Elisabeth U Dexter

University of Iowa, Iowa City, IA, United States

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Publications (7)19.55 Total impact

  • Franklin Dexter, Elisabeth U Dexter, Johannes Ledolter
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    ABSTRACT: Multiple logistic regression studies frequently are performed with duration (e.g., operative time) included as an independent variable. We use narrative review of the statistical literature to highlight that when the association between duration and outcome is presumptively significant, the procedure itself (e.g., video-assisted thoracoscopic lobectomy or thoracotomy lobectomy) needs to be tested for inclusion in the logistic regression. If the procedure is a true covariate but excluded in lieu of category of procedure (e.g., lung resection), estimates of the odds ratios for other independent variables are biased. In addition, actual durations are sometimes used as the independent variable, rather than scheduled (forecasted) durations. Only the scheduled duration is known when a patient would be randomized in a trial of preoperative or intraoperative intervention and/or meets with the surgeon and anesthesiologist preoperatively. By reviewing the literature about logistic regression and about predicting case duration, we show that the use of actual instead of scheduled duration can result in biased logistic regression results.
    Anesthesia and analgesia 08/2011; 113(5):1197-201. · 3.08 Impact Factor
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    Franklin Dexter, Elisabeth U Dexter, Johannes Ledolter
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    ABSTRACT: Predictive variability of operating room (OR) times influences decision making on the day of surgery including when to start add-on cases, whether to move a case from one OR to another, and where to assign relief staff. One contributor to predictive variability is process variability, which arises among cases of the same procedure(s). Another contributor is parameter uncertainty, which is caused by small sample sizes of historical data. Process variability was quantified using absolute percentage errors of surgeons' bias-corrected estimates of OR time. The influence of procedure classification on process variability was studied using a dataset of 61,353 cases, each with 1 to 5 scheduled and actual Current Procedural Terminology (CPT) codes (i.e., a standardized vocabulary). Parameter uncertainty's sensitivity to sample size was quantified by studying ratios of 90% prediction bounds to medians. That studied dataset of 65,661 cases was used previously to validate a Bayesian method to calculate 90% prediction bounds using combinations of surgeons' scheduled estimates and historical OR times. (1) Process variability differed significantly among 11 groups of surgical specialty and case urgency (P < 0.0001). For example, absolute percentage errors exceeded the overall median of 22% for 57% of urgent spine surgery cases versus 42% of elective spine surgery cases. (2) Process variability was not increased when scheduled and actual CPTs differed (P = 0.23 without and P = 0.47 with stratification based on the 11 groups), because most differences represented known (planned) options inherent to procedures. (3) Process variability was not associated with incidence of procedures (P = 0.79), after excluding cataract surgery, a procedure with high relative variability. (4) Parameter uncertainty from uncommon procedures (0-2 historical cases) accounted for essentially all of the uncertainty in decisions dependent on estimates of OR times. The Bayesian method moderated the effect of small sample sizes on uncertainty in estimates of OR times. In contrast, from prior work, the use of broad categories of procedures reduces parameter uncertainty but at the expense of increased process variability. For procedures with few historic data, the Bayesian method allows for effective case duration prediction, permitting use of detailed procedure descriptions. Although fine resolution of scheduling procedures increases the chance of performed procedure(s) differing from scheduled procedure(s), this does not increase process variability. Future studies need both to address differences in process variability among specialties and accept the limitation that findings from one may not apply to others.
    Anesthesia and analgesia 04/2010; 110(4):1155-63. · 3.08 Impact Factor
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    ABSTRACT: Surgeon estimates of case durations are important for operating room (OR) management decision making because many cases are rare combinations of procedures with few or no historical data. Thoracic and spine surgeons updated their scheduled OR times on the day of surgery just before the "time out" in the OR. All elective (scheduled) general thoracic (n = 39) and spine surgery (n = 48) cases at 1 hospital were studied over 3-month and 1.5-month periods, respectively. Among cases with a change in predicted duration, most changes were made based on updates to the surgical or anesthetic procedures (thoracic 85%, spine 86%). For thoracic surgery, there was overall no significant median reduction in absolute prediction error (median 0 minutes, 95% confidence interval [CI] 0-0 minutes). Among the 37% of cases with changed predicted durations, there was a significant reduction in absolute error (median 38 minutes, 95% CI >7.5 minutes). For spine surgery, there was overall no reduction in the absolute error (median 0 minutes, 95% CI 0-0 minutes). Among the 29% of cases with changed predicted durations, absolute error was no worse, but not significantly better (point estimate of median reduction 34 minutes, 95% CI >0 minutes). Secondary observations made were no effect of updates on bias, frequent rounding of scheduled durations to the nearest half hour, and increased predictive error caused by decisions that reduced expected overutilized OR time. A systematic program of routinely and/or always asking for updated case duration predictions will not substantively improve OR management decision making. However, when a change in surgical approach, surgical procedure, or anesthetic procedure is identified (e.g., at the intraoperative briefing before case start), the updated estimate of case duration should be used, because such updates are not worse and often better than original estimates.
    Anesthesia and analgesia 02/2010; 110(4):1164-8. · 3.08 Impact Factor
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    ABSTRACT: The economic costs of reducing first case delays are often high, because efforts need to be applied to multiple operating rooms (ORs) simultaneously. Nevertheless, delays in starting first cases of the day are a common topic in OR committee meetings. We added three scientific questions to a 24 question online, anonymous survey performed before the implementation of a new OR information system. The 57 respondents cared sufficiently about OR management at the United States teaching hospital to complete all questions. The survey revealed reasons why personnel may focus on the small reductions in nonoperative time achievable by reducing tardiness in first cases of the day. (A) Respondents lacked knowledge about principles in reducing over-utilized OR time to increase OR efficiency, based on their answering the relevant question correctly at a rate no different from guessing at random. Those results differed from prior findings of responses at a rate worse than random, resulting from a bias on the day of surgery of making decisions that increase clinical work per unit time. (B) Most respondents falsely believed that a 10 min delay at the start of the day causes subsequent cases to start at least 10 min late (P < 0.0001 versus random chance). (C) Most respondents did not know that cases often take less time than scheduled (P = 0.008 versus chance). No one who demonstrated knowledge (C) about cases sometimes taking less time than scheduled applied that information to their response to (B) regarding cases starting late (P = 0.0002). Knowledge of OR efficiency was low among the respondents working in ORs. Nevertheless, the apparent absence of bias shows that education may influence behavior. In contrast, presence of bias on matters of tardiness of start times shows that education may be of no benefit. As the latter results match findings of previous studies of scheduling decisions, interventions to reduce patient and surgeon waiting from start times may depend principally on the application of automation to guide decision-making.
    Anesthesia and analgesia 04/2009; 108(4):1257-61. · 3.08 Impact Factor
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    ABSTRACT: Previous studies of operating room (OR) information systems data over the past two decades have shown how to predict case durations using the combination of scheduled procedure(s), individual surgeon and assistant(s), and type of anesthetic(s). We hypothesized that the accuracy of case duration prediction could be improved by the use of other electronic medical record data (e.g., patient weight or surgeon notes using standardized vocabularies). General thoracic surgery was used as a model specialty because much of its workload is elective (scheduled) and many of its cases are long. PubMed was searched for thoracic surgery papers reporting operative time, surgical time, etc. The systematic literature review identified 48 papers reporting statistically significant differences in perioperative times. There were multiple reports of differences in OR times based on the procedure(s), perioperative team including primary surgeon, and type of anesthetic, in that sequence of importance. All such detail may not be known when the case is originally scheduled and thus may require an updated duration the day before surgery. Although the use of these categorical data from OR systems can result in few historical data for estimating each case's duration, bias and imprecision of case duration estimates are unlikely to be affected. There was a report of a difference in case duration based on additional information. However, the incidence of the procedure for the diagnosis was so uncommon as to be unlikely to affect OR management. Matching findings of prior studies using OR information system data, multiple case series show that it is important to rely on the precise procedure(s), surgical team, and type of anesthetic when estimating case durations. OR information systems need to incorporate the statistical methods designed for small numbers of prior surgical cases. Future research should focus on the most effective methods to update the prediction of each case's duration as these data become available. The case series did not reveal additional data which could be cost-effectively integrated with OR information systems data to improve the accuracy of predicted durations for general thoracic surgery cases.
    Anesthesia and analgesia 05/2008; 106(4):1232-41, table of contents. · 3.08 Impact Factor
  • Ruth E Wachtel, Elisabeth U Dexter, Franklin Dexter
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    ABSTRACT: Most surgical and anesthesia groups are interested in expanding their practices and recruiting more patients. Methods have been developed to help hospitals identify surgical specialties with the potential for growth by determining whether the hospital is performing fewer of certain types of procedures than expected in a given specialty. However, these methods are not appropriate for physicians who may practice at more than one hospital and want to determine the potential for growth in their regions. We examined potential markets for growth of surgical and anesthesia practices in Iowa and New York State using state discharge abstract data. Several patient demographic groups and several surgical specialties were examined. Each state was divided into regions, and data were analyzed three ways: (1) A similarity index compared each region to the rest of the state. (2) The number of procedures performed on patients who left their home regions for care was determined. (3) A similarity index compared procedures performed on patients who left their home regions for care with procedures performed on patients who remained within their home regions. The methods successfully identified several geographic regions with previously unrecognized growth potential. Access to care was limited in these regions. The methods correctly showed few opportunities for growth in geographic regions where expansion was already known to be unlikely. A count of the number of procedures performed on patients who left their home regions, in combination with the similarity index, is a useful method for screening state discharge abstract data to identify geographic regions where surgical and anesthesia practices could grow.
    Anesthesia and analgesia 06/2007; 104(5):1157-70, tables of contents. · 3.08 Impact Factor
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    ABSTRACT: Inpatient and outpatient data were used to create market segments consisting of hierarchical combinations of surgical procedure, then type of payer, and then location of patients' residences. The competitive effect of one hospital's caseload for a given surgical specialty on the caseload of another hospital was determined from the numbers of patients in each segment. Earlier methods for estimating surgical competition that ignored market segments over-estimated the competitive effects of one hospital on another. Thus, results differed from those obtained previously for all types of hospital admissions. When actual market segments with homogeneous groups of patients are used, competitive effects of hospitals in the same market area are far less than expected.
    Health Care Management Science 06/2005; 8(2):121-31. · 1.05 Impact Factor

Publication Stats

98 Citations
19.55 Total Impact Points

Institutions

  • 2010
    • University of Iowa
      • Department of Anesthesia
      Iowa City, IA, United States
  • 2005–2009
    • State University of New York Upstate Medical University
      • Department of Surgery
      Syracuse, NY, United States