Enlisting New Teachers in Clinical Environments (ENTICE); novel ways to engage clinicians.

PURPOSE: To explore the barriers and incentives that affect primary care providers who precept students in outpatient clinics in the US. METHOD: In 2013, leadership of our large primary care group sent a 20-question survey via e-mail to all of the 180 providers within the network. The survey assessed provider demographics, precepting history, learner preferences, and other issues that might affect future decisions about teaching. RESULTS: The response rate was 50% (90 providers). The top reasons for precepting in the past were enjoyment for teaching and personal interaction with learners. The most commonly cited reason for not precepting previously was a perceived lack of time followed by increased productivity demands. When questioned about the future, 65% (59 respondents) indicated that they were likely to precept within the next 6 months. A desired reduction in productivity expectations was the most commonly cited motivator, followed by anticipated monetary compensation and adjusted appointment times. A top barrier to future precepting was a belief that teaching decreases productivity and requires large amounts of time. CONCLUSION: This survey represents an opportunity to study a change in focus for a cohort of busy clinicians who were mostly new to teaching but not new to clinical practice. The survey provides further insight into clinician educators' perceptions regarding the education of a variety of different learners. The results align with data from previous studies in that time pressures and productivity demands transcend specific programs and learner backgrounds. This information is critical for future clerkship directors and hospital administrators in order to understand how to increase support for potential preceptors in medical education.

Tool for evaluating research implementation challenges: a sense-making protocol for addressing implementation challenges in complex research settings.

BACKGROUND: Many challenges arise in complex organizational interventions that threaten research integrity. This article describes a Tool for Evaluating Research Implementation Challenges (TECH), developed using a complexity science framework to assist research teams in assessing and managing these challenges. METHODS: During the implementation of a multi-site, randomized controlled trial (RCT) of organizational interventions to reduce resident falls in eight nursing homes, we inductively developed, and later codified the TECH. The TECH was developed through processes that emerged from interactions among research team members and nursing home staff participants, including a purposive use of complexity science principles. RESULTS: The TECH provided a structure to assess challenges systematically, consider their potential impact on intervention feasibility and fidelity, and determine actions to take. We codified the process into an algorithm that can be adopted or adapted for other research projects. We present selected examples of the use of the TECH that are relevant to many complex interventions. CONCLUSIONS: Complexity theory provides a useful lens through which research procedures can be developed to address implementation challenges that emerge from complex organizations and research designs. Sense-making is a group process in which diverse members interpret challenges when available information is ambiguous; the groups' interpretations provide cues for taking action. Sense-making facilitates the creation of safe environments for generating innovative solutions that balance research integrity and practical issues. The challenges encountered during implementation of complex interventions are often unpredictable; however, adoption of a systematic process will allow investigators to address them in a consistent yet flexible manner, protecting fidelity. Research integrity is also protected by allowing for appropriate adaptations to intervention protocols that preserve the feasibility of 'real world' interventions.

CONNECT for quality: protocol of a cluster randomized controlled trial to improve fall prevention in nursing homes.

BACKGROUND: Quality improvement (QI) programs focused on mastery of content by individual staff members are the current standard to improve resident outcomes in nursing homes. However, complexity science suggests that learning is a social process that occurs within the context of relationships and interactions among individuals. Thus, QI programs will not result in optimal changes in staff behavior unless the context for social learning is present. Accordingly, we developed CONNECT, an intervention to foster systematic use of management practices, which we propose will enhance effectiveness of a nursing home Falls QI program by strengthening the staff-to-staff interactions necessary for clinical problem-solving about complex problems such as falls. The study aims are to compare the impact of the CONNECT intervention, plus a falls reduction QI intervention (CONNECT + FALLS), to the falls reduction QI intervention alone (FALLS), on fall-related process measures, fall rates, and staff interaction measures. METHODS/DESIGN: Sixteen nursing homes will be randomized to one of two study arms, CONNECT + FALLS or FALLS alone. Subjects (staff and residents) are clustered within nursing homes because the intervention addresses social processes and thus must be delivered within the social context, rather than to individuals. Nursing homes randomized to CONNECT + FALLS will receive three months of CONNECT first, followed by three months of FALLS. Nursing homes randomized to FALLS alone receive three months of FALLs QI and are offered CONNECT after data collection is completed. Complexity science measures, which reflect staff perceptions of communication, safety climate, and care quality, will be collected from staff at baseline, three months after, and six months after baseline to evaluate immediate and sustained impacts. FALLS measures including quality indicators (process measures) and fall rates will be collected for the six months prior to baseline and the six months after the end of the intervention. Analysis will use a three-level mixed model. DISCUSSION: By focusing on improving local interactions, CONNECT is expected to maximize staff's ability to implement content learned in a falls QI program and integrate it into knowledge and action. Our previous pilot work shows that CONNECT is feasible, acceptable and appropriate. TRIAL REGISTRATION: ClinicalTrials.gov: NCT00636675.