Decentralized Autonomous Organizations (DAOs) have seen exponential growth and interest due to their potential to redefine organizational structure and governance. Despite this, there is a discrepancy between the ideals of autonomy and decentralization and the actual experiences of DAO stakeholders. The Information Systems (IS) literature has yet to fully explore whether DAOs are the optimal organizational choice. Addressing this gap, our research asks, "Is a DAO suitable for your organizational needs?" We derive a gated decision-making framework through a thematic review of the academic and grey literature on DAOs. Through five scenarios, the framework critically emphasizes the gaps between DAOs' theoretical capabilities and practical challenges. Our findings contribute to the IS discourse on blockchain technologies, with some ancillary contributions to the IS literature on organizational management and practitioner literature.
Ravi Naik, Kaizhe Jin, Alexandros Kogkas, Hutan Ashrafian · 6 authors
The operating room represents a high-risk environment centred around the safe and efficient delivery of patient care. It is a complex ecosystem that encompasses many factors including communication within the multidisciplinary surgical team often led by the operating surgeon as well as execution of precise technical surgical skill. These factors are associated with the mental or cognitive workload (CWL) of the surgeon. CWL, also described as the mental effort exerted while undertaking a task, is a construct derived from the cognitive load theory first described in the eighties during problem solving exercises [1]. There has been a growing emphasis on the measurement of CWL since then on individuals working in high-stake environments such as aviation [2]. Measurement of CWL in surgery is moving from solitary traditional subjective measures such as the Surgical Task Load Index (SURG-TLX) to objective measurement of physiological parameters secondary to changes in the CWL of the surgeon which are less exposed to subjective bias [3]. These have included heart rate variability (HRV), pupil metrics, electromyography (EMG), electroencephalography (EEG), skin conductance and functional near-infrared spectroscopy (fNIRS). More recently, there is increasing evidence to demonstrate the use of multiple sensors, or a multimodal sensor system designed to measure CWL with greater accuracy [4]. The aim of this paper is to demonstrate the use of a pilot synchronised system of multiple sensors to measure the real-time cognitive workload of surgeons in a simulated setting to demonstrate a proof of concept and to discuss the early findings.
Paul B. Romesser, Grace L. Smith, Christopher H. Crane
Locally advanced rectal cancer (LARC) treatment traditionally includes preoperative chemoradiation, radical surgery, and postoperative chemotherapy (1). Although highly effective, this standard leads to substantial rates of long-term morbidity, including permanent colostomy, low anterior resection syndrome, urinary dysfunction, and sexual dysfunction (1–3). Patients with mid- and low rectal cancers who achieve a clinical complete response (cCR) present a dilemma to the thoughtful surgeon because the preoperative discussion must include the very real possibility that the specimen would not contain cancer. Patients’ refusal of radical surgery and surgeons’ desire to balance oncologic outcomes with quality of life (QOL) have led investigators to embark on organ preservation strategies in complete responders. Although the nonoperative management (NOM, also known as watch-and-wait) strategy has been increasingly accepted, radical surgery is still considered the standard, and randomized evidence determining the comparative effectiveness of NOM remains to be established. In this issue of the Journal, Miller and colleagues evaluate other important considerations of effectiveness, specifically the cost-effectiveness and quality-adjusted survival of NOM (4). The NOM strategy was initially introduced by Habr-Gamma and colleagues in 2004 (5). Patients with LARC who achieved a substantial clinical response to chemoradiation were offered NOM if they were agreeable to stringent monthly clinical reassessments (5). The 5-year overall survival and disease-free survival of the 72 patients who underwent NOM were 100% and 92%, respectively (5). Importantly, all of the patients with regrowth of the primary tumor were successfully salvaged with mesorectal excision. Although initially controversial, this pioneering work provided proof of principle that the majority of patients who achieve a cCR to preoperative chemoradiation can be managed without radical surgery. Multiple groups have subsequently reported 70–80% durable local disease control in LARC complete responders on NOM, and close surveillance has led to timely salvage surgery with no clear oncologic disadvantage (6–13). Miller and colleagues developed a decision-analytic Markov model to evaluate cost-effectiveness of this strategy compared with standard radical surgery (4). Importantly, with respect to clinical interpretation and application, this analysis includes only patients who have achieved cCR to neoadjuvant therapy. NOM was found to have incremental cost savings of $28 500 and $32 100 and incremental benefit in quality-adjusted-life years of 0.527 and 0.601 compared with low anterior resection and abdominoperineal resection, respectively. The main cost differences were predominantly driven by the upfront cost of surgery, which was absent or deferred in patients managed with NOM and statistically significantly offset the added cost of enhanced screening. Results were presented from a US payer perspective and validate similar findings demonstrating cost-effectiveness of NOM from a UK payer perspective (14). The model by Miller and colleagues (4) further suggests quality-adjusted survival benefit from NOM, based on population-based QOL and health utility data. This finding highlights the need for ongoing studies of NOM to detail patient-reported QOL measures that encompass pain, symptoms, body image, and sexual function, data that will ultimately be needed to inform individual treatment decision making about NOM for clinically eligible patients. In the setting of unavoidably increasing limits on health-care resources, the authors’ finding of the incremental cost savings of NOM after cCR also suggests that developing therapeutic strategies to optimize and expand cCR rates could substantially affect not only individual patient outcomes but also public health. There is considerable interest in expanding the pool of eligible patients for organ preservation by increasing the cCR. One strategy that appears to be successful is giving the adjuvant chemotherapy component of treatment before surgery, either before chemoradiation (induction) or after completion of chemoradiation (consolidation). This strategy has become known as total neoadjuvant therapy (TNT). The TIMING trial (NCT00335816) showed that delivering increasing number of cycles (zero, two, four, or six cycles) of consolidative FOLFOX in sequential cohorts of patients with LARC increased the pathologic complete response (pCR) rate to 25%, 30%, and 38%, respectively, compared with 18% for chemoradiation alone, suggesting that giving all of the chemotherapy upfront while increasing the time to surgery increases the pCR (15). A recent meta-analysis of 10 comparative studies demonstrated that TNT increased the likelihood of a pCR by 39% (16). An ongoing, randomized phase II trial of TNT compares sequencing 5-Fluorouracil, Leucovorin, and Oxaliplatin before or after chemoradiation, in which patients with LARC who achieve a cCR are offered NOM (NCT02008656) (17). A similar trial has recently been reported with planned surgery showing a slight increase in pCR with consolidation (18). Efforts also continue in earnest to develop selective radiosensitizers that may improve pCR. NRG-GI002 is an ongoing phase II study evaluating sequential experimental arms integrating radiosensitizers into a TNT platform in patients with high-risk LARC. (NCT02921256). Although TNT strategies have resulted in higher response rates, approximately two-thirds of patients with LARC still require radical surgery (19). As efforts continue, better regimens will likely emerge that will improve complete response rates, which will change the proportion of patients eligible for NOM and ultimately increase the acceptance of the NOM approach. Although the results from Miller et al. will not likely persuade nonbelievers, they do provide an important contribution to our understanding of the advantages of a NOM strategy (4). Although the concept of organ preservation for patients with LARC is appealing, prospective randomized cooperative group studies are needed to confirm the oncologic noninferiority of NOM and the applicability of NOM to routine community oncological practice. In Brazil, Cecconello and colleagues are conducting a randomized phase II trial comparing the 3-year disease-free survival of NOM and radical surgery in LARC patients who achieve a cCR after preoperative chemoradiation (NCT02052921) (20). Although this is an important first step, widespread adoption of NOM will likely be limited and the use of NOM will likely remain controversial until randomized phase III data demonstrate noninferiority and improved patient reported outcomes. The feasibility of randomizing patients to radical surgery vs organ preservation will likely be challenging in the United States because of patient preferences either for or against radical surgery. Nevertheless, outcomes data from prospective randomized trials are critical to provide the knowledge needed to harmonize the insights gained from a cost-effectiveness model of NOM with real-world clinical practice. P. B. Romesser is a consultant for EMD Serono for work on radiation sensitizers. The other authors have no disclosures.
It is our contention that the authors of many clinical trials in anesthesia are not fully considering the implications of the minimum effect size of interesta entered in their power calculations, and are therefore making conclusions that are not supported by their findings. In this paper we use hypothetical examples to explain how the choice of minimum effect size of interest in the design of clinical trials sets conditions on their interpretation, including the threshold for clinical relevance, the magnitude of effect sizes that can be confidently excluded, and the discrimination between primary and secondary outcomes. We then use examples from a sample of recent highly cited anesthesia trials to show that this is a problem, which as a specialty we should be addressing. THE MINIMUM EFFECT SIZE OF INTEREST IN THE DESIGN OF CLINICAL TRIALS Let us suppose that a group of researchers wishes to investigate whether a new short-acting antihypertensive drug has a clinically worthwhile treatment effect for the attenuation of the arterial blood pressure response to tracheal intubation. The first step in planning their study is to estimate the sample size required to detect a clinically worthwhile difference for their primary outcome (in this case the blood pressure response), with adequate power.1–4 The alternative, using a confidence interval (CI) approach, is to predefine an acceptable CI width for the primary outcome.b5,6 After careful consideration of all factors, including drug costs, they decide that the minimum clinically worthwhile difference or “threshold for clinical relevance” is 10 mm Hg. This is their minimum effect size of interest. They perform a t test with a null hypothesis that there is no difference between the new drug versus control. They accept a type I error rate of 5% (α = 0.05; i.e., P < 0.05 will be considered significant), and a type II error rate of 20% (β = 0.2, i.e., power = 80% [power = the probability of getting a statistically significant result if there is a true difference ≥ the specified minimum effect size of interest]).1–4 They anticipate that the SD in both groups will be about 10 mm Hg. With these variables they require n = 16 in each group.7 (If they had chosen a minimum effect size of interest = 5 mm Hg, they would have required n = 63 in each group; alternatively, with n = 16 in each group, their power would be reduced to around 29%.)7 THE ROLE OF THE MINIMUM EFFECT SIZE OF INTEREST IN THE INTERPRETATION OF CLINICAL TRIALS Scenario 1: No Significant Difference In a hypothetical scenario, the authors find that the mean difference is 6 mm Hg (95% CI −0.4 to 12.4 mm Hg), P = 0.06. They accept (or more correctly, fail to reject) their null hypothesis and conclude that there is “no difference” between the groups. This conclusion is not correct. A nonsignificant finding does not support a conclusion of no difference without qualification.2–4,6 Nonsignificant findings are qualified by the minimum effect size of interest entered in the power calculation, and the power. This is because the minimum effect size of interest entered in the power calculation is also the “minimum detectable difference” of the trial.1–4 The trial does not exclude or confirm a difference up to this value (in this case 10 mm Hg). Moreover, the power (1 – β, in this case 80%) defines the likelihood of a type II error (β, in this case 20%). In other words, in this scenario there is still a 20% chance that there is a true difference ≥10 mm Hg, even though the investigators failed to find a statistically significant difference. This is very different from an unqualified conclusion of “no difference”! Moreover, while the null hypothesis may not have been rejected, the actual P value continues to provide important information about the probability of observing the finding (or one more extreme) given that the null hypothesis is true. For example, in this case the probability is 6%; in contrast, if the P value were 0.6 rather than 0.06, it would be 60%. Furthermore, the observed point estimate and its 95% CI remain the best estimate of the difference between groups, whether or not statistical significance is reached. For example, an observed 95% CI of −5.4 to 7.4 mm Hg would likewise have been “not statistically significant,” but would have suggested that the true point estimate is closer to 1 mm Hg, which would be very different than the actual example, where the most likely point estimate is about 6 mm Hg. The importance of the minimum effect size of interest and power when interpreting nonsignificant findings becomes clearer when we consider the possibility of smaller true effect sizes. For example, let us say that the drug cost turns out to be lower than expected and that most clinicians would accept a 5 mm Hg difference as clinically worthwhile, rather than the 10 mm Hg used by the authors. How then does the information from this trial help them in their decision whether to use the drug? The answer is very little. The trial was designed to have sufficient probability of detecting a difference, given that the true difference is ≥10 mm Hg. It provides little information on the probability of detecting a difference, given a true difference <10 mm Hg. Scenario 2: Significant Difference and Observed Effect ≥ Minimum Effect Size of Interest In another hypothetical scenario, the authors find a mean difference of 12 mm Hg (95% CI 0.5 to 23.5 mm Hg), P = 0.04. They reject their null hypothesis (acknowledging a 5% chance that they are making a type I error) and correctly conclude that there is a statistically significant treatment effect. They also correctly conclude that this treatment effect is likely to be clinically worthwhile, because the point estimate is at least as large as their predefined minimum clinically worthwhile difference (= minimum effect size of interest stipulated in their power calculation). Scenario 3: Significant Difference and Observed Effect < Minimum Effect Size of Interest In yet another hypothetical scenario, the authors find a difference of 6 mm Hg (95% CI 0.3 to 11.7 mm Hg), P = 0.04. They correctly conclude that there is a statistically significant treatment effect. But what should they conclude about whether the effect is clinically worthwhile? The hypothesis they tested was that there was no difference between the groups (null). The P value <0.05 supports rejection of this hypothesis. However, it does not provide information on the likely magnitude of the effect or whether it is clinically worthwhile. To assess whether the observed effect size is worthwhile, it is necessary to refer to the minimum clinically worthwhile difference decided before the trial began, and which was entered as the minimum effect size of interest in the power calculation (in this case 10 mm Hg). Therefore, the correct conclusion is that while there is a statistically significant effect in this particular trial (i.e., P < 0.05), the observed effect (6 mm Hg) is too small to be clinically worthwhile (i.e., <10 mm Hg). The authors may not accept this conclusion. They may argue that the 95% CI around their point estimate of 6 mm Hg includes 10 mm Hg, so their finding is still compatible with a clinically worthwhile difference. However, they would have to concede that the same 95% CI would be compatible with a range of effect sizes as small as 0.3 mm Hg, and that the true effect size is most likely closer to the point estimate of 6 mm Hg (i.e., <10 mm Hg). The authors may also argue that their original 10 mm Hg was not a true minimum clinically worthwhile difference, and was chosen only for pragmatic reasons to limit the required sample size. They may argue that 5 mm Hg is a more realistic value in any case. However, their trial did not have adequate power (i.e., only around 29%) to detect a ≥5 mm Hg difference.7 The authors may argue that the power is now irrelevant, because they have observed a statistically significant difference. This is a misconception, because there is no guarantee that a repeat trial with the same sample sizes would provide another significant result.8,9 The likelihood of reproducing a significant result (assuming that a true difference ≥ the stipulated minimum effect size of interest exists) is equal to the power of the trial.8 For example, with 80% power, if the trial were repeated using different samples of the same size, there would be an 80% chance of again observing a significant difference.8 In contrast, with 29% power, the likelihood would be only around 29%.8 It would be difficult to be conclusive about any finding with this low level of replicability. For this reason, the power of a study is important for both negative and positive findings. Reducing the minimum effect size of interest after the fact reduces the power, thereby reducing the likelihood of replicating a finding. In effect, the authors are faced with a dilemma. Either they accept that the observed effect size is too small to be clinically worthwhile, or they accept that they cannot be confident that the finding has >80% chance of being repeated. Neither of these supports a conclusion of a reproducible clinically worthwhile effect. THE MINIMUM EFFECT SIZE OF INTEREST AND PRIMARY VERSUS SECONDARY OUTCOMES Let us say the authors also assessed the heart rate (HR) response, but because this was a secondary outcome, they did not perform a power calculation. They found that the mean difference in HR was 6 beats per minute (bpm) (95% CI −1 to 13 bpm), P = 0.10. How should they interpret this finding? To interpret it correctly they need to refer to the minimum effect size of interest (= minimum detectable difference) in the power calculation and the power. Clearly, if these values are not presented, it is not possible to make a meaningful interpretation. Similarly, it would be difficult to interpret a P value <0.05, because without knowing the power and the minimum effect size of interest, it would not be clear how likely the result could be replicated.8,9 Unfortunately, it is not possible to extrapolate power from other outcomes.8,9 Moreover, it is not appropriate to perform a power calculation once the results are already known.6 The authors may argue that a power calculation is not necessary, because the CI alone provides sufficient information. This is another misconception. The CI does not provide sufficient information on the adequacy of the sample size, a major determinant of the CI width.5,6 Perhaps a larger sample size for the HR outcome (with the same sample variability) would have reduced the CI width around the same point estimate sufficiently for the lower limit to be above zero? In fact, ensuring an adequate sample size is equally important using CI as it is using inferential tests, as is predefining a clinically worthwhile difference.5,6 For these reasons, it is not possible to make conclusions about secondary outcomes, unless they are accompanied by this information.9,10 They might still be important (depending on the point estimate and the actual P value or the 95% CI), but remain as “observations” until confirmed or excluded in future studies.9,10 Nevertheless, let us say that in this particular trial the authors made conclusions about both blood pressure and HR responses without specifying which was the primary outcome. A quick check of the minimum effect size of interest would identify the blood pressure response as the primary outcome (i.e., the outcome for which the power had been calculated, the sample size estimated, and the threshold for a clinically worthwhile difference set).9 In this way the minimum effect size of interest discriminates between primary and secondary outcomes. A SAMPLE OF HIGHLY CITED ANESTHESIA TRIALS We identified 20 highly cited prospective anesthesia trials by interrogating the ISI Web of knowledge (http://apps.isiknowledge.com/, accessed December 2010) using the following search strategy: topic = anesthesia or anaesthesia; journal = Lancet, New England Journal of Medicine, Anesthesiology, Anesthesia and Analgesia, or British Journal of Anaesthesia; year of publication = 2001 to 2010, with ranking of trials by number of citations.11–30 Publications other than prospective clinical trials were excluded. We scrutinized the top 20 most cited trials for conclusions that were not supported by the minimum effect size of interest stipulated in their power calculations. We did not recheck any statistical analysis or assess any other aspect of the trials. Conclusions Based on Secondary Outcomes There were 10 trials that based 1 or more conclusions on secondary outcomes (for which no minimum effect size of interest or power was provided) (Table 1).15,17,18,21,22,24,27–30 Three trials even included the findings of a secondary outcome in their title (Table 1).15,17,27 In many cases, it was not possible to differentiate between primary and secondary outcomes without reference to the minimum effect size of interest in the power calculation.Table 1: Studies that Include Secondary Outcomes in Their ConclusionsConclusions Based on Statistically Significant Findings too Small to Be Clinically Worthwhile There were 5 trials with statistically significant findings for their primary outcome, but with an observed effect size less than their minimum effect size of interest (Table 2).13,17–19,30 For example, Myles et al. chose a minimum effect size of interest of 0.9% “because uptake into routine practice would require convincing proof of benefit.“13 Yet they observed a mean effect size of only 0.74%.13 Similarly, Carli et al. specifically chose a “minimum effect size of interest” of 36 m walked in 6 minutes, because this difference produced “a meaningful impact” on long-term exercise capacity.17 Yet they observed a mean difference of only 33.6 m at 3 weeks, and 18 m at 6 weeks.17 Neither Myles et al. nor Carli et al. concluded that their observed effect was too small to be clinically worthwhile. Similar considerations apply to the other 3 trials in this category (Table 2).18,19,30 Only Myles et al. presented the 95% CI for their observed effect size. The remainder either presented no CI for the observed effect size, or presented CI in a different metric to the minimum effect size of interest (Table 2).Table 2: Studies with a Statistically Significant Primary Outcome but an Observed Effect Size Less than the Minimum Effect Size of InterestConclusions Based on Nonsignificant Findings—Unable to Exclude All Clinically Worthwhile Effect Sizes Four trials had nonsignificant findings for their primary outcomes (Table 3).11,22,26,27 Scrutiny of their minimum effect size of interest indicated that none could confidently exclude all clinically worthwhile effect sizes. For example, they were powered to detect differences in the incidence of morbidity and mortality ≥10%, length of stay ≥2.5 days, awareness incidence ≥0.9%, and block success rate ≥23%, respectively. Yet effect sizes below these ranges might still be considered clinically worthwhile. (e.g., mortality and morbidity reduction of 9%, length of stay reduction of 2 days, incidence of awareness reduction of 0.8%, block success rate improvement of 22%). Only Rigg et al. explained that they could not confidently exclude the possibility of a worthwhile true effect size less than the minimum effect size of interest stipulated in their power calculation.11 Only Avidan et al. presented the 95% CI for their observed effect size (which was instead of a P value from an inferential test).26Table 3: Studies with Nonsignificant Findings for the Primary OutcomeConclusions Based on Findings with no Power Analysis or Stipulation of Minimum Effect Size of Interest There were 4 trials with no power calculation or minimum effect size of interest for any outcomes.12,16,23,25 None of these explained that their findings could not be fully interpreted without this information. CONCLUSION To fully interpret a clinical trial in which inferential statistics are used, it is necessary to go beyond effect size, and consider also the minimum effect size of interest stipulated in the power calculation. This is an important value, which not only has a major influence on the required sample size, but also defines the threshold for clinical relevance for positive findings, and the minimum detectable difference for negative findings (Fig. 1). Readers should also scrutinize the value chosen by the authors, to determine if it is appropriate. Failure to consider the minimum effect size of interest may result in erroneous conclusions, such as conclusions based on secondary outcomes, on outcomes that are statistically significant but not clinically worthwhile, or on nonsignificant findings that do not exclude the possibility of a smaller, but nevertheless true clinically worthwhile treatment effect. We have provided examples of such conclusions in a sample of highly cited anesthesia trials in a selection of high-impact-factor journals. Given their criteria for selection, it is unlikely that these trials represent a negatively biased sample in terms of quality of statistical reporting. We suspect that similar findings would be found in any sample of anesthesia trials. To address this situation, we recommend greater rigor in the design and interpretation of clinical trials, with closer scrutiny of the minimum effect size of interest (by both authors and readers), adequate power, and a focus on primary rather than secondary outcomes. For key secondary outcomes, we recommend that additional a priori power calculations be provided, along with their minimum effect sizes of interest. The use of CI for the observed effect size has advantages, because CI provide information on the most likely true effect size and the range of likely true effect sizes for both primary and secondary outcomes. Nevertheless, the same principle of defining the minimum clinically worthwhile effect size before the trial commences applies, as well as holding to this value when interpreting outcomes, and ensuring that an adequate sample size was used.Figure 1: The central role of the minimum effect size of interest in the design and interpretation of clinical trials. Once chosen, the minimum effect size of interest determines the sample size required (for any given level of power, α, and sd of the samples). The threshold for clinical relevance for the observed effect size and the minimum effect size detectable (given the power) are mathematically equal to this value. As sample size cannot be changed once the trial is completed, none of the values can be altered post hoc without affecting the trial's power.DISCLOSURES Name: Neville M. Gibbs, MD, FANZCA. Contribution: Study design, conduct of study, data analysis, and manuscript preparation. Name: William M. Weightman, MB, FANZCA. Contribution: Study design, conduct of study, data analysis, and manuscript preparation. This manuscript was handled by: Franklin Dexter, MD, PhD.
Rarely in medicine does one observe the adoption of a new technology as it moves from infancy (and a domain of early adopters) into the realm of widespread, general use. Anesthesiologists may be an exception; they have long been in the vanguard of new technology adoption as a part of an ongoing quest for improved patient safety. More recently, however, technological developments in anesthesiology have involved information systems. These systems' potential to improve patient care is not so traditionally obvious as something such as a new physiologic monitor or a better anesthesia machine. Hence, the adoption of anesthesia information management systems (AIMS) has been slow, in part, because they are regarded as expensive, “optional” technology with little direct patient benefit. However, a new study by Halbeis et al. indicates a sharp uptick in the number of academic anesthesia departments that are either in the process of installing an AIMS, or have allocated resources to do so in the near future.1 The authors suggest that adoption of AIMS in academic departments is passing through a “tipping point,” as defined by Gladwell, wherein a new idea catches on and penetrates the culture widely.2 In other words, AIMS appear on the verge of completing the adoption lifecycle. Suddenly, anesthesia departments are finding themselves heavily involved in information systems (IS) either as clients or, in many cases, as the “business owners” of their own IS groups. Once installed, AIMS applications quickly become critical to the department's financial health and daily clinical activities. This focuses a sharp lens on the resources required to operate an AIMS. Limited IS funds and competition for priority are frequently cited reasons for delayed or deferred AIMS adoption in the Halbeis et al. study.1 This state of affairs commands attention from potential AIMS adopters, as every center with any substantial AIMS experience has learned that the acquisition and implementation costs are only part of the total cost of AIMS ownership. There is also a continuing requirement for application and system support that must be reliably met, so that the AIMS continues to meet changing clinical and administrative demands. What are the resources required to ensure initial and ongoing AIMS success? An AIMS requires dedicated personnel, not just for implementation, but also for ongoing support of the software, the associated hardware, maintenance and modifications of the user interface, and development and implementation of new functionalities. The specifics of how these resources are provided, which budget(s) they are supported by, and under whose jurisdiction they fall in the organizational chart differ widely, ranging from all support provided by hospital-wide IS departments to all AIMS activities being supported by the anesthesia department. Despite the disparate organizational features of the AIMS-dedicated IS resources, there are key roles that are common and easily identified in organizations with a successful AIMS. These roles must be anticipated and filled by departments considering an AIMS installation. First, there must be a competent, committed clinical champion—an individual familiar with the anesthesia workflow of the department who can both set up the AIMS interface and keep the interface up to date as the needs of the department change. This person must understand the capabilities and limitations of the AIMS well enough to know what can and cannot be accomplished when setting up the AIMS in order to match the operating room workflow. Almost always, this person is an anesthesiologist with facility in software, computer hardware, medical device interfaces, or database management. Given that none of these topics is addressed during anesthesia residency, such individuals are rare. The AIMS clinical champion should participate in product selection, so that their expertise regarding local anesthesia workflow, practices, and expectations may influence the selection of an AIMS whose capabilities most closely match the clinical setting. Here we encounter a Catch-22. How would the AIMS expertise required to make an informed selection develop in a department preparing to select its first AIMS? Frequently, a clinician with some prior interest and acknowledged ability in personal computing is nominated, and an informal consultation network with existing AIMS users is established. The process repeats for each new department selecting an AIMS; very few centers have been through the process more than once. Fundamental questions such as “How much of the clinician's time will selection and implementation require?” are negotiated anew each time. Given the financial and practice-impact issues at stake, AIMS selection is an area ripe for the development of capability and professionalism. The clinical champion must either be capable of maintaining the AIMS software and databases themselves, or be assisted by a software engineer, database administrator or programmer analyst with sufficient cross-training to work in all of the aforementioned specialties. For a multi-specialty anesthesia practice, this “AIMS engineer” role typically requires a full-time professional. Increasingly, hospital IS include electronic health records, provider order entry systems, and computerized lab result systems, all of which must interface with the AIMS. This increases the complexity of the programming/engineering services required, and potentially calls for more than one full-time equivalent person in the AIMS engineer role. The AIMS is literally and figuratively at the interface between medical devices and medical information systems. Thus, a successful AIMS requires constant attention from biomedical personnel (usually a biomedical engineer) who has sufficient IS background to set up, maintain and troubleshoot the physical connections and interfaces between the anesthesia equipment, intraoperative monitors, and the AIMS. Problems with these connections have resulted in medico-legal liability and losses that offset the value of the AIMS.3 Because this maintenance and troubleshooting capability must be available, or at least on call during all times the AIMS is in use, multiple individuals are typically required. Behind the scenes, perhaps the largest end-user of any AIMS is actually the anesthesia billing office. Because the AIMS functions required to support a successful billing operation are quite distinct from the clinical implementation, maintenance and development efforts, one or more separate, dedicated programmer analysts are often required to support the business functions. As mentioned above, no two organizations are alike in the exact configuration, governance, and funding of the resources supporting the AIMS. However, each of the half-dozen departments with established, successful AIMS implementations have either provided or secured personnel to fill these roles. For many early adopters, the resources were secured “on the fly,” as it became clear that the AIMS would founder without them. In successful programs the resources applied are not aberrations but, practically speaking, are quite homogeneous with respect to full-time equivalent clinicians, engineers, programmers and analysts across the various institutions. Every organization contemplating an AIMS installation should plan for these requirements or risk appearing ill-prepared when they must be urgently met. Installing an AIMS brings the anesthesia department into the world of operating room medical information systems demanding new personnel and capabilities, and ongoing resources to support this new operation. Can there be additional benefits, beyond the obvious (better charting) from the new expertise and expense? The Halbeis et al. study provides a hint: upcoming AIMS adopters strongly value improved data collection for clinical, quality assurance, and safety purposes, and to support clinical research as reasons for installing an AIMS.1 The early adopters have demonstrated the added benefits of having an AIMS, with examples such as easy retrospective searches for Quality Assurance/Quality Improvement purposes, easy reporting for “pay-for-performance” purposes, a platform for active quality management (including documentation quality related to billing, which justifies the cost), and a platform for managerial decision support.4–10 However, in virtually every case reported, the “out of the box” AIMS product was insufficient to provide the extra value. Instead, the considerable resources applied by the early adopters were used to modify or extend the capabilities of the AIMS. In some cases, AIMS vendors have incorporated new functionalities into their products in response to user examples or demands.3 However, the current offerings still do not perform all of the functions that a department will desire. The take-home message is that when planning for AIMS acquisition, anesthesia departments and hospitals must specify in their requests for proposals the additional personnel and list the additional functionalities to be developed, in addition to the capital and software acquisition and installations costs. Departments should develop their own requirements for additional AIMS functionalities, but should start by searching the medical literature. Almost without exception, what is known about AIMS modifications and additional functionalities has been published in peer-reviewed journals. In other words, the fundamental proof-of-concept reports about various additional functionalities and their operational and/or financial impacts are readily available. This is not to say that there is nothing more to be learned; the available reports merely scratch the surface, but the current body of knowledge is available and searchable. Thus, when developing additional requirements, the key reliance should be on the applicable scientific literature. In addition to the selected examples cited above, review of the AIMS-related literature indicates that AIMS-mediated improvements in anesthesia are related to the process of care (e.g., on-time antibiotics), billing, managerial decision-support, etc. In contrast to electronic health record systems in primary care settings, the time course of the data flow from (input) → AIMS → (output) is seconds to minutes as compared to hours to weeks. This compressed time frame may be a key differentiator between AIMS and other electronic medical record systems. A traditional medical informatics approach may not be ideally suited to advancing knowledge and capabilities. Instead, the early AIMS adopters are moving towards automated process monitoring and process control. The general form is as follows: Process modeling to create a reference process against which actual process progress can be compared, seeking noteworthy exceptions. Data integration of multiple electronic sources and different data types. Continuous process monitoring by recursive queries of the AIMS and other databases to identify process exceptions. Pushing data to key stakeholders, seeking to provide the right information to the person who needs it, at the time when it is most useful. The skills required to build these capabilities are closer to industrial engineering and scientific programming than to medical informatics. Anesthesia departments contemplating AIMS adoption must also think about how to get that expertise into their organizations. There is a significant risk to AIMS success that is still at hand, but little discussed. All of the successful AIMS implementations that have produced added value beyond simpler charting have been systems that were either developed by the implementers themselves, were products that the vendors modified in response to customer requests, were products that allowed additional software to be run on top of the AIMS, or some combination of these. Each of these AIMS products could be considered an anesthesiology-oriented product, and is frequently a standalone application. However, many hospital IS departments are seeking to cover all of the hospital's needs with one monolithic solution from a single vendor. Thus, there is a potential conflict among AIMS-users and AIMS-purchasers (i.e., the hospital) over a fundamental choice between vendors producing systems that serve anesthesia well (but are mute with respect to the rest of the hospital's needs), and vendors producing systems that cover more areas but may not perform the AIMS function very well. Depending on the hospital IS department's orientation, the larger software vendors' products may have a significant sales advantage. However, the AIMS adopters who have reported value-adding successes in the peer-reviewed literature have, to date, voted with their feet in favor of products over which they have the most control. Although AIMS adoption may have tipped in favor of implementation at academic centers, the technology as a whole is still vulnerable, perhaps more so because of the increased exposure to demanding users and high expectations for benefits that the out-of-the-box products do not provide. The potential for frustration and missed opportunities is high, as not all centers will succeed in selecting an optimal product, or in securing the resources to adapt the AIMS to best meet their needs. Hence, AIMS vendors would be well advised to attend to the users' needs themselves.