Uncertain of role in change in healthcare

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Again, this may lead to a greater reliance on relationships, in turn leading to a greater impact of the relationship infrastructure on patient outcomes. Completing more routine tasks may be less uncertain, and thus rely less on relationships.

In primary care offices, delivery of preventive care or chronic disease care that is generally recommended may be inherently less uncertain than dealing with a new, undifferentiated complaint.

The degree of routine versus non-routine care, and its impact in terms of uncertainty, may also be context-dependent, and we must consider what is routine or not routine in specific clinical settings. A mismatch between the level of uncertainty inherent in the task and the types of tasks typically performed in the setting may lead to a greater degree of uncertainty. For example, providing initial care for a patient found to be in diabetic ketoacidosis in an outpatient setting may be more uncertain than providing that care in an emergency department, because the emergency department has routinized this type of care in a way that the typical primary care setting has not.

It may be possible to deliver care effectively across levels of task uncertainty within the same setting if an appropriate organizational structure is in place. For example, in many patient-centered medical home implementations, routine and preventive, low-complexity care is delivered by non-physician providers, while physicians focus on delivering care that is more highly uncertain.

Thus, we must match the uncertainty of the work to be done with an organizational structure that can effectively navigate that level or levels of uncertainty.

Our analysis builds on the literature to date regarding uncertainty in healthcare or clinical situations. Uncertainty has been described in terms of illness or clinical progression, using terms such as ambiguity, inconsistency, vagueness, unpredictability, lack of information, and unfamiliarity [ 40 ].

A second way that uncertainty has been described is in terms of risk and risk assessment. In this approach, risk and uncertainty are often discussed as interchangeable, yet they differ in important ways [ 41 ]. A decision made under risk occurs when one can list all possible outcomes associated with a particular decision and assign a probability to each possible outcome. Managing risk is usually thought of as an information, or numeracy, activity where people have or can obtain the data required to support analyses for optimal decision-making.

In contrast, uncertainty exists when one cannot list all possible outcomes or assign accurate probabilities to different outcomes. To manage risk, more information is generally effective, but the same is not true for managing uncertainty. When we discuss system uncertainty, we do not refer to situations where uncertainty exists solely because of lack of information.

Instead, we refer to situations that are inherently unpredictable. Uncertainty can often be reduced with information, but it cannot be eliminated. Similarly, high-risk clinical situations in which outcomes may encompass life or death scenarios are not necessarily high uncertainty situations. For example, a critically ill patient may be high risk, but there may be relatively little clinical uncertainty.

More recently, Han et al. While this taxonomy expands our conceptualizations of uncertainty in healthcare systems, it does not delve into the ways that these categories may vary in specific contexts. It also does not explicitly suggest strategies for navigating uncertainty or managing performance improvement in the face of these different types sources of uncertainty.

We defined uncertainty in terms of unpredictability. Our analysis suggests that uncertainty is an important aspect of clinical systems that must be considered in designing approaches to improve healthcare system function. The recognition of complexity in the delivery of healthcare provides the insight that improvement efforts must take uncertainty into account. Because uncertainty may vary depending on the disease or task and how they come together in specific settings, these interdependencies must be considered in intervention design.

Understanding the patterns of task, disease, and the interdependencies among them in specific contexts that are associated with greater uncertainty will allow us to more effectively utilize relationally based approaches to improvement. To more effectively design interventions to improve patient outcomes, we propose approaching improvement in terms of impacting system interdependencies.

These interdependencies include not only the processes of care and resources in the system, but also the relationship infrastructure among individuals in the system.

The relative role of the uncertainty will vary as a function of the task, disease, the local context, and interdependence among them.

We propose considering improvement efforts in terms of changing the interdependencies in the system. These interdependencies include three elements: the resources available in the system; the processes utilized to accomplish work in the system; and the relational infrastructure among individuals in the system. The resources in the system will impact how the system functions and influence the approach taken to improvement efforts.

For example, the physical layout of a clinic or inpatient unit will influence the communication patterns among providers [ 43 ]. The processes are the ways in which work is done in the system. These might include care pathways or protocols, or physical movement of individuals or materials throughout a system.

Finally, the relationship infrastructure includes ways that providers relate to each other and to their patients. All of these aspects of a system influence each other. Resources will influence processes, processes influence ways that providers relate, and ways that people relate in turn influence processes and resource decisions.

Finally, resources can be brought to bear to reinforce either processes or relationships in the system. Implications of these differences in uncertainty for the role of process, resource, and relationship-based approaches for healthcare improvement are summarized in Table 6. We note that because any type of change leads to uncertainty, it may be helpful to consider the relational infrastructure and how individuals make sense and learn in any change effort, but targeting relationships as a key change intervention may not be necessary in low-uncertainty situations.

Our focus in this work is not change efforts generally but rather on how varying manifestations of uncertainty in tasks, diseases, and settings being improved will influence the need for a focus on relationships in the intervention itself.

Delivery of preventive care, recommended chronic disease management, and guideline-concordant population-based care seem to be relatively standardized, routine, low-uncertainty activities. Process-based interventions may be most useful in these circumstances in which the target of improvement is one that is applicable to almost all patients.

For example, in primary care settings, urine screening for microalbuminuria in diabetic patients is recommended for all diabetic patients and may be well-suited to process-based interventions such as clinical reminders, automated order sets, or clinical protocols. Resource allocation in these contexts may focus on infrastructure that improves access to care, or implementation of technologies to improve guideline-concordant care. The delivery of preventive and chronic disease care in the VA illustrates the effectiveness of process-based interventions on routine care delivery.

The VA has made considerable investment in clinical reminders and other processes that put delivery of preventive and chronic disease care at the forefront of the primary care delivery system.

Our own results in VA primary care show that markers of preventive and chronic disease care were high and not associated with between-clinic differences in provider and staff relationships. Low uncertainty situations may have high clinical risk. For example, trauma patients are high-risk for mortality, but well-established protocols that are generally applicable guide initial assessment and care.

In these types of low uncertainty but high-risk situations, processes of care that ensure that all patients receive recommended care are critical. Paying attention to provider relationships may be more important in settings where there is a higher level of uncertainty, making improvement through the application of processes or resources alone less effective.

These circumstances include those where there is a greater need to share work and where clinical issues are non-routine, customized, and quickly evolving.

In nursing homes, a requirement that staff continuously respond to individual needs of very diverse residents may lead to situations with high levels of uncertainty.

In hospital settings, treatment of the patient often occurs without stopping. This leads to a focus on assessments, handoffs, and transitions that involve many providers across and within specialties. In these contexts, the ways that the providers relate and make sense are critical to good outcomes, in turn requiring a relationship infrastructure that enables effective communication. This need to distribute care across providers may not occur to the same degree in primary care settings, particularly in the delivery of routine or preventative services.

Alternatively, managing the workup or treatment of a patient among multiple providers in the primary care setting, or to and from another setting to primary care, may be quite complex. The greater the requirement for coordination or care sharing among providers, the more impact the provider relationships will have on patient outcomes. In these circumstances, resources might be better deployed to improve the ways that providers relate to each other and make sense of non-routine issues.

High uncertainty situations require an adaptive approach. Adaptive problems require more emphasis on relationships, as well as how providers make sense of what is happening [ 21 ],[ 44 ], improvise [ 45 ], and learn [ 46 - 48 ]. Improving care for chronically ill patients who interact frequently with the healthcare system for acute care services exemplifies the ways in which uncertainty might influence the types of approaches that are most likely to be effective.

Some aspects of care, such as periodically recommended reassessment of ejection fraction, nutritional counseling, or weight monitoring, are more standardized and routine in outpatient than in acute settings.

Efforts to improve those more routine aspects of care are well-suited to process-based approaches such as reminders or decision support, or resource-based approaches of adding nutritional education resources or home monitoring support.

However, patients have a high degree of control over outcomes, and improving the relationships between the patient, family, and providers are likely to be important for optimal self-management. Acute exacerbations can be unpredictable, and thus their workup will likely require some customization and coordination among providers that is dependent on provider relationships and sensemaking.

Finally, once admitted, patients with heart failure are at high risk for readmission, and successful transitions to home are likely to require some customization of discharge plans for individual patients.

In this example, uncertainty manifests differently in different aspects of heart failure care delivery, and improving outcomes for heart failure patients requires attention to the interdependencies that are required for improvement. While we had a diverse sample of studies conducted across a number of healthcare settings, our studies were predominantly conducted in primary care settings, and not all settings were represented. However, our approach provided us with rich data that informed our analysis that could not be obtained from a less in-depth approach.

Our primary care studies also did not examine issues of multi-morbidity or care transitions between settings. Our framework for considering uncertainty based on the interplay between disease, task, and setting would apply to those issues. Additionally, relationships have multiple aspects and characteristics. These various characteristics may also have different degrees of importance in improvement interventions based on the task, disease, or setting.

We do not explore those potential nuances, but they are important areas of further development. Recognizing healthcare systems as complex systems highlights the uncertainty and unpredictability inherent in healthcare delivery.

It also highlights the patterns of uncertainty that exist. Uncertainty has been described in terms of system non-linearities, limits of scientific knowledge, and unpredictable trajectories of disease. This paper adds to the literature on uncertainty in healthcare systems by developing an empirically grounded approach to understanding how patterns of uncertainty might vary depending on the task being done, disease being treated, or setting in which care is delivered, leading to low or high uncertainty situations.

Pace of evolution of disease and degree of patient control over outcomes may be ways to consider the unpredictable trajectory of disease and the limits of scientific knowledge. While all diseases have some level of unpredictability in their trajectories, customized and non-routine care may have the greatest. Task-related uncertainties may be examples of the types of uncertainty inherent in the system.

Our analyses have implications for efforts to improve healthcare system performance and patient outcomes. Understanding differences in the ways that uncertainty is manifest in different clinical scenarios will lead to an improved understanding of the types of improvement efforts that will be most likely to be effective.

Differences in uncertainty levels based on task, disease, setting, and their interdependence should be considered when selecting improvement strategies in healthcare. Recognizing patterns of how task, disease, and their interdependence come together from the perspective of uncertainty will afford a greater ability to understand local patterns of self-organization and recognize when paying attention to the relational aspects of care delivery will be critical for intervention success [ 11 ].

In these cases, fostering sensemaking, learning, and improvising could be important strategies for improvement. For example, understanding the degree to which a change impacts routine versus non-routine care or requires the sharing of work across multiple providers may be helpful in deciding what approaches are most likely to be effective. Trying to improve non-routine care with only process-based interventions may not be as successful as an intervention based on reshaping the relationships among providers.

Being more deliberate about these interdependencies and their role will lead to improved interventions, particularly in the context of reimbursement and policy changes that promote effective coordination and communication among providers. Better matching of improvement strategies to the nature of the system improvement will increase the likelihood of success.

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J Manag Stud. Health Care Manage Rev , 38 1 ,. Download references. We would like to thank Ms. Shannon Provost, Dr. Edward Anderson, and Dr. We would also like to thank Dr. Carlos Jaen for his input on our analysis. Finally, we thank our reviewers for their thoughtful comments. Their insights and suggestions have strengthened our manuscript. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.

You can also search for this author in PubMed Google Scholar. Correspondence to Luci K Leykum. All authors were involved in the discussions that led to the framing and conceptualization of this paper and in data analysis and interpretation.

LL wrote the initial draft of this manuscript, and all authors provided substantive comments and edits, producing the final version. All authors read and approved the final manuscript. This article is published under license to BioMed Central Ltd. Reprints and Permissions. Leykum, L. Manifestations and implications of uncertainty for improving healthcare systems: an analysis of observational and interventional studies grounded in complexity science. Implementation Sci 9 , Download citation.

Received : 13 June Accepted : 27 October Published : 19 November Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative.

Skip to main content. Search all BMC articles Search. Download PDF. Download ePub. Abstract Background The application of complexity science to understanding healthcare system improvement highlights the need to consider interdependencies within the system. Methods We analyzed eight observational and interventional studies in which our author team was involved as the basis of our analysis, using a set theoretical qualitative comparative analytic approach.

Results We identified system-level uncertainty as a defining characteristic of complex systems through which we interpreted our results. Conclusions Uncertainty is an important aspect of clinical systems that must be considered in designing approaches to improve healthcare system function.

Background Results of efforts to improve healthcare systems remain inconsistent and disappointing. Table 1 Characteristics of complex system and their application to our work Full size table. Methods We analyzed the group of studies in which any member of the research team participated as the potential set of studies to examine. Table 2 Studies examining the association between relationships and outcomes and their results Full size table. Figure 1. Analytic approach.

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Role uncertain healthcare change in of in cigna primary care physician near me

Where is baxter iowa	The measurement of uncertainty in illness. All studies are focused on healthcare providers, including physicians, nurses, clinic rolf staff, pf ancillary services. For https://indi-infantformula.com/nuances-meaning-in-english/4956-expressive-nuances-definition.php, while the setting alone check this out not be associated with strength of findings with regard to provider relationships, the setting might interact with task and disease in a way that the type of activity that is routine in the setting may be related to the importance of relationships as a strategy to improve care. Our settings included primary care, inpatient medical care, and skilled nursing care. The challenge of complexity in health care. We show how this new taxonomy facilitates an organized approach to the problem of uncertainty in health care by clarifying its nature and chajge, and suggesting appropriate strategies for its analysis and management.
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Centers for medicaid and medicare services grand rapids	Furthermore, no taxonomy encompasses all of the salient issues in which uncertainty is manifest in health care. For example, because these sites are typically used for planned procedures, the NHS can require patients to self-isolate for several days before their treatment. Merriam-Webster Online. Yet the ultimate and most challenging task in managing uncertainty is not to establish its heakthcare or prognosis in the minds of patients and clinicians, but to help each party cope with uncertainty. It is willingness of a party to be vulnerable to the action of another party based on the expectation that the other will perform a particular action that is important to the trustor. The healtncare of this paper is to summarize major theoretical insights relevant to understanding uncertainty specific to health just click for source, and to develop an integrative conceptual taxonomy that visit web page serve as a useful framework, based on a review of literature from the fields of decision science, psychology, chanbe, engineering, and health services research.
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Uncertain of role in change in healthcare	Intolerance for uncertainty scale: Psychometric properties of the English version. We operationalized uncertainty to refer to situations when the outcome cannot be controlled or predicted. Discussion Our analysis builds on the literature to date regarding uncertainty in healthcare or clinical situations. Clearly, it would be better to reduce roe degree of uncertainty in the first place. We operationalized this focus on interdependencies in our studies in terms of understanding and influencing relationships among individuals.

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Growth and margins for providers are already strained due to this dynamic, and the impact is likely to worsen. The analysis includes a range of million to million annual cases, of which 10 to 15 percent require outpatient treatment; 4, to 6, per day require a non-intensive care unit ICU hospital admission; and to per day require an ICU admission.

There is significant uncertainty in ascertaining prevalence and resulting cost impact of long COVID, and data continue to become available on a frequent basis as more research is conducted. Testing and vaccine estimates are based on costs per test and per vaccine and data from US Department of Health and Human Services and the US Centers for Disease Control and Prevention as to annual demand for testing and boosters. For this factor, higher utilization of testing times per person per year would result in an estimate at the higher end of the range.

All figures are scaled to nominal estimates. End payers, already struggling to afford healthcare, have limited ability to absorb this potential acceleration in costs. Employers have continued to shift the cost of healthcare to employees. For example, 18 percent of employees were enrolled in high-deductible health plans in Value reflects enrollment in consumer-driven health plans, which primarily consist of health savings account—eligible high-deductible health plans.

In , 40 percent of employees were enrolled in these health plans. In addition, in , the average family contribution to coverage was 32 percent for employees at companies with more than workers and 53 percent at those with less than workers.

In our recent survey, 95 percent of employers stated that they would adjust benefits if cost increases were 4 percent or higher, with the most common changes being increasing employee cost sharing, shifting to high-deductible health plans, and optimizing the provider network. Consumers already face significant exposure to healthcare costs, as noted above, with the rising level of cost sharing in employer-sponsored insurance.

Estimate based on US Census Bureau household data and Brookings Institution household finance data; this estimate is subject to fluctuation, including during depressed spending periods due to the COVID pandemic. Moreover, while US workers are seeing nominal wage increases, inflation has eroded the gains, resulting in negative real earnings growth.

Wage and inflation indicators from Federal Reserve Bank of St. The government may also not be prepared to fund the increase in healthcare costs. Recent implementation of 2 percent Medicare sequestration cuts illustrate this issue. If the Medicare trust fund needs to pay for additional healthcare spending, this timeline for trust fund insolvency could accelerate.

In addition, federal debt stands at percent of GDP. Total public debt as percent of gross domestic product, Federal Reserve Bank of St.

Louis, accessed September 6, As the Federal Reserve raises interest rates and shrinks its balance sheet, interest payments on federal debt are expected to double as a proportion of the US budget between to Congressional Budget Office, accessed September 6, It is not clear that end payers—employers, consumers, and government funders—will be able to bear this increase, leaving industry players to address the additional spending or face significant EBITDA risk.

It provides the best avenue to improve healthcare for all stakeholders and alleviate the potential margin pressure on the industry. Four areas make up this opportunity:. The headwinds for healthcare are significant and the risks for the industry are sizeable.

But the size of the opportunity outstrips those challenges. The challenge for the industry is to scale up these innovative models at speed. Never miss an insight. We'll email you when new articles are published on this topic.

Skip to main content. Method: A survey of nursing practice in person-centred health-policy implementation is presented. Findings: Despite much being written about managing health-professional resistance to policy implementation, there is a gap between what is being asked of nurses and the resources made available to them to deliver.

In this milieu, nurses are utilising their discretion and leading from the front-line in championing change. Conclusions: Empowering nurses who seek to lead patient involvement could be the key to unlocking health-care improvement. Implications for nursing management: Health services tend to be over-managed and under-led and there is a need to harness the potential of front-line nurses by facilitating leadership development through appropriate organisational support.

Keywords: change champions; front-line nurses; health policy; leadership; person-centred.