Contemporary educational technologies such as online learning and technology-enhanced simulation offer tools that might help practicing physicians in their continuous professional development (CPD). Research across a wide spectrum of health professions learners confirms that both approaches offer consistent and significant benefits when compared with no intervention.1,2 Online learning (i.e., use of the Internet to support and mediate educational activities1,3) is, on average, neither more nor less effective than other educational approaches in promoting knowledge, skills, and behaviors,1,4 whereas learning with technology-enhanced simulation (“an educational tool or device with which the learner physically interacts to mimic an aspect of clinical care for the purpose of teaching or assessment”5) is associated with modest and statistically significant increases in these outcomes.5 These approaches differ substantially in their efficiency, flexibility, cost, and complexity, and support complementary learning strategies. Online and simulation-based tools can also be used to assess learning and thus identify gaps in knowledge, skill, and practice performance.6,7 Information technologies, including online learning and clinical decision support tools embedded in electronic health records, have been further proposed as potentially helpful in managing the exponential growth of medical information.8–11
Although these technologies are widely used in undergraduate and postgraduate physician training,1,2,12,13 it is less clear how well practicing physicians accept and use these technologies to guide the identification and remediation of CPD gaps.14 Few studies have explored the attitudes and beliefs of practicing physicians regarding their use of educational technologies, and those studies have incompletely addressed relevant issues. A survey in 2013 of practicing U.S. physicians found respondents moderately likely to participate in an online course, and that time spent seeking online information had increased from 2009 to 2013.15 In a 2008 survey of users of an online pediatric continuing medical education (CME) Web site, physicians indicated that the most important features of an online course were that it be free of charge and address an important topic.16 A survey in 2000 of physicians in a large U.S. health network found that 30% believed online learning was useful,17 and a survey of German physicians in 2003 found that technology was not a barrier to online learning.18 A review of online CME courses in 2008 found that a small minority of Web sites accounted for the majority of offerings, most were free or charged very little, and few required learner interactivity.19 This paucity of evidence leaves a significant gap in our understanding of the beliefs and expectations of practicing physicians regarding educational technologies.
To address this gap, we conducted a nationwide, cross-specialty survey of U.S. physicians to determine their current use of, past experiences with, and anticipated future use of online learning and simulation-based education, and how these experiences and beliefs vary by age, practice type, and specialty.
We hypothesized that:
- Older physicians would have less favorable attitudes about educational technologies;
- Physicians in academic practice would have had more, and more favorable, experiences with both online learning and simulation-based education than physicians in group or self-employed practice;
- Surgeons would perceive a greater role for simulation-based education than generalists and nonsurgical specialists;
- Self-employed physicians would report less support for online learning and lower integration with their practice environment, but would anticipate a greater role for online learning in the future;
- Physicians would prefer technology-mediated activities that are short, case based, and can be done without leaving the office or buying special equipment;
- Prior favorable experiences with online learning and simulation, and current integration/support, would be associated with interest in future offerings.
From September 2015 through April 2016, we surveyed licensed physicians in the United States about their CPD beliefs and experiences. Survey results regarding broad issues in CPD have been published separately20,21; this report focuses on the unpublished subset of items dedicated to educational technologies.
Sampling and participants
We identified a random sample of 4,648 licensed U.S. physicians from the LexisNexis Provider Data Management and Services database (LexisNexis Risk Solutions, Alpharetta, Georgia). We obtained the name, contact information, specialty, age, and gender for each physician. Internet survey completion was tracked, but responses were anonymized upon receipt. Paper surveys were entirely anonymous. All participants were offered a small gift (a book valued at about $12). The Mayo Clinic Institutional Review Board approved the study.
The authors, representing diverse CME leadership experiences working within academic medical centers, integrated managed care networks, and medical specialty boards, collaborated to create survey items addressing three domains: prior experiences with online learning and simulation-based education; beliefs about educational technology effectiveness, personal preparedness for technology use, and workplace supports; and anticipated future use of specific, diverse educational technology innovations. All items consisted of seven-point bipolar Likert-type items (1 = strongly disagree; 7 = strongly agree). The survey did not provide definitions of “online learning” or “simulation-based education.” The number of items exceeded what we anticipated would be acceptable to many respondents. Thus, to allow advertising of a shorter survey and thereby encourage participation, we divided the questionnaire into two sections and allowed participants to submit the survey after completing the first section (“primary items”). This article reports five primary items; the remaining items come from the second half (optional or “secondary” items), for which the response rate was lower.
Four CME experts at nonaffiliated institutions reviewed the draft survey for content (i.e., important omitted topics or irrelevant items). Mayo Clinic Survey Research Center personnel with expertise in questionnaire development reviewed each item to verify structure and wording. We asked 17 physicians (representing anesthesiology, dermatology, emergency medicine, family medicine, internal medicine, neurology, pathology, psychiatry, and surgery) to pilot test the survey and provide feedback on item relevance and wording. We revised the survey at each stage of testing.
We used Qualtrics (www.qualtrics.com), a survey research tool, to administer the Internet survey. We sent each physician an individually tracked link via e-mail, and sent follow-up e-mail reminders to nonrespondents. Those not responding to the Internet survey within three months were mailed a paper questionnaire that had no identifying information, and a stamped return envelope.
In reporting demographic characteristics we used respondent-reported information when available, and filled in missing data using information from LexisNexis. To evaluate the representativeness of the sample, we compared the distribution of respondents’ specialties against the national distribution published in the Association of American Medical Colleges 2014 Physician Specialty Data Book.22 We explored possible differences between respondents and nonrespondents in two ways. First, we used the chi-square test to compare respondents and nonrespondents for demographic features available from the LexisNexis dataset (specialty, practice location, age, and gender). Second, we compared the responses of late responders (the last 15% of responses) with those responding earlier, since research suggests that the beliefs of late responders closely approximate the beliefs of nonrespondents.23 To estimate the representativeness of the secondary item findings (since only about half the respondents completed these items), we compared primary item responses and respondent demographics for those who did versus did not complete the secondary items. We also compared survey responses for Internet and paper modalities.
We planned a priori analyses exploring variation in responses by age (< 45 years, 45–59 years, or ≥ 60 years); specialty (generalist [nonsubspecialist family medicine, internal medicine, and pediatrics], surgical specialist [surgery, anesthesiology, and obstetrics–gynecology], or nonsurgical specialists [all others]); and practice type (self-employed, group, or academic). We also planned to evaluate potential associations among past experiences and beliefs about effectiveness, usefulness, and expected future use; desired use of online learning and online learning support, skills, and integration with the practice environment; online learning support and online skills; and beliefs about online learning and simulation.
We used general linear models to test associations between opinions (outcomes) and respondent characteristics (predictors), including selected demographics and whether or not they completed the second half of the survey. We evaluated the correlation among survey items using Spearman rho. Because of the large sample size and multiple comparisons, we used a two-tailed alpha of 0.01 as the threshold of statistical significance in all analyses. We used SAS version 9.4 (SAS Institute Inc., Cary, North Carolina) for statistical analyses.
Survey response and sample characteristics
Of 4,648 attempted contacts, 646 e-mail invitations and 223 paper questionnaires were undeliverable, and 65 invitations were undeliverable via either e-mail or paper. We received 631 responses via Internet and 357 via paper. After excluding the 65 invitations undeliverable by either method, our response rate was 988/4,583 (21.6%). A less conservative estimate excluding all 934 undeliverable invitations suggests a response rate of 26.6% (988/3,714).
The demographic characteristics of respondents and nonrespondents were similar, except that older physicians were less likely to respond and pediatric subspecialists were more likely (see Table 1). The distribution of respondents’ specialties was similar to that of published data for all U.S. physicians22 (P > .06), except that our sample contained relatively fewer family medicine and general internal medicine physicians (absolute difference about 4% for both; P < .001). We compared the responses to the five primary survey items for those responding early versus late in the survey period and found no statistically significant differences. Finally, we compared the five primary items between Internet and paper survey modalities, and again found no statistically significant differences.
The secondary items were explicitly labeled “optional.” To determine whether the 444 (44.9%) respondents who completed at least one secondary item were similar to those responding only to the primary items, we compared both item responses and demographics. We found no statistically significant differences in responses to the five primary items (P ≥ .04) among those who did versus did not complete the secondary items. Relatively fewer respondents ≥ 60 years old completed the secondary items (38.5%, compared with 48.1% of those < 45 and 52.2% of those 45–59; P = .01). We found no statistically significant differences in completion of secondary items for other demographics (specialty, practice type, gender, location, practice size, or revenue model).
Prior experience with educational technologies
In the preceding five years, 97.1% of respondents (429/442) had used online learning for their professional development, 92.1% (407/442) had used online learning for personal purposes, and 84.2% (372/442) had used simulation-based education (Table 2). Among those with experience, the mean rating (1 = strongly disagree, 7 = strongly agree) for online learning effectiveness was 5.2 (standard deviation [SD], 1.5) compared with 4.5 (1.7) for simulation-based education. Supplemental Digital Appendix 1 (http://links.lww.com/ACADMED/A464) contains responses in the full 1–7 scale for all survey items.
Physicians generally agreed that point-of-care learning is vital to effective patient care (mean 5.3 [SD 1.3]; see Table 3) and that they have adequate workplace resources to answer patient-related questions (5.9 [1.1]). Only 39.0% (165/423) agreed that they currently use objective performance data to guide their CPD choices, although 64.6% (605/936) agreed that such information would be useful.
Perceived effectiveness and future role of online learning and simulation for CPD
Desire for more online learning was modest (mean 4.6 [SD 1.5]), as was desire for more simulation-based education (4.2 [1.7]; see Table 3). Physicians’ impressions of the effectiveness of online learning and simulation-based education were similar (5.2 [1.4] vs. 5.0 [1.4]; P = .06), yet they anticipated a more vital role for online learning in CPD compared with simulation (5.7 [1.1] vs. 5.1 [1.4]; P < .0001). They perceived that they currently possess adequate access to (5.4 [(1.3]), skills for (5.8 [1.2]), and technical support for (5.5 [1.4]) online learning.
Anticipated use of specific technology innovations
We asked physicians to rate their anticipated use of various specific innovations (Table 4). The most highly rated innovations provided support for formal CME activities: a central repository for listing CME opportunities (mean 5.7 [SD 1.2]), tracking CME completion (5.7 [1.3]), and receiving credit for answering patient-focused questions (5.2 [1.7]). Other highly rated innovations included 5-minute and 20-minute clinical updates and an e-mailed “question of the week.”
Variation by age
There were no significant differences across age groups in the number of physicians with prior experience using online learning or simulation (P > .35), or in physicians’ ratings of these prior experiences (P ≥ .03; see Table 2).
Responses regarding effectiveness of, access to, and future role of educational technologies varied minimally across age groups (see Table 3). We found small but statistically significant differences across age groups in self-perceived skill for online learning (older physicians perceived lower skills) and interest in information about patient outcomes (younger physicians indicated stronger interest). Other differences across age groups did not reach statistical significance.
For nearly all of the technology innovations, physicians < 45 years old rated the helpfulness or likelihood of regular use highest, and those ≥ 60 rated these lowest (Table 4). These differences by age group were statistically significant for six innovations: an app with case-based questions, an app with patient-focused questions, a 5-minute clinical update, an app that monitored clinical practice, an educational game, and a central CME tracking repository.
Variation by practice type and specialty
We report subgroup analyses by practice type and specialty in Supplemental Digital Appendix 2 (http://links.lww.com/ACADMED/A464). Ratings of prior experiences with online learning and simulation-based education were similar by practice type (P ≥ .06) and specialty (P ≥ .46). As expected, we found a statistically significant difference across practice types in access to point-of-care knowledge resources (P < .0001), with self-employed physicians reporting less access, and physicians in academic practice reporting greater access, than those in group practices. We found an unanticipated difference in the perceived benefit of patient outcomes information, which was higher for physicians in group practice than for self-employed physicians (P = .002). We did not find any other statistically significant differences by practice type.
We found an unanticipated difference across specialties in the perceived value of point-of-care learning, with generalists and nonsurgical specialists rating this higher than surgeons (P = .0005). Generalists reported better access to point-of-care knowledge resources than surgeons or nonsurgical specialists (P = .0004). We did not confirm the anticipated differential preference of surgeons for simulation-based education, nor did we identify any other significant differences across specialty (P > .06).
Associations with other technology beliefs
We explored associations among selected survey ratings. Ratings for the effectiveness of prior online professional development activities correlated significantly with ratings of the future effectiveness (rho = 0.73), desired use (rho = 0.36), and vital role (rho = 0.60) of online learning (all P < .0001). Likewise, ratings of the effectiveness of prior simulation-based education correlated with ratings of the future effectiveness (rho = 0.69), desired use (rho = 0.45), and vital role (rho = 0.63) of simulation (all P < .0001). We found only weak correlations (explaining ≤ 3.6% of the score variance [R2 ≤ 3.6]) between desired use for online learning and current online learning integration (rho = 0.19, P < .0001), support (rho = 0.04, P = .37), and personal skills (rho = 0.16, P = .0006). Online learning support was significantly associated with personal skills (rho = 0.54, P < .0001). Finally, we found relatively strong correlations between online learning and simulation in terms of beliefs about past effectiveness (rho = 0.44) and future desired use (rho = 0.44; P < .0001).
In this national survey, we found that nearly all responding physicians had used online learning, and the vast majority had experience with simulation-based education. Although perceptions of past experience with online learning and simulation were similar, physicians anticipated a greater role for future online learning. Physicians generally perceived adequate personal skills and access to support for online learning. Specific innovations that were highly rated included a central repository for listing CME opportunities and tracking their CME completion, an app awarding credit for answering patient-focused questions, 5-minute and 20-minute clinical updates, and an e-mail “question of the week.” By contrast, responses regarding current and future use of clinical practice data were only moderately favorable. Responses for nearly all beliefs and past experiences were similar across age groups. Differences across practice types and specialties were few, small, and showed no meaningful pattern.
The survey response rate was lower than ideal, and those choosing to respond might have been systematically different from those who did not respond. However, the demographic characteristics of those invited and those responding were similar, and respondents largely reflected the national distribution of specialties. Additionally, to the extent that those responding late have opinions similar to those who never respond,23 our finding of similar responses among early and late responders suggests that our findings do not misrepresent nonrespondents. Moreover, the invitation to complete the survey did not specifically mention educational technologies, such that decisions to complete the survey would be unlikely to be based on particular interests in or beliefs about this topic. Most of the data in this report derive from survey items completed by only half the respondents (secondary items), but those who did not respond to these items were similar to those who did in both demographic characteristics and actual responses.
Strengths include the national cross-specialty sample, large sample size, planned subgroup analyses, robust process for questionnaire development (including external expert review and pilot testing by physicians in multiple specialties), and adherence to best practices in survey implementation and delivery (including use of a dedicated Survey Research Center).
Integration with prior work
Our findings corroborate prior research suggesting that practicing physicians are willing to use online learning and simulation in their CPD.15–19,24,25 Two qualitative studies highlighted the importance of being able to trust the quality of content,26,27 and suggested that physicians may be slow to adopt a new technology if their current approach seems to be working.27 Although we did not directly address the issue of cost, other studies indicate that physicians prefer (and believe they can find) free online CME,16 and that free online CME is readily available.19
Our study also highlights interest in technology for learning at the point of care.15,28,29 Technology innovations that integrate learning and patient care include knowledge resources and search tools that facilitate finding information to answer clinical questions,14,30,31 clinical alerts and practice advisories that provide information before it is requested,32,33 and patient data reports that highlight gaps in knowledge or performance.34,35 Most clinical decision support systems are designed to expedite immediate patient-related decisions rather than to promote actual learning (i.e., linking new information with existing knowledge structures to promote retention and transfer to new settings).36 When and how to optimize point-of-care information technologies to promote learning constitutes an important topic for future study.
Our findings have implications for education practice and future research. First, practicing physicians generally seem receptive to using a variety of educational technologies. They seem especially attracted to short, high-relevance, patient-focused activities, and innovations that automate administrative tasks (e.g., monitoring or awarding CME credit). Favorable past experiences appear to be strong predictors of the anticipated future effectiveness and role of these technologies. Respondents rated simulation-based education slightly lower than online learning, and although we lack data to explain this finding directly, we speculate that it may reflect the perceived high cost and low accessibility of simulation (e.g., the need for specialized equipment or inability to complete tasks on-site). Resources and skills are currently perceived as adequate for online learning.
Second, although physicians believe that patient outcomes information would help them make better CPD choices, they expressed only modest interest in a technology solution to provide such information. We lack data to fully explain this disparity, but propose two potential explanations. First, physicians might have had or heard about poor experiences using such technologies. If true, then improving the technology and highlighting advantages of the new approach could address the concern. Alternatively, this could reflect a general resistance to feedback,37 in which case better technology alone will not solve the problem. We need to better understand this issue, and the related issues of self-assessment versus external guidance.38–40
Third, physicians of all ages seem to have interest, willingness, and capability to use online learning and simulation-based education. Although older physicians generally reported a lower likelihood of regularly using our example innovations than younger physicians, beliefs about effectiveness and future roles were similar across age groups.
Fourth, beliefs about educational technologies vary little across practice types or specialties. Even those in small practices seem interested in and capable of using new technologies, and integration of and local support for online learning are weak predictors of anticipated future use. Nonetheless, there appears to be room for improvement in the integration of online learning activities into most physicians’ practices and in the availability of point-of-care resources (especially for self-employed practitioners).
Finally, building it won’t guarantee that they will use it. The findings in Table 4 suggest potential interest in software to support point-of-care learning, clinical updates, or CME tracking. Yet, avowed desires may not translate to actual future usage, and physicians might not really know what they do or don’t want or need. This is especially important given the up-front infrastructure investment that some technologies entail. We need to focus on true educational needs rather than the hype and glamour of the latest innovation, listening attentively to our potential customers—practicing physicians—to understand the problems they face, and then iteratively test and refine potential technology solutions. We need further research on what to build and how to build it, focusing beyond technical issues to consider matters of usability, implementation, integration, and educational effectiveness.
The authors thank Graham McMahon (Accreditation Council for Continuing Medical Education), Paul Mazmanian (Virginia Commonwealth University School of Medicine), the late Alex Djuricich (Indiana University School of Medicine), and one anonymous reviewer for providing external expert review of the survey questionnaire. Additionally, the authors thank Ann Harris and Wendlyn Daniels (Mayo Clinic Survey Research Center) for their help in planning, testing, and implementing the survey.
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