Adoption of health care information technology has been shown to improve both clinical outcomes and operational efficiency in a variety of settings.1,2 In the operating room, anesthesia information management systems (AIMS) have been shown to improve patient care and, in some cases, the financial performance of the anesthesia department.3,4
The primary function of an AIMS is to collect intraoperative data that enables anesthesia care providers to create a more reliable and accurate anesthesia record, especially with respect to vital signs, which are automatically recorded while providers attend to other aspects of patient care.5 In addition, AIMS also have been used to establish reference limits during anesthetic care,6–8 to enhance the quality of care delivered by facilitating appropriate clinical care through clinical decision support,9–19 to improve quality assurance and billing documentation,20–26 to allow research studies involving operating room management issues,27–31 and to provide data for simulation studies evaluating implications of perioperative decision support.32,33
There have only been a few published studies documenting potential disadvantages of AIMS.15,34 Barriers to the adoption of AIMS have been identified from several prior surveys and have largely been related to financial and resource constraints.35–37 Although medical-legal concerns related to artifacts recorded by AIMS and other inaccuracies have sometimes been raised, the consensus is that AIMS will help more than hinder in the defense of malpractice allegations.38 Potential benefits from health information technology, including AIMS, and barriers to adoption have recently been reviewed.39 An annotated bibliography (updated monthly) describing all published AIMS studies with online links is available.a
AIMS adoption by all U.S. anesthesia residency programs was last surveyed in 2007.35 Based on a response rate of 51% and considering all programs as the denominator, this survey estimated that at least 16% of programs were using this technology, with at least an additional 9% in the process of installing, and at least 19% in the process of looking for a system. This contrasts with an adoption rate of 1% in 2001, and 5% in 2006.35 A sharp uptick in the rate of adoption in 2007 was interpreted by the authors that the momentum for adoption had reached a tipping point40 in academic anesthesiology departments. An accompanying editorial by Sandberg41 commented that “AIMS appear on the verge of completing the adoption lifecycle.”
In contrast, adoption of AIMS outside of academic programs has been much slower, with only 24% of respondents to a 2011 random survey of 5000 American Society of Anesthesiologists members indicating that they were currently using an AIMS.42 Due to the design of that study (no ability to detect multiple submissions either by individuals or from institutions), response bias (higher response rate more likely among adopters than nonadopters), and a low survey completion rate (12.3%), the percentage of anesthesia practices with an AIMS could not be determined.
Adoption of a myriad of innovations has been shown to follow an S-shaped curve and is explained by the diffusion theory of innovations.43 Rogers44 describes diffusion as “…the process in which an innovation is communicated through certain channels over time among the members of a social system.” The roots of diffusion theory were planted around the turn of the 20th century,45,46 and came into prominence in the 1940s, largely as a result of studies published by rural sociologists related to the adoption of hybrid corn seeds (with much higher yields than standard corn) by Iowa farmers.47 Since this time, diffusion theory has been applied to innovations as diverse as the British navy providing limes to sailors to eliminate scurvy in the 18th century, prescription of new antibiotics by physicians, and adoption of personal computers by consumers.44 How ideas get communicated, why they are adopted or rejected, and whether use is sustained over time are at the core of this field. What has been repeatedly shown for successful technologic innovations is that the life cycle follows an S-shaped curve, in which there is an initial period of slow adoption by “innovators” followed by sequential periods of increased adoption (early adopters, early and late majority, laggards; Fig. 1). Once a critical mass has been achieved (typically at 10%–20% of the market), further adoption tends to be self-sustaining.44
Curiously, despite the widespread adoption of technology in anesthesiology over the past several decades, we found no publications (searching either PubMed or Googleb) where diffusion theory has been applied to the adoption of innovations such as oximetry, capnometry, Bispectral Index, or neuromuscular monitoring in anesthesia. Most technology adoption occurs through a process of imitation (i.e., based on the experiences of peers) rather than through independent research or reading the scientific literature.44 Thus, the time course and extent to which adoption of new technologies can be predicted is of interest to anesthesiologists. Diffusion theory provides a well-tested and extensively validated framework to understand the successes and failures of technology adoption. Implementation of AIMS by academic anesthesia departments is a prototypical innovation to highlight the relevance of diffusion in the specialty, and the methodology likely has considerable relevance to the evaluation of adoption of other technologies in anesthesia.
In this study, we updated the AIMS adoption figures in U.S. anesthesia residency programs. We chose to study this group in anticipation that it would be possible to achieve a 100% sample size. We hypothesized that the historical trend of AIMS deployment would follow the S-shaped curve predicted by diffusion theory. We also explored quantitatively the potential utility of extrapolating regression curves from AIMS adoption at various times to predict future adoption.
A request for exemption as nonhuman subjects research was granted by the Vanderbilt IRB. We designed a brief questionnaire (Table 1) to identify programs currently using an AIMS, the year of installation, and for programs without an AIMS, whether a system would be installed within the next 12 months. Since our primary objective was to determine the adoption status for every U.S. anesthesiology residency program, allowing a robust exploration of the time course of AIMS diffusion, we deliberately omitted peripheral questions relating to AIMS adoption and use (e.g., reasons for lack of installation, benefits). A lengthy survey likely would have precluded obtaining the desired response rate.
The target for the survey was all U.S. anesthesiology residency programs approved by the Accreditation Council for Graduate Medical Education (ACGME) as of June 30, 2012.c Questionnaires were sent to residency program directors or alternative contact individuals, identified online, at the 130 qualifying programs. Responses were collected and updated from February 2013 through May 2013. For programs not responding to the initial request, follow-up e-mails were sent. Subsequently, for programs that had not answered, individuals in the anesthesia department were personally contacted by e-mail or phone (by JME or RHE) until a 100% response rate was obtained. Programs responding with unclear, vague, or incompletely answered questionnaires were recontacted to obtain interpretable answers. Where respondents indicated that their program used an AIMS at some but not all hospitals at which their residents did rotations, we counted the program as having an AIMS. Respondents were assured in the survey cover letter that the individual AIMS adoption status of their program would not be disclosed.
To adjust percent adoption figures for yearly changes in the number of academic anesthesiology residency programs, we determined the accredited programs for each year between 1987 and 2013 by counting the anesthesia programs listed in the ACGME Green Bookd (Table 2). We started with 1987 because that was the year of the first commercial installation of an AIMS (Arkive, Diatek Inc. [now defunct], San Diego, CA) in an academic anesthesiology program (Duke University Hospital, Durham, NC).48
Statistical calculations were performed using R version 3.0.0 (The R Foundation for Statistical Computing, Vienna, Austria).
Using data through 2012 (the last full year for which adoption data were available), we calculated several regression models. Standard logistic regression for the fraction of adoption
was calculated using
as the independent variable. Residual deviance was analyzed by the χ2 test, with goodness of fit assessed by P > 0.05.
We also fit the data using the nonlinear least squares R function nls to the curve
where F = fraction adoption, t = number of years since introduction, p = “coefficient of innovation” and q = “coefficient of imitation,” as derived by Bass49e and validated for many consumer purchases.50 This formula is derived from innovation theory that predicts that “the probability that an initial purchase will be made at [time] T given that no purchase has yet been made [by the consumer] is a linear function of the number of previous buyers.”48,f As there was an unexpectedly high correlation (−0.99) between the 2 parameters p and q in the Bass model, resulting in the numerator of the ratio being very close to 1, we also calculated a simplified Bass model in which only 1 effective parameter (alpha2) was estimated (setting alpha1 to produce the adoption percentage at year 0 as the intercept). The equation for the simplified Bass model was
We computed logistic regression curves and standard errors for data through 2000, 2005, 2007, 2010, and 2012 and extrapolated results to determine the estimated year and 95% confidence intervals for adoption of an AIMS by at least 50% (“most programs”) and by 84% of programs. We chose the latter percentage because a cumulative adoption rate of 84% marks the transition from the “late majority” to the “laggard” phase of technology adoption, as defined in the diffusion theory model45 (Fig. 1). The function dose.p in the R library MASSg was used analogously to its use in lethal dose experiments, substituting year for dose, and percent adoption for mortality rate.
Responses were received from all 130 anesthesiology residency programs accredited as of July 1, 2012, with 87 (67%) indicating that they were using an AIMS. Ten programs currently without an AIMS responded that they would be installing an AIMS within 12 months, tabulated (conservatively) as to occur by the end of 2014. The yearly cumulative percentage of accredited residency programs that had implemented an AIMS is shown in Figure 2. Programs indicating that they would be installing within 12 months of the survey were included in the 2014 numerator.
The observed AIMS adoption percentages for the 26 years between 1987 and 2012 were well fit by the logistic regression curve above, with b0 = –445.1303 and b1 = 0.2214 (residual deviance = 15.770, 24 degrees of freedom, P = 0.90). The fit was almost the same when ln(Year) was used as the independent variable (residual deviance = 16.00, P = 0.89). This is reflected by the nearly overlapping regression plots displayed in Figure 2. The Bass fit was better than the logistic fits, both qualitatively (Fig. 3) and quantitatively (residual sum-of-squares = 0.008, P = 1), as was the simplified Bass fit (residual sum-of-squares = 0.021, P = 1).
Regression curves extrapolated from logistic fits of the data through 2000, 2005, 2007, 2010, and 2012 are shown in Figure 2. The extrapolated plots produced had the current study been done between 2000 and 2007 (the date of the last academic AIMS adoption survey) markedly underestimated the subsequent actual adoption percentages. The estimated times to adoption of AIMS by at least 50% and at least 84% of academic anesthesia programs based on fitting the data to logistic regression curves using data up to years 2000, 2005, 2007, and 2010 are shown in Table 3. Predictions for at least 50% adoption from data through 2000 and 2005 (when AIMS prevalence was <20%) had wide confidence intervals (17 and 8 years, respectively) and thus would have provided marginal utility for planning or projection purposes. However, the confidence intervals for 84% adoption from the 2010 and 2012 regressions were much narrower (3–4 years, respectively), making them more useful to predict the transition to the laggard phase of adoption.
Our study indicates that after a prolonged period of sluggish adoption from 1987 to approximately 2004, acceleration in AIMS deployment began. By 2014, approximately 75% of U.S. academic anesthesiology departments will be using an AIMS, based on survey responses of impending implementation. If the current rate of adoption continues, as predicted by the logistic regression curve, such systems will be in use in at least 84% of anesthesiology residency programs sometime between 2018 and 2020. However, predicted adoption from the logistic regression curves from 2000 to 2007, when overall adoption was much <50%, greatly underestimated the subsequent adoption rate. Thus, the utility of this approach to assess the future time course of adoption of technology in anesthesia is questionable when done at times before widespread adoption has already been completed.
AIMS have been embraced by academic anesthesiology departments, in contrast to nonacademic practices, where adoption has continued to be slow.36 Given that most U.S. anesthesia residency graduates are already using AIMS and many have limited experience in completing manual anesthesia records, this gap between private and academic practice with respect to adoption of the technology may impact the quality of paper anesthesia records completed by newly trained anesthesiologists. For example, there are many items that are automatically charted in an AIMS (e.g., via “macros” that add additional documentation when a single button is clicked) that such providers may forget to document when entering data manually. However, we are not aware of any studies that directly address this potential concern. The high prevalence of AIMS implementation in academic anesthesia residency programs may put programs without such technology at a disadvantage in their efforts to attract highly qualified residents.
Also lagging in AIMS adoption are European university-affiliated hospitals, where it was estimated that a minimum of 15% of hospitals had already installed, were in the process of implementation, or were actively searching for an AIMS as of 2010.51
Reasons for the differential adoption of AIMS by academic and private practice anesthesia groups in the United States may relate to factors identified in studies of successful innovations in other fields.44 Most of the opinion leaders favoring AIMS adoption have been from academic programs, and may have exerted less influence on their private practice peers, an important factor in adoption. The typical academic hospital is much larger than the typical private practice hospital, with attendant greater financial resources, information technology infrastructure, and resources necessary for implementation. “Clinical champions,” necessary for successful implementation, need to have sufficient nonclinical time to address the many configuration issues that arise during AIMS installation, and such time may not be as available in private practice as opposed to academic settings. Finally, although both private practice and academic anesthesiologists share the same concerns about delivery of high-quality anesthesia care, there are important differences in orientation, because academic anesthesiologists are more focused on teaching and research than their counterparts. To the extent that AIMS function to serve those objectives, the technology may be viewed as providing lesser value in the private practice community. For both groups in the United States and in Europe, money and resources have been the largest barrier to implementation.35–37
Since the current study had a response rate of 100% and was adjusted for the number of active ACGME programs each year, our adoption statistics are a more accurate historical assessment of the rate of AIMS adoption by academic anesthesiology residency programs than the Egger Halbeis et al. survey.35 That study, by design, underestimated the adoption rate, and consequently missed by a few years the start of the expansion phase of AIMS implementation.
The slow rate of AIMS adoption is not unexpected, because the switch from paper to electronic charting is a dramatic change in documentation of clinical care during anesthesia. As articulated by Rogers in 1962,43 the adoption curve of an innovation shows a long period of dormancy (the innovators), a slow sustained rise (the early adopters) and then a rapid rise (early and late majority; Fig. 1). The final 16% of adopters are considered to be “laggards.” Whether the time course of final adoption as predicted by the 2012 logistic regression curve will be maintained, or if external financial pressures such as initiatives that are part of the 2009 Health Information Technology for Economic and Clinical Health (HITECH) Acth (e.g., “meaningful use”) will result in a more timely completion of the adoption life cycle remains to be determined. We expect, however, that the rate of adoption by anesthesia residency programs will exceed predictions from the extrapolation of the logistic regression curve (Fig. 3, Table 2), because the increasing number of academic hospitals moving to enterprise-wide electronic health records have been implementing an AIMS as part of the new system. Even though most anesthesiologists will not qualify for current HITECH Stage 2 and 3 financial incentives as a result of AIMS adoption (due to current requirements),i hospitals are being driven to full implementation of electronic health records, which often extends to the operating room.
We were unable to find any published data on the life cycle of adoption by other technologies now in nearly universal use during anesthesia (e.g., oxygen analyzers, pulse oximeters, capnometers). Although we strongly suspect that adoption followed the S-shaped pattern predicted by the diffusion of innovation theory, these technologies are somewhat different from AIMS adoption in that usage was mandated in some states by statutory requirement and for all anesthesiologists by monitoring standards of care developed by the Committee on Standards and Practice Parameters and endorsed by the House of Delegates of the American Society of Anesthesiologists.52
Our study has several limitations. First, the dates of implementation reported by respondents may be slightly inaccurate (e.g., off by 1 year), especially for programs whose AIMS have been in place for many years. However, this would have a minimal effect on the cumulative curve for the current estimate of adoption. We have no reason to suspect that any programs deliberately misrepresented their current AIMS adoption status. Second, it is possible that there may have been a few programs that closed who had implemented an AIMS, which we would not have captured. However, programs that close are generally those that do not meet ACGME standards, and are not likely to have been on the leading edge of innovation. This would not have affected the 2012 adoption rate, which is based on all currently accredited programs. Third, our adoption rate for 2013 was estimated based on programs indicating that implementation was expected within 12 months of the survey response would deploy in 2014, not 2013. Thus, the 2013 point estimate may be slightly low, as the numerator is based on installations completed by May 2013. Finally, the estimate of the implementation percentage by the end of 2014 is based on no change from 2013 in the number of accredited anesthesia residency programs and that all programs indicating implementation within 12 months achieve that objective by the end of 2014.
In conclusion, adoption of AIMS by academic anesthesiology residency programs has reached the final stages of adoption, with a large majority of programs having such technology in place. AIMS adoption has closely followed the theory of the diffusion of innovation, framework that may prove useful for evaluation of adoption of other technologies in anesthesia. However, attempts to predict the time course of future adoption at relatively low degrees of prevalence are subject to wide confidence intervals and may be best to defer until adoption is close to 50%.
Name: Ilana S. Stol, BA.
Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.
Attestation: Ilana S. Stol has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
Name: Jesse M. Ehrenfeld, MD, MPH.
Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.
Attestation: Jesse M. Ehrenfeld has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
Name: Richard H. Epstein, MD.
Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.
Attestation: Richard H. Epstein has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.
This manuscript was handled by: Franklin Dexter, MD, PhD.
We appreciate the assistance of Waldo Wentz, Operations Manager at the National Resident Matching Program. We also thank Franklin Dexter, MD, PhD, for suggesting the simplification of the Bass formula and application of the lethal dose statistical methodology for estimating 95% confidence intervals for the projected AIMS adoption percentages.
a Dexter F. AIMS Annotated Bibliography. Available at: http://www.franklindexter.net/bibliography_AIMS.htm. Accessed July 21, 2013.
b PubMed search: (oximet* OR capnomet* OR “BIS” OR monitor* OR neuromuscular OR blockade OR technolog*) AND (anesthesi*) AND (diffusion OR Rogers EM[au] OR Bass FM[au]).
c Accreditation Counsel for Graduate Medical Education. Programs by Specialty. Available at: https://www.acgme.org/ads/Public/Reports/ReportRun?ReportId=1&CurrentYear=2012&SpecialtyId=3. Accessed July 21, 2013.
d Accreditation Counsel for Graduate Medical Education. Historical Documents: The Graduate Medical Education Directory also known as “The Green Book.” Available at: http://www.acgme.org/acgmeweb/tabid/126/About/AMAGreenBooks.aspx. Accessed July 21, 2013.
e Bass, FM. Which Bass model should I use? Available at: http://www.bassbasement.org/BassModel/WhichBassModelEquation.aspx#SM1986. Accessed August 12, 2013.
f As a special case of the Bass model, when p = 0, the models reduce to the logistic distribution, and when q = 0, to the exponential distribution. Bass diffusion model. Available at: http://en.wikipedia.org/wiki/Bass_diffusion_model#Relationship_with_other_s-curves. Accessed August 13, 2013.
g Ripley B. Support functions and datasets for Venables and Ripley’s MASS. Available at: http://cran.r-project.org/web/packages/MASS/index.html. Accessed August 12, 2013.
h Department of Health and Human Services. Health Information Technology for Economic and Clinical Health Act. Available at: http://www.hhs.gov/ocr/privacy/hipaa/understanding/coveredentities/hitechact.pdf. Accessed July 18, 2013.
i Mira T. RAC Rules: HITECH Act Electronic Health Record Incentive Program—Bonus or Penalty for Anesthesiologists? Available at: http://www.anesthesiallc.com/publications/ealerts/170-hitech-act-electronic-health-record-incentive-program-bonus-or-penalty-for-anesthesiologists. Accessed August 13, 2013.
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