Fluent Essays

DUE IN 24 HOURS INSTRUCTIONS ATTACHED Successful Change Versus a Dismal Failure Using the University of Arizona Global Campus Library, locate and discuss a scholarly article that describes a successful change initiative within an organization. Next, locate and discuss a scholarly article that describes a failed change initiative within an organization. Compare and contrast the implementation theories used for the change events discussed in the two articles. Provide a summation of what could have been done to make the failed initiative a success. Your paper should be three to four pages in length (excluding the title and reference pages). Your paper must be formatted according to APA style as outlined in the Writing Center, and it must include references and in-text citations for at least two scholarly sources from the University of Arizona Global Campus Library, in addition to the course text Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Managing employee involvement and total quality Sandelands, Eric Management Decision; 1994; 32, 5; ProQuest Central pg. 42 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Hospital Pharmacy 599 Hosp Pharm 2016;51(7):599–603 2016 © Thomas Land Publishers, Inc. www.hospital-pharmacy.com doi: 10.1310/hpj5107–599 Director’s Forum Big Data: Implications for Health System Pharmacy Laura B. Stokes, PharmD*; Joseph W. Rogers, PharmD, MS†; John B. Hertig, PharmD, MS, CPPS‡; and Robert J. Weber, PharmD, MS, BCPS, FASHP, FNAP§ Big Data refers to datasets that are so large and complex that traditional methods and hardware for collecting, sharing, and analyzing them are not possible. Big Data that is accurate leads to more confident decision making, improved operational efficiency, and reduced costs. The rapid growth of health care information results in Big Data around health services, treatments, and outcomes, and Big Data can be used to analyze the benefit of health system pharmacy services. The goal of this article is to provide a perspective on how Big Data can be applied to health system pharmacy. It will define Big Data, describe the impact of Big Data on population health, review specific implications of Big Data in health system pharmacy, and describe an approach for pharmacy lead- ers to effectively use Big Data. A few strategies involved in managing Big Data in health system pharmacy include identifying potential opportunities for Big Data, prioritizing those opportuni- ties, protecting privacy concerns, promoting data transparency, and communicating outcomes. As health care information expands in its content and becomes more integrated, Big Data can enhance the development of patient-centered pharmacy services. INTRODUCTION Health care is becoming increasingly complex with the advent of new treatments, evolving provider roles, changing legislation and payment models, and health information technology. The electronic medi- cal record (EMR) is a form of health care technology used by providers and patients to access and manage medical information. Computer desktops, laptops, and mobile devices provide efficient and effective ways of viewing and processing medical information. EMRs also have interoperable capabilities; various applications can be viewed in one program providing a comprehensive view of a patient’s medical history. Big Data has been associated with nuclear phys- ics, supercomputers, and defense simulations. Tech- nology advances, particularly in banking and retail, enable the use of Big Data in business. For example, retail vendors use Big Data to enhance the understand- ing of consumer demand; purchasing patterns can be tracked using reward cards and loyalty programs so that decision makers can more effectively interpret customer needs.1 The Human Genome Project was the first science and health care program to use Big Data; the magnitude of computing required to sequence the massive amount of genetic information challenged paradigms in data-processing capacity. Big Data can also be a powerful tool for phar- macy directors regardless of the size of their depart- ments. Leaders must understand the concept of Big Data, how organizations can use it, and pharmacy’s role in that plan. The goal of this article is to provide perspective on how Big Data can be applied to health system pharmacy. It will define Big Data, describe the impact of Big Data on population health, review specific implications of Big Data in health system pharmacy, and describe an approach for leaders to effectively use Big Data. As health care information expands and becomes more integrated, Big Data will enable the growth and expansion of patient-centered pharmacy services. BIG DATA DEFINITION Big Data is defined as data sets that are so large and complex that traditional computing methods are *PGY1 Health System Pharmacy Administration Resident; The University of Texas MD Anderson Cancer Center, Houston, Texas; † PGY2 Health System Pharmacy Administration Resident, Memorial Hermann Health System, Houston, Texas; ‡Asso- ciate Director, Purdue University College of Pharmacy, Fishers, Indiana; §Administrator, Pharmacy Services, The Ohio State University Wexner Medical Center, Columbus, Ohio hpj5106599-603.indd 599 21/07/16 7:37 AM Director’s Forum 600 Volume 51, July-August 2016 not capable of handling the information.2 Big Data results from the rapid assimilation of information that is complex, is large in byte size, and is from many different sources. Big Data gathers and stores health care outcomes resulting from advances in technology, treatments, and analytics and transmits this infor- mation very quickly to data warehouses. Common examples of how Big Data is used today include Net- flix’s ability to predict how you will rate a given film using a variety of complex variables3 and Facebook’s use of subscriber information to target posts and advertisements of interest.4 A few examples of Big Data in health care include the demonstration of how medication prescribing patterns differ in relation to outcomes in various locations or the prediction of a patient’s therapy success based on models built from data from similar patients. Some major sources for Big Data in health care are the electronic medical record (EMR), administra- tive claims information, and clinical decision support databases, like First Databank. EMRs include real- time clinical data, such as patient identifiers (admis- sion numbers), vital signs, diagnoses, and coded therapies. Administrative data include information from the Centers for Medicare & Medicaid Services or commercial insurance claims. All of these data are relevant and accessible to health system phar- macy leaders. Table 1 summarizes different types and sources of health care data.5 IMPACT OF BIG DATA ON POPULATION HEALTH Population health is defined as the health out- comes of a group of individuals, including the distribu- tion of such outcomes within the group.6 Big Data may provide population health statistics for various medi- cation-related indicators such as readmissions, patient triage and prioritization, adverse drug events, and expensive medications. Bates and colleagues described the need for enhanced methods of identifying high- cost and high-risk patients to ensure effective interven- tion. If patients are incorrectly identified as high risk, the benefit of enhanced care management interven- tions is lost. Additionally, in the case of readmission and triage, clinical workflow algorithms can be used to help identify patient-specific parameters, prioritize patients according to risk factors, and minimize the time to appropriate therapy. Big Data may be used to measure the effectiveness of this algorithm.7 Innovation in health care is influenced by finan- cial models, patient needs, provider motivation, and technology advances. Big Data may play an impor- tant role in driving health care innovation. Table 2 summarizes how Big Data supports health care inno- vation through data demand, supply, technology, and government assistance.8 Future cost projections suggest that health care expenditures in the United States will continue to increase. The aging baby boomer population, increased prevalence of chronic disease states, technological advances, costly end-of-life care, and other operational issues contribute to this projection.1 Health care legis- lation is moving toward measuring quality health care outcomes. Additionally, incentives and reimbursement for services depend on quality outcomes. In a chang- ing health care landscape, where accountable care organizations and patient-centered medical homes are becoming the norm, the implications of Big Data are at the forefront of progressive operational change. Big Data may be most effectively used to pro- vide patients and providers with information based on population statistics to determine the best treat- ment options.2 EMR data can be used on a large scale to provide insight on patient outcomes. These data include length of stay, risk factors for compli- cations, or disease progression; all of these data can be extrapolated to populations to predict outcomes. Using real-time data in intensive care units or emer- gency departments to rapidly detect changes in patient status and apply appropriate treatments is another example of the use of Big Data.1 Table 1. Types of health care data5 Type Source Attributes Administrative •   Government (CMS) •   National surveys (Medical Expenditure Panel Survey) •   Commercial vendors (health plans, PBMs, etc) •   Insurance billing •   Financial reporting •   Lacks clinical detail Clinical •   Hospital EMR •   Physician EMR •   Integrated delivery network EMR •   Clinical database •   Clinical data collected for patient care Note: CMS = Centers for Medicare & Medicaid Services: EMR = electronic medical record; PBMs = pharmacy benefit manager. hpj5106599-603.indd 600 21/07/16 7:37 AM Director’s Forum Hospital Pharmacy 601 Each stakeholder within the health system has different goals for the use of Big Data. Patients may utilize these data to empower their health care deci- sions, whereas providers may use Big Data to obtain real-time information about their patients or for deci- sion support. Health applications – or “apps” – have been created to help manage many chronic disease states, including diabetes, and to enable smoking ces- sation and improve nutrition. Ochsner Health Sys- tem, New Orleans, Louisiana, has implemented a stand-alone “O Bar,” where patients can walk in at any time to ask questions about health apps they are using and receive recommendations about other apps and technologies for wellness.9 Pharmaceutical and medical device companies may use the data to better understand the mechanisms of diseases and their progression to develop safe, targeted therapies with the fewest possible adverse events. Analysis of large datasets can enable predictive modeling, which will result in a more efficient medica- tion therapy development process. Advanced statisti- cal analysis and optimized study designs will result in stronger therapy trials with medications and their intended populations. Big Data will also be a useful tool in surveillance for adverse medication events, both before and after market. The ideal impact of Big Data on the pharmaceutical industry is to ensure that the right patient has the right medication at the right time.1 Medical device companies market prod- ucts for home monitoring of medication levels and as surrogate markers of chronic diseases. A health care provider often transmits data electronically from the patient’s home device to the EMR for review. Providers review the data and make adjustments to medications or the therapy plan over the phone. Providers can monitor patients for safety and adverse events without seeing them in person. This gives patients increased access to health care without the cost and inconvenience of an in-office visit. This is common practice in blood glucose level monitoring and international normalized ratio (INR) testing for warfarin therapy; in the future, this mechanism of care delivery will be increasingly prevalent. Pay- ers may use Big Data to develop sustainable business models secondary to the transition away from the fee-for-service payment model. Finally, governmen- tal agencies may use Big Data to reduce health care costs and enforce health care regulations related to the quality of patient outcomes.1 Each stakeholder within the health system will use Big Data in unreal- ized capacities in the future. As information technology grows more capable of translating Big Data, evidence-based guidelines may not be as applicable to patient care. Some of the popu- lation health outcomes may demonstrate treatment approaches different than those suggested by some guidelines. Providers may use clinical decision support tools to help ensure the safety and appropriate use of medication therapy given patient-specific factors such as weight and creatinine clearance. As Big Data becomes more prevalent in practice, providers will use this infor- mation to develop treatment plans for each patient. Another challenge related to the use of Big Data is the impact on Health Insurance Portability and Accountability Act of 1996 (HIPAA) privacy standards. There is debate among health care lead- ers as to the public benefit of Big Data and the pro- tection of patient privacy (HIPAA concerns). The integration of multiple systems and the combina- tion of data from a variety of sources is concerning to some patients. HIPAA does not directly address the complexity of data manipulation today; much work is necessary to truly understand the real pri- vacy risks associated with using Big Data in popu- lation health. APPLICATION OF BIG DATA TO HEALTH SYSTEM PHARMACY Most EMRs have clinical decision support tools to guide the most appropriate prescribing of medication. Table 2. Aligning Big Data to support health care innovation8 Demand for data •   Cost pressures •   Need to act quickly (first movers show impact, while late adopters only “keep up”) Supply of data •   Clinical data available across continuum of care •   Non–health care consumer data readily accessible Technological capability •   Advances in combining administrative claims data with clinical data •   Analytics Market changes •   Government emphasis on transparency •   Interoperable standards between competitors in the private sector hpj5106599-603.indd 601 21/07/16 7:37 AM Director’s Forum 602 Volume 51, July-August 2016 Clinical decision support tools related to pharmacy include medication dose buttons, medication alerts such as duplicate therapy or drug-drug interaction alerts, and warning pop-ups to alert users of poten- tially unsafe practice. Clinical decision support tools can also be used in other aspects of patient care to increase efficiency and improve the quality of care provided.1 Potential applications include database maintenance, increased metrics and tools available in inpatient and outpatient areas, and tools targeting pop- ulation trends. The clinical data maintained within the EMR can be used to identify patients who are at risk for complications and would benefit from early inter- ventions or proactive care. This concept will become more applicable with increasing advances in informa- tion technology and advanced analytical skills.2 EMRs also contain financial, operational, and genetic data that can be used to evaluate the appropriateness of treatment options for individual patients. Over the past 5 years, the amount of data generated has significantly increased. As information technology can better organize and analyze the data, the opportunity to use data as benchmarking tools between institutions will continue to increase. Clini- cal benchmarking databases can be used to identify best practices, sources of waste within a workflow, and resource utilization.10 These data can be used by directors of pharmacy to identify problems and devise strategies for resolution or cost reduction. GUIDING PRINCIPLES FOR USING BIG DATA As health care moves toward utilizing Big Data to optimize patient care outcomes while reducing health care costs, pharmacy leaders can use the following guid- ing principles to effectively use Big Data (Figure 1).11 Improve the core business first. By evaluating the core business, leaders can identify all of the potential opportunities for Big Data within their organization. If this step is not taken, all of the benefits of the data may not be realized due to unidentified applications. Optimization of clinical decision support tools Formulary management and adverse event monitoring Measure the quality of care and patient outcomes Better care transitions via data related to medication adherence Enhanced application of evidence-based medicine Claims and cost data that provide information on utilization Big Data uses within Health System Pharmacy Figure 1. Using Big Data to promote health system pharmacy operations and management.11 hpj5106599-603.indd 602 21/07/16 7:37 AM Director’s Forum Hospital Pharmacy 603 Define organizational model and talent strategy. Deter- mining who will design and implement Big Data initiatives within an organization is one of the first steps necessary when planning a process change. Successful strategies include initiatives led within or across business units, through functional groups, or at the executive level. Make it a priority. Especially within health care, it is imperative that leaders buy in to the application of Big Data as a tool to improve patient care outcomes. If the application of Big Data is a priority beyond the initial planning stages, the organizational workforce will be ready for the changes. Additionally, leadership should focus on recruitment of the right talent and concentrate significant efforts on strategic areas. Establish a vision from the top-down; establish innovation from the bottom-up. When leaders set a vision and a strat- egy, it allows end users to innovate and increases oppor- tunities. However, this is only possible in an environ- ment where local innovation is the culture. The vision set for the use of Big Data must go beyond performance metrics and should encompass transformational change within the institution in order to get necessary buy in from front-line personnel. Engaging employees to exe- cute the vision results in empowered employees com- mitted to the established goals. Set diverse goals. Goals associated with the implica- tions of Big Data should vary in size and target date. By establishing short-, medium-, and long-term goals, momentum is established early on and maintained throughout the life of the project. Strive for transparency. Data transparency in health care is important in terms of regulatory compliance. To meet regulatory standards in health care, the data must be HIPAA compliant and secure. Understanding how the data is procured and providing transparency about the strengths and limitations of the data is nec- essary for health care leaders. Additionally, making data available for internal and external benchmark- ing can be mutually beneficial for patient outcomes, workflow efficiencies, resource utilization, and health care expenditure. Internal and external communication. Transparent communication with stakeholders, both internal and external, will help leaders refine viewpoints and ensure congruency of agendas. CONCLUSION Big Data is generated by the rapid assimilation of technology from a variety of resources that range from simple to complex in their content. Health sys- tem pharmacy leaders must understand Big Data and how they can use its elements to improve their phar- macy practice model. Medication use outcomes can be predicted by Big Data provided there is a strategy for its application. The strategies offered in this arti- cle should guide the pharmacy director in the future when handling Big Data. As health care information expands in its content and becomes more integrated, Big Data will be a valuable tool for enhancing the development of patient-centered pharmacy services. REFERENCES 1. Feldman B. Big data in healthcare: Hype and hope. DrBonnie360.com. https://www.ghdonline.org/uploads/big- data-in-healthcare_B_Kaplan_2012.pdf. Published October 2012. Accessed April 1, 2016. 2. Bates D, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs. 2014;33(7):1123-1131. 3. Krumholz H. Big data and new knowledge in medicine: The thinking, training, and tools needed for a learning health system. Health Affairs. 2014;33(7):1163-1170. 4. De Montcheuil T. Facebook: A decade of big data. Wired. http://www.wired.com/insights/2014/03/facebook-decade- big-data/. Published March 2014. Accessed April 26, 2016. 5. Aparasu R, ed. Research Methods for Pharmaceutical Prac- tice and Policy. Gurnee, IL: Pharmaceutical Product Press; 2010. 6. Kindig D, Stoddart G. What is population health? Am J Publ Health. 2003;93(3):380-383. 7. Raghupathi W, Raghupathi V. Big data analytics in health- care: Promise and potential. Health Information Sci Syst. 2014;2(3):1-10. 8. Groves P, Kayyali B, Knott D, Van Kuiken S. The big data revolution in healthcare. Center for US Health System Reform Business Technology Office. http://www.mckinsey. com/industries/healthcare-systems-and-services/our-insights/ the-big-data-revolution-in-us-health-care. Published January 2013. Accessed April 1, 2015. 9. Ochsner’s O bar uses interactive health technology to enhance patient engagement. New Orleans, LA: Ochsner Health System Public Relations Department; July 30, 2014. https://news.ochsner.org/news-releases/ochsners-o-bar-uses- interactive-health-technology-to-enhance-patient-engage/. Accessed May 11, 2016. 10. O’Neal B, Weber R. Director’s Forum: Benchmarking drug prescribing using clinical databases: A tool for practice model enhancement. Hosp Pharm. 2011;46(10):809-814. 11. Ma C, Smith HW, Chu C, Juarez D. Big data in pharmacy practice: Current use, challenges, and the future. Integrated Pharm Res Pract. 2015;4:91-99.  hpj5106599-603.indd 603 21/07/16 7:37 AM ORGANIZATIONAL TRAINING AND PERFORMANCE Using Faculty Professional Development to Foster Organizational Change: a Social Learning Framework M. Aaron Bond1 & Samantha J. Blevins2 Published online: 6 December 2019 # Association for Educational Communications & Technology 2019 Abstract Change is a constant presence within today’s higher education institutions. Local faculty professional development opportunities may provide avenues for enacting these organizational changes through personal continuous improvement. While working to meet these organizational priorities, it is also essential that professional development must provide opportunities for individual growth. Combining systems thinking and social learning theories may provide solutions for meaningful faculty development that also meets identified organizational priorities. This article will describe the creation of a conceptual framework that utilizes systems thinking and change in combination with social learning theories that may be employed to encourage meaningful development engagements for faculty in order to lead change in higher education institutions. In addition to defining each component of the framework, suggestions for intervention strategies, change strategies and design considerations are also offered. Action planning pages provided within the framework will help practitioners make decisions regarding their own individual higher education settings. Keywords Professional development . Organizational change . Higher education . Systemic change . Social learning As universities grapple with rapid change and external pressures, faculty professional development is a method that can be employed to foster organizational change and scaffold individual behaviors. Though many professional development initiatives utilize social theories to foster learning, there are gaps when trying to foster change aligned with organizational priorities. Similarly, when organizations seek to foster change, professional development and the value of socialization of ideas are often underutilized. Examining the organization and the significant role that faculty play within the system may lead to professional development opportunities that help drive individual and organi- zation changes. Many university departments responsible for of- fering faculty development opportunities are working in siloed entities within a larger institution. Instructional designers, faculty developers, and others involved in supporting faculty are unique- ly situated within the institution to influence change with indi- vidual faculty and the larger organization, especially when the change is related to the sphere of influence of those practitioners (Gillespie 2010; Kowch 2005). Finding ways to link organiza- tional priorities with targeted development can be challenging, but this linkage is critical for meaningful change (Baron 2005; Kowch 2016). In order for professional development to have an impact, designers and facilitators must seek an understanding of organizational priorities, individual performance, and the desired change for all stakeholders (Bond and Lockee 2014). Professional development efforts that foster change can be grounded in systems change and social learning theories (Gioia and Thomas 1996; Wenger 1998; Kezar 2001; Senge 2006). Using practical application of theory, we seek to introduce the use of a conceptual professional development framework that incorporates systems thinking and social learning theories to in- form the design and development of change oriented profession- al development. This conceptual framework provides guidance for education leaders at universities for grounding faculty devel- opment efforts in social theories of change and learning. This conceptual framework may provide guidance to help profession- al development practitioners incorporate systems thinking and social learning in a more strategic way. This article will describe how the framework may be employed to encourage meaningful development engagements with faculty in order to influence change in higher education institutions. * M. Aaron Bond [email protected] 1 Virginia Tech, Blacksburg, VA, USA 2 Radford University, Radford, VA 24141, USA TechTrends (2020) 64:229–237 https://doi.org/10.1007/s11528-019-00459-2 http://crossmark.crossref.org/dialog/?doi=10.1007/s11528-019-00459-2&domain=pdf http://orcid.org/0000-0002-9430-3218 mailto:[email protected] Theoretical Foundations Systems Thinking and Change Theories The field of systems thinking contains a breadth of theories, approaches and methodologies that are often related to the specific discipline of a given researcher (Cabrera et al. 2015). A simplified definition of systems thinking is a system- atic approach to understanding of the whole organization or system and its interdependent parts. This definition, however, does not adequately describe the complexity of systems think- ing. Cabrera et al. identify four components that underlie sys- tem thinking models: distinction, system, relationship, and perspective (Cabrera et al. 2015). In order to understand a problem, organization, or system it is imperative that one un- derstand how to consciously make distinctions between things or ideas, recognize systems in part or whole, identify relation- ships in action and reaction, and metacognitively take or ex- plore different perspectives or views (Cabrera et al. 2015). This process can seem daunting, but building understanding of the processes, people, culture, infrastructure, and external systems is crucial to enacting change. A key component of any organizational system is people. Finding ways to influence change often starts with the people who interact with and within the system. Senge (2006) iden- tifies the importance of considering the mental models of in- dividuals in a system as key to changing the larger system. When individuals have a better understanding of systems and the role they play within the system, they are better able to identify the areas of focus that lead to desired outcomes (Senge 2006; Stroh 2015). Taking a systems thinking ap- proach when planning organizational change can provide in- sights and analysis that might be overlooked otherwise. In order to be successful, professional development efforts must consider the whole system and designers must understand how to gain support of the organization in which they exist (Kowch 2005; Stroh 2015). Employing a systems thinking approach when planning for professional development can help ensure alignment with organizational strategic priorities. Additionally, taking perspective and seeking to understand the mental models that an audience may hold, along with account- ing for interconnected relationships may lead to more success- ful change initiatives promoted within a learning organization (Senge 2006). The utilization of these practices can help fac- ulty development leadership focus on a distributed change management strategy that incorporates the social aspects of relationships. This new focus can influence individual and organizational change in a higher education context. Systems Thinking and Change in Higher Education Considering the culture of the organization or system is essen- tial when championing change. Higher education institutions are often seen as particularly resistant to change (Kezar and Eckel 2002). The unique governing structure in higher educa- tion institutions may be the source of this resistance. Brown (2013) discussed findings that identify particular considerations concerning such resistance to change within a university culture that values collegiality and fuzzy lines of accountability: (1) managing tends to be by consent and through gradual change, (2) decisions tend to be committee-based and generally consen- sual, (3) the status of potential change agents is often derived from personal credibility and their standing in a subject com- munity, and (4) high value is placed on dialogue and the legit- imacy of critique. The shared governance structure with faculty make higher education institutions unique and often immune to traditional change management strategies. Change efforts within any context should consider how the change will impact all of the other independent parts of the system, including those that who will be affected by and implementing the change (Senge 2006). Change in the univer- sity is often slow and met with strong resistance from faculty (Marshall 2010). Dashborough et al. (2015) found that faculty have three attitudes toward change: promising (something to look forward to), a threat (something to manage for), or inev- itable (something that cannot be avoided) and that these atti- tudes are often informed by conversations with peers. It is imperative to involve faculty in discussions of an impending change and to capitalize on social networking in order to help foster faculty buy in (Dashborough et al. 2015). Instructional designers and other faculty development professionals are of- ten uniquely positioned to make organizational change through their work with faculty and organization leaders (Kowch 2005). Understanding the organization culture (Kezar and Eckel 2002), the mental modes of the faculty (Senge 2006), and the desire for change may have implica- tions for successful professional development activities. Many initiatives fail because the focus of change tends to be on the change itself and not the people impacted by the change (Senge 2006). Given the resistance typically present within higher education settings, finding innovative ways to influ- ence change through social learning professional development strategies may be of benefit to higher education institutions. Social Learning Social learning is often discussed through two different, but closely related theories: social cognition and social constructionism (Jonassen et al. 2007; Rogoff 1990). Though there are many researchers who advocate the impor- tance of social interaction and its impact on learning, which theory they use to situate the social learning varies depending on the educational outcome. Generally, social cognitive theo- ries as defined by Albert Bandura (1977), focus on group observation and the cognitive or behavior change that can happen from exposure in a social group (Ormrod 2008). 230 TechTrends (2020) 64:229–237 Social constructivist theories encourage co-creation of knowl- edge through interaction, collaboration, and authentic work. The main concern of learning research focuses on the individ- ual, the behavioral and, cognitive processes associated with personal learning (Jonassen et al. 2007). Initially, constructiv- ist theory was concerned with individual meaning making, but has come to include collaborative knowledge construction (Suthers 2006). Constructivists argue that while there is an important role for behaviorist and cognitivist strategies for learning, knowledge is both individually constructed and so- cially co-constructed from interactions and experiences with the world (Jonassen et al. 2007). For the purpose of this con- versation, we will use the term “social learning theory” when referring to suggested strategies. Social learning theories of change stress the power of social interactions for creating change (Gioia and Thomas 1996; Kezar 2001; Wenger 1998). Building on the theories of Lev Vygotsky (1978) and constructivist concepts, social learning theorists have argued that social context in which cognitive activity takes place is an integral part of the learning process (Brown et al. 1989; Lave and Wenger 1991; Brown and Duguid 2001). Many such authentic collaborative learning experiences are found both in the workplace and in educational settings everywhere. Cognitive apprenticeships, communities of prac- tice, learning communities, and even computer supported col- laborative learning environments are used in adult learning and in daily informal and formal learning situations (Sawyer 2006). Such interventions have been used in higher education to foster change in pedagogical practice and curriculum design (Micomonaco and Austin 2010; Bond and Lockee 2014). The utilization of these practices in order to manage organizational change builds on the research-based interventions in organiza- tional learning using social learning strategies. Conceptual Framework Development Even though social cognitive and social constructivist theories have long-informed how learning is conducted within organi- zations, little research has been conducted around how profes- sional learning can be combined with change management and systems thinking within higher education institutions (Kezar and Eckel 2002). By combining systems thinking with social learning and change theories, it may be possible to develop social cognitive professional development opportuni- ties that are driven by organizational needs and priorities to help bring about individual and organizational changes. Much of systems thinking and change theory involves empathizing with the perspective of individuals in the system and the atti- tudes and/or beliefs the individuals hold about any given change initiative (Boland and Tenkasi 1995; Cabrera et al. 2015; Senge 2006). Providing faculty who are in the midst of organizational change the opportunity to interact with and learn from other faculty engaged in the same predicament around change can be beneficial. Linking accepted theory and practical application to inform the development of a sys- tems thinking and/or social cognition approach may have im- plications on the way we design and develop meaningful pro- fessional development. Practical professional development opportunities grounded in social learning theory include, but are not limited to: con- versational connections; communities of practice; learning communities; and mentorship opportunities. Using systems thinking to understand institutional goals and how faculty fit within the organizational structure may directly impact the way professional development conversational strategies are employed to foster change. Additionally, the authors chose to include reflective practice in the conceptual framework, as such practice is encouraged by both social learning and change management theories (Collins 2006; Kerawalla et al. 2009; Senge 2006). The importance of reflective practice can- not be overlooked, as it encourages each faculty member to think individually about their experience and how they will carry that experience forward into new learning, practice, and professional development contexts. Following an extensive review of the literature, a concep- tual framework to support those designing and facilitating faculty development opportunities was created (see Fig. 1). Generally defined as the ideas and principles that form the structure for a plan (Cambridge Dictionary, n.d.), this frame- work incorporates social theories of learning and change for strategically planning faculty development. The resulting framework (see Figs. 2 and 3) is divided into four professional development strategies that have been identified as profes- sional development opportunities grounded in social learning Fig. 1 Conceptual Framework for Using Faculty Professional Development to Foster Organizational Change Components TechTrends (2020) 64:229–237 231 Fig. 2 Conceptual Framework for Using Faculty Professional Development to Foster Organizational Change Strategies (Conversational Connections and Communities of Practice Components) Fig. 3 Conceptual Framework for Using Faculty Professional Development to Foster Organizational Change Strategies (Learning Communities and Mentorship Opportunities Components) 232 TechTrends (2020) 64:229–237 theory and can be utilized to foster organizational change. In addition to defining each of the four components, the frame- work also offers practical examples of each type of profession- al development opportunity, change strategies that can be uti- lized within that professional development opportunity, as well as design considerations for offering these types of pro- fessional development opportunities. There are some strategies for conducting social learning engagements that are not explicitly contained in an individual set of interventions or explicitly addressed within the frame- work. However, these considerations are pertinent to all four of the interventions and are included in the action planning page (see Fig. 4) of the framework. These techniques include understanding of audience, building trust with that audience, and development of strong facilitators to lead the initiative. Understanding of faculty audience is essential to the design and deployment of faculty development opportunities. Many faculty members are stretched for time, with numerous aca- demic commitments including, but not limited to: teaching, research, committee appointments, external organizations in their professional field, and grant opportunities. Faculty de- velopment interventions must be well designed in order to ensure that faculty do not feel participation is wasted effort that could instead be put toward their already full list of com- mitments. Another important consideration that crosses all four interventions is building trust. Building trust among par- ticipants is a key finding in systems thinking and change literature as well as social learning research (Senge 2006; Booth 2012). Finally, research stresses the importance of a strong facilitator or core membership who are versed in fos- tering dialogue, as well as open-minded champions for the initiative, topic, or general practice (Cox 2004; Gray 2004; Wenger et al. 2002). For each of the suggested interventions, make sure a strong facilitator, mentor, or core group of mem- bers are prepared for debate and discussion facilitation. These items of importance should be employed across all four interventions. The framework also offers an action planning page that can be utilized to help the user strategically think through change initiatives currently happening within an organization, as well as professional development opportunities that can be used within an organization in order to assist in the change effort. An area for identifying next steps, target completion dates for each step, as well as notes, is also provided. This action plan is meant to be a job aid to help start the conversation around social learning interventions with a systematic view of poten- tial organizational changes. Conversational Connections The opportunity to offer connections through conversation as part of professional development cannot be overlooked (Watland et al. 2008). These types of sessions can often serve Fig. 4 Conceptual Framework for Using Faculty Professional Development to Foster Organizational Change Strategies (Action Planning Page) TechTrends (2020) 64:229–237 233 as a gateway to other professional development opportunities by bringing together professionals from different siloed enti- ties across a campus. Participants are encouraged to get to know each other and build connections, while also exploring the surface of many different topics. Often, these sessions can help participants connect with each other, eventually leading to greater collaboration opportunities and the desire to more deeply explore topics of interest. Examples of conversational connections include, but are not limited to: panel discussions, facilitated dialogue discus- sions, topic discussions, and lunch and learn sessions. Specific strategies for designing and deploying conversational connec- tions are discussed in Fig. 2. Learning Communities Learning communities are defined as a group with shared learning goals and attitudes (Palloff and Pratt 1999). These types of groups meet on a semi-regular basis in order to hold discussion, debate, and extended conversation on a specific topic. Within higher education, this type of community often acts as a cohort of faculty exploring a topic, tool, or strategy to improve individual practice while allowing for interdisciplin- ary conversations and collaborations (Cox 2004). Information sharing is critical to organizational learning. Adult learners may find learning communities to be an effective form of professional development. Long before Lave and Wenger (1991) introduced the concept of communities of prac- tice, Malcolm Knowles understood the important role that com- munity plays in the learning process for adults. According to Knowles (1950), “attitudes and opinions are formed primarily in the study groups, work groups, and play groups with which adults affiliate voluntarily” (p.9). More recently, Knowles et al. (2005) insist that adult learners are more responsive to learning when it is grounded in authentic tasks. Learning communities provide an opportunity for participants to develop a shared knowledge and often are applicable in real world situations. Faculty members who become involved and active participants in learning communities report positive results. Wildman et al. (2000) explored a faculty collaboration group at a large Research One institution and reported greater problem solving among faculty participants after participation in a learn- ing community. Participants in faculty communities find col- laboration across academic units, new ideas, and lasting friend- ships (Cox 2004). Instructional designers, faculty developers, or others who work with faculty may find such interactions to be of benefit when promoting a new or changing priority at the institution. As with all social learning and change related activ- ities, giving faculty a voice and a place to hear the perspectives of others can help them more easily navigate an uncomfortable situation. Learning communities can offer faculty a place to learn and to grapple with changing priorities. Specific strategies for creating a learning community are discussed in Fig. 2. Communities of Practice A community of practice is defined as a group of people that interact on an ongoing basis in order to deepen their individual knowledge and expertise (Wenger 1998). The purpose of their interaction can be for a variety of reasons, including: a shared concern, a shared set of problems that need to be solved, or a shared passion regarding a topic (Wenger 1998). At first, com- munities of practice may seem similar to learning communi- ties. Remarkably, learning communities can lead to or inform communities of practice. However, there are differences be- tween the two. For instance, in a learning community, partic- ipants learn through conversation about a given topic. While communities of practice must consist of both participation (conversations, activities, reflections) and creation efforts ar- tifacts, documents, processes, and methods to improve prac- tice (Wenger et al. 2002). Additionally, participants in com- munities of practice engage around a specific domain or joint enterprise. The act of building and growing a community of practice may have positive implications for addressing change in higher education institutions. Developing communities of practice for university faculty can help faculty adjust to the rigor of academic life, improve teaching practices, participate in collabora- tive research opportunities, and better understand orga- nizational priorities. Communities of practice have been used widely in higher education for faculty professional development, but many of these efforts do not explicitly make the connection between professional development, institutional priorities, and desired change (Eib and Miller 2006; Steinert 2010). Kezar et al. (2018) de- scribes the shift from communities of practice to com- munities of transformation, but much of the literature about communities of practice involves small or incre- mental changes. For example, Bond and Lockee (2018) described communities of practice for faculty develop- ment around technology implementation and pedagogi- cal approaches without explicitly connecting the desired change with institutional priorities. There is a gap in the literature for communities of practice in the form of professional development as catalysts for change for higher education institutions. If an institution is under- going a major change in organizational structure or gov- ernance, deploying a major technological shift, or en- couraging a change in practice, a community of practice may provide a venue to allow faculty a place to use systems thinking to explore the planned change. Communities of practice offer faculty the space to ex- plore a topic in-depth, co-create knowledge and arti- facts, while engaging in perspective taking through so- cial interactions. Figure 2 provides guidance and insight into how to operationalize a community of practice in a higher education institution to assist with change. 234 TechTrends (2020) 64:229–237 Mentorship Opportunities Defined by Kram (1983), mentoring is the developmental as- sistance offered to a junior employee by someone more senior and experienced in an organization. Mentoring can happen either informally or formally. Regardless of the type of mentoring relationship, it is important to note that the mentor and mentee is typically made up of three types of behaviors: career development, social support, and role modeling (Raabe and Beehr 2003). Research also suggests that the relationship with a mentor can help mentees further develop both socially (Thomas 1993) and professionally (Kram 1985). Many formal mentoring programs convey the idea that having one strong mentor relationship is the key to enhancing personal and professional development (Raabe and Beehr 2003). In a study of a group of scholars navigating the aca- demic tenure process through peer mentorship, Driscoll et al. (2009) reported that through collaboration and mentoring, individuals found independence and clear sense of direction for scholarship. However, Kram (1985) proposes that it is possible to have and learn from more than one mentor. Encouragingly, some research now shows that individuals learn in all types of relationships, including from peers and supervisors (Darwin and Palmer 2009). Mentoring circles, defined as a mentor working with a group of mentees or groups of people working to mentor each other, have also been successfully used as an alternative to formal or informal mentoring programs (Darwin and Palmer 2009). This is an innovative revisioning of the mentoring pro- cess, moving away from formal and informal programs and can be beneficial to those who are comfortable working in a collaborative group environment (Darwin and Palmer 2009). Specific strategies for designing mentorship opportunities are provided in Fig. 4. Reflective Practice Reflective practice is defined as contemplating on an experi- ence, through which the learner gains a general knowledge of the concepts encountered during the experience, and then carries forward this new knowledge into a new experience (Kolb 1975). In this way, the learner transforms their knowl- edge and informs their own professional practice, making meaning of each experience and how those experiences con- nect together. Professional practice has been described by Donald Schön as a flat place where we can’t see very far into the distance, while professionals work blindly by trial and error, living with the consequences (1987). Leveraging reflective practice as part of professional life can help practitioners make sense of where they are within their organization, as well as where they want to go. Employing reflective practice can also help when solving complex problems, as examining a problem from many viewpoints, including the self-viewpoint, is encouraged. Most importantly, it should be noted that reflective practice is not an inherent action. However, as a learner practices reflec- tion, this practice becomes part of their life and their work. The act of reflective practice enables professionals to learn from a multitude of their own experiences, including personal and pro- fessional, and then apply those lessons to other areas of their lives as well as to future experiences (Bolton 2014; Senge 2006). As the landscape of higher education continues to change, higher education professionals will continue to face complex and often unpredictable situations, and will need com- plex and diverse reflective processes in order to discover previ- ously unseen solutions (Bolton 2014). The engagement in re- flective practice gives a holistic experience to higher education professionals. This holistic experience can bring greater satis- faction and unity to the practitioner, as well as greater empathy between themselves and their students (Bolton 2014). Throughout professional development sessions, reflection should be encouraged in order to assist participants in making meaning out of each experience. This reflection can happen in a multitude of ways, including: electronic portfolio entries, blog or video blog posts, and journaling. Most importantly, it should be noted that participants should answer three key questions for each experience: what?; so what?; now what?. These three questions encourage capturing the impact of the activity without extensive investment. Conclusion and Suggested Next Steps Finding innovative ways to influence change in higher educa- tion institutions may be of interest to instructional designers, faculty developers, and others who support faculty through changes in strategies, pedagogies, technologies, and other orga- nizational changes. Using systems thinking and change litera- ture to inform social learning strategies may offer a solution, providing guidance for designing faculty professional develop- ment that results in organizational and individual change. … Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. SUBJECT TO REVIEW: Factors that lead to success or failure in healthcare projects Creasy, Todd Quality Progress; Feb 2017; 50, 2; ProQuest Central pg. 24 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.