Organizational theory for dissemination and implementation research

Abstract Background Even under optimal internal organizational conditions, implementation can be undermined by changes in organizations’ external environments, such as fluctuations in funding, adjustments in contracting practices, new technology, new legislation, changes in clinical practice guidelines and recommendations, or other environmental shifts. Internal organizational conditions are increasingly reflected in implementation frameworks, but nuanced explanations of how organizations’ external environments influence implementation success are lacking in implementation research. Organizational theories offer implementation researchers a host of existing, highly relevant, and heretofore largely untapped explanations of the complex interaction between organizations and their environment. In this paper, we demonstrate the utility of organizational theories for implementation research. Discussion We applied four well-known organizational theories (institutional theory, transaction cost economics, contingency theories, and resource dependency theory) to published descriptions of efforts to implement SafeCare, an evidence-based practice for preventing child abuse and neglect. Transaction cost economics theory explained how frequent, uncertain processes for contracting for SafeCare may have generated inefficiencies and thus compromised implementation among private child welfare organizations. Institutional theory explained how child welfare systems may have been motivated to implement SafeCare because doing so aligned with expectations of key stakeholders within child welfare systems’ professional communities. Contingency theories explained how efforts such as interagency collaborative teams promoted SafeCare implementation by facilitating adaptation to child welfare agencies’ internal and external contexts. Resource dependency theory (RDT) explained how interagency relationships, supported by contracts, memoranda of understanding, and negotiations, facilitated SafeCare implementation by balancing autonomy and dependence on funding agencies and SafeCare developers. Summary In addition to the retrospective application of organizational theories demonstrated above, we advocate for the proactive use of organizational theories to design implementation research. For example, implementation strategies should be selected to minimize transaction costs, promote and maintain congruence between organizations’ dynamic internal and external contexts over time, and simultaneously attend to organizations’ financial needs while preserving their autonomy. We describe implications of applying organizational theory in implementation research for implementation strategies, the evaluation of implementation efforts, measurement, research design, theory, and practice. We also offer guidance to implementation researchers for applying organizational theory

Sharing patient-generated data with healthcare providers: findings from a 2019 national survey.

OBJECTIVE: Our study estimates the prevalence and predictors of wearable device adoption and data sharing with healthcare providers in a nationally representative sample. MATERIALS AND METHODS: Data were obtained from the 2019 Health Information National Trend Survey. We conducted multivariable logistic regression to examine predictors of device adoption and data sharing. RESULTS: The sample contained 4159 individuals, 29.9% of whom had adopted a wearable device in 2019. Among adopters, 46.3% had shared data with their provider. Individuals with diabetes (odds ratio [OR], 2.39; 95% CI, 1.66-3.45; P \u3c .0001), hypertension (OR, 2.80; 95% CI, 2.12-3.70; P \u3c .0001), and multiple chronic conditions (OR, 1.55; 95% CI, 1.03-2.32; P \u3c .0001) had significantly higher odds of wearable device adoption. Individuals with a usual source of care (OR, 2.44; 95% CI, 1.95-3.04; P \u3c .0001), diabetes (OR, 1.66; 95% CI, 1.32-2.08; P \u3c .0001), and hypertension (OR, 1.78; 95% CI, 1.44-2.20; P \u3c .0001) had significantly higher odds of sharing data with providers. DISCUSSION: A third of individuals adopted a wearable medical device and nearly 50% of individuals who owned a device shared data with a provider in 2019. Patients with certain conditions, such as diabetes and hypertension, were more likely to adopt devices and share data with providers. Social determinants of health, such as income and usual source of care, negatively affected wearable device adoption and data sharing, similarly to other consumer health technologies. CONCLUSIONS: Wearable device adoption and data sharing with providers may be more common than prior studies have reported; however, digital disparities were noted. Studies are needed that test implementation strategies to expand wearable device use and data sharing into care delivery

Psychometric assessment of three newly developed implementation outcome measures

Abstract Background Implementation outcome measures are essential for monitoring and evaluating the success of implementation efforts. Yet, currently available measures lack conceptual clarity and have largely unknown reliability and validity. This study developed and psychometrically assessed three new measures: the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM). Methods Thirty-six implementation scientists and 27 mental health professionals assigned 31 items to the constructs and rated their confidence in their assignments. The Wilcoxon one-sample signed rank test was used to assess substantive and discriminant content validity. Exploratory and confirmatory factor analysis (EFA and CFA) and Cronbach alphas were used to assess the validity of the conceptual model. Three hundred twenty-six mental health counselors read one of six randomly assigned vignettes depicting a therapist contemplating adopting an evidence-based practice (EBP). Participants used 15 items to rate the therapist’s perceptions of the acceptability, appropriateness, and feasibility of adopting the EBP. CFA and Cronbach alphas were used to refine the scales, assess structural validity, and assess reliability. Analysis of variance (ANOVA) was used to assess known-groups validity. Finally, half of the counselors were randomly assigned to receive the same vignette and the other half the opposite vignette; and all were asked to re-rate acceptability, appropriateness, and feasibility. Pearson correlation coefficients were used to assess test-retest reliability and linear regression to assess sensitivity to change. Results All but five items exhibited substantive and discriminant content validity. A trimmed CFA with five items per construct exhibited acceptable model fit (CFI = 0.98, RMSEA = 0.08) and high factor loadings (0.79 to 0.94). The alphas for 5-item scales were between 0.87 and 0.89. Scale refinement based on measure-specific CFAs and Cronbach alphas using vignette data produced 4-item scales (α’s from 0.85 to 0.91). A three-factor CFA exhibited acceptable fit (CFI = 0.96, RMSEA = 0.08) and high factor loadings (0.75 to 0.89), indicating structural validity. ANOVA showed significant main effects, indicating known-groups validity. Test-retest reliability coefficients ranged from 0.73 to 0.88. Regression analysis indicated each measure was sensitive to change in both directions. Conclusions The AIM, IAM, and FIM demonstrate promising psychometric properties. Predictive validity assessment is planned