Care coordination for complex cancer survivors in an integrated safety-net system: a study protocol

Implement a system-level EHR-driven intervention for 500 complex cancer survivors at Parkland, combining three evidence-based care coordination strategies.

Test effectiveness of the strategies on system-and patient-level outcomes using a rigorous, quasi-experimental design with outcomes measured before and after implementation.

Elucidate system and patient factors that facilitate or hinder implementation and result in differences in experiences of care coordination between complex patients with and without cancer.

To facilitate transitions between oncology and primary care, we will develop an EHR-based registry and workflow [38] using the Epic Reporting Workbench to identify eligible patients and deliver team-based coordinated care (Fig. 1). The Oncology and Primary Care Medical Directors will provide clinical expertise to develop the EHR registry and associated workflow and will also champion implementation of the care coordination strategies. During Year 1, Parkland will educate clinicians and staff about the proposed system change and inform primary care physicians on the ways that the health system seeks to address this need through new patient pathways and access. This training will be included in one of each primary care clinic’s monthly training modules.

Oncology will designate a full-time registered nurse (“nurse coordinator”) with competencies in care coordination [39] as a key member of the patients’ care team [40]. The nurse coordinator will be tasked primarily in clinical care coordination, continuity/transition management [41‐43], and assisting patient self-management. [39, 44, 45] The nurse coordinator will review a list of patients from the registry who have initiated cancer treatment in ambulatory oncology using the EHR algorithm and confirm eligibility for this research (Fig. 1). We will confirm the algorithm’s ability to identify Stage I-III complex patients via chart review audit of 50 patients. For cancer survivors established within community primary care, the nurse coordinator will first notify the primary care physician of the survivor’s completion of initial cancer treatment and transition to the cancer survivorship phase. Patients diagnosed with cancers referred in or diagnosed through the emergency department (ED) will be assigned directly to the nurse coordinator who will establish a primary care provider and home within the Parkland system. In-person meetings at the start of cancer treatment will be established by the nurse coordinator when patients begin to attend oncology visits.

At the end of active cancer treatment, the nurse coordinator will initiate an EHR-driven treatment summary and follow-up guidelines, encourage continual interaction with primary care, and recommend transition to the care team post-treatment. S/he will systematically track patients to ascertain completion of initial cancer treatment and, each week, coordinate appointments and lab tests between primary and specialty clinics. Once appointments are made with the primary care physician and social worker, the nurse coordinator will track upcoming appointment results via the EHR-driven registry. Eligible patients will be seen again at treatment end by the nurse coordinator for a transition review. At the visit, the nurse coordinator will synthesize patients’ medical and cancer history, and educate them on follow-up for cancer recurrence, self-management for chronic diseases, and long-term effects of their cancer treatment [44]. The nurse coordinator will also coordinate appropriate specialty care referrals, including smoking cessation, and health behaviors/psychosocial counseling resources already available at Parkland. Information and education will be culturally and literacy-level appropriate, per Parkland standards, as a healthcare provider for diverse and vulnerable communities.

An external practice facilitator will provide coaching and technical assistance to enhance team-based care [46‐48]. Ongoing support from expert quality improvement trainers is critical to successfully implement and sustain system changes [41]. Prior to implementation launch, the coach will meet with the nurse coordinator in-person to start the rapport-building process and establish goals and expectations for the coordinator/coach relationship. Then, during the implementation phase, the nurse coordinator and coach will hold regular (30 min, weekly) meetings aimed at providing motivational work and direct recommendations to challenges as they arise, as well as providing specific guidance to identify and assess patients, clarify team member roles, facilitate communication, manage care transitions, and monitor or adjust work flow [49]. The external coach will draw on the principles of process improvement cycles (e.g. Plan-Do-Study-Act), appreciative inquiry, and other coaching strategies over the course of the study to help the nurse coordinator and larger team learn continuously from on-the-ground implementation [50‐52].

We used the empirically informed Primary Care Practice Change Model, developed by Cohen and colleagues, to guide our mixed-methods evaluation design [53]. Built on principles of complex adaptive systems theory [53‐56] and drawing from results of prior primary care system-level interventions, the model emphasizes the role of multi-level factors influencing implementation. Specifically, it identifies four key elements: internal motivators, external motivators, resources and opportunities for change [56]. These elements can act independently but, because system resources and opportunities are highly interconnected, a change in one can reverberate across the system [53]. For example, resources available to Parkland to support change (e.g. leadership and relationships among practice members) may influence the extent to which clinicians and staff adapt work flow to implement the new EHR-driven strategy to link survivors with primary care. Coupled with availability of a new care coordinator within a dedicated complex care team, this may improve care within the system. Similarly, motivated staff may identify new ways to foster implementation of the strategies. Using provider interviews and structured observations, we will examine how and why motivation of clinic members influences implementation. Using mixed-methods, we will track how these different elements inside and outside the system interact to influence change [57], and support or hamper sustainability of the intervention strategies [58‐61]. We will identify system- and patient-level factors influencing implementation of the care coordination strategies to systematically pinpoint barriers amenable to ongoing quality improvement and to enable subsequent dissemination to other safety-net settings [62].

Gathering qualitative and quantitative data before, during, and after implementation will enable us to assess the factors that influence implementation at the system- and patient-levels [56, 63]. Using an intervention mixed methods approach to evaluation will enable us to observe how contextual factors shift during the course of the study [57, 64], to systematically pinpoint facilitators as well as barriers amenable to ongoing quality improvement and to generate transportable lessons for dissemination [62]. To inform real-world implementation intervention, it is important to: (a) describe how the system-level strategies are implemented; (b) elucidate contextual factors affecting implementation at system- and patient-levels; and (c) identify alternate explanations for observed effects on outcomes to generate counterfactual inference.

We will use segmented logistic regression and linear mixed regression models to assess effect of care coordination strategies on system and patient outcomes. To evaluate system-level hypotheses, we will first use chi-square tests to compare proportion of eligible patients meeting guidelines at 12 and 24 months before implementation to proportion of patients meeting guidelines 12, 24 and 36 months after system-level strategies are in place. We will then use segmented logistic regression models [83] to identify the time point at which observed trend in proportion of patients meeting guidelines (trajectory rate) markedly changes. We will then test trend for proportion of patients meeting guidelines before implementation of system-level strategies.

For patient-level hypotheses, we will use linear mixed models to estimate effects on change in scores. Through specification of fixed effects, the mixed model allows for control of confounding variables, and can account for sources of natural heterogeneity by adding random effects that are unique to a particular individual. We will test different covariance matrices for random effects and will choose the matrix yielding the smallest Akaike information criterion or largest restricted maximum likelihood estimate. Residual analyses and plots will examine adequacy of the final model. Retrospective data might be missing. If data are missing at random (MAR), a linear mixed regression model is appropriate to address the issues of repeated measures and missing data in longitudinal studies. If the missing data are not missing at random (NMAR), we will explore the missing data pattern and a technique such as pattern mixture model will be used to adjust for NMAR bias.

We will combine qualitative and quantitative methods in a convergent interactive analytic design to examine interdependencies between conceptual model elements. Further, we will conduct observations at multiple time points to assess the gradual nature of implementing system change [87], staggering data collection over time, enabling each method to cross-inform our overall strategy [88]. For instance, interim analyses indicating successful implementation will inform provider interviews to elicit contextual data regarding factors contributing to increases in receipt of guideline-appropriate care [64]. Triangulating such data strengthens validity and decreases deficiencies or biases that might arise from any single method [89‐91]. Our multi-disciplinary research team minimizes researcher bias by bringing together experts with differing perspectives to review and critique synthesis and interpretation of qualitative findings. By examining “factors that affect interpretation of what happened during the study (internal validity) [92] and consider what others need to know to transport the study elsewhere (external validity),” [57] our study will generate the evidence base for care coordination among complex cancer survivors and facilitate dissemination.

We will use NVivo 9.0 (QSR International, AUS) to collate and analyze qualitative data. Use of this software makes the analytic process transparent so that investigators can track evolving analyses [93]. Research staff will transcribe field notes from structured observations and gather existing protocols and documents relating to care of complex patients to enter into the database. We will triangulate across diverse data sources, to understand how providers adapt the strategies to suit the Parkland context [56, 67]. To focus our analysis of model elements and their interdependencies, we will develop a matrix of key concepts, populating cells with observational data and inserting brief excerpts of raw text to substantiate claims or interpretations. This analytic step facilitates cross-case comparisons (e.g. pre- and post-implementation, or between patient types) and in-depth explorations of specific concepts [94]. Through monthly meetings, we will test emergent themes and interpretation against the knowledge base of our study team [95]. We will review coding agreement and resolve discrepancies through consensus [96, 97].