Mapping Multi-Site Clinic Workflows to Design Systems-Enabled Interventions

Intervention delivery for embedded pragmatic clinical trials: Development of a tool to measure complexity

BACKGROUND

Conducting an embedded pragmatic clinical trial in the workflow of a healthcare system is a complex endeavor. The complexity of the intervention delivery can have implications for study planning, ability to maintain fidelity to the intervention during the trial, and/or ability to detect meaningful differences in outcomes.

METHODS

We conducted a literature review, developed a tool, and conducted two rounds of phone calls with NIH Pragmatic Trials Collaboratory Demonstration Project principal investigators to develop the Intervention Delivery Complexity Tool. After refining the tool, we piloted it with Collaboratory demonstration projects and developed an online version of the tool using the R Shiny application (https://duke-som.shinyapps.io/ICT-ePCT/).

RESULTS

The 6-item tool consists of internal and external factors. Internal factors pertain to the intervention itself and include workflow, training, and the number of intervention components. External factors are related to intervention delivery at the system level including differences in healthcare systems, the dependency on setting for implementation, and the number of steps between the intervention and the outcome.

CONCLUSION

The Intervention Delivery Complexity Tool was developed as a standard way to overcome communication challenges of intervention delivery within an embedded pragmatic trial. This version of the tool is most likely to be useful to the trial team and its health system partners during trial planning and conduct. We expect further evolution of the tool as more pragmatic trials are conducted and feedback is received on its performance outside of the NIH Pragmatic Trials Collaboratory.

Clinic Factors Associated With Mailed Fecal Immunochemical Test (FIT) Completion: The Difference-Making Role of Support Staff

PURPOSE

Mailed fecal immunochemical test (FIT) programs can facilitate colorectal cancer (CRC) screening. We sought to identify modifiable, clinic-level factors that distinguish primary care clinics with higher vs lower FIT completion rates in response to a centralized mailed FIT program.

METHODS

We used baseline observational data from 15 clinics within a single urban federally qualified health center participating in a pragmatic trial to optimize a mailed FIT program. Clinic-level data included interviews with leadership using a guide informed by the Consolidated Framework for Implementation Research (CFIR) and FIT completion rates. We used template analysis to identify explanatory factors and configurational comparative methods to identify specific combinations of clinic-level conditions that uniquely distinguished clinics with higher and lower FIT completion rates.

RESULTS

We interviewed 39 clinic leaders and identified 58 potential explanatory factors representing clinic workflows and the CFIR inner setting domain. Clinic-level FIT completion rates ranged from 30% to 56%. The configurational model for clinics with higher rates (≥37%) featured any 1 of the following 3 factors related to support staff: (1) adding back- or front-office staff in past 12 months, (2) having staff help patients resolve barriers to CRC screening, and (3) having staff hand out FITs/educate patients. The model for clinics with lower rates involved the combined absence of these same 3 factors.

CONCLUSIONS

Three factors related to support staff differentiated clinics with higher and lower FIT completion rates. Adding nonphysician support staff and having those staff provide enabling services might help clinics optimize mailed FIT screening programs.

Assessment of organizational readiness to implement an electronic health record system in a low-resource settings cancer hospital: A cross-sectional survey

BACKGROUND

Organizational readiness for change is a key factor in success or failure of electronic health record (EHR) system implementations. Readiness is a multifaceted and multilevel abstract construct encompassing individual and organizational aspects, which makes it difficult to assess. Available tools for assessing readiness need to be tested in different contexts.

OBJECTIVE

To identify and assess relevant variables that determine readiness to implement an EHR in oncology in a low-and-middle income setting.

METHODS

At the Uganda Cancer Institute (UCI), a 100-bed tertiary oncology center in Uganda,we conducted a cross-sectional survey using the Paré model. This model has 39 indicator variables (Likert-scale items) for measuring 9 latent variables that contribute to readiness. We analyzed data using partial least squares structural equation modeling (PLS-SEM). In addition, we collected comments that we analyzed by qualitative content analysis and sentiment analysis as a way of triangulating the Likert-scale survey responses.

RESULTS

One hundred and forty-six clinical and non-clinical staff completed the survey, and 116 responses were included in the model. The measurement model showed good indicator reliability, discriminant validity, and internal consistency. Path coefficients for 6 of the 9 latent variables (i.e. vision clarity, change appropriateness, change efficacy, presence of an effective champion, organizational flexibility, and collective self-efficacy) were statistically significant at p < 0.05. The R2 for the outcome variable (organizational readiness) was 0.67. The sentiments were generally positive and correlated well with the survey scores (Pearson's r = 0.73). Perceived benefits of an EHR included improved quality, security and accessibility of clinical data, improved care coordination, reduction of errors, and time and cost saving. Recommended considerations for successful implementation include sensitization, training, resolution of organizational conflicts and computer infrastructure.

CONCLUSION

Change management during EHR implementation in oncology in low- and middle- income setting should focus on attributes of the change and the change targets, including vision clarity, change appropriateness, change efficacy, presence of an effective champion, organizational flexibility, and collective self-efficacy. Particularly, issues of training, computer skills of staff, computer infrastructure, sensitization and strategic implementation need consideration.