Interprofessional learning

Literature review: Quality improvement education and interprofessional learning

QIE and IPL in a connected world. As described earlier, QIE and IPL have 45-plus years of history behind them. Therefore, our perception of them is to a significant extent shaped by how they looked, acted and interacted during the pre-Internet era. It was a very different world from today. We can now videoconference with peers on another continent, or use one-click access to read up-to-date detailed dynamic reports, activities that would be seen as science fiction by earlier generations. In the past 25 years, technology has reshaped how we communicate, learn, live and perceive the world around us (Siemens, 2005). In that context, we can revisit how QIE and IPL look today.

Knowing that the traditional CPD has a limited impact on quality of care (Hager et al., 2008; IoM, 2010; Macy, 2013) and is focused on individuals, it is fair to say that IPL and QIE have different learning formats and different goals than traditional CPD.

QIE and IPL have numerous similarities. They assume that the best way to ensure individual and system-wide professional development and QI is to have a well-integrated and coordinated system (Shortell, Bennett, & Byck, 1998), where healthcare workers from all professions are connected and focused on meeting the needs of individuals and communities (Macy, 2013). They are both described as great tools to address the same three goals: better care, better health and reduced cost (Batalden & Davidoff, 2007; IoM & 2013). Finally, WHO (2010) presented IPL as an important prerequisite for a high-performing collaborative practice and continuous quality improvement. Therefore, QIE and IPL can be viewed as two different entry/view points of the same system-wide QI system (learning/networked health system) – as Figure 6 illustrates. IPL will start with creation of a skilled, collaborative, practice-ready workforce that can practice quality improvement and deliver optimal health services. On the other hand, QIE will start with system changes that require the collaborative practice-ready workforce IPL can produce. Ultimately, they should be treated as two related parts of the same system. Further on, I will refer to them as QIE/IPL.

Figure 6. IPL and QIE entry point or lenses into Health and Learning Health Systems. Left lens is more focused on IPL. Right lens is more focused on QIE. Together they provide the full picture (Adopted from: WHO, 2010, p. 9).

The QIE roadmap confirms the same assumption. The Alliance for Continuing Education in the Health Professions (ACEHP) in 2015 launched the Alliance QIE Initiative (Sulkes, 2014) and Roadmap (Figure 7). As Figure 6 illustrates, QIE by ACEHP is a continuation of the gradual evolution of CME to CPD – from didactic lectures to practice-based activities with real impact on clinical performance. It assumes incorporation and integration of education professionals, tools, resources and methods into system-wide QI efforts. Since successful QIE changes are usually system-wide, and involve multiple professions, the QIE roadmap presented below (Diamond et al., 2015) predicts the current model of siloed education of healthcare professionals will evolve into interprofessional education during next 10-15 years. In other words, implementation of QIE and IPL is happening simultaneously, and we cannot separate them.

Figure 7. Alliance QIE Initiative: A Transformation Shift – toward interprofessional team-based QIE  Source: The Quality Improvement Education (QIE) Roadmap: A Pathway to Our Future: http://www.acehp.org/page/qie-roadmap, (Diamond et al., 2015).

During that process, current pedagogies focused on content transmission and didactic events that are not well-integrated in clinical work will be replaced with pedagogies that integrate quality improvement, clinical practice, interprofessional collaboration, and student- and team-centric approaches (Ladden, Bednash, Stevens, & Moore, 2009).

1.1     Networked learning and quality

As mentioned, the CPD of healthcare professionals in the U.S. and CPD services the ASA provides are going through significant changes. Arguably, enhancing connections among healthcare professionals and the system is an important part of that process (Margolis & Parboosingh, 2015). Thus far, professional organizations are serving as learning networks (Margolis & Parboosingh, 2015). However, depending on our perspective, visibility of networked learning will change. For example, Jackson and Temperley (2007) argue that if a professional organization and profession is perceived as an established indivisible entity, then facilitating productive discussions about networked learning may be a challenge. However, if we perceive a profession as a network of professionals spread through numerous institutions and communities of practices, most of them interprofessional, then we may say that the profession is primed for networked learning.

Recently Curtis A Olson and Tooman (2012) provided a series of vivid examples explaining how didactic CME has an impact on clinical outcomes. Although participation in didactic sessions at live learning conferences was an important part of all three cases they chose, in all three instances it was just a small part of continuous networked learning. Yet, the cases were used to demonstrate the value of didactic leaning, and did not even mention networked learning. That illustrates how, depending on our ontological and epistemological perspective, the same event can be seen as networked learning and an example of didactic learning.  Arguably, the cases presented how networked and social learning, which encompasses didactic lectures and uses conferences to connect people and build social capital, can improve clinical practice.

As the examples suggest, the main, and usually the only, networking “technology” have been live meetings (face-to-face conferences). Therefore, the impact of those learning networks has been limited to a specific time and place. The first Journal of Continuing Education in the Health Profession article using keyword networked learning was recently published by Margolis and Parboosingh (2015). The article highlights how networked learning initiated through live meetings can be enhanced through an online community.

Fully-featured networked learning is “learning in which information and communication technology (ICT) is used to promote connections: between one learner and other learners, between learners and tutors; between a learning community and its learning resources” (Goodyear, Banks, Hodgson, & McConnell, 2006, p. 1). Such a network can foster a shared vision, create collaborative space used to discuss solutions for complex issues, support CPD of participants and help them built trusting relationships (Margolis & Parboosingh, 2015).

Dirckinck-Holmfeld, Jones, and Lindström (2009) explained that development of networked learning environments is essential for successful networked learning. Due to cultural, organizational, legal and technological issues, wide adoption of such an environment hasn’t happened thus far. However, in all four areas, positive changes are happening very quickly. For example, the healthcare social media landscape is very versatile and dynamic (Fogelson, Rubin, & Ault, 2013). And the number of healthcare professionals using social media is growing exponentially. Sermo.com provides secure networking and crowdsourcing opportunities to 550,000 credentialed  physicians from 25 countries (sermo.com, 2016). Steele et al. (2015) argues that social media has become a necessary component of surgery practice. Furthermore, gaps in networked care between healthcare teams and groups have been recognized as a serious challenge. Knowing that strategies to support networked systems are well-established among some non-healthcare groups, Braithwaite (2015) systematically reviewed non-healthcare literature.

Team-based and networked learning for healthcare teams
Arguably, team-based education and networked learning have many characteristic of QIE and IPL (Bornkessel et al., 2014). Many programs delivered through team-based education and/or networked learning are in essence QIE/IPL modalities (Bate, 2000; Carter, Ozieranski, McNicol, Power, & Dixon-Woods, 2014). Therefore, they can be used as a basis for future development of QIE/IPL. A few promising examples are described in the following paragraphs.

MOOCs. The potential of Massive Open Online Courses (MOOCs) in medical education that focus on specific topics is recognized. For example, Murphy and Munk (2013) convincingly argue that radiology residents get only limited teaching of medical imaging, radiology management, economics and technology. They propose MOOCs as an optimal solution to address those learning gaps, engage all professions (physicians, nurses and technologists) and have a positive impact on U.S., Canadian and international radiology education. In the same manner, Liyanagunawardena and Williams (2014) reviewed 98 MOOCS offered on healthcare topics in 2013. A significant majority of the MOOCs were offered in English-speaking institutions from the developed world, primarily in North America, and focused on introductory-level material. Numerous examples prove the potential of MOOCs to provide continuous professional development of healthcare professionals, students, the public and patients. Some courses offered CPD credits for healthcare professionals. “Collaboration and Communication in Healthcare: Interprofessional Practice” is a good example. That MOOC, created by the University of California, San Francisco, has run since 2014. Based on those characteristics of successful healthcare MOOCs, it is fair to expect that more advanced MOOCs on IPL and QIE will be successful – especially if the offerings are associated with CME credits.
On the other hand, Davidson (2014) warns that reliance primarily on xMOOCs (extendable MOOCs) instead of cMOOCs (connectivity MOOCs) will allow  a few providers to monopolize learning. cMOOCs and xMOOCs are massive online courses, but they are based on different pedagogies (Rodriguez, 2013) and different levels of openness. xMOOCs are built around video streaming and automated MCQ exams, while cMOOCs are more open and built around collaboration and connectivism. As a result, instead of delivering connected, collaborative education, MOOCs may promote keeping more with forms of siloed education specific for the Taylorized 19th-century industrial era, than for the current era (Davidson, 2014).
Therefore, depending on which MOOCs modality we choose, the target audience, how activities are organized and how outcomes are connected with quality improvement, MOOCs can have (or not) each of these characteristics: networked, IPL and QIE.

Simulation education proves to be a great context for interprofessional, quality-focused team-based training (Hinde, Gale, Anderson, Roberts, & Sice, 2016; Navedo, Pawlowski, & Cooper, 2015). In that context, multiple healthcare professions (physicians, nurses) and associated healthcare professionals (computer science, law, etc.) can learn together through highly interactive, hands-on learning experience (Paige et al., 2014). Liaw, Siau, Zhou, and Lau (2014) showed that simulations can promote mutual respect, open communication and shared decision-making, while breaking down stereotypes toward physician-nurse collaboration. At the same time, the impact of simulations can be significantly improved it they are well-integrated into reflective, collaborative learning and working, if they reflect the cultural and social context of a team, and if participants are in a network of peers, teachers and resources while they are implementing changes in their local environment (Stocker, Burmester, & Allen, 2014; Zigmont, Kappus, & Sudikoff, 2011).
The ASA maintains the ASA Simulation Education Network – a network of high-fidelity simulation providers of exercises for the purpose of maintenance of certification. After an interprofessional simulation exercise and reflection, all participants must create a performance improvement plan. All three elements – simulations, IPL and QIE – are very noticeable. A framework that will network participants with peers, tutors and resources should be developed in 2016.

Quality improvement initiatives have been significantly promoted with the new pay for performance reimbursement system (Britton, 2015). Healthcare providers are required to track their performance and are awarded for QI initiatives. Therefore, QI initiatives have become mainstream. Almost as a rule, QI initiatives are multiprofessional. However, QI initiatives do not have direct connections with CME and maintenance of the certification credit system. Therefore, the American Board of Medical Specialties has created Multi-Specialty Portfolio Approval Program. Through that program, participants can get MOC credits for institutional, multispecialty-team-based quality-improvement activities (Irons & Nora, 2015).

Experiential learning. (G. Campbell, 2016) convincingly argues that any form of experiential learning in a digital age is at least partially built on participation “within a digitally mediated network of discovery and collaboration” (G. Campbell, 2016, p. 71) – therefore it is a form of networked learning.

Furthermore, Campbell reminds us that we still use a collection of pre-digital networked learning practices, the library. “Enter the stacks, and run your fingers along the spines of the books on the shelves. You’re tracing nodes and connections. You’re touching networked learning — slow-motion and erratic, to be sure, but solid and present and, truth to tell, thrilling.” (G. Campbell, 2016, p. 70)
(Bates, 2015) criticizes that argument, stating that Campbell’s high-level pedagogical justification of networked learning lacks detailed support. The quality of networked learning and experiential learning can vary – just as the quality of any teaching method can vary. Therefore, it is fair to say that Campbell’s statement is correct only if we can deliver high-quality networked learning. Otherwise, we should consider alternative modalities.

This paper will build on those thoughts, and look for ways to deliver more effective networked learning modalities – so that networked learning can become a viable CME option.

References

1. Batalden, P. B., & Davidoff, F. (2007). What is “quality improvement” and how can it transform healthcare? Quality and safety in health care, 16(1), 2-3.
2. Bate, P. (2000). Changing the culture of a hospital: from hierarchy to networked community. Public Administration, 78(3), 485-512.
3. Bates, A. W. T. (2015, 1/27/2016). Is networked learning experiential learning?   Retrieved from http://www.tonybates.ca/2016/01/27/is-networked-learning-experiential-learning/
4. Bornkessel, A., Furberg, R., & Lefebvre, R. C. (2014). Social media: opportunities for quality improvement and lessons for providers—a networked model for patient-centered care through digital engagement. Current cardiology reports, 16(7), 1-9.
5. Braithwaite, J. (2015). Bridging gaps to promote networked care between teams and groups in health delivery systems: a systematic review of non-health literature. BMJ open, 5(9), e006567.
6. Britton, J. R. (2015). Healthcare Reimbursement and Quality Improvement: Integration Using the Electronic Medical Record: Comment on” Fee-for-Service Payment-an Evil Practice That Must Be Stamped Out?”. International journal of health policy and management, 4(8), 549.
7. Campbell, G. (2016). Networked learning as experiential learning. Educause Review, Vol. 51 No., 50(1, January 11), 1.
8. Carter, P., Ozieranski, P., McNicol, S., Power, M., & Dixon-Woods, M. (2014). How collaborative are quality improvement collaboratives: a qualitative study in stroke care. Implementation Science, 9(1), 32.
9. Davidson, C. N. (2014). Why Higher Education Demands a Paradigm Shift. Public Culture, 26(1 72), 3-11.
10. Diamond, L., Kues, J., & Sulkes, D. (2015). The Quality Improvement Education (QIE) Roadmap: A Pathway to Our Future. Retrieved from http://www.acehp.org/p/cm/ld/fid=209
11. Dirckinck-Holmfeld, L., Jones, C., & Lindström, B. (2009). Analysing networked learning practices in higher education and continuing professional development: Sense Publishers.
12. Fogelson, N. S., Rubin, Z. A., & Ault, K. A. (2013). Beyond likes and tweets: an in-depth look at the physician social media landscape. Clinical obstetrics and gynecology, 56(3), 495-508.
13. Goodyear, P., Banks, S., Hodgson, V., & McConnell, D. (2004). Advances in Research on Networked Learning (Vol. 4). Boston, USA: Springer Science & Business Media.
14. Hager, M., Russell, S., Fletcher, S. W., & Macy Jr, J. (2008). Continuing education in the health professions: improving healthcare through lifelong learning: Josiah Macy, Jr. Foundation.
15. Hinde, T., Gale, T., Anderson, I., Roberts, M., & Sice, P. (2016). A study to assess the influence of interprofessional point of care simulation training on safety culture in the operating theatre environment of a university teaching hospital. Journal of interprofessional care, 1-3.
16. IoM. (2010). Institute of Medicine: Redesigning Continuing Education in the Health Professions (9780309140782). Retrieved from http://www.ama-assn.org/resources/doc/cme/iom-report-cme.pdf
17. 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.
18. Irons, M. B., & Nora, L. M. (2015). Maintenance of Certification 2.0—strong start, continued evolution. New England Journal of Medicine, 372(2), 104-106.
19. Jackson, D., & Temperley, J. (2007). From professional learning community to networked learning community. In K. S. L. Louise Stoll (Ed.), Professional learning communities: Divergence, depth and dilemmas (pp. 45-62). UK: McGraw-Hill Education.
20. Ladden, M. D., Bednash, G., Stevens, D. P., & Moore, G. T. (2009). Educating interprofessional learners for quality, safety and systems improvement. Journal of interprofessional care.
21. Liaw, S. Y., Siau, C., Zhou, W. T., & Lau, T. C. (2014). Interprofessional simulation-based education program: a promising approach for changing stereotypes and improving attitudes toward nurse–physician collaboration. Applied Nursing Research, 27(4), 258-260.
22. Liyanagunawardena, T. R., & Williams, S. A. (2014). Massive open online courses on health and medicine: Review. Journal of Medical Internet Research, 16(8).
23. Macy, Josiah Macy Jr. Foundation. (2013). Transforming Patient Care:  Aligning Interprofessional Education with Clinical Practice Redesign. Paper presented at the Macy Conference on Transforming Patient Care: Aligning Interprofessional Education with Clinical Practice Redesign, January 2013.
24. Margolis, A., & Parboosingh, J. (2015). Networked Learning and Network Science: Potential Applications to Health Professionals’ Continuing Education and Development. Journal of Continuing Education in the Health Professions, 35(3), 211-219.
25. Murphy, K., & Munk, P. L. (2013). Continuing medical education: MOOCs (Massive Open Online Courses) and their implications for radiology learning. Canadian Association of Radiologists Journal, 3(64), 165.
26. Navedo, A., Pawlowski, J., & Cooper, J. B. (2015). Multidisciplinary and Interprofessional Simulation in Anesthesia. International anesthesiology clinics, 53(4), 115-133.
27. Olson, C. A., & Tooman, T. R. (2012). Didactic CME and Practice Change: Don’t Throw That Baby Out Quite Yet. Advances in health sciences education, 17(3), 441-451.
28. Paige, J. T., Garbee, D. D., Kozmenko, V., Yu, Q., Kozmenko, L., Yang, T., . . . Swartz, W. (2014). Getting a head start: high-fidelity, simulation-based operating room team training of interprofessional students. Journal of the American College of Surgeons, 218(1), 140-149.
29. Rodriguez, O. (2013). The concept of openness behind c and x-MOOCs (Massive Open Online Courses). Open Praxis, 5(1), 67-73.
30. sermo.com. (2016). WHAT IS SERMO?   Retrieved from sermo.com
31. Shortell, S. M., Bennett, C. L., & Byck, G. R. (1998). Assessing the impact of continuous quality improvement on clinical practice: what it will take to accelerate progress. Milbank Q, 76(4), 593-624, 510.
32. Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1).
33. Steele, S. R., Arshad, S., Bush, R., Dasani, S., Cologne, K., Bleier, J. I., . . . Kelz, R. R. (2015). Social media is a necessary component of surgery practice. Surgery, 158(3), 857-862.
34. Stocker, M., Burmester, M., & Allen, M. (2014). Optimisation of simulated team training through the application of learning theories: a debate for a conceptual framework. BMC Medical Education, 14(1), 69.
35. Sulkes, D. (Producer). (2014, 10/26/2014). ACEhHP’s Quality Improvement Education Initiative, October 24, 2014. Alliance Town Hall Webinar. Retrieved from http://www.acehp.org/imis15/acme/pdfs/Alliance_Town_Hall_Oct2014.pdf
36. WHO, World Health Organization. (2010). Framework for Action on Interprofessional Education and Collaborative Practice. Retrieved from http://www.who.int/hrh/resources/framework_action/en/
37. Zigmont, J., Kappus, L., & Sudikoff, S. (2011). Theoretical foundations of learning through simulation. Semin Perinatol, 35, 47 – 51.