Cumulative Complexity Model Research Articles

Women living with multi-morbidity in the Greater Accra Region of Ghana: a qualitative study guided by the Cumulative Complexity Model.

Defined as the co-occurrence of more than two chronic conditions, multi-morbidity has been described as a significant health care problem: a trend linked to a rise in non-communicable disease and an ageing population. Evidence on the experiences of living with multi-morbidity in middle-income countries (MICs) is limited. In high-income countries (HICs), multi-morbidity has a complex impact on health outcomes, including functional status, disability and quality of life, complexity of health care and burden of treatment. Previous evidence also shows that multi-morbidity is consistently higher amongst women. This study aimed to explore the perceptions and experiences of women living with multi-morbidity in the Greater Accra Region, Ghana: to understand the complexity of their health needs due to multi-morbidity, and to document how the health system has responded. Guided by the Cumulative Complexity Model, and using stratified purposive sampling, 20 in-depth interviews were conducted between May and September 2015 across three polyclinics in the Greater Accra Region. The data were analysed using the six phases of Thematic Analysis. Overall four themes emerged: 1) the influences on patients' health experience; 2) seeking care and the responsiveness of the health care system; 3) how patients manage health care demands; and 4) outcomes due to health. Spirituality and the stigmatization caused by specific conditions, such as HIV, impacted their overall health experience. Women depended on the care and treatment provided through the health care system despite inconsistent coverage and a lack of choice thereof, although their experiences varied by chronic condition. Women depended on their family and community to offset the financial burden of treatment costs, which was exacerbated by having many conditions. The implications are that integrated health and social support, such as streamlining procedures and professional training on managing complexity, would benefit and reduce the burden of multi-morbidity experienced by women with multi-morbidity in Ghana.

The Cumulative Complexity Model and Repeat Falls: A Quality Improvement Project.

The Cumulative Complexity Model and Repeat Falls: A Quality Improvement Project.

The Cumulative Complexity Model and Repeat Falls: A Quality Improvement Project.

The purpose of this article is to demonstrate the effectiveness of the Cumulative Complexity Model as a framework to build an Excel tool and a Pareto tool that will enable inpatient case managers to predict the increased risk for and prevent repeat falls. The Excel tool is based on work explained in a previous article by and uses a macro to analyze the factors causing the repeat falls and then calculate the probability of it happening again. This enables the case manager to identify trends in how the patient is transitioning toward goals of care and identify problems before they become barriers to the smooth transition to other levels of care. Thus, the case manager will save the facility money by avoiding unneeded days of care and avoiding the costs that result from rendering medical care for the patient who has fallen. In July 2015, a group of nurses at a small Veterans Health Administration Hospital in the Northwest collaborated to find ways to reverse a trend of increasing falls and repeat falls. A retrospective chart review of all falls and repeat falls (N = 73) that happened between January 2013 and July 2015 was used to generate a list of top 11 contributing variables that enabled evaluation of the data. A bundle of 3 interventions was instituted in October 2015: (1) development of a dedicated charge nurse/resource nurse, (2) use of a standardized method of rounding, and (3) use of a noncontact patient monitoring system ("virtual nurses"). Falls pre- and postimplementation (N = 109) were analyzed using linear and logistic regression analyses. Data were entered into an Excel sheet and analyzed to identify the major contributing factors to falls and repeat falls and to identify trends. These data were also evaluated to find out whether length of stay and nurse workload contributed to falls. Fifteen months after implementation of the aforementioned interventions, falls on the unit went down from 30 aggregate falls in 2015 to 17 aggregate falls in 2016. Repeat falls in 2015 went from 9 repeat falls after admission to the unit down to 2 repeat falls in 2016. Each additional extrinsic variable that was present added an additional 1.43 to the odds ratio (OR) for a fall. Similarly, each additional intrinsic variable present added 2.08 to the OR for a fall. The linear regression of length of stay and falls demonstrated that 17.5% of falls correlated with length of stay, F(1,36) = 7.63, p = .009, R = .175, adjusted R = .152. Workload correlated with work 17% of the time, as measured by using ward days of care, F(1,100) = 20.84, p = .00001, R = .17, adjusted R = .16. Two examples of the how to use these tools are located in the "Discussion" section of the article.

Living with, managing and minimising treatment burden in long term conditions: a systematic review of qualitative research.

Background‘Treatment burden’, defined as both the workload and impact of treatment regimens on function and well-being, has been associated with poor adherence and unfavourable outcomes. Previous research focused on treatment workload but our understanding of treatment impact is limited. This research aimed to systematically review qualitative research to identify: 1) what are the treatment generated disruptions experienced by patients across all chronic conditions and treatments? 2) what strategies do patients employ to minimise these treatment generated disruptions?Methods and FindingsThe search strategy centred on: treatment burden and qualitative methods. Medline, CINAHL, Embase, and PsychINFO were searched electronically from inception to Dec 2013. No language limitations were set. Teams of two reviewers independently conducted paper screening, data extraction, and data analysis. Data were analysed using framework synthesis informed by Cumulative Complexity Model. Eleven papers reporting data from 294 patients, across a range of conditions, age groups and nationalities were included. Treatment burdens were experienced as a series of disruptions: biographical disruptions involved loss of freedom and independence, restriction of meaningful activities, negative emotions and stigma; relational disruptions included strained family and social relationships and feeling isolated; and, biological disruptions involved physical side-effects. Patients employed “adaptive treatment work” and “rationalised non-adherence” to minimise treatment disruptions. Rationalised non-adherence was sanctioned by health professionals at end of life; at other times it was a “secret-act” which generated feelings of guilt and impacted on family and clinical relationships.ConclusionsTreatments generate negative emotions and physical side effects, strain relationships and affect identity. Patients minimise these disruptions through additional adaptive work and/or by non-adherence. This affects physical outcomes and care relationships. There is a need for clinicians to engage with patients in honest conversations about treatment disruptions and the ‘adhere-ability’ of recommended regimens. Patient-centred practice requires management plans which optimise outcomes and minimise disruptions.

Attaining minimally disruptive medicine: context, challenges and a roadmap for implementation.

In this second of two papers on minimally disruptive medicine, we use the language of patient workload and patient capacity from the Cumulative Complexity Model to accomplish three tasks. First, we outline the current context in healthcare, comprised of contrasting problems: some people lack access to care and others receive too much care in an overmedicalised system, both of which reflect imbalances between patients' workloads and their capacity. Second, we identify and address five tensions and challenges between minimally disruptive medicine, the existing context, and other approaches to accessible and patient-centred care such as evidence-based medicine and greater patient engagement. Third, we outline a roadmap of three strategies toward implementing minimally disruptive medicine in practice, including large-scale paradigm shifts, mid-level add-ons to existing reform efforts, and a modular strategy using an existing 'toolkit' that is more limited in scope, but can fit into existing healthcare systems.

Preventing early readmissions.

Forthesecondconsecutiveyear,morethan2000UShospitalswill be collectively penalized upward of $200million for readmissions of Medicare patients within 30 days of discharge.1 The fines have been levied under the Medicare Hospital Readmissions Reduction Program (HRRP),whichwas establishedby theAffordableCare Act as part of a larger effort to slow the growth of health care spendingwhile improving the qualityof care.Under theHRRP,hospitals arepenalized forhigher-thanexpected readmissions rates compared with the national average. In part because of these incentives, many health systems are reexamining their approach to prevention of early readmissions. Since the HRRP has been in effect, the rate of all-cause Medicare readmissions within 30 days has declined, from about 19% in 2007-2011 to 17.5% in 2013.2 However, reducing readmissions has proven difficult for many hospitals because the underlying causes of readmissions are complex and not completely understood. Onemodel that conceptualizes readmissions is the cumulative complexitymodel,whichposits thathealthoutcomesare influenced bythebalanceofapatient’sworkloadandcapacity.3Workloadincludes the tasks and responsibilities required as a patient (eg, understanding a plan of care, making clinical appointments, and self-care) and capacity refers to the ability to meet those demands (eg, social and financial resources, literacy, and cognitive function). Themodel hypothesizes that having aworkload that exceeds capacitywill lead to poor health outcomes—including early readmissions. In the May 2014 issue of JAMA Internal Medicine, Leppin and colleagues4 reported their findings from a systematic review and meta-analysis of randomized trials of interventions to reduce early hospital readmissions—and also tested how well elements of the Related article page 1305 JAMA INTERNALMEDICINE

Preventing 30-day hospital readmissions: a systematic review and meta-analysis of randomized trials.

Reducing early (<30 days) hospital readmissions is a policy priority aimed at improving health care quality. The cumulative complexity model conceptualizes patient context. It predicts that highly supportive discharge interventions will enhance patient capacity to enact burdensome self-care and avoid readmissions. To synthesize the evidence of the efficacy of interventions to reduce early hospital readmissions and identify intervention features--including their impact on treatment burden and on patients' capacity to enact postdischarge self-care--that might explain their varying effects. We searched PubMed, Ovid MEDLINE, Ovid EMBASE, EBSCO CINAHL, and Scopus (1990 until April 1, 2013), contacted experts, and reviewed bibliographies. Randomized trials that assessed the effect of interventions on all-cause or unplanned readmissions within 30 days of discharge in adult patients hospitalized for a medical or surgical cause for more than 24 hours and discharged to home. Reviewer pairs extracted trial characteristics and used an activity-based coding strategy to characterize the interventions; fidelity was confirmed with authors. Blinded to trial outcomes, reviewers noted the extent to which interventions placed additional work on patients after discharge or supported their capacity for self-care in accordance with the cumulative complexity model. Relative risk of all-cause or unplanned readmission with or without out-of-hospital deaths at 30 days postdischarge. In 42 trials, the tested interventions prevented early readmissions (pooled random-effects relative risk, 0.82 [95% CI, 0.73-0.91]; P < .001; I² = 31%), a finding that was consistent across patient subgroups. Trials published before 2002 reported interventions that were 1.6 times more effective than those tested later (interaction P = .01). In exploratory subgroup analyses, interventions with many components (interaction P = .001), involving more individuals in care delivery (interaction P = .05), and supporting patient capacity for self-care (interaction P = .04) were 1.4, 1.3, and 1.3 times more effective than other interventions, respectively. A post hoc regression model showed incremental value in providing comprehensive, postdischarge support to patients and caregivers. Tested interventions are effective at reducing readmissions, but more effective interventions are complex and support patient capacity for self-care. Interventions tested more recently are less effective.

Examining health promotion interventions for patients with chronic conditions using a novel patient-centered complexity model: protocol for a systematic review and meta-analysis

BackgroundSuccessful chronic care self-management requires adherence to healthy lifestyle behaviors, but many healthcare-based health promotion interventions have resulted in small and unsustainable changes in patient behavior. Patients with chronic conditions may already be overwhelmed by burdensome illnesses and treatments, and not have the capacity to respond well to the additional work required of behavior modifications. To explore this phenomenon, we will apply the cumulative complexity model (CCM), a patient-centered model of patient complexity, to a systematic review and meta-analysis of healthcare-based health behavior interventions.Methods/DesignThis systematic review will include randomized trials published between 2002 and 2012 that compared healthcare-based interventions aimed at improving healthy diet and physical activity in community dwelling adult patients with chronic conditions. After extracting study and risk of bias features from each trial, we will classify the interventions according to the conceptual model. We will then use meta-analysis and subgroup analysis to test hypotheses based on the conceptual model.DiscussionHealthcare providers need evidence of successful health promoting interventions for patients with chronic conditions who display common behavioral risk factors. To better understand how patients respond to interventions, we will apply the CCM, which accounts for both the capacity of patients with chronic conditions and their treatment-related workload, and posits that a balance between capacity and workload predicts successful enactment of self-care. Analysis will also include whether patients with multiple chronic conditions respond differently to interventions compared to those with single chronic conditions. The results of this review will provide insights as to how patients with chronic conditions respond to health-promoting interventions.Review registrationPROSPERO registration number: CRD42012003428

Cumulative complexity: a functional, patient-centered model of patient complexity can improve research and practice

ObjectiveTo design a functional, patient-centered model of patient complexity with practical applicability to analytic design and clinical practice. Existing literature on patient complexity has mainly identified its components descriptively and in isolation, lacking clarity as to their combined functions in disrupting care or to how complexity changes over time. Study Design and SettingThe authors developed a cumulative complexity model, which integrates existing literature and emphasizes how clinical and social factors accumulate and interact to complicate patient care. A narrative literature review is used to explicate the model. ResultsThe model emphasizes a core, patient-level mechanism whereby complicating factors impact care and outcomes: the balance between patient workload of demands and patient capacity to address demands. Workload encompasses the demands on the patient's time and energy, including demands of treatment, self-care, and life in general. Capacity concerns ability to handle work (e.g., functional morbidity, financial/social resources, literacy). Workload-capacity imbalances comprise the mechanism driving patient complexity. Treatment and illness burdens serve as feedback loops, linking negative outcomes to further imbalances, such that complexity may accumulate over time. ConclusionWith its components largely supported by existing literature, the model has implications for analytic design, clinical epidemiology, and clinical practice.