Measure Twice, Change Once: Using Simulation to Support Change Management in Rural Healthcare Delivery.

Barriers to adequate healthcare in rural areas remain a grand challenge for local healthcare systems. In addition to patients' travel burdens, lack of health insurance, and lower health literacy, rural healthcare systems also experience significant resource shortages, as well as issues with recruitment and retention of healthcare providers, particularly specialists. These factors combined result in complex change management-focused challenges for rural healthcare systems. Change management initiatives are often resource intensive, and in rural health organizations already strapped for resources, it may be particularly risky to embark on change initiatives. One way to address these change management concerns is by leveraging socio-technical simulation models to estimate techno-economic feasibility (e.g., is it technologically feasible, and is it economical?) as well as socio-utility feasibility (e.g., how will the changes be utilized?). We present a framework for how healthcare systems can integrate modeling and simulation techniques from systems engineering into a change management process. Modeling and simulation are particularly useful for investigating the amount of uncertainty about potential outcomes, guiding decision-making that considers different scenarios, and validating theories to determine if they accurately reflect real-life processes. The results of these simulations can be integrated into critical change management recommendations related to developing readiness for change and addressing resistance to change. As part of our integration, we present a case study showcasing how simulation modeling has been used to determine feasibility and potential resistance to change considerations for implementing a mobile radiation oncology unit. Recommendations and implications are discussed.

Impact of Merging Into a Comprehensive Cancer Center on Health Care Teams and Subsequent Team-Member and Patient Experiences.

PURPOSE: Comprehensive health care centers are increasingly popular as they offer inclusive health care services under one roof. Often, these centers are formed by merging previously separate clinics. However, there is a lack of systematic guidance on the interprofessional, and interteam and intrateam dynamics that may develop during such an organizational change process. Using team process literature, we identify a possible model to explain how merging into a comprehensive cancer center (CCC) might influence health care teams and their subsequent outcomes, including patient experience. METHODS: We used a mixed-method research design. Qualitative data were collected via semistructured interviews from 20 health care professionals employed at a recently merged CCC. During the time frame the interviews were collected, quantitative data were collected from 50 patients receiving treatment at the cancer center through anonymous paper-pencil surveys. Qualitative interviews were analyzed using thematic analysis, on the basis of the input-process-output team dynamics framework. Descriptive statistics were calculated for patient experience data. Trends between data collections were identified. RESULTS: On the basis of our qualitative analysis, we provide an input-process-output framework that documents positive and negative aspects of interteam and intrateam dynamics associated with the merger process. Additionally, a number of connections were found between health care professional perceptions and quality patient experiences (eg, merger impacts on interteam and patient communication). CONCLUSION: Our findings and model may assist in future merging efforts. Future CCCs may use the proposed framework to better understand and visualize their postmerger progress, in particular from the aspects of interprofessional, and interteam and intrateam dynamics.

Techno-Economic Feasibility Analysis of a Fully Mobile Radiation Oncology System Using Monte Carlo Simulation.

PURPOSE: Disparities in radiation oncology (RO) can be attributed to geographic location, socioeconomic status, race, sex, and other societal factors. One potential solution is to implement a fully mobile (FM) RO system to bring radiotherapy to rural areas and reduce barriers to access. We use Monte Carlo simulation to quantify techno-economic feasibility with uncertainty, using two rural Missouri scenarios. METHODS: Recently, a semimobile RO system has been developed by building an o-ring linear accelerator (linac) into a mobile coach that is used for temporary care, months at a time. Transitioning to a more FM-RO system, which changes location within a given day, presents technical challenges including logistics and quality assurance. This simulation includes cancer census in both northern and southeastern Missouri, multiple treatment locations within a given day, and associated expenditures and revenues. A subset of patients with lung, breast, and rectal diseases, treated with five fractions, was simulated in the FM-RO system. RESULTS: The FM-RO can perform all necessary quality assurance tests as suggested in national medical physics guidelines within 1.5 hours, thus demonstrating technological feasibility. In northern and southeastern Missouri, five-fraction simulations' net incomes were, in US dollars (USD), $1.55 ± 0.17 million (approximately 74 patients/year) and $3.65 USD ± 0.25 million (approximately 98 patients/year), respectively. The number of patients seen had the highest correlation with net income as well as the ability to break-even within the simulation. The model does not account for disruptions in care or other commonly used treatment paradigms, which may lead to differences in estimated economic return. Overall, the mobile system achieved a net benefit, even for the most negative simulation scenarios. CONCLUSION: Our simulations suggest technologic success and economic viability for a FM-RO system within rural Missouri and present an interesting solution to address other geographic disparities in access to radiotherapy.