What is a system?
Merriam-Webster Dictionary defines the word “system” as: a regularly interacting or interdependent group of items forming a unified whole. Examples: number system, digestive system, river system, computer system, etc.
Use DSRP to answer this question. What is a part? What is a whole? How can you apply it in health care administration?
To grasp the meaning of “system,” it is helpful to use an example in detail. The United States Health Care Industry is an example of a system. It can be explained using the DSRP method of thinking. DSRP stands for Distinctions, Systems, Realationships, and Perspectives
Distinctions (which consist of an identity and an other) – The U.S. Health Care System is unique among advanced industrialized countries. The U.S. does not have a uniform health system, has no universal health care coverage, and only recently enacted legislation mandating healthcare coverage for almost everyone. Most health care, even if publicly financed, is delivered privately.
Systems (which consist of part and whole) – The U.S. Health Care Industry System View/Diagram (Figure 1.1, page 10 of textbook) is a vast and complex network of Customers/Patients, Hospital and Physicians, Regulators, Public Health Agencies, Suppliers, and Buyers.
Relationships (which consist of action and reaction) – Orders, deliveries, executions, payments, policies and regulation of services flow and cycles through the interlinked “players” – Customers/Patients, Hospitals and Physicians, Regulators, Public Health Agencies, Suppliers, and Buyers.
Perspectives (which consist of point and view) – Different perspectives from consumers, medical practitioners, regulators, agencies, suppliers, and buyers affect the US Health Care System. According to a Health Affairs journal by Reinhard Priester (1992), A set of six influential values has shaped the U.S. Health Care System since World War II.
- Professional autonomy includes both clinical autonomy of practitioners (that is, independence in making treatment decisions) and regulatory autonomy of the profession itself.
- Patient autonomy refers to patients’ right to information that is material to making an informed decision about medical care—including the right to refuse care.
- Consumer sovereignty includes individuals’ freedom to choose both their health insurance plan and their own physician.
- Patient advocacy connotes a mix of values, including caring, service, benevolence, beneficence, fidelity, and effacement of self-interests. It requires health care professionals to single-mindedly pursue the best interests of individual patients, regardless of costs or other societal considerations. The advocacy role has traditionally been limited to benefiting patients (some suggest, patients who pay) and has not included the ill who are not someone’s patients.
- High-quality care historically has been assessed with reference to its process and structure (that is, how and in what settings medicine is practiced). In the past few years, this focus has broadened to include outcome (that is, the effect of care on patients’ functional status and quality of life).
- Access to care, relative to the other five values, has been vaguely defined. Confusion over its meaning is compounded by the mingling of two separate but related aspects of access. One refers to providing care to more people (universality); the other, to offering more services (comprehensiveness). Most policy discussions on access have focused on the progress toward universal access. Determining those services to which people should have access has come to the fore only since resources have been declared finite.
As demonstrated above, the DSRP method of thinking can be applied in health care administration. Seeing the whole system in detail and its many interlinked parts and how each “player” actions affect the entire system is very important!
What is Measure in DMAIC? How can you apply it in Healthcare Administration?
DMAIC is an abbreviation of the five improvement steps it comprises: Define, Measure, Analyze, Improve and Control. All of the DMAIC process steps are required and always proceed in the given order.
Measure – The purpose of this step is to objectively establish current baselines as the basis for improvement. This is a data collection step, the purpose of which is to establish process performance baselines. The performance metric baseline(s) from the Measure phase will be compared to the performance metric at the conclusion of the project to determine objectively whether significant improvement has been made. The team decides on what should be measured and how to measure it. It is usual for teams to invest a lot of effort into assessing the suitability of the proposed measurement systems. Good data is at the heart of the DMAIC process.
Data collection (measurable information) in healthcare allows health systems to create holistic views of patients, personalize treatments, advance treatment methods, improve communication between doctors and patients, and enhance health outcomes. The healthcare industry involves not only providers and physicians, but also third parties in the face of insurance companies, registries, etc. Quality data raises the quality of care for patients and performance of care providers; it also improves the quality of the health care organization as a whole.
What are three common myths or misconceptions about innovation?
Beware of three common myths or misconceptions about innovation: (1) innovation is good; (2) there is a formula, and (3) innovation is linear (Burns, L.R. et. al. 2011, p. 234-235).
- Innovation is Good – Not all innovations are economically or socially beneficial (e.g., crack cocaine). Some have harmful unanticipated consequences, even when used correctly (e.g., Vioxx). Others have disastrous unintended consequences, especially when overly diffused or improperly used (e.g., financial derivatives). The process of innovation itself is disruptive and fraught with risk for organizations and managers alike.
- There is a Formula – Management experts first identify successful companies and then try to reconstruct what these companies did to set themselves apart from their less-accomplished peers. The problem with this sort of retrospective analysis is that management experts interpret what they see in the glow (i.e., the halo effect) of what they already know about the companies’ performance. The halo that surrounds these companies can lead experts to accept uncritically the simple formula or rules of thumb that executives use to describe the secrets of the company’s success.
- Innovation is Linear – The most pervasive misconception of the innovation journey is that it follows a logical, predictable sequence of stages or phases of activity. Stage models can be useful as heuristics for talking about the innovation journey. But, the simplicity, clarity, and predictability of linear stage models belie the complex, uncertain, and indeterminate nature of the innovation journey revealed by an empirical research. The Minnesota Innovation Research Project (MIRP) revealed a complex, messy progression of events that they observed in terms of three broad phases of activity: genesis, development, and Although they parsed the innovation journey into three phases for the purposes of description and reporting, they emphasized that these phases were fuzzy, incomplete, and nonlinear.
What is complexity? What is a complex system? Provide examples of a complex system.
Merriam-Webster Dictionary defines “complexity” as complexness, complicacy, complicatedness, complication, elaborateness, intricacy, intricateness, involution, knottiness, sophistication. Weather systems, ant colonies, human economies, and health care organizations are all complex systems.
In the case of Health Care Organizations, there are three characteristics of complex systems: interdependence, nonlinearity, and dynamism (Burns, L.R. et. al. 2011, p. 224).
- Interdependence – system elements are connected in many different ways, and these connections vary in levels of responsiveness. Some connections respond rapidly and strongly, some slowly and weakly, and some moderately in terms of timeliness and intensity. Management theories have long recognized that organizations are comprised of numerous, diverse, interdependent parts. Complexity theory suggests that organizations are more than simply open systems; they are “massively entangled” systems.
- Nonlinear – In linear systems, output is directly proportional to its input. Small causes have small effects. Big causes have big effects. By contrast, in nonlinear systems, output is not directly proportional to its input. Small changes can produce big effects, small effects, or no effects at all, depending on the complex chain of cause-and-effect loops operating in the system. Nonlinearity, combined with dense interconnectedness, makes the behavior of complex systems impossible to reliably predict
- Dynamism – Not only do systems have the capacity to change, but prior states can influence present events. Management theories are often criticized as discounting the importance of time and history. Complexity theory suggests that a system’s history cannot be ignored. The set of decisions one faces for any given circumstance is limited by the decisions one has made in the past, even when those past circumstances no longer exist.
These characteristics—interconnectedness, nonlinearity, and dynamism—make complex systems unpredictable and challenging to manage.
How are complexity and feedback related? What is a reinforcing feedback loop vs a balancing feedback loop?
There are two basic types of feedback loops. Reinforcing feedback loops amplify or intensify whatever is happening in a system.
- Reinforcing feedback loops are self-fulfilling prophecies, also known as the “Pygmalion Effect.” For instance, a physician group practice that delivers high-quality care develops a positive reputation, which, through positive word of mouth, generates more referrals. More referrals, in turn, generate more resources that could be invested to further increase quality of care.
- Balancing feedback loops counteract or oppose whatever is happening in a system. For example, when the physicians in a group practice see more patients than they can realistically manage, patient satisfaction and possibly quality of care begin to suffer. Over time, negative word of mouth leads to fewer referrals and lighter schedules.
Whereas reinforcing feedback loops drive a system toward disequilibrium and constitute the engines of accelerating growth or accelerating the decline, balancing feedback loops drive a system toward equilibrium and constitute the engines of steady states or goal-oriented behavior. It is important to emphasize that reinforcing feedback loops can produce desirable or undesirable consequences. So, too, can balancing feedback loops.
What does organizational learning mean? How can organizations promote this?
Organizational learning is a multilevel phenomenon. Moreover, different factors influence knowledge creation, retention, and transfer within and across different levels. Individual learning is influenced, for example, by experience, feedback, and deliberate practice (i.e., focusing on techniques and understanding principles). Group learning is influenced by group member diversity, intergroup linkages, and group norms, such as psychological safety. Organizational learning is influenced by organizational leadership, culture, policies, and routines. Although the factors that facilitate or stymie learning at various levels are intertwined, there are five distinct management practices that characterize a learning organization.
Five “disciplines” promote organizational learning: (1) systems thinking, (2) personal mastery, (3) mental models, (4) shared vision, and (5) team learning. A powerful way to spread the discipline of systems thinking is to encourage organizational members to learn and make use of systems archetypes.
Organizations can promote learning and innovation by nurturing foresight, fostering improvisation, maximizing serendipity, and learning from mistakes. Actively look for mistakes—unanticipated, unwanted consequences—from new programs. Repeatedly let your staff members know that mistakes are bound to occur, and the most essential thing is to understand how they occurred as a result and why. These mistakes may prove the most fertile ground for future innovation.
What is double-loop learning? What is the OODA Loop? Provide examples of how you can use it in health care administration.
Double-loop learning, occurs when problem-solvers attempt to close the gap between desired and actual states of affairs by questioning and modifying those organization’s policies, plans, values, and rules that frame organizational problems and guide organizational action. Changes in underlying values and assumptions, in turn, prompt changes in action strategies.
The OODA loop was a tool developed by military strategist John Boyd to explain how individuals and organizations can win in uncertain and chaotic environments. It is an Acronym that explains the four steps of decisions making: Observe, Orient, Decide, and Act.
As a military spouse for many years, I became accustomed to the OODA Loop way of thinking as my military family lived for seventeen years overseas in high-threat regions of the world. My naval aviator husband taught me and our two sons situational awareness using OODA Loop. Today, I find myself continuing to use it as I deal with high-stress and unpredictable classroom situations as a Special Education Instructional Assistant.
In health care delivery, OODA Loop is applicable. Medical professionals observe and orient their patients, decide on a diagnosis and treatment, and then act on that treatment and observe their results or try something else if unsuccessful. There is no reason why OODA Loop is not applicable in health care administration/management. The principles are the same when it comes to managing organizational responsibilities, employees, training, assets, records, finances, and resources.
What is the difference between single-loop learning and double-loop learning? How are they related to adaptive learning and generative learning?
Single-loop learning (adaptive learning) occurs when problem solvers compare desired states of affairs with actual results and seek to close the gap.
Double-loop learning (generative learning) occurs when problem-solvers attempt to close the gap between desired and actual states of affairs by questioning and modifying the underlying conditions that contribute to the actual state, such as an organization’s policies, plans, values, and the rules that frame organizational problems.
From what you have seen so far, how does this (adaptive and generative learning — as well as single and double-loop learning) relate to Swarm Learning?
This class has already demonstrated adaptive and generative learning by students sharing their individual thoughts and experiences, as well as giving constructive feedback on each other’s work (posts, maps, etc). We also learn from each other ideas that we may have not otherwise considered if there was no discussions/interactions among class members and Professor Schwandt. Grades are not the top priority. The goal is for each of us, individually, and as a group, to have a firm grasp of the concepts presented. The previous assignment, FM4 Feedback Map, is a practical example of single and double-loop learning and Swarm Learning. All the assignments and online tools that we have used so far demonstrate what Swarm Learning is supposed to be. I could see bees working individually and collectively as a group.
What is emergence? Why is important? Provide examples.
Complexity-oriented management scholars contend that organizations also exhibit bounded instability. That is, organizations never quite settle into a stable equilibrium, but they generally do not fall apart, either. Between order (stability) and chaos (instability), there is an intermediate zone that some scholars poetically call “the edge of chaos.” When organizations operate at the edge of chaos, new ideas, products, practices, and relationships can spontaneously emerge that are neither predicted nor anticipated by participants or observers. Complexity theorists refer to this phenomenon as emergence. The challenge for managers, complexity-oriented management scholars say, is not to give in to the pull of either order (stability) or chaos (anything goes), but rather to sustain organization at the edge of chaos, where continuous innovation and adaptability are possible.
Burns, L. R., Bradley, E. H., Weiner, B. J., Shortell, S. M., & Kaluzny, A. D. (2011). Shortell and Kaluznys health care management: organization design & behavior. Clifton Park, NY: Delmar.
DMAIC. (2019, July 29). Retrieved from https://en.wikipedia.org/wiki/DMAIC
DSRP. (2019, September 9). Retrieved from https://en.wikipedia.org/wiki/DSRP
Priester, R. (1992). A Values Framework for Health System Reform. Health Affairs, 11(1), 84– 107. doi: 10.1377/hlthaff.11.1.84
Taylor Pearson. (2019, July 30). The OODA Loop: How to Turn Uncertainty into Opportunity. Retrieved from https://taylorpearson.me/ooda-loop/
The U.S. Health Care System: An International Perspective. (n.d.). Retrieved from https://dpeaflcio.org/programs-publications/issue-fact-sheets/the-u-s-health-care-system-an-international-perspective/
Josefina Howard’s Link to Plectica Presentation:
Josefina Howard’s Tableau Dashboard