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Complexity science
Published in Frances Rapport, Robyn Clay-Williams, Jeffrey Braithwaite, Implementation Science, 2022
There are several take-home messages. Discussing the core challenges of translating research into practice in a nuanced way, as has been attempted here, means rejecting a linear depiction of the world in favour of a complexity science approach (Long et al. 2019). It means embracing ideas such as path dependence, adaptation over time, dynamic change, modelling behaviours via tools such as Social Network Analysis, and working through and with multiple stakeholders in complex ecosystems. It also means having a tolerance for uncertainty and unpredictability. Complex problems in complex settings are emergent and resist simplistic solutions. That’s a lesson every implementation scientist needs to absorb.
Medicaid and the Children’s Health Insurance Program
Published in Kant Patel, Mark Rushefsky, Healthcare Politics and Policy in America, 2019
Research suggests that while partisanship has played a significant role in the expansion decision, it is not the only factor. There are other factors besides partisanship that have also played a role in state governments’ decisions. Callaghan and Jacobs (2014) found that the presence of a Republican legislature had a negative influence on the expansion decision, while the presence of a Republican governor had a more nuanced effect. Conversely, the stronger the Democratic Party control over state government, the more likely a state was to implement Medicaid expansion. After controlling for party domination, they found that the trajectory of established policy for the vulnerable population and state learning about the process of intergovernmental bargaining were other factors that also exercised influence on the expansion decision. States with well-established and generous social programs for low-income individuals create self-reinforcing and positive feedback (path dependency), making such states more willing to expand their Medicaid program. Similarly, states with experience in negotiating and working with CMS and HHS over Medicaid changes are more likely to decide to expand their Medicaid program, having learned the skills to effectively bargain with the federal government. Finally, they also find that affluence/economic conditions of the state and state institutional and administrative capacity have also played some role in expansion decisions.
Medical leadership and reducing variation in healthcare
Published in Jill Aylott, Jeff Perring, Ann LN Chapman, Ahmed Nassef, Medical Leadership, 2018
The UK NHS is a complex system with a history of constant change. Scholes and colleagues (2011) comment that a useful way of thinking about the role and influence of history is through the concept of ‘Path dependency’ (Figure 9.2). Path dependency is where early events and decisions establish ‘policy paths’ that have lasting effects on subsequent events and decisions (Arthur, 1989).
A review of innovation strategies and processes to improve access to AT: Looking ahead to open innovation ecosystems
Published in Assistive Technology, 2021
Catherine Holloway, Dafne Zuleima Morgado Ramirez, Tigmanshu Bhatnagar, Ben Oldfrey, Priya Morjaria, Soikat Ghosh Moulic, Ikenna D. Ebuenyi, Giulia Barbareschi, Fiona Meeks, Jessica Massie, Felipe Ramos-Barajas, Joanne McVeigh, Kyle Keane, George Torrens, P. V.M. Rao, Malcolm MacLachlan, Victoria Austin, Rainer Kattel, Cheryl D Metcalf, Srinivasan Sujatha
It is also important to recognize that a complex system, such as AT provision, “clearly does not change merely because someone devises and then mandates a purpose designed solution … Instead, the system alters over time and to its own rhythm (idiosyncratically and locally)” (Braithwaite, 2018). Indeed, due to their complexity, systems may be resistant to change (WHO, 2009), and such resistance to change was identified in our findings. For example, path dependence (David, 2007) – a prevalent concept in the innovation literature (Kingston, 1977) – may be used to explain resistance to change within a system. As noted by (Uusitalo & Lavikka, 2020, p. 1), past decisions have “been found to lock organisations onto pathways that constrain future choices and limit their ability to respond to changes.” For instance, path dependency has been used to explain inadequate healthcare policies (Bevan & Robinson, 2005).
Imagining maternity care as a complex adaptive system: understanding health system constraints to the promotion of respectful maternity care
Published in Sexual and Reproductive Health Matters, 2020
Anteneh Asefa, Barbara McPake, Ana Langer, Meghan A. Bohren, Alison Morgan
A complex adaptive system is a dynamic system that consists of a wide variety of elements, and in which the behaviour of each is responsive to the actions of others within the system (adaptive); interactions are nonlinear; and responses or changes are unpredictable (complex).23,26,28Table 1 shows a brief description of selected complex adaptive system concepts. Nonlinearity refers to the heterogeneous and multiple levels of interaction between system agents which makes system behaviour unpredictable.26,30 Small changes in inputs may lead to large changes in outputs. Conversely, large changes in inputs may result in small changes in output.28 Gear and colleagues describe feedback loops as “recursive mechanisms arising from multiple agent interactions over time that either reinforce (positive) or undermine (negative) each other. Positive feedback loops support a change trajectory while negative feedback loops tend to undermine or negate change”.26 When system elements interact, the system displays a new aggregate behaviour that cannot be seen at the individual element level. This property is called emergence23,26 and such repeated interactions over time make the system adapt to the behaviour of its elements; this is labelled as self-organisation.28 Sometimes, past system events or circumstances manifest their desirable or undesirable influence on current system behaviours or events – path dependence.30,31
Applications of Machine Learning Methods to Predict Readmission and Length-of-Stay for Homeless Families: The Case of Win Shelters in New York City
Published in Journal of Technology in Human Services, 2018
Boyeong Hong, Awais Malik, Jack Lundquist, Ira Bellach, Constantine E. Kontokosta
Finally, integration of data-driven decision-making into organizations not accustomed to these tools and processes creates significant barriers to using analytics for day-to-day operations. Part of this is the result of human and computational infrastructure limitations: in many cases, employees simply do not have the skills needed to interpret and apply data analytics to their work, and information technology—including database management—is often characterized by legacy systems using outmoded software. The more significant challenge to data-driven decision-making is created by rigid, bureaucratic processes, often present in social service agencies, that dis-incentivize innovation and foster a lack of trust in new methods (Provost & Fawcett, 2013). While there is a rationale for this type of path dependent approach in light of the seriousness of these agencies’ responsibilities, the aversion to new ways of organizational management can limit relatively low-cost and low-risk opportunities for the use of data.