosing a staffing solution to minimize the chance of patients waiting in ORs for PACU admission. Tampering with PACU staffing more often than every 4 months is unlikely to result in improvements in OR efficiency and may harm recruitment and retention of nursing staff.

Operating Rooms↗

PubMed2002

Statistical power analysis to estimate how many months of data are required to identify operating room staffing solutions to reduce labor costs and increase productivity.

UNLABELLED: We performed a statistical power analysis to determine how many historical data are needed for optimal operating room (OR) management decision making. The work applies to hospitals that provide service for all of its surgeons' elective cases on whatever workday the surgeons and patients choose. The hospital and anesthesia group adjust OR staffing and patient scheduling to care for the patients while minimizing OR staffing costs and maximizing labor productivity. Two years of data were obtained from a seven-OR surgical suite. The data were repeatedly split into training and testing datasets. The optimal staffing solution was calculated for each training dataset to maximize the efficiency of OR time usage and was then applied to the corresponding testing dataset. Training datasets ranged in size from 30 to 270 consecutive workdays. With 30 workdays of data, the statistical method identified staffing solutions that had an average of 35% decreased costs and 27% increased productivity as compared to the existing staffing plan. There was no significant improvement in performance with more than 210 workdays (10 mo) of data. With 30 workdays of OR or anesthesia group data, the optimization method can significantly reduce staffing costs and increase productivity compared with existing staffing. When applied routinely for adjusting staffing (e.g., on a quarterly basis), 9 to 12 mo of data should be used. IMPLICATIONS: With 30 workdays of operating room or anesthesia group data, the optimization method can propose staffing solutions that significantly decrease costs and increase productivity compared with existing staffing solutions. We recommend that, when the statistical method is applied routinely for adjusting staffing (e.g., on a quarterly basis), 9 to 12 mo of data be used.

Efficiency↗

PubMed2002

Uncertainty in knowing the operating rooms in which cases were performed has little effect on operating room allocations or efficiency.

UNLABELLED: At many US surgical facilities, applying the previously published method that maximizes the efficiency of use of operating room (OR) time is an effective way to optimize the allocation of OR time. Results are resistant to small errors in recorded OR times. However, at some facilities, the OR information systems data have as much as a 10% error in the correct OR where each case took place. This decreases the total OR time attributed to each service, which is the basis for the allocation method. Such errors could result in incorrect OR allocations and increased OR staffing costs. Expensive and time-consuming data-cleaning steps may be required to resolve the actual OR allocation for each case. We used 1 yr of data from a large, tertiary academic hospital to investigate, through simulation, how increasing levels of error in the correct OR affect OR efficiency and allocations. To apply noise to the data, the actual ORs were changed randomly to unique, "unknown" rooms. At a 30% error level, OR allocations decreased by 4.8%, and costs increased by 1.4% relative to knowing the actual location of every case. Only 1 of 11 surgical services had an allocation decrease at room error rates of less than 25%. We conclude that, in most circumstances, data-cleaning steps to resolve uncertainty in OR locations are not necessary to make accurate OR allocations. IMPLICATIONS: Up to a 30% uncertainty in knowing the actual operating room (OR) in which cases were performed had a minor effect on OR allocations to maximize OR efficiency and on the resulting staffing costs. Thus, facilities with this common error in their OR information systems data will generally be able to use their existing data for accurate OR allocations.

Efficiency↗

PubMed2002

Costs and risks of weekend anesthesia staffing at 6 independently managed surgical suites.

We previously developed a statistical method that managers can use to assure that nurse anesthetists are on call on weekends for as few hours as possible while providing a specified level of care for operating room (OR) patients. The statistically derived staffing solutions are optimal, meaning that the total number of staffed hours is guaranteed to be as low as possible to achieve the specified risk of being unable to care for patients as promptly as they had in the recent past. We used the statistical method to review nurse anesthetist weekend staffing at 6 surgical suites that were part of a healthcare system with a cost-conscious management team. Four of the suites had already made staffing changes resulting in a greater than 6% risk of being understaffed. One suite had adequate current staffing but slightly exceeded the minimum total staffing hours. One suite had more anesthetist coverage than was needed, resulting in excess staffing costs greater than $200,000 per year. We conclude that the principal value of the statistical method may be in helping healthcare system administrators and anesthetists quantify the impact of contemplated reductions in staffing on their risk of understaffing and prologing patients' wait for OR care.

Hospital Costs↗