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Franklin Dexter

Publications and source records attributed to Franklin Dexter.

At least 37 records · Page 2Linked to original sources
PubMed2005

Criteria for identification of comprehensive pediatric hospitals and referral regions.

OBJECTIVE: To identify comprehensive pediatric hospitals on the basis of publicly available data. STUDY DESIGN: We developed identification criteria for comprehensive pediatric hospitals, then evaluated the number of hospitals meeting these selection criteria. Criteria for a comprehensive pediatric hospital included pediatric residency accreditation, pediatric inpatient volume, and diversity of pediatric disorders at each hospital. New York State hospital administrative discharge data were analyzed for patients 0 to 14 years of age, excluding neonatal diagnoses. RESULTS: Infants and children (n = 125,588) with 375 different diagnosis-related groups were discharged from 230 hospitals in 2000. Through the use of higher selective criteria (educational accreditation plus both high volume and diversity in the top decile), 11 comprehensive pediatric hospitals were identified. These hospitals serve populations of 1.7 +/- 0.3 million (mean +/- SD) each, with 8 referral regions throughout the state, collectively providing care for 29% of all pediatric statewide hospitalizations. CONCLUSIONS: Comprehensive pediatric hospitals serve the population of New York widely and evenly. The ability to identify pediatric hospitals will permit evaluation of the relative quality of care and suggest appropriate regulatory interventions to improve pediatric hospital utilization.

Accreditation↗
PubMed2005

Estimating the incidence of prolonged turnover times and delays by time of day.

BACKGROUND: Prolonged turnover times cause frustration and can thereby reduce professional satisfaction and the workload surgeons bring to a hospital. METHODS: The authors analyzed 1 yr of operating room information system data from two academic, tertiary hospitals and Monte-Carlo simulations of a 15-operating room hospital surgical suite. RESULTS: Confidence interval widths for the mean turnover times at the hospitals were negligible when compared with the variation in sample mean turnover times among 31 hospitals. The authors developed a statistical method to estimate the proportion of all turnovers that were prolonged (> 15 min beyond mean) and that occurred during specified hours of the day. Confidence intervals for the proportions corrected for the effect of multiple comparisons. Statistical assumptions were satisfied at the two studied hospitals. The confidence intervals achieved family-wise type I error rates accurate to within 0.5% when applied to between five and nineteen 4-week periods of data. The diurnal pattern in the proportions of all turnovers that were prolonged provided different, more managerially relevant information than the time course throughout the day in the percentage of turnovers at each hour that were prolonged. CONCLUSIONS: Benchmarking sample mean turnover times among hospitals, without the use of confidence intervals, can be valid and useful. The authors successfully developed and validated a statistical method to estimate the percentage of turnover times at a surgical suite that are prolonged and occur at specified times of the day. Managers can target their quality improvement efforts on times of the day with the largest percentages of prolonged turnovers.

Academic Medical Centers↗
PubMed2005

Financial implications of a hospital's specialization in rare physiologically complex surgical procedures.

BACKGROUND: The authors previously identified a hospital that has a unique role in its region for surgical care. In children aged 0-2 yr, the hospital performed 64% of all physiologically complex procedures statewide (>or= 8 American Society of Anesthesiologists Relative Value Guide basic units). For all age groups combined, 48% of the physiologically complex procedures performed at that hospital were rare, defined as < 1/workday statewide. METHODS: The authors tested the hypothesis that financially important differences can result from performing relatively large numbers of such specialized procedures. Methods were developed to compare contribution margin (revenue from facility and professional fees minus variable costs) per operating room hour (CM/OR hour) between patient groups and different types of surgical procedures. RESULTS: CM/OR hour was significantly larger by a financially important amount (> 250 dollars/OR hour) for pediatric versus geriatric patients (P <or= 0.002), primarily because of higher professional reimbursements, with no difference in hospital reimbursements. Unexpectedly, CM/OR hour was also significantly greater by at least 250 dollars when a rare procedure was involved (P < 0.001 for all ages combined), primarily because of greater hospital reimbursements. For cases involving implant charges of 10,000 dollars or greater, overall CM/OR hour was negative because increased revenues did not compensate for the high variable costs. CONCLUSIONS: Other hospitals can use these methods to perform a similar analysis of the financial impact of those patient populations or surgical procedures that are unique to their own roles in their regional healthcare systems, and to identify the sources of financial losses and gains experienced by the hospital.

Child↗
PubMed2005

Bayesian prediction bounds and comparisons of operating room times even for procedures with few or no historic data.

BACKGROUND: Lower prediction bounds (e.g., for fasting), upper prediction bounds (e.g., to schedule delays between sequential surgeons), comparisons of operating room (OR) times (e.g., when sequencing cases among ORs), and quantification of case uncertainty (e.g., for sequencing a surgeon's list of cases) can be done accurately for combinations of surgeon and scheduled procedure(s) by using historic OR times. The authors propose that when there are few or no historic data, the predictive distribution of the OR time of a future case be centered at the scheduled OR time, and its proportional uncertainty be based on that of other surgeons and procedures. When there are a moderate or large number of historic data, the historic data alone are used in the prediction. When there are a small number of historic data, a weighted combination is used. METHODS: This Bayesian method was tested with all 65,661 cases from a hospital. RESULTS: Bayesian prediction bounds were accurate to within 2% (e.g., the 5% lower bounds exceeded 4.9% of the actual OR times). The predicted probability of one case taking longer than another was estimated to within 0.7%. When sequencing a surgeon's list of cases to reduce patient waiting past scheduled start times, both the scheduled OR time and the variability in historic OR times should be used together when assessing which cases should be done first. CONCLUSIONS: The authors validated a practical way to calculate prediction bounds and compare the OR times of all cases, even those with few or no historic data for the surgeon and the scheduled procedure(s).

Algorithms↗
PubMed2005

Operating room efficiency and scheduling.

PURPOSE OF REVIEW: The review focuses on six papers published in 2004 that pertain to operating room (OR) efficiency. RECENT FINDINGS: When to release OR time was much less important than was having the correct OR allocations in the first place. If OR time must be released, then this decision should be based on the historical scheduled workload. Several interventions combined can reduce average turnover times by 7 min per case, probably reducing labor costs by approximately 1.5%. Use of a block room for patients undergoing brachial plexus anesthesia for upper extremity surgery reduces overall OR time by 4 min per upper extremity case. Improving prediction of case duration can reduce over-utilized OR time by just 5.4 min per OR per workday. SUMMARY: Interventions studied included changing when OR time was released, reducing surgical times, reducing turnover times, reducing OR times with block rooms, and improving prediction of case duration. The incremental improvements in OR efficiency and reductions in labor costs were small as compared with allocating OR time and scheduling cases appropriately.

Journal Article↗
PubMed2005

Tactical decision making for selective expansion of operating room resources incorporating financial criteria and uncertainty in subspecialties' future workloads.

We considered the allocation of operating room (OR) time at facilities where the strategic decision had been made to increase the number of ORs. Allocation occurs in two stages: a long-term tactical stage followed by short-term operational stage. Tactical decisions, approximately 1 yr in advance, determine what specialized equipment and expertise will be needed. Tactical decisions are based on estimates of future OR workload for each subspecialty or surgeon. We show that groups of surgeons can be excluded from consideration at this tactical stage (e.g., surgeons who need intensive care beds or those with below average contribution margins per OR hour). Lower and upper limits are estimated for the future demand of OR time by the remaining surgeons. Thus, initial OR allocations can be accomplished with only partial information on future OR workload. Once the new ORs open, operational decision-making based on OR efficiency is used to fill the OR time and adjust staffing. Surgeons who were not allocated additional time at the tactical stage are provided increased OR time through operational adjustments based on their actual workload. In a case study from a tertiary hospital, future demand estimates were needed for only 15% of surgeons, illustrating the practicality of these methods for use in tactical OR allocation decisions.

Decision Making↗
PubMed2005

Validation of statistical methods to compare cancellation rates on the day of surgery.

UNLABELLED: We investigated the validity of several statistical methods to monitor the cancellation of electively scheduled cases on the day of surgery: chi(2) test, Fisher's exact test, Rao and Scott test, Student's t-test, Clopper-Pearson confidence intervals, and Chen and Tipping modification of the Clopper-Pearson confidence intervals. Discrete-event computer simulation over many years was used to represent surgical suites with an unchanging cancellation rate. Because the true cancellation rate was fixed, the accuracy of the statistical methods could be determined. Cancellations caused by medical events, rare events, cases lasting longer than scheduled, and full postanesthesia or intensive care unit beds were modeled. We found that applying Student's two-sample t-test to the transformation of the numbers of cases and canceled cases from each of six 4-wk periods was valid for most conditions. We recommend that clinicians and managers use this method in their quality monitoring reports. The other methods gave inaccurate results. For example, using chi(2) or Fisher's exact test, hospitals may erroneously determine that cancellation rates have increased when they really are unchanged. Conversely, if inappropriate statistical methods are used, administrators may claim success at reducing cancellation rates when, in fact, the problem remains unresolved, affecting patients and clinicians. IMPLICATIONS: Operating room cancellation rates can be monitored statistically by considering the number of canceled and performed cases during each 4-week period, performing a transformation of each period's cancellation rate, and then applying Student's t-test. Methods such as the Fisher's exact test and {chi}2 test should be avoided for this application because they can give erroneous results.

Algorithms↗
PubMed2005

An algorithm for processing vital sign monitoring data to remotely identify operating room occupancy in real-time.

We developed an algorithm for processing networked vital signs (VS) to remotely identify in real-time when a patient enters and leaves a given operating room (OR). The algorithm addresses two types of mismatches between OR occupancy and VS: a patient is in the OR but no VS are available (e.g., patient is being hooked up), and no patient is in the OR but artifactual VS are present (e.g., because of staff handling of sensors). The algorithm was developed with data from 7 consecutive days (122 cases) in a 6 OR trauma center. The algorithm was then tested on data from another 7 consecutive days (98 cases), against patient in- and out-times captured by OR surveillance videos. When pulse oximetry, electrocardiogram, and temperature readings were used, OR occupancy was correctly identified 96% (95% confidence interval [CI] 95%-97%) and OR vacancy >99% of the time. Identified patient in- and out-times were accurate within 4.9 min (CI 4.2-5.7) and 2.8 min (CI 2.3-3.5), respectively, and were not different in accuracy from times reported by staff on OR records. The algorithm's usefulness was demonstrated partly by its continued operational use. We conclude that VS can be processed to accurately report OR occupancy in real-time.

Algorithms↗
PubMed2005

How do we sequence urgent cases?

Because the medical deadline is determined in hours, there is a precise mathematical definition for OR efficiency, and the impact on patient delays can be estimated. The priorities described above will give a single answer to a sequence of urgent cases.

Appointments and Schedules↗
PubMed2004

Market capture of inpatient perioperative services using DEA.

We develop and validate a method to measure "market capture" of inpatient, elective surgery. Data envelopment analysis (DEA) is used to measure the efficiency of the market capture for Perioperative Services at 53 non-metropolitan Pennsylvania hospitals. Eight procedures are studied, representing a wide spectrum of elective, scheduled, inpatient surgery (e.g., abdominal aortic aneurysm resection and hip replacement). Our results address issues in operating room management, such as: How should additional resources be allocated to each surgical specialty? Given existing market conditions, for which specialties can we expect to be able to increase our current workloads? Our results demonstrate DEA's potential as a valuable tool for operating room managers' strategic decision-making.

Benchmarking↗
PubMed2004

Using length of stay data from a hospital to evaluate whether limiting elective surgery at the hospital is an inappropriate decision.

STUDY OBJECTIVE: At hospitals without detailed managerial accounting data but with overall longer than average diagnosis-related groups (DRG)-adjusted lengths of stays (LOS), some administrators do not aggressively hire the nurses needed to maintain surgical hospital capacity. The consequence of this (long-term) decision is that day-of-surgery admit cases are delayed or cancelled from a lack of beds. The anesthesiologists suffer financially. In this paper, we show how publicly released national LOS data can be applied specifically to these cases. DESIGN: We applied the method to 1 year of data from two academic hospitals. Each case's LOS was compared to the United States national average LOS for cases with the same DRG. MEASUREMENTS AND MAIN RESULTS: A total of 8,050 and 10,099 hospitalizations, respectively. Among all surgical admissions, mean LOS was 2.5 days longer than the national average for Hospital #1 (95% confidence interval [CI], 2.1 to 2.8) and 3.1 days longer for Hospital #2 (95% CI, 2.8 to 3.4). Among patients undergoing elective, scheduled surgery with day of surgery admission, mean LOS was 0.7 days less than average for Hospital #1 (0.6 to 0.9) and 1.2 days less than average for Hospital #2 (1.1 to 1.4). CONCLUSIONS: This method can be used by anesthesiologists to show that LOS are not longer than average among patients whose surgeries may be cancelled or delayed for a lack of hospital ward staff.

Anesthesia Department, Hospital↗
PubMed2004

Quantifying net staffing costs due to longer-than-average surgical case durations.

BACKGROUND: Anesthesiology departments incur staffing costs that are not covered by revenue because the operating room (OR) time allocation and case scheduling are not done to maximize OR efficiency and because surgical durations are longer than average. The purpose of this article is to demonstrate a method to quantify net anesthesia staffing costs due to longer-than-average surgical durations and evaluate the factors that influence staffing costs. METHODS: Data collected from two anesthesiology departments in academic hospitals for 1 yr included date of surgery, time that patients entered the OR, time that patients exited the OR, surgical service, and the Current Procedural Terminology code for the primary surgical procedure. Anesthesia care performed outside the main surgical suite and services not billed with American Society of Anesthesiologists units were excluded. National average surgical durations were determined from the Current Procedural Terminology code from the Centers for Medicare and Medicaid Services' database. Actual surgical durations were then used to determine staffing solutions to maximize OR efficiency; national average surgical durations were then used to determine a second solution. The difference in staffing costs between these two staffing solutions represented the staffing costs attributable to longer surgical durations. Costs were converted to dollar amounts using compensation values reported in a national compensation survey. The differences in revenue were determined by applying conversion factors to the differences in surgical durations. The annual net cost attributable to longer surgical durations equaled the staffing costs minus the revenue produced by longer durations. Net staffing costs were estimated for two hospitals using median staffing compensation and median payer mix. Net staffing costs were then recalculated by varying the parameters (conversion factors, limits on differences between actual and average surgical duration, levels of compensation, surgical service size of OR allocation). RESULTS: Using the median compensation of staff and an average conversion factor, the net annual staffing costs attributable to longer surgical durations were $672,100 for the first hospital. However, if staff members were highly compensated and the payer mix was unfavorable, the net staffing costs were $1,688,000. Reducing the difference between actual and average duration resulted in lower staffing costs. Net staffing costs were less in a second hospital studied that had many low-volume surgical services. CONCLUSIONS: Longer-than-average surgical durations can increase net staffing costs for anesthesiology groups. The increase is dependent on factors such as staffing compensation and payer mix.

Anesthesia Department, Hospital↗
PubMed2004

Differentiating among hospitals performing physiologically complex operative procedures in the elderly.

BACKGROUND: The authors previously showed how a statewide discharge abstract database could be used to quantify for stakeholders how surgical practices differ among hospitals. The two pediatric hospitals in Iowa differ from other hospitals in Iowa based on their providing a more diverse, comprehensive, and physiologically complex selection of procedures in younger patients. Physiologically complex surgery performed in children aged 0-2 yr has been regionalized to a few high-volume facilities. METHODS: The same inpatient discharge abstract database was used to quantify physiologically complex operative procedures performed throughout Iowa in patients aged 80 yr and older during January through June 2001. RESULTS: In contrast to earlier results with pediatric patients using the same database, hospitals performing physiologically complex procedures in the elderly could not readily be differentiated from one another based on the numbers and types of procedures performed (P < 0.001 when comparing geriatrics vs. pediatrics in terms of the distributions of numbers of procedures, the distributions of numbers of different types of procedures, or the distributions of numbers of rare procedures performed at different hospitals). Additional analyses showed that one hospital did perform relatively more rare procedures in geriatric patients and had a relatively larger percentage of patients who traveled beyond their local county to reach it. CONCLUSIONS: Results observed for geriatric patients provide further evidence of the validity of these methods and the usefulness of discharge abstract data for comparing surgical practices among facilities. A hospital can use discharge abstract data to assist governmental agencies, charitable organizations, philanthropists, insurers, etc., in appreciating the unique contributions of individual hospitals to surgical care.

Aged↗
PubMed2004

Making management decisions on the day of surgery based on operating room efficiency and patient waiting times.

The authors review the scientific literature on operating room management operational decision making on the day of surgery. (1) Some decisions should rely on the expected (mean) duration of the scheduled case. Other decisions should use upper prediction bounds, lower prediction bounds, and other measures reflecting the uncertainty of case duration estimates. One single number cannot be used for good decision making, because durations are uncertain. (2) Operational decisions can be made on the day of surgery based on four ordered priorities. (3) Decisions to reduce overutilized operating room time rely on mean durations. Limited additional data are needed to make these decisions well, specifically, whether a patient is in each operating room and which cases are about to finish. (4) Decisions involving reducing patient (and surgeon) waiting times rely on quantifying uncertainties in case durations, which are affected highly by small sample sizes. Future studies should focus on using real-time display of data to reduce patient waiting.

Appointments and Schedules↗
PubMed2004

When to release allocated operating room time to increase operating room efficiency.

UNLABELLED: We studied when allocated, but unfilled, operating room (OR) time of surgical services should be released to maximize OR efficiency. OR time was allocated for two surgical suites based on OR efficiency. Then, we analyzed real OR schedules. We added new hypothetical cases lasting 1, 2, or 3 h into OR time of the service that had the largest difference between allocated and scheduled cases (i.e., the most unfilled OR time) 5 days before the day of surgery. The process was repeated using the updated OR schedule available the day before surgery. The pair-wise difference in resulting overutilized OR time was calculated for n = 754 days of data from each of the two surgical suites. We found that postponing the decision of which service gets the new case until early the day before surgery reduces overutilized OR time by <15 min per OR per day as compared to releasing the allocated OR time 5 days before surgery. These results show that when OR time is released has a negligible effect on OR efficiency. This is especially true for ambulatory surgery centers with brief cases or large surgical suites with specialty-specific OR teams. What matters much more is having the correct OR allocations and, if OR time needs to be released, making that decision based on the scheduled workload. IMPLICATIONS: Provided operating room (OR) time is allocated and cases are scheduled based on maximizing OR efficiency, then whether OR time is released five days or one day before the day of surgery has a negligible effect on OR efficiency.

Ambulatory Surgical Procedures↗