Staying focused on a wealth of data. As a research analyzing a very complex, very dynamic phenomenon of great importance for society, this research was crafted as a delicate balance between two opposites: (1) insight to just a fraction of data and (2) too much data to analyze. While, from my perspective, the balance seems to be well established, from the perspective of other stakeholders, a different balance may seem more suitable. Combining the outcomes of this research with the results of similar research, especially similar future research, is probably the best approach to address this limitation.
Bias. As a phenomenographic researcher working for the ASA, there is a risk that my personal or ASA bias might influence the research. Since per social constructivism, perceived reality is a social construct that exists on an individual and group/organizational level, my goal is to recognize how my and the ASA’s realities look and how the two realities compare with the realities of other individuals and organizations involved in this research. Ultimately, the goal is not to neutralize them but to recognize and combine them with the realities of other stakeholders.
The main steps to address bias are the following:
- Actively hold back my assumptions and theories that I would get a better insight into how the phenomenon is understood by respondents (Sandbergh, 1997) without the influence of personal perspectives, material world, and subjects (Chan, Fung, & Chien, 2013).
- Use phenomenographic research to gather insight into the realities of other stakeholders and, through follow-up interviews, ask them to evaluate how I interpreted their reality.
- Share a part of my findings with selected leaders and the public and ask them for feedback.
The data from those four clinical professions will allow me to define the main themes and interactions among different professions while remaining focused and on scope; however, insight from other professions will be very valuable. Therefore, future research should include perspectives from the following:
- Other clinical groups involved in care, such as operating room nurses, surgical technicians, pharmacists, and nonclinical professions like IT and management
- Students—future professionals and residents
Chan, Z. C., Fung, Y.-l., & Chien, W.-t. (2013). Bracketing in phenomenology: only undertaken in the data collection and analysis process? The Qualitative Report, 18(30).
Sandbergh, J. (1997). Are phenomenographic results reliable? Higher Education Research & Development, 16(2), 203-212.Read More
The key to recruiting participants and obtaining successful interviews is to gain their trust and respect. To achieve that, the initial contact with a group or individual included a clear statement of the research goals, format, and ethical considerations. I made it clear that their involvement in the research is voluntary, their participation in the study is anonymous, and the results will be presented in a way that assures confidentiality.
Identity protection. The participants were informed that they can cease participation up to four weeks after they receive the transcript and ask that their data be destroyed. The interviews were recorded with a Galaxy 5 password-protected Android smartphone. A few hours after the interview, the audio files were erased from the smartphone. The data were stored in a password-protected and encrypted Google for a business server. The recordings and transcripts were anonymized, and a separate digital file not stored on the computer was used to connect transcripts, recordings, and participants. That measure assured that even in the case of the computer being hacked, the anonymity of participants would be assured.
The ethics for this study were approved by Lancaster University’s Department of Educational Research. The ASA Committee on Professional Oversight was informed about the project.Read More
Data analysis was based on three segments (Figure 1):
- Phenomenographic research was used to draw a picture of how QIE/IPL is perceived. If there are moderate differences among groups, I will reflect on them. If those differences are significant, it may be possible to create separate outcome spaces for each group.
- A case study of each professional organization involved in the research was focused on QIE/IPL-related practices and technology and the official policy used in the organization. That can help us better interpret data from phenomenographic research and gather insight into what is possible in reality. For example, in my recent research (Hlede, 2015), I found that in the ASA, all research participants indicated that IPL is the preferred way to go; however, the official policy of the organization in 2015 did not reflect that.
- The interaction among groups and existing and potential QIE/IPL projects was analyzed through activity theory so we can get a better picture of interprofessional activities that, at this moment, shape perceptions of each profession.
Phenomenographic analysis. During the interviews and the analysis of the transcripts, the focus was on what the informants said about the phenomenon and how they talked about it (Larsson & Holmström, 2007) and how they perceive the relationships among them. The first step was to become familiar with each transcript and all concepts mentioned in the transcript (Figure 5).
Following that, during the compilation phase, passages that provided comments about QIE/IPL were tagged with short descriptions. These descriptions were grouped into categories based on concepts to which they were referring. To optimize the validity of phenomenographic research, the categories were created as logically separate and exclusive, and they correspond to a significant degree with the data from the literature on IPL/QIE and health-care reform. Therefore, as suggested by Ornek (2008), the probability of categories being considered by other researchers is high.
A unique color was assigned to each category, and an NVivo graphical display was used to track categories and sets of categories. This system helped with cross-referencing categories and estimating the theme, thematic field, and margin of each category (Sjöström & Dahlgren, 2002).
Figure 2. Object and process of phenomenographic research based on Sjöström and Dahlgren (2002) and Bowden (2005)
The outcome space is created as an image illustrating interrelations among categories. All categories are tightly connected. Their themes and thematic fields (Sjöström & Dahlgren, 2002) are of varying size in individual transcripts; however, after summarizing the transcripts, that difference was not very noticeable, even inside each interviewee group. Therefore, the outcome space was presented as a summary of all groups. The outcome spaces specific for each profession may be created in follow-up research.
Tools Used for Data Analysis
Qualitative data analysis software (QDAS). NVivo 11 Pro for Windows, 64-bit (QSR, 2016), was used in the research. The QDAS can significantly enhance qualitative data analysis (Yuen & Richards, 1994). Although analysis and theory construction is a task for the researcher and not the software (Zamawe, 2015), the software creates an additional layer over established research methods and can alter outcomes (Paulus, Woods, Atkins, & Macklin, 2015); therefore, how the software was used is worth mentioning.
Selection. I selected NVivo because it is with ATLAS.ti, one of the two QDAS options supported by Lancaster University. The university has provided valuable lessons on NVivo best practices, and a wealth of materials is available online.
Utilization. NVivo was used for two groups of tasks. The first group covers data management and support for data analysis. The process of coding and analysis was, in many ways, identical as if it was done on Google Docs or paper. The benefit was that the process was easier and faster, and it is easier to track progress. Another group of tasks covers activities that are hardly possible with traditional pen and paper toolset. A quantitative analysis of content and data visualization is the most important example.
Data visualization. In this case, the role of NVivo is enhanced compared to traditional QDAS usage. Knowing that Paulus et al. (2015) stated that 87.5% of 763 articles they analyzed reported only the software name, it is fair to believe that in most cases, QDAS is used only to ease the process of standard phenomenographic practice and that a significant number of researchers did not master advanced features of the software. In this research, I will showcase some of the unique features NVivo provides, primarily data visualization and the utilization of dynamic connections between data from interviews and external resources. There are five main reasons for that approach:
- Graphics can enhance the understanding of connections and differences between various elements of the complex health-care system, which are the focus of this research.
- Data visualization is emerging as an important tool in online research (Kennedy & Allen, 2016).
- Advanced NVivo functionality is underreported in research (Zamawe, 2015).
- Majority of CME/CPD readers are not familiar with QDAS.
- Contemporary visual culture expects well-visualized materials. Addressing that expectation may help bridge the gap between the methodology used in this research and a still-strong preference toward a positivist worldview among health-care professionals.
- Bowden, J. A. (2005). Reflections on the phenomenographic research process. In J. A. Bowden & P. Green (Eds.), Doing Developmental Phenomenography. Melbourne, Victoria: RMIT University Press.
- Chan, Z. C., Fung, Y.-l., & Chien, W.-t. (2013). Bracketing in phenomenology: only undertaken in the data collection and analysis process? The Qualitative Report, 18(30).
- Hlede, V. (2015). Interprofessional Learning: Anesthesiologists’ Perspectives. Assignment, Doctoral Programme in E-Research and Technology Enhanced Learning. Department of Educational Research. Lancaster University.
- Kennedy, H., & Allen, W. (2016). Data Visualisation as an Emerging Tool for Online Research. In N. G. Fielding, R. M. Lee, & G. Blank (Eds.), The SAGE Handbook of Online Research Methods (pp. 307-326). London, UK: SAGE Publications.
- Larsson, J., & Holmström, I. (2007). Phenomenographic or phenomenological analysis: Does it matter? Examples from a study on anaesthesiologists’ work. International Journal On Qualitative Studies On Health And Well-being, 2(1), 55-64. doi:10.1080/17482620601068105
- Ornek, F. (2008). An overview of a theoretical framework of phenomenography in qualitative education research: An example from physics education research. Asia-Pacific Forum on Science Learning and Teaching, 2(11).
- Paulus, T., Woods, M., Atkins, D. P., & Macklin, R. (2015). The discourse of QDAS: reporting practices of ATLAS. ti and NVivo users with implications for best practices. International Journal of Social Research Methodology, 1-13.
- QSR, I. (2016). NVivo 11 Pro for Windows.
- Sandbergh, J. (1997). Are phenomenographic results reliable? Higher Education Research & Development, 16(2), 203-212.
- Sjöström, B., & Dahlgren, L. O. (2002). Applying phenomenography in nursing research. Journal of Advanced Nursing, 40(3), 339-345. doi:10.1046/j.1365-2648.2002.02375.x
- Yuen, H. K., & Richards, T. J. (1994). Knowledge representation for grounded theory construction in qualitative data analysis. Journal of Mathematical Sociology, 19(4), 279-298.
- Zamawe, F. C. (2015). The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections. Malawi Medical Journal, 27(1), 13-15.
The literature states that there is “no prescriptive sample size for a phenomenographic study” (Yates, Partridge, & Bruce, 2012, p. 103). Bowden (2005) suggests that the sample should be large enough to find sufficient variation in perceptions but small enough that the amount of data is manageable. A concept close to sufficient variation in perceptions is the saturation point. The research will achieve the saturation point when additional perceptions cannot be detected (Kaapu & Tiainen, 2012).
Study participants. Following that recommendation, I interviewed five to eight members of each of the four groups: physician anesthesiologists, nurse anesthetists (NA), anesthesiologist assistants (AA), and surgeons. Majority of the participants are clinicians and leaders who are clinically active. The rest are CPD professionals and staff leaders.
This study analyzed twenty-two transcripts, gained through interviewing
- five anesthesiologist assistants (AAs),
- four surgeons and one CPD expert involved in the education of surgeons,
- five anesthesiologist physicians and two CPD professionals, and
- seven certified, registered nurse anesthetists (CRNAs).
In addition to that, because of technical issues, the recordings with one anesthesiologist and one CRNA were damaged and were not included in the analysis.
The in-depth, open-ended interviews were recorded and transcribed verbatim. The interviews lasted between 22 minutes and 115 minutes, and in all cases, a state of mutual understanding was achieved (discussion was exhausted) (Booth, 1997). The variation of length was because of different backgrounds and experiences with IPL. The participants involved in education and leadership had longer interviews than those working solely as clinicians. There are two possible explanations for that: (a) they are more familiar with the complexities of QIE and IPL, and (b) clinicians, especially physicians, usually give very precise and short answers that, in many ways, mimic the way they talk in OR.
The opening sessions during the last three ASA Annual Meetings (2014, 2015, and 2016) have tackled answers to the questions in this research. Since the speeches were delivered as opinion-shaping contemplations of leaders—and the profession in general—I decided to treat those presentations as separate interviews. Additional reasons for that decision are the following:
- The actions of top leaders are critical to the success or failure of IPL (IoM, 2013).
- The provided messages were very clear and can be paired with the questions asked in this research, and those messages have influenced how IPL and QIE are viewed.
- The key speeches delivered during yearly meetings of professional societies and promoted on their websites suggest the direction a profession is taking.
- The videos of those sessions are available online.
The interviews I did for my research on perception of IPL among anesthesiologists (Hlede, 2015) provide information that complement well with this research; therefore, I included them in the analysis.
Special label. Knowing that it is not a standard phenomenographic procedure, I clearly labeled all the comments I received through those channels.
The participants were recruited through references and direct personal contact, mainly through LinkedIn. A few leaders were willing to help me recruit participants. In comparison to my previous research participant recruitment campaigns, the references had a much smaller impact. Prior to this research, I have completed three phenomenographic papers, and I did not have problems recruiting participants. Actually, while I was doing phenomenographic research on anesthesiologists’ perspectives of IPL (Hlede, 2015), I had more leaders willing to be interviewed than I was able to handle. I was not able to replicate that ease of recruitment in this case.
Updating recruitment protocol. In an attempt to improve recruitment, on July 7, 2016, I requested permission to
- update the research participant recruitment scripts,
- offer participants’ insight into the summary of research data (described in the scripts), and
- reward participation in the research with $60 Visa gift cards.
As reasoning for that change, I used the following:
- This research requires interviews with eight groups of people. These include both clinicians and education professionals from four professions—physician anesthesiologists, surgeons, nurse anesthetists, and anesthesiologist assistants—a total of eight groups of participants. The recruitment of participants from all groups can be challenging.
- Sensitive nature of the study topic. Interprofessional politics is an important factor at this moment. As health-care reform progresses and team-based care become standard, anesthesiologists, surgeons, and nurse anesthetists have very passionate discussions about the new team structure and payment models. For example, the debate on the nursing scope of practice (what nurses can do without the supervision of physician anesthesiologists) (Nader, Massarweh, & Safety, 2016) was culminating during this research; therefore, the mere mention of IPL and QIE as elements that assume more intensive collaboration with other professions may be perceived negatively.
- Not comfortable talking about the topic. Interprofessional learning and QIE are still emerging phenomena; therefore, those topics are not commonly discussed among potential interviewees who are not accustomed to explaining their positions on this topic (Namageyo-Funa et al., 2014).
- Extensive health-care reform creates instability, and users are exposed to numerous surveys and interviews related to the reform; therefore, they are not interested in additional surveys/interviews.
- Concerns about confidentiality, a common issue, are exacerbated by the perception that I am an employee of a competing professional group (ASA) with an ulterior motive.
- Perceptions of bias may be exacerbated because of my work for the ASA.
- No-participation policy. Wiebe, Kaczorowski, and MacKay (2012) found that more than one-third of physicians may have an office policy of not participating in research. It is fair to assume that the same policy affects all clinicians in the organization.
- Make participation a revenue-neutral experience. U.S. clinicians are well-paid and busy professionals; however, in the case of anesthesiologists or surgeons, they start their career in their early thirties with significant student debt that, with interest, could exceed $350,000 (AAMC, 2014). Therefore, they are more likely to prefer opportunities where they will be paid for their time.
- Reducing nonresponse bias. Nonresponse bias or the likelihood that the survey respondent group may be significantly different from the population in this research (VanGeest & Johnson, 2013) can be reduced if payment can encourage additional participants.
- Build trust by showing respect and underline the seriousness of this research. Trust is the key in participant recruitment. This initiative can contribute to trust by respecting their time and presenting this as a serious research with a budget.
On July 18, 2016, the change was approved.
As a result, one surgeon and one anesthesiologist assistant were recruited through references. All other participants were recruited through LinkedIn or through previous contacts. The representatives of all organizations were willing to participate in the research except the AANA. That was at odds with the fact that the recruitment of nurse anesthetists through LinkedIn was the most productive.
The questions below were selected to provide critical variation among participants. Those variations were categorized and organized in an outcome space (Cope, 2004):
- Can you please reflect on your previous experience of working in multi-professional teams?
- How would you describe IPL and QIE? For example, what is the purpose of each? Is it the same purpose?
- What are the differences or similarities?
- How about benefits and challenges?
- Can technology help us address those challenges or enhance the benefits? If yes, how?
- What, in your opinion, are the most important benefits and challenges associated with the perioperative surgical home (PSH)?
- How is PSH related to IPL and/or QIE? Is it related?
- Is there something QIE/IPL related that members of your profession can learn from those other professions?
The in-depth, open-ended interviews were recorded and transcribed verbatim.
Since this is a phenomenographic research, the participants were informed that there are no right or wrong answers (Daly, 2008).
- Booth, S. (1997). On phenomenography, learning and teaching. Higher Education Research & Development, 16(2), 135-158.
- Bowden, J. A. (2005). Reflections on the phenomenographic research process. In J. A. Bowden & P. Green (Eds.), Doing Developmental Phenomenography. Melbourne, Victoria: RMIT University Press.
- Cope, C. (2004). Ensuring validity and reliability in phenomenographic research using the analytical framework of a structure of awareness. Qualitative Research Journal, 4(2), 5-18.
- Daly, S. R. (2008). Design Across Disciplines. Purdue University. Engineering, Education, Ann Arbor, MI, U.S. Retrieved from http://books.google.hr/books?id=dSE4uvnBskMC
- Hlede, V. (2015). Interprofessional Learning: Anesthesiologists’ Perspectives. Assignment, Doctoral Programme in E-Research and Technology Enhanced Learning. Department of Educational Research. Lancaster University.
- IoM, Institute of Medicine. (2013). Interprofessional Education for Collaboration: Learning How to Improve Health from Interprofessional Models Across the Continuum of Education to Practice: Workshop Summary: National Academies Press.
- Kaapu, T., & Tiainen, T. (2012). Phenomenography: Alternative Research Approach for Studying the Diversity of Users’ Understandings. Paper presented at the European Conference on Information Systems, http://aisel.aisnet.org/ecis2012/29.
- Nader, N., Massarweh, M., & Safety, M. E. (2016). Veterans Affairs Proposed Rule for Advanced Practice Registered Nurses in the Operating Room A Step Forward or Overstepping?
- Namageyo-Funa, A., Rimando, M., Brace, A. M., Christiana, R. W., Fowles, T. L., Davis, T. L., . . . Sealy, D.-A. (2014). Recruitment in qualitative public health research: Lessons learned during dissertation sample recruitment. The Qualitative Report, 19(4), 1-17.
- VanGeest, J. B., & Johnson, T. P. (2013). Surveying clinicians: An introduction to the special issue. Evaluation and the Health Professions, 36(3), 275-278.
- Wiebe, E. R., Kaczorowski, J., & MacKay, J. (2012). Why are response rates in clinician surveys declining? Canadian Family Physician, 58(4), e225-e228.
- Yates, C., Partridge, H., & Bruce, C. (2012). Exploring information experiences through phenomenography. Library and Information Research, 36(112), 96-119.
In previous chapters, the purpose of the study was described and the literature reviewed. In the continuation, this chapter will present the methodology used in this study: a set of methods and tools I will use to collect and analyze data and deliver conclusions. A methodology is the activity “of choosing, reflecting upon, evaluating and justifying the methods you use” (Wellington, 2015, p. 33).
The theoretical framework used in this research is described first since the theoretical background can heavily influence the selection of methods, how methods are used, and data interpretation. I will use the theoretical framework as a lens to observe and analyze the world. As described in the literature review, theories are important contributors of all our intellectual endeavors; therefore, they should be well-defined, whether they are well-constructed and publicly evaluated concepts or personal hunches, fears, or beliefs.
The methods rooted in that theoretical framework, data collection, analysis, and interpretation practices are presented. Since this research uses data from multiple sources, data triangulation and the validation of findings are described.
The ethical issues and protection of research participants and critical elements of research are described in the last section of this chapter.
The completion of this chapter will serve as the preparation for research activity and data collection.
Importance of ontology and epistemology. Our personal, professional, and societal perceptions and interpretations of the world around us and our interpretation of the educational process have a crucial impact on our intellectual endeavors and research; therefore, knowing them may help interpret and evaluate findings (Cleland & Durning, 2015; Guba & Lincoln, 1994). That is especially important in the context of the Continuing Professional Development (CPD) of health-care professionals in the United States, where, as it is described in the literature review, the culture of quantitative, positivist research is very strong and where qualitative, interpretative, social research methodologies are perceived with suspicion. Positivists argue that educational research and social science should follow positivist methods used in the natural science and deliver “hard” quantitative data (Wellington, 2015).
Feminist research tradition. On the other hand, health-care professions that historically have the majority of their constituents as females (e.g., nurses) are arguably more prone to feminist research (Hall & Stevens, 1991) that promotes collaboration, equality, subjectivity, emancipation, and egalitarian qualitative research (Cohen, Manion, & Morrison, 2007). It is focused on the feelings and experiences of individuals in their unique social and historical context (Holloway & Wheeler, 2013).
Stereotypes. Arguably, that bipolarization is exacerbated by historic stereotypes where the medical profession is male dominated while nursing is a typically female occupation and where relationships between doctors and nurses were typically described as dominant-subservient relationships with man-woman stereotypes (Carpenter, 1993; Gjerberg & Kjølsrød, 2001; Vasey & Mitchell, 2015).
Influence of stereotypes. Although in recent decades, we can see a significant shift from that male-female norm, cultures of professions are still heavily influenced by those stereotypes (Braun, O’Sullivan, Dusch, Antrum, & Ascher, 2015). For example, Vasey and Mitchell (2015) note that not so long ago, in a teaching hospital theater room in 2008, it was stated, “There are only two types of women in surgery—those who shouldn’t be surgeons and those who shouldn’t be women.”
Clash of medical civilizations. That internal cultural and ontological debate is exacerbated with external forces. For example, in his paper on the clash of medical civilizations, (McKenna, 2012) argues that the neoliberal movement has converted the U.S. health-care system into a highly competitive business and has contributed to the creation of a hidden medical curriculum that promotes hierarchy and focuses solely on biomechanical elements of disease while discouraging students from social critique.
Ultimately, the contexts in which this research is conducted is marked by strong ontological and epistemological conflicts; therefore, a clear definition of points this research will take will ease the understanding of how data are analyzed.
Ontology. Social constructivism is my dominant worldview. It assumes that meanings are created in social interaction. They are constructed on individual and group/organizational/professional levels and influenced by numerous historical, cultural, and technological factors.
Epistemology. The social constructivism learning theory (Curran, Fleet, & Kirby, 2010; Vygotsky, 1978) is associated with that worldview. I will use it as a lens to analyze the potential challenges of Quality Improvement Education and Interprofessional Learning (QIE/IPL).
Social constructivism assumes that groups actively construct knowledge through social interaction internally among team members and as a team interacting with the external world. In that process, they create a group culture, a collection of shared artifacts and mental models. Ultimately, according to a social constructivist view, society exists simultaneously as subjective and objective reality (Andrews, 2012).
Phenomenography. The social constructivist worldview allows me to focus “on the participants’ views of the situation being studied” (Creswell, 2009). It suggests open-ended questioning to find out what people think or do in their daily lives. A phenomenographic approach is a good tool for that task, providing insight into the more subjective side of the world and insight on how the phenomena of QIE and IPL are preceded at this moment. Since QIE/IPL is a social construct happening in a very complex environment, that insight can help us understand the status, trends, and ways we can address those trends.
Case study. On the other hand, although IPL and QIE have become quite well-known concepts, most interviewees have not had a chance to practice them. They have not experienced them as something real and objective. To better understand the objective aspects of the phenomenon, the case study focused on how their professions and their professional associations are tackling that issue I used.
Methodology of Choice
The methodology of choice is a qualitative, interpretive, multiple-case case study (Yin, 2003) that encompasses phenomenographic analysis. Activity theory is used as a lens to analyze interrelations among multiple elements in this complex system.
Roles. Phenomenography will help me map the territory by analyzing how people perceive that phenomenon. The case study will help analyze the most important elements of the system and craft “the big picture.” Knowing that the system is extremely complex and very dynamic, with multiple stakeholders and multiple contradictions, it is essential to select a toolset to analyze relationships and processes in such a system. Complex metatheory and activity theory are selected as a match to that task. Complex metatheory will serve as a lamp that exposes multiple interconnected elements of this dynamic system, including interactions among different methods and methodologies (Bleakley & Cleland, 2015). Activity theory will, as a lens, help us analyze interactions among different elements of the system.
The image on the right illustrates how methods and theories are nested in this research.
Research design and learning questions. The interaction among those elements of research design will contribute to all three questions; however, it is fair to say that specific elements of research frameworks are associated with specific research questions. Those connections are the following:
- Phenomenography will mainly address question 1: How are QIE/IPL and technologies and policies that shape QIE/IPL perceived by four groups involved in perioperative teams: anesthesiologists, surgeons, anesthesiologist assistants, and nurse anesthetists?
- The case study has the dominant role to address question 2: How is technology-enhanced collaborative learning used and perceived in the context of QIE/IPL and perioperative teams?
- Activity and complexity theories provide a general context for the whole study and are essential tools for the last research question: How are professional cultures and contextual factors related to collaborative learning influencing the implementation of technology-enhanced QIE/IPL?
Multiple-case design is the approach of choice (Yin, 2003). QIE/IPL-related practices and learning technology that can support QIE/IPL used by each profession are analyzed as independent cases. The four cases are
- the American Society of Anesthesiologists (ASA),
- the American Association of Nurse Anesthetists (AANA),
- the American College of Surgeons (ACS), and
- the American Academy of Physician Assistants (AAPA).
An alternative solution was to use a single case with embedded multiple units of analysis. That approach would be appropriate if we had collaboration and shared programs among two or more of the specialties in place. In that situation, the case would be shared QIE/IPL-related practices and learning technology that can support QIE/IPL of all professions together. Activities specific to each profession would be embedded units of analysis.
Since now I am not aware of a formal interprofessional collaboration on QIE/IPL, the QIE/IPL activities in each specialty were analyzed as separate entities.
The case study data sources were interviews with staff and physician members and a professional association’s website and published literature. Two interviews (nonphenomenographic) were scheduled with representatives of the three involved professions. The representatives of the American Association of Nurse Anesthetists were not able to participate. Phenomenographic interviews also served as a source of data over and above standard phenomenographic research.
Phenomenographic analysis focused on how CPD professionals, clinicians (anesthesiologists, nurse anesthetists, surgeons, and anesthesiologist assistants), and their respective leaders perceive QIE/IPL; and technology that supports those practices is the central part of the case study. Phenomenography appears to be the optimal method for this approach because although at this point QIE/IPL is in its early stage, it is a very hot topic, and human perceptions are the dominant factor.
The additional reasons are the following:
- Attempts to implement QIE/IPL in the U.S. health-care system have a long but troubling history, and drivers influencing the implementation of QIE/IPL create a very complex picture. The phenomenographic approach is recognized as a good tool to analyze changes in such a complex system (Bunniss & Kelly, 2010; Stenfors‐Hayes, Hult, & Dahlgren, 2013).
- Understanding the perceptions of groups involved in the learning and teaching process can enable us to address current and emerging challenges in that dynamic environment (Richardson, 2005).
- QIE/IPL is ultimately a social endeavor (Hean, Craddock, & O’Halloran, 2009).
Phenomenography and medical education. During the past two decades, phenomenography proved to be very useful in medical education (Stenfors‐Hayes et al., 2013). It provides insight into the different ways that people perceive phenomena in the world around them and how those perceptions relate one to another (Marton, 1981; Marton & Booth, 1997); therefore, it can serve as a lens to analyze a specific research question and direct how research is carried out. In a medical setting, phenomenographic research is valuable for topics like clinical practice, communication, health-care learning, and especially the processes and outcomes of learning (Larsson & Holmström, 2007; Richardson, 1999). According to Stenfors‐Hayes et al. (2013), phenomenography can serve as a link among three important elements this research is tackling—research, organizational change, and educational development. That feature can be especially valuable in the context where, as the Macy (2013) expert team concluded, huge changes affecting the U.S. medical system are not linked effectively with changes affecting the CPD of health-care professionals in the United States.
Alternatives. Phenomenography was chosen over phenomenology because QIE/IPL is, in this context, an emerging concept; therefore, we can expect numerous, sometimes contradicting ways in which QIE/IPL is perceived (Larsson & Holmström, 2007). Furthermore, the research is interested in the differences of how the phenomena are perceived among four different professions, and phenomenography is an optimal tool for that task.
Two additional approaches that I considered but did not include as the first choice are action research and realist evaluation. As described below, I considered those methods as potential augmentation of the research framework, and they may help me address possible changes, for example, if the leadership of the ASA decided to start an interprofessional program during this research. While not used in this paper, the methodologies below can be very beneficial to the studies that may follow.
The action research method is a collaborative study focused on solving a problem through a cyclical and reflective process built around the following:
- Research and planning>>action>>collecting and analyzing evidence>>reflecting>>research and planning
Through those cycles, the researcher actively works with the participants to prepare and implement changes (Boet, Sharma, Goldman, & Reeves, 2012; Coghlan & Brannick, 2014). Focusing on QIE/IPL as an emerging concept, which the ASA has considered implementing, suggests that action research may be a method of choice.
On the other hand, the collaboration between the participants and the researcher is a potential source of political and ethical challenges affecting researchers and participants (Williamson & Prosser, 2002). Since the changes of CPD are described primarily as a political process (Balmer, 2013; Cervero & Moore Jr., 2011), using action research as a dominant methodology would be too risky an approach. It can affect the researcher and the participants, and political forces may stop what is needed for action research.
The realist method is a theory-driven approach used for the evaluation of complex social intervention in the field of health care (Marchal, van Belle, van Olmen, Hoerée, & Kegels, 2012). Some research asks simplistic questions like “Does IPL work?” The realist method attempts to answer if something works under specific social circumstances. It asks how and why something works. It attempts to dig deeper and find what works for whom, under what circumstances, and to what extent. It tries to determine how to improve or reduce the impact of what is being studied (Wong, Greenhalgh, Westhorp, & Pawson, 2012). The realist method may be useful for this research since the implementation of QIE/IPL is a very complex process influenced by numerous societal factors and affecting a variety of different stakeholders. On the other hand, per Wong et al., the realist method will be especially beneficial if we have the new intervention and a rich source of qualitative data in place. Hopefully, in a few years, we will have a wealth of data on QIE/IPL, but that is not the case now.
Complexity and Activity Theory
It is complex. For quite a long time, we have been rediscovering that the majority of learning for health-care professionals happens in the work environment and that it is socially constructed (Engeström, 2001; Fenwick, 2014). That is especially true for QIE and IPL; however, we have been experiencing challenges implementing learning designed for that context. Therefore, very often, our QIE and IPL attempts would result in retreat to the comfort zone of the traditional content-focused learning modalities. For example, the Accreditation Council for Continuing Medical Education annual report revealed that in 2015, only 0.7% of accredited learning activities were performance/quality improvement activities (ACCME, 2016). Arguably, the lack of tools that can help us analyze how health-care learning systems work is the reason why we are prone to forget the importance of workforce learning until the next research rediscovers that fact again.
Complexity and activity theories can help us address that challenge by providing insight on how learning embedded in a complex system works and how various factors of such a system interact.
Lens to analyze complexity. Activity theory (AT, sometimes called cultural historical activity theory or CHAT) is the third element of the research framework. AT is a descriptive sociopsychological framework taking into account all elements of a complex activity/work system (Johnston & Dornan, 2015). It explains divisions between the material and the mental, history and present, theory and praxis, and—for interprofessional education, especially troubling issue—the individual and the group (Stetsenko, Arievitch, & Blunden, 2014). Examples of such an activity system may be teams like a perioperative surgical home team or organizations such as the ASA. AT can help us analyze interactions among professionals in the system—in our case, that can be doctors, nurses, and patients—and their learning shaped by interpersonal, cultural, economic, political, and historical aspects (Foot, 2014); therefore, activity theory can serve as a lens to analyze collective, culturally mediated, and object-oriented human activities in such a complex and dynamic system (Barab, Evans, & Baek, 2004; Jonassen & Rohrer-Murphy, 1999).
Activity theory is probably the most complicated, and for some readers, it can be the most abstract tool in my research toolset; therefore, I will describe it in more detail.
Under the umbrella of complexity. With communities of practices and actor-network theory, activity theory is nested under the umbrella of complexity, a metamethodology described below (Bleakley & Cleland, 2015; Jonassen & Land, 2012).
Ontology. Activity theory, same as phenomenography, rejects positivist approach and Cartesian dualism. Cartesian dualism is the idea that our body and our mind are two separate entities and that our mind can objectively analyze everything happening in the “real world” without being affected by real-world activities (Baker & Morris, 2005). AT is rooted in the idea that our perception of the world is interwoven with our physical existence in it; therefore, AT provides a map to understand the main drivers that may influence our interaction with and perception of the world (Roth & Lee, 2007), a map to help us understand processes, individuals, and teams used to manage change and learning in daily (clinical) practice (Engeström, 2001).
History. Activity theory has had a dynamic and troubling evolution. It is rooted in Vygotsky’s sociocultural psychology (Verenikina, 2010) and Marxist’s praxis-focused dialectical materialism (DeVane & Squire, 2012). It was crafted during the 1920s and the early 1930s in Soviet Russia. Dialectical materialism assumes that progress is built through a clash between opposites (Spirkin, 1983). Sociocultural psychology/theory explains that teaching and learning (and QI projects) are embedded in the cultural and historical context of learners’ daily practices and intimately connected with the way learners interact with peers, teachers, and society.
Political context – complex network society. The Western opinion of early Soviet Russia is usually based on recollections of Cold War and the relatively small economical, intellectual, and cultural impact socialistic Russia had on the world. Early Soviet Russia, located between the dictatorship of the czar and the dictatorship of the Communist Party led by Lenin, may give a different picture. During the 1920s, Russia seemed to be a place of significant intellectual production (Johnston & Dornan, 2015). The country was going through massive and complex reforms: the imperial political structures crashed and they were replaced informal personal networks (Easter, 1996); the belief that the new system will be better that the old monarchy was crushed by failed reforms, subsequent famines that took many millions of lives (Brooks & Gardner, 2004), and the rising, cruel dictatorship of the new government. Arguably, the country was well networked and on the edge of chaos; and that, as explained under complexity theory, created a great context for innovation. One important innovation initiated during that time is AT.
Suppression in SSSR. During early postrevolutionary Soviet Russia, the first generation of activity theory was created by Vygotsky and collaborators. Because of the Cold War and the suppression of dialectical materialist psychology in SSSR (Bickley, 1977), AT was not well known to the West until the 1980s (Engeström, Miettinen, & Punamäki, 1999).
The development of activity theory went through three generations. Each generation created a more complex extension of the previous one. Each generation has a bigger, more inclusive, and more complex unit of analysis; therefore, each generation is worth mentioning for a better understanding of AT.
The first generation of activity theory is focused on the interaction between individuals and the world. That interaction is never direct. We need psychological tools to communicate our thoughts and/or technical tools to physically communicate with the world. The most common psychological tool is language. Technical tools are physical artifacts we use in our daily life. The pen, the fork, and the scalpel are just a few examples. We use those tools to achieve a specific objective. Therefore, the map of the first generation of AT looks like this (Engeström et al., 1999):
The second generation of AT extends a notion of context by adding rules, division or labor, and community to the picture. Those are integral elements of each activity system, and they may have a crucial impact on how we interact with the world. The second generation of the AT map is presented in the image below. The lines with arrowheads symbolize connections and contradictions, which make the system dynamic and create a fuel for progress.
The second generation of AT can be used, for example, to analyze how an operation room team interacts in the attempt to achieve, for example, object: patient safety. The inherited limitation in that analysis may be the differences among groups in that team (e.g., surgery, anesthesia, management); each one is with their own unique culture, and rules and roles are not recognized. That limitation is addressed in the third-generation AT.
The third generation of activity theory is specifically interesting for this research because it is focused on how different activity systems interact (Engeström, 2001). Each profession (anesthesiologists, nurse anesthetists, surgeons, etc.) and patient or the public can be analyzed as a separate activity system. The third generation of activity theory can help us understand how those systems interact during work or QIE/IPL activities.
AT and health care. Activity theory is often used to analyze complex interactions among and inside health-care activity systems (Bardram & Doryab, 2011; de Feijter, de Grave, Dornan, Koopmans, & Scherpbier, 2011; Engestrom, 2000; Skipper, Musaeus, & Nøhr, 2016). That link between activity theory and health care was noticeable since the early beginning. For example, the paper introducing the third generation of activity theory (Engeström, 2001) uses interaction among health-care activity systems (hospital, patient’s family) as the main example.
An example of the third-generation AT diagram is presented below:
Figure. Two interacting activity systems are the minimal model for the third generation of activity theory (Source: Engeström, 2001). Each profession can be analyzed as a separate activity system. The outcomes (Object2) of each profession interact, creating the outcome of collaboration—Object3.
In our perioperative model, the interaction between the patient’s anesthesiology and surgery activity systems may look like this:
AT is a system-based design. Instead of being a predictive theory, activity theory can serve better as metatheory or a framework we can use to understand cultural and historical aspects of relations in complex social systems (Iivari & Linger, 1999). Since it is focused on activity systems, a concept of collective and socially and object-mediated human activity, AT can bridge the gap between individual actors and very complex, socially constructed, and technology-enhanced reality. For example, AT has proven to be a powerful tool for researching how people adapt and learn in the workplace (Engeström, 2001; Engeström, Virkkunen, Helle, Pihlaja, & Poikela, 1996).
Expansive learning. Finally, the last important element of AT is expansive learning. Expansive learning theory was created as an application of activity theory to showcase how workforce learning and innovation arise. AT showcases multiple voices, multiple drivers, and multiple contradictions among them. When those voices and contradictions merge and create something new, innovatively, that is called expansive learning (Engeström, 1987). In the other words, expansive learning is a “constant comparative process or adaptation” (Johnston & Dornan, 2015).
Challenges and opportunities of expansive learning. While expansive learning as a complex model may be hard to grasp and may not be the best solution for industrial, standardized learning products delivery (Hean et al., 2009), it serves as a promising model to explain and improve learning in complex systems such as health care. We are well aware that in an increasingly complex world, our attempts to have complete control or chunk our world into smaller, isolated, “manageable” entities play a central cause of numerous failures (Dorner, Nixon, & Rosen, 1990).
Change laboratory. To address the challenges of learning in complex systems, Virkkunen and Newnham (2013) have created The Change Laboratory: A Tool for Collaborative Development of Work and Education. The Change Laboratory method is built on AT and the theory of expansive learning. In this model, the outcomes are developed by participants while they create expansive answers to contradictions (read: QI challenges) in their activity system (read: local context). That is significantly different from the traditional learning development approach, whereby teachers or instructional designers create a learning activity for learning objectives they have selected and activities happen in time and space separated from daily practice.
The big, final piece of methodology is complexity theory. It serves as a metatheory that provides insight on how multiple dynamic elements of a complex system interact.
About complex health-care education, the very formalized word of surgery and perioperative medicine and its cousin, the world of medical education is complex (Bleakley & Cleland, 2015; Plsek & Wilson, 2001). It is created as a dynamic network of multiple teams—anesthetic, surgical, laboratory, medical technology, radiology, pathology, management, and physiotherapy—shaped by five dominant cultures. In addition to management, education, and research, the three cultures described by Bleakley and Cleland, the cultures of health-care professions and QI play significant roles. An addition to that very dynamic complex is the empowered patients with their beliefs, knowledge (or lack of knowledge), and expectations (Wald, Dube, & Anthony, 2007). Therefore, to make a positive and sustainable change in that system, we must understand how complexity works.
Complexity has two important sides. It is harder to analyze it, but it can be very beneficial for innovation.
It is complex. From one side, analyzing and managing complex systems is much harder than analyzing simple or complicated systems. That is why when we have a choice between a complex system and a simple or complicated system, our preference will go toward a simpler one (image below). Senge (2006, p. 73) explains it: “The reality is made of circles, but we see straight lines.” In other words, reality is complex, but we see only a simple and complicated process. Very often we will deconstruct a complex system on a set of simple processes and attempt to analyze those processes as separate entities. While on short run, those attempts may provide valuable information, at the end we may understand elements of the system, but we do not understand the system. Fortunately, as described in this chapter, we have tools to analyze complex systems; therefore, we can use Prochaska’s words: “The days of searching for simple solutions to complex problems should be behind us” (Prochaska & DiClemente, 1986, p. 4).
Complexity and innovation. On the other hand, complexity provides a framework for innovation. Complex systems, especially highly complex systems on the edge of chaos, have the highest potential for creativity. Looking through the lens of activity theory, dynamic complex systems have the highest amount of contradictions, which drive change and innovation. Kauffman (1996) convincingly argues that the edge of chaos, a space between order and complete randomness, is a context where serendipity has the highest potential. The same context is forcing us to critically analyze our actions and impact on the world around us, increasing our potential for learning and discovery. That explains why some of the most valuable contributions Russia made to the world’s science and culture (especially avant-garde) happened during 1920 (Von Hagen, 1996).
Treating complex as complicated. On the other hand, we usually experience challenges in that innovative side of complexity. Glouberman and Zimmerman (2002) use examples of Brazil, France, and Canada health-care systems to point out that health-care systems are complex; therefore, emerging health-care challenges should be treated as complex issues. Unfortunately, most health-care fixes perceive health-care systems as merely complicated systems. Thus, those complicated interventions serve primarily as a waste of time, opportunity, and resources and contribute to the deterioration of health care.
A vivid example and scale of such a misconception was given by Mr. Donald Trump on February 24, 2017, when he noted, “Nobody knew that health care could be so complicated” (Howell, 2017). That showcases that after seven years of intensive debate over U.S. health care, part of policymakers and the person who acts as the president of the United States perceive health care merely as a complicated system. Consecutive to that misconception, the American Health Care Act of 2017, a U.S. Congress bill aimed to replace the Patient Protection and Affordable Care Act (ACA) known as Obamacare, failed to deliver on some of the main promises made by the administration. Therefore, the U.S. Congressional Budget Office estimated that instead of “insurance for everybody” (Costa & Goldstein, 2017), the bill promises twenty-four million more uninsured Americans by 2016 (CBO, 2017); instead of lower health-care costs, insurance can rise significantly, especially for older Americans (Sarlin, 2017). As result, the law did not pass in its original form.
The drive back to the simple zone. The image below illustrates that drive from complexity to a “simple, evidence-based zone” (O’Riordan et al., 2011). Very often, we end up in the evidence-based zone simply by neglecting complexity and ignoring everything that is not certain or generally accepted. In a stage of high uncertainty like the U.S. health-care system is experiencing recently, the drive to go to a safe zone can be much stronger, blocking the possibility to make the needed change (Engel, 1997).
Fortunately, the forces pushing in the other direction toward complexity are becoming stronger and better supported by science.
The evolution of dominant epistemologies has contributed to the increased awareness of learning complexities. Traditional learning theories like behaviorism and cognitivism were focused on the individual student, usually perceived as a completely autonomous entity separated from the rest of the world and her possibility to “absorb” knowledge (Siemens, 2005). Learning was perceived as a simple, longitudinal, and content transmission (Jonassen & Land, 2012). During the last four decades, our perception of learning has transformed. Now dominant constructivist and sociocultural learning theories perceive learning as an active, social process where, instead of knowledge reproduction, students collaborate on knowledge production, helping one another access and evaluate distributed knowledge. This is a significantly more complex process that involves socialization, identity formation (Cruess, Cruess, & Steinert, 2015), and (for CME/CPD context) new course delivery formats (Curran et al., 2010).
Personal and professional epistemologies are an important element of the complexity mosaic. They are processes in which individuals and groups do or do not construct knowledge from learning experiences. Those epistemologies are shaped by learners’ ambitions, interests, capacities, identities, and social structures (Billett, 2009). Because of significant variations of personal and group epistemologies, the impact on a learning event, as a society-constructed activity, can significantly vary.
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