A New Paradigm for Nurse Leader Decision-Making Within Complex Adaptive Systems

Health care is a complex and ever-changing environment for nurse leaders and other health care industry decision-makers. The prevailing leadership and decision-making models, rooted in Industrial Age principles, often struggle to adapt to the complexities of modern health care. This article explores the foundations of complexity science and its application to health care decision-making, highlighting the importance of understanding systems dynamics and embracing complexity. Drawing from systems knowledge, the Cynefin Sensemaking Framework, and understanding how to develop enabling constraints, nurse leaders can navigate the complexities of health care by identifying the nature of the problem and applying appropriate decision-making strategies, fostering agility and innovation. By embracing complexity and adopting adaptive leadership approaches, nurse leaders can pragmatically navigate the complexities of modern health care and drive transformative change. This manuscript provides methods for nurse leaders to enhance decision-making within the dynamic landscape of health care as a complex adaptive system.

Defining nursing workload predictors: A pilot study

Aim

To explore predictors of perceived nursing workload in relation to patients, nurses and workflow.

Background

Nursing workload is important to health care organisations. It determines nurses' well‐being and quality of care. Nevertheless, its predictors are barely studied.

Methods

A cross‐sectional prospective design based on the complex adaptive systems theory was used. An online survey asked nurses to describe perceived workload at the end of every shift. Data were gathered from five medical‐surgical wards over three consecutive weeks. We received 205 completed surveys and tested multivariate regression models.

Results

Patient acuity, staffing resources, patient transfers, documentation, patient isolation, unscheduled activities and patient specialties were significant in predicting perceived workload. Nurse‐to‐patient ratio proved not to be a predictor of workload.

Conclusions

This study significantly contributed to literature by identifying some workload predictors. Complexity of patient care, staffing adequacy and some workflow aspects were prominent in determining the shift workload among nurses.

Implications for nursing management

Our findings provide valuable information for top and middle hospital management, as well as for policymakers. Identification of predictors and measurement of workload are essential for optimizing staff resources, workflow processes and work environment. Future research should focus on the appraisal of more determinants.

Missed nursing care and complexity theory: a conceptual paper

Background

Missed nursing care is a complex healthcare problem. Extant literature in this area identifies several interventions that can be used in acute hospital settings to minimise the impact of missed nursing care. However, controversy still exists as to the effectiveness of these interventions on reducing the occurrence of missed nursing care.

Aim

This theoretical paper aimed to provide a conceptual understanding of missed nursing care using complexity theory.

Methods

The method utilised for this paper is based on a literature review on missed care and complexity theory in healthcare.

Results

We found that the key virtues of complexity theory relevant to the missed nursing care phenomenon were adaptation and self-organisation, non-linear interactions and history. It is suggested that the complex adaptive systems approach may be more useful for nurse managers to inform and prepare nurses to meet uncertain encounters in their everyday clinical practice and therefore reduce instances of missed care.

Conclusions

This paper envisions that it is time that methods used to explore missed care changed. Strategies proposed in this paper may have an important impact on the ability of nursing staff to provide quality and innovative healthcare in the modern healthcare system.

The Power Law in Operating Room Management

The Acute Care Surgery model has been implemented by many hospitals in the United States. As complex adaptive systems, healthcare systems are composed of many interacting elements that respond to intrinsic and extrinsic inputs. Systems level analysis may reveal the underlying organizational structure of tactical block allocations like the Acute Care Surgery model. The purpose of this study is to demonstrate one method to identify a key characteristic of complex adaptive systems in the perioperative services. Start and end times for all surgeries performed at the University of Vermont Medical Center OR1 were extracted for two years prior to the transition to an Acute Care Surgery service and two years following the transition. Histograms were plotted for the inter-event times calculated from the difference between surgical cases. A power law distribution was fit to the post-transition histogram. The Kolmogorov-Smirnov test for goodness-of-fit at 95% level of significance shows the histogram plotted from post-transition inter-event times follows a power law distribution (K-S = 0.088, p = 0.068), indicating a Complex Adaptive System. Our analysis demonstrates that the strategic decision to create an Acute Care Surgery service has direct implications on tactical and operational processes in the perioperative services. Elements of complex adaptive systems can be represented by a power law distributions and similar methods may be applied to identify other processes that operate as complex adaptive systems in perioperative care. To make sustained improvements in the perioperative services, focus on manufacturing-based interventions such as Lean Six Sigma should instead be shifted towards the complex interventions that modify system-specific behaviors described by complex adaptive system principles when power law relationships are present.