Scheduled Start Time Research Articles

An Analysis of Risk Factors for Patient Complaints About Ambulatory Anesthesiology Care

Anesthesiology groups continually seek data sources and evaluation metrics for ongoing professional practice evaluation, credentialing, and other quality initiatives. The analysis of patient complaints associated with physicians has been previously shown to be a marker for patient dissatisfaction and a predictor of malpractice claims. Additionally, previous studies in other specialties have revealed a nonuniform distribution of complaints among professionals. In this study, we describe the distribution of complaints among anesthesia providers and identify factors associated with complaint risk in pediatric and adult populations. We performed an analysis of a complaint database for an academic medical center. Complaints were recorded as comments during postoperative telephone calls to ambulatory surgery patients regarding the quality of their anesthesiology care. Calls between July 1, 2006 and June 30, 2010 were included. Risk factors were grouped into 3 categories: patient demographics, procedural, and provider characteristics. A total of 22,871 calls placed on behalf of 120 anesthesiologists were evaluated, of which 307 yielded a complaint. There was no evidence of provider-to-provider heterogeneity in complaint risk in the pediatric population. In the adult population, an unadjusted test for the random intercept variance component in the mixed effects model pointed toward significant heterogeneity (P = 0.01); however, after adjusting for a prespecified set of risk factors, provider-to-provider heterogeneity was no longer observed (P = 0.20). Several risk factors exhibited evidence for complaint risk. In the pediatric patient model, risk factors associated with complaint risk included a 10-year change in age, the use of general anesthesia (versus not), and a 1-hour change in the actual minus scheduled start times. Odds ratios were 1.47 (95% confidence interval (CI), 1.04-2.08), 0.22 (95% CI, 0.07-0.62), and 1.27 (95% CI, 1.10-1.47), respectively. In the adult patient model, risk factors associated with complaint risk included male gender, general anesthesia, a 10-year change in provider experience, and speaking with the patient (rather than a family member). Odd ratios were 0.66 (95% CI, 0.47-0.92), 0.67 (95% CI, 0.47-0.95), 1.18 (95% CI, 1.01-1.38), and 1.96 (95% CI, 1.17-3.29), respectively. There was apparent evidence in adult patients to suggest heterogeneity in provider risk for a patient complaint. However, once patient, procedural, and provider factors were acknowledged in analyses, such evidence for heterogeneity is diminished substantially. Further study into how and why these factors are associated with greater complaint risk may reveal potential interventions to decrease complaints.

Improving On‐time starts for patients scheduled with general anesthesia in a MRI suite

We applied quality improvement methodology to identify unnecessary, redundant parts of processes that can lead to delayed on-time starts for patients scheduled with general anesthesia (GA) in the radiology department. To address the issue of delayed on-time starts by improving work flow for the first patient scheduled with GA. Unplanned imaging in a high-volume MRI suite can result in a significant ripple effect throughout the day. Delayed on-time starts can lead to patient, family, and staff dissatisfaction due to significant wait times. The team conducted a 5 month improvement project. Baseline data were obtained from pilot time studies allowed the team to identify reasons why the first case was not starting on time and to identify several key drivers to improve the process. Using the framework of small tests of change or the Plan-Do-Study-Act model, our key interventions primarily focused on standardizing the processes for completing the preimaging evaluation and for anesthesia induction. The primary objective measure of successful on-time start was defined as obtaining the first MRI image within 10 min of the scheduled start time, for the first patients of the day scheduled with GA. The secondary outcome measure was the extent of the delay quantified in minutes. Prior to the initiation of the project, only 36% of the first patients scheduled with GA each day met the primary objective measure. At the conclusion of the project 84% started on time. The secondary measure also showed significant improvement. Process improvement projects in anesthesia can yield positive results, using small incremental standardized changes. We used a quality improvement methods to successfully improve on-time start for patients scheduled with GA in high-volume MRI suite.

The Impact of Late-Starts and Overruns on Theatre Utilisation Rates

The NHS Management Executive recommends that hospitals should aim to use 90% of planned theatre time and that theatre utilisation should be used as a key performance indicator. This study aims to investigate the impact of late-starts and overruns on theatre utilisation rates. Data were retrieved from a prospectively updated theatre database for all elective plastic surgical main theatre operating sessions carried out over a one year period. Theatre list utilisation was calculated as the percentage of the total allocated session time that was used for anaesthesia and operating. A total of 2,944 elective main theatre operations were performed in one year. Total theatre utilisation was 90.9%. Utilisation of lists starting less than one hour after the scheduled start time was similar to the utilisation of sessions starting more than one hour late (90.1% versus 91.7% respectively, p = 0.527). In contrast, overrunning lists demonstrated much higher utilisation rates than those that finished before the end of the session (96.7% versus 76.6% respectively, p < 0.001). The study shows that late-starts and overruns represent obvious sources of theatre inefficiency yet their impact on utilisation is misleading: overruns exaggerate theatre usage and late-starts have little impact upon it. We conclude that the use of utilisation as a marker of theatre performance requires caution.

States of Exception: Anxiety, Panic, and the Nation

Although it happened over ten years from this writing ( January, 2012), the event we now call ‘‘9/11’’ still seems to have happened very recently. On that terrible day, I was teaching a large lecture class on the West coast of the United States. By 12:30 p.m. (the scheduled start time), the students would all have known about the attacks and, presumably, I thought as I walked from my office to the other end of campus where the class met, they would choose to spend the day with friends, with family, on the phone, glued to the television, at a bar—certainly not at a 90-minute class on Mary Rowlandson’s captivity narrative. I wasn’t even sure why I was going. There were about 120 students enrolled in that class. I would say about 140 showed up. We talked about the Event for the entire 90 minutes. Iw ill never forget the very fi rst comment made in that meeting: ‘‘We have to recognize we are now in a state of emergency.’’ ‘‘Now,’’ he said. Still? One would have to be very daft or very deft neither to recognize nor experience the lingering effects of 9/11 in the everyday life of the United States and Canada today. ‘‘Security’’ has become the stated goal of policy makers, an aspirational ideal of the managers of the republic where not a category of normalized identity for the managed: Airport security; Border Security; ‘‘Security Moms.’’ Public discourse encourages greater attentiveness to the monstrous and the mundane: Amber alerts; alertness to the ‘‘suspicious’’; alertness to unattended bags in train stations. The nation has even been renamed in and by a newly popular and official parlance alarming because largely unremarked: The Homeland: Department of, Defence of, Love of, America’s Blithe Acceptance of that Loaded Term as Description of. We now listen to ‘‘God Save America’’ rather than

Turnover time in arthroscopic shoulder surgery: A reduction of 18% by three simple strategies

Purpose: The aim of the study was to determine whether 3 strategies such as parallel anesthetic induction, improving first case start times, and dedicating porters to surgical theatres could reduce turnover times. Methods: A prospective interventional clinical study of operating room processes was conducted in a day surgery facility during arthroscopic shoulder surgery. The study included a 10-week observation period, then a 2-week education period, followed by a 10-week intervention period. Three intervention strategies were used as (1) parallel anesthetic induction, defined as performing the anesthetic induction on the next patient during the intraoperative period of the currently anesthetized patient; (2) a dedicated porter system, in which the porter completed his duties of room clean up and next patient prep before leaving the room; and (3) starting the first case within 10 minutes of scheduled start time. The major outcome was turnover time—the time between end of skin closure of the first patient to knife to skin of the next patient in the same theater. Minor outcomes included porter time (wheel out to wheel in), first case start times, and cost savings. Results: Intervention of the 3 strategies reduced turnover time by 9 minutes (18%), from 51±10 minutes (mean±SD) to 42±9 minutes. Porter times were reduced by 6 minutes (27%). First case start times improved by 12 minutes, although this did not affect turnover time per se. Total time savings per day was 61 minutes. The reduction in turnover time and improvement in first case start times resulted in a minimum net financial benefit of $430 per day to the day surgery facility.

Delays and Efficiency in the Bronchoscopy Suite

Procedural delays or late start times in the bronchoscopy suite can impact operational efficiencies and potentially patient care. To date, little data have been published examining the potential causes of such delays or ways to prevent them in the bronchoscopy unit. In a quality assessment project at a major medical center, we surveyed members of the bronchoscopy team to determine perceptions of the existence of delays in the bronchoscopy suite and possible causes. For comparison, we prospectively observed procedures in the bronchoscopy suite and retrospectively reviewed the records of past procedures to identify delays in the delivery of care. A total of 31 procedures were prospectively observed, with delays (defined a priori as procedures beginning 15 minutes after the scheduled start time) occurring 80% of the time. Two hundred thirty-four procedures were retrospectively reviewed, with delays occurring 71% of the time. In both data sets, the largest portion of time the patient spent in the bronchoscopy suite was during the preprocedure period. Delays often occur in the bronchoscopy suite, with preprocedure activities identified as an area for potential improvement.

Optimizing the Arrival, Waiting, and NPO Times of Children on the Day of Pediatric Endoscopy Procedures

Research in predictive variability of operating room (OR) times has been performed using data from multidisciplinary, tertiary hospitals with mostly adult patients. In this article, we discuss case-duration prediction for children receiving general anesthesia for endoscopy. We critique which of the several types of OR management decisions dependent on accuracy of prediction are relevant to series (lists) of brief pediatric anesthetics. OR information system data were obtained for all children (aged 18 years and younger) undergoing a gastroenterology procedure with an anesthesiologist over 21 months. Summaries of data were used for a qualitative, systematic review of prior studies to learn which apply to brief pediatric cases. Patient arrival times were changed to be based on the statistical method relating actual and scheduled start times (Wachtel and Dexter, Anesth Analg 2007;105:127-40). Even perfect case-duration prediction would not affect whether a brief case was performed on a certain date and/or in a certain OR. There was no evidence of usefulness in calculating the probability that one case would last longer than another or in resequencing cases to influence postanesthesia care unit staffing or patient waiting from scheduled start times. The only decision for which the accuracy of case-duration prediction mattered was for the shortest time that preceding cases in the OR may take. Knowledge of the preceding procedures in the OR was not useful for that purpose because there were hundreds of combinations of preceding procedures and some cases cancelled. Instead, patient ready times were chosen based on 5% lower prediction bounds for ratios of actual to scheduled OR times. The approach was useful based on a 30% reduction in patient waiting times from scheduled start times with corresponding expected reductions in average and peak numbers of patients in the holding area. For brief pediatric OR anesthetics, predictive variability of case durations matters principally to the extent that it affects appropriate patient ready times. Such times should not be chosen by having patients start fasting, arrive, and be ready fixed numbers of hours before their scheduled start times.

Influence of the Operating Room Schedule on Tardiness from Scheduled Start Times

Tardiness from scheduled start times in a surgical suite is a common source of frustration for both operating room personnel and patients. Data from two surgical suites were used to investigate the relative importance of various factors that contribute to tardiness, including average case duration, time of day, prolonged turnovers, whether a surgeon follows himself or another surgeon, the potential for starting cases early, concurrency (e.g., number of residents supervised simultaneously), expected under-utilized or over-utilized time, and case duration bias. Average tardiness per case did not depend on the individual durations of preceding cases or on the relative numbers of long and short cases. In contrast, the total duration of preceding cases was important in determining tardiness. Tardiness per case grew larger as the day progressed because the total duration of preceding cases increased, but began to decline for cases scheduled to commence 6 h after the start of the workday. Tardiness was not affected by prolonged turnovers, differences in average case duration among services, or whether a surgeon followed himself or another surgeon in the same operating room. Tardiness was affected by expected under-utilized or over-utilized time at the end of the workday and by case duration bias. Factors associated with the largest numbers of cases had the biggest influence on tardiness. Greater understanding of these factors aided in the development of several mathematical interventions to reduce tardiness in the two surgical suites. These interventions and their applicability for reducing tardiness are described in a companion article. At two surgical suites, tardiness from scheduled start times did not depend on average case duration or prolonged turnovers. Tardiness did depend on the total duration of preceding cases, expected under-utilized or over-utilized time at the end of the day, and case duration bias.

Reducing Tardiness from Scheduled Start Times by Making Adjustments to the Operating Room Schedule

Tardiness from scheduled start times is a common source of frustration for both operating room (OR) personnel and patients. Factors that influence tardiness were quantified in a companion paper and have been used to develop interventions that have the potential for reducing tardiness. Data from two surgical suites were used to compare the effectiveness of several interventions to reduce tardiness, including i) moving cases to different ORs on the afternoon of surgery, ii) recalculating the OR schedule when it is published to correct for average lateness in first cases of the day, iii) recalculating the OR schedule when it is published to correct for average service-specific case duration bias, and iv) scheduling a gap (time buffer) before the cases of a "to follow" surgeon if the day is expected to end early. These last three interventions involve creation of a modified schedule with revised start times that are more accurate for both patient and "to follow" surgeon. The surgeon performing the first case of the day would not be affected. Moving cases to different ORs when a room was running late produced a 50%-70% reduction in the tardiness for those cases that were moved. However, overall tardiness in each suite was reduced by only 6%-9%, because few cases were moved. Scheduling a gap between surgeons if the day was expected to end early reduced tardiness by more than 50% for those cases that were preceded by gaps. However, overall tardiness in each suite was reduced by only 4%-8%, because few gaps could be scheduled. In contrast, correcting for the combination of lateness in first cases of the day and service-specific case duration bias reduced overall tardiness in each suite by 30%-35%. Interventions which involve small numbers of cases have little potential to reduce overall tardiness. Generating a modified or auxiliary OR schedule that compensates for known causes of tardiness can significantly reduce patient and "to follow" surgeon waiting times. Modifying the OR schedule to create revised start times for patients and "to follow" surgeons involves interventions that are simple to perform. The official schedule is not changed and case sequencing is not altered. Results do not depend on changing surgeon, anesthesia provider, or nursing behavior.

Transmission of a Scheduled Message Using a Foundation Fieldbus Protocol

In the last decade, process instrumentation field devices such as sensors and actuators that have a direct contact with industrial processes have become increasingly intelligent. An important property of such intelligent field devices is their ability to be networked. The Foundation fieldbus (FF) is one of the most widely used fieldbus protocols for process instrumentation and control systems applications. This paper presents a study that validates the effectiveness of a new scheduling method that satisfies the performance requirements for a scheduled message using an FF protocol. Our scheduling method generates a schedule list that records the scheduled starting time and the scheduled period of each scheduled message. Then, it allocates scheduled message traffic to a bandwidth-limited fieldbus medium. As part of this study, an experimental model of an FF network was developed. The experimental results from our model confirmed that our scheduling method restricts the delay of a scheduled message to a prespecified bound.

A Simple Method for Deciding When Patients Should Be Ready on the Day of Surgery Without Procedure-Specific Data

Deciding when patients should arrive for same-day-admission or ambulatory surgery is a problem at many hospitals and surgery centers. Although staff can often start cases earlier than scheduled, the potential start times are not known when each case is scheduled. Patient availability must therefore be balanced against patient waiting times and fasting times. Knowing the earliest time that a case might begin, given its scheduled start time, provides a rational basis for telling patients when to report for surgery and when to refrain from eating or drinking before their procedure. We describe and validate a simple method for determining the earliest possible start time for a case, with only a 5% probability that staff would be able to start the case even earlier. Calculations use nonparametric methods to determine the 0.05 lower prediction bound for the start time of a case, using historical values for the scheduled and actual start times of cases performed by the same surgical suite/surgical service/day of the week combination as the case of interest. Information is not needed regarding the preceding cases performed in the same operating room. No patient or surgeon identifiable information is used. We use results from earlier studies to provide a derivation and theoretical justification for these methods. New data confirm the validity of the results obtained and show that the required calculations are easy to implement. Individualized patient instructions can be accessed via a public website without disclosing confidential information. We have developed a simple method for determining when patients should be ready on the day of surgery based on the start times of historical cases performed by the same surgical suite/surgical service/day of the week combination as the case of interest.

MONITORING WAITING TIMES (MEAN TARDINESS) ON THE DAY OF ELECTIVE SURGERY

Operating room management decisions on the day before and on the day of surgery are well understood and effective at reducing patient waiting from scheduled start times. We investigated monitoring each surgeon's patients’ mean tardiness. Discrete-event simulation shows that mean tardiness can be monitored and accurate confidence intervals obtained using 6 four-week periods of data. As mean tardiness was primarily determined by the scheduled workday's duration, the duration needs to be controlled for in benchmarking. There was no need to control for: 1) average OR time; 2) similarity of OR times among cases; or 3) whether cases can start early.

Incidence and causes of ocular surgery cancellations in an ambulatory surgical center

To report the incidence and analyze potentially preventable causes of ocular surgery cancellations. Ambulatory Care Surgical Center of the Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA. A retrospective review of the ambulatory surgical center cancellation records and patient medical records from December 2001 to December 2003 was conducted. The primary statistical analysis was conditional logistic regression. Three hundred seventy-nine of 7153 (5.3%) ambulatory ophthalmic surgeries were cancelled within 24 hours of the scheduled start time. Cancellation rates varied by patient age, with the rate among children being highest (8.7%) and that among older patients (age 60+) lowest (4.9%; P = .08). Surgeons who performed at least 4 surgeries per month on average had the lowest cancellation rate (P = .08). Cancellations occurred less frequently in warmer months (June, 3.3%; August, 4.2%) than during the rest of the seasons (P<.001). The highest incidence of cancellations occurred in February (7.8%) and the lowest in June (3.3%). Of the total causes, 41% were considered "preventable," 45% "unpreventable," and 14% "no reason given." Cancellations deemed preventable were lower in general anesthesia cases (1.0%) than in local anesthesia cases (2.0%; P = .02). Preventable cancellation rates also varied by procedure and were statistically significant. Among ambulatory ophthalmic surgeries, there was a higher incidence of late cancellations in pediatric cases. Late cancellation rates were highest in cases scheduled in the winter, especially in February. Of the reasons documented for cancellations, 41% were considered "preventable" with proper preoperative counseling and instructions. The costs of late cancellations to the particular institution are estimated to be at least $100 000 per year, or nearly 1 month of scheduled surgeries in a 2-year period.

Bayesian Prediction Bounds and Comparisons of Operating Room Times Even for Procedures with Few or No Historic Data

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. This Bayesian method was tested with all 65,661 cases from a hospital. 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. 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).

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).

Improving anesthesiologist performance through profiling and incentives

Study objective To determine the influence of profiling and incentives on anesthesiologist behavior in relation to several key indicators of performance. Design Prospective collection and analysis of operational data before and after implementation of a physician profiling, reporting, and incentive program. Setting University hospital. Measurements An intervention consisting of two components was studied with the intent of stimulating a high level of performance in relation to a peer group. The first component, a monthly report of physician performance via an individualized performance report, was provided to each physician for each of 6 months. The second component consisted of a financial incentive. For each month in the study, physicians were eligible to receive a variable financial incentive of between $0 and $500 per month depending on individual performance based scoring in relation to each other. Physician performance was tracked in five areas: 1) percentage of first cases of the day in the room at or before the scheduled in-room time, 2) percentage of cases with an anesthesia prep time less than a target, 3) percentage of cases delayed due to waiting for an anesthesiology patient evaluation, 4) percentage of cases delayed during the anesthesiology controlled time, and, 5) percentage of cases delayed due to waiting for the anesthesiology attending. Results were reported to each physician on a monthly basis, by e-mail distribution, of an individualized perioperative efficiency summary report. A monthly financial incentive was awarded to the top performing physicians in the form of a credit to the physician's personal CME/expense account. Also, all physicians received a rank order list of their performance on each indicator at the end of each month. Main results 31 anesthesiologists, comprising the multispecialty division, and covering all services with the exception of obstetrics, pediatrics, and cardiothoracic anesthesia were tracked for 6 months. Compared to the first month, the percent of first cases of the day in the room at or before the scheduled start time and the percent of cases with an anesthesiology prep time less than target increased significantly (19 ± 4.6%, vs. 61 ± 6.5%, 95% CI, p < 0.001; and 57 ± 5.3%, vs. 73 ± 5.1%, 95% CI, p < 0 .001) during the sixth month. The mean number of cases per physician with a delay during anesthesiology controlled time decreased (14.9 ± 2.9 vs. 3.3 ± 1, p < 0.001), no change occurred in the number of cases with a delay due to waiting for an anesthesiology patient evaluation or number of cases delayed due to waiting for the anesthesiology attending in the sixth month compared with the first month. Conclusion Tracking and rewarding physician performance with monthly profiling and a financial incentive given to the best in a peer group improves anesthesiologist performance in several key areas.

Presence technology: More than just instant messaging

Presence technology has long been an integral part of instant messaging. The technology lets users determine if their contacts are online, signed onto their IM applications, and ready to receive messages. This makes IM different from traditional forms of communication, in which users send out messages or calls without knowing whether the recipient is present to receive them. Now, presence technology is starting to appear in numerous programs other than IM. For example, Microsoft is adding presence to Word, Excel, and other applications. In addition, IBM is adding presence capabilities to Lotus Notes 6.5. Thus, for example, when the scheduled starting time for a user's meeting arrives, the system could show people that the user is unavailable.

Irrigation Scheduling Using Mixed-Integer Linear Programming

A mixed-integer program is presented for scheduling canal irrigation among a group of users where the duration of flow of each outlet and a target start time is specified by the users. Two models are developed. The first is a single-period model which uses as input a minimized demand at the head of the canal. This will allow the discharge at the head of the canal to be set once at the beginning of the irrigation period. Within this constraint, the single-period model minimizes the sum over all outlets of the difference between the scheduled start time of flow to each user and the requested target start times. The second model is a multiperiod model. This model favors those users who had been disadvantaged in the previous irrigation periods by giving them priority in scheduling their actual start times with target start times for the subsequent irrigation period. The proposed models can be used to support decisions in irrigation schemes operating on an arranged demand.

A COMIT Model Utilization to Improve First-Case Start Time

The purpose of this article is to review the problems and processes impacting the deviation in first-case actual start times when compared to first-case scheduled start times. This article will also discuss the Continuous Outcomes Measurement and Improvement Technique (COMIT) model for problem resolution and share the experiences encountered by a group of collaborative nurse manager leaders in their outcome improvement efforts. The overall process improvements are discussed using the eight-step COMIT model, which showed the overall success of the project.

The six levers for managing organizational culture

Schein describes three levels of culture: artifacts'; shared values; and shared basic assumption& • Art i fac ts are visible, audible, tactile manifestations of underlying cultural assumptions, such as behavior patterns, rituals, physical environment, dress codes, stories, and myths. They are relatively easy to understand. For example, many firms have dress codes, such as uniforms or proper business attire, that are indicative of some underlying culture. When IBM and Lotus merged some years ago, the prediction of a cultural clash was due, in part, to this outward manifestation of their cultures--IBM's dress code that stipulated proper business attire, and Lotus employees wearing T-shirts and sandals. More recently, a merger between Beth Israel and Deaconess Hospitals in Boston encountered a clash in cultures, one outward manifestation of which was arrival time for meetings. At Beth Israel, meetings started in which meant they began about 15 to 20 minutes after their scheduled start time. There was no such artifact at Deaconess, and shortly after the merger, individuals from Deaconess would become irate while waiting for BI people to arrive so that a meeting could begin. • S h a r e d va lues , also quite easily understood, are the espoused reasons why things should be as they are, such as norms, codes of ethics, company value statements, and so on. For example, many firms have goal or mission statements boldly emblazoned in their reception areas for all to see, informing both their customers and their employees what the firm stands for, and saying something about the espoused culture as well. Similarly, some firms have codes of ethics, which are often related to the professional norms of their employees. Doctor-patient and lawyer-client relationships are examples. Other firms have rather rigid rules of behavior, some of which constitute grounds for dismissal when broken. A company might forbid its employees to discuss their salaries, with termination as the consequence of breaching the rule. • Basic a s s u m p t i o n s are somewhat more difficult to define and examine. They comprise the invisible but identifiable reasons why group members perceive, think, and feel the way they do about external survival and internal operational issues, such as a mission, means of problem solving, relationships, time, and space. They can be viewed most easily in terms of behavior that one would find totally unacceptable, as well as, of course, its counterpart: highly desirable behavior. The importance of saving face (especially in Asia), the relevance of financial rewards as performance motivators, and a subordinate's ability to question a supervisor's decisions are all examples of these basic assumptions.