| Literature DB >> 20735859 |
Luci K Leykum1, Michael Parchman, Jacqueline Pugh, Valerie Lawrence, Polly H Noël, Reuben R McDaniel.
Abstract
BACKGROUND: Despite applications of models of care and organizational or system-level interventions to improve patient outcomes for chronic disease, consistent improvements have not been achieved. This may reflect a mismatch between the interventions and the nature of the settings in which they are attempted. The application of complex adaptive systems (CAS) framework to understand clinical systems and inform efforts to improve them may lead to more successful interventions. We performed a systematic review of interventions to improve outcomes of patients with congestive heart failure (CHF) to examine whether interventions consistent with CAS are more likely to be effective. We then examine differences between interventions that are most effective for improving outcomes for patients with CHF versus previously published data for type 2 diabetes to explore the potential impact of the nature of the disease on the types of interventions that are more likely to be effective.Entities:
Year: 2010 PMID: 20735859 PMCID: PMC2936445 DOI: 10.1186/1748-5908-5-66
Source DB: PubMed Journal: Implement Sci ISSN: 1748-5908 Impact factor: 7.327
Characteristics of Complex Adaptive Systems Abstracted
| Characteristic | Definition | Example |
|---|---|---|
| Agents who Learn | • People can and will process information, as well as react to changes in information | • 'Health Buddy' with educational content |
| Interconnections | • Change in pattern of interactions, including non-verbal communication, among agents | • Letters to patients |
| Self-organization | • Order is created in a system without explicit hierarchical direction | • Flexibility in tailoring intervention to individual patients |
| Co-evolution | • The system and the environment influence each other's development | • Individualized 'HOME' treatment plan that changes over time |
Criteria used to classify intervention effectiveness, with examples of outcomes reflecting each level of effectiveness
| Outcome Score | Criteria | Example |
|---|---|---|
| 0 | • No statistically differences between control and intervention groups, or between intervention and baseline, on process or outcome measures | • No difference in adherence, NYHA class, # visits, or # hospitalizations |
| 0.5 | • Trends without significance | • Significant improvement in adherence, trends for CHF-related admission and total number of hospital days |
| 1 | • Statistically significant improvement: | • Significant reduction in all-cause mortality and all-cause admissions at 3 months |
Figure 1Articles eligible and ineligible at each stage of review.
Distribution of CAS and intervention effectiveness for CHF studies
| Total CAS | Rating of Intervention Effectiveness | Total No. Studies with each CAS Score | ||
|---|---|---|---|---|
| 1 | 2 | 0 | 3 | |
| 6 | 13 | 0 | 19 | |
| 0 | 10 | 3 | 13 | |
| 0 | 1 | 10 | 11 | |
P < 0.001
Association between individual CAS characteristic and intervention effectiveness for type 2 diabetes [22] and CHF
| CAS characteristic | type 2 diabetes22 | CHF | ||
|---|---|---|---|---|
| Proportion of studies utilizing | Association with effectiveness | Proportion of studies utilizing | Association with effectiveness | |
| Learning | 80% | p = 0.07 | 76% | p = 0.05 |
| Interconnections | 77% | p = 0.05 | 93% | P = 0.72 |
| Self-organization | 27% | p = 0.58 | 35% | p < 0.001 |
| Co-evolution | 70% | p = 0.003 | 65% | p = 0.002 |
Potential differences between type 2 diabetes and CHF with regards to uncertainty, and how they might influence CAS characteristic effectiveness
| CAS characteristic | type 2 diabetes | CHF |
|---|---|---|
| Learning | Treatment is nuanced and complex, making efforts to improve outcomes through learning more difficult. | Less uncertainty in treatment guidelines allows more prescriptive, algorithmic approaches to management that may be more easily learned. |
| Interconnections | Greater degree of uncertainty in terms of symptoms and management, leading to greater reliance on interconnections to manage disease. | Lesser degree of uncertainty in terms of symptoms and management may lead interventions focused on interconnections less effective. |
| Self-organization | Greater uncertainty in management and symptoms of exacerbation may make efforts to self-organize more difficult. | Less uncertainty regarding management and symptoms of exacerbation may make efforts to self-organize more effective. |
| Co-evolution | Course and symptoms evolve over time in unique trajectory. | Course and symptoms evolve over time in unique trajectory. |