Highlights Thinking through when to let go: theory for identifying interventions that may not add value.Examples of interventions ideal for discontinuation in public health and social service settings.De‐implementation of interventions in the context of dissemination and implementation science.

Introduction Due to issues related to informed research consent, older adults with cognitive impairments are often excluded from high-quality studies that are not directly related to cognitive impairment, which has led to a dearth of evidence for this population. The challenges to including cognitively impaired older adults in research and the implications of their exclusion are a transdisciplinary issue. Discussion The ethical challenges and logistical barriers to conducting research with cognitively impaired older adults are addressed from the perspectives of three different fields – social work, emergency medicine, and orthopaedic surgery. Issues related to funding, study design, intervention components, and outcomes are discussed through the unique experiences of three different providers. Clinical Implications A fourth perspective – medical research ethics – provides alternatives to exclusion when conducting research with cognitively impaired older adults such as timing, corrective feedback and plain language, and capacity assessment and proxy appointments. Given the increasing aging population and the lack of evidence on cognitively impaired older adults, it is critical that researchers, funders, and institutional review boards not be dissuaded from including this population in research studies.

Background: Increasingly, scholars argue that de-implementation is a distinct concept from implementation; factors contributing to stopping a current practice might be distinct from those that encourage adoption of a new one. One such distinction is related to de-implementation outcomes. We offer preliminary analysis and guidance on de-implementation outcomes, including how they may differ from or overlap with implementation outcomes, how they may be conceptualized and measured, and how they could be measured in different settings such as clinical care vs. community programs. Conceptualization of outcomes: We conceptualize each of the outcomes from Proctor and colleagues' taxonomy of implementation outcomes for de-implementation research. First, we suggest key considerations for researchers assessing de-implementation outcomes, such as considering how the cultural or historical significance to the practice may impact de-implementation success and, as others have stated, the importance of the patient in driving healthcare overuse. Second, we conceptualize de-implementation outcomes, paying attention to a number of factors such as the importance of measuring outcomes not only of the targeted practice but of the deimplementation process as well. Also, the degree to which a practice should be de-implemented must be distinguished, as well as if there are thresholds that certain outcomes must reach before action is taken. We include a number of examples across all outcomes, both from clinical and community settings, to demonstrate the importance of these considerations. We also discuss how the concepts of health disparities, cultural or community relevance, and altruism impact the assessment of de-implementation outcomes. Conclusion: We conceptualized existing implementation outcomes within the context of de-implementation, noted where there are similarities and differences to implementation research, and recommended a clear distinction between the target for de-implementation and the strategies used to promote de-implementation. This critical analysis can serve as a building block for others working to understand de-implementation processes and de-implement practices in real-world settings.

Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

The rapid spread of COVID-19 across the United States demands a similarly rapid scientific response to mitigate the impact. While the initial scientific discourse about the virus appropriately focused on virology, clinical features, and therapeutics, there is now equal, if not greater, attention on the public health practices that are immediately needed to slow the spread as the response shifts from "containment" to "mitigation." 1 To mitigate the spread of the virus, numerous states have enacted policies that restrict their residents statewide, such as closing all schools, limiting gatherings over a certain amount, or issuing "shelter in place/stay at home" orders for all nonessential activities. These "social or physical distancing" practices are seen by many public health experts as the most effective tools currently available to slow the transmission of the virus. At the time of writing, the majority of COVID-19 cases in the United States are located in urban areas, with relatively few cases found in rural areas. This is, of course, not due to an inherent immunity found in rural areas, but rather due to characteristics of urban areas such as high population density and the frequency of travel in and out of urban areas. This stark contrast in COVID-19 prevalence between urban and rural areas is potentially problematic in the context of public health prevention measures that are implemented statewide, across both highly affected urban areas and largely unaffected rural areas. There is the potential for residents of rural areas to, understandably, not perceive themselves at high risk of COVID-19 and thus not heed the warnings and follow the recommended prevention practices.

Objective To understand the frequency of social determinants of health (SDOH) diagnosis codes (Z‐codes) within the electronic health record (EHR) for patients with prediabetes and diabetes and examine factors influencing the adoption of SDOH documentation in clinical care. Data Sources EHR data and qualitative interviews with health care providers and stakeholders. Study Design An explanatory sequential mixed methods design first examined the use of Z‐codes within the EHR and qualitatively examined barriers to documenting SDOH. Data were integrated and interpreted using a joint display. This research was informed by the Framework for Dissemination and Utilization of Research for Health Care Policy and Practice. Data Collection/Extraction Methods We queried EHR data for patients with a hemoglobin A1c > 5.7 between October 1, 2015 and September 1, 2020 (n = 118,215) to examine the use of Z‐codes and demographics and outcomes for patients with and without social needs. Semi‐structured interviews were conducted with 23 participants (n = 15 health care providers; n = 7 billing and compliance stakeholders). The interview questions sought to understand how factors at the innovation‐, individual‐, organizational‐, and environmental‐level influence SDOH documentation. We used thematic analysis to analyze interview data. Principal Findings Patients with social needs were disproportionately older, female, Black, uninsured, living in low‐income and high unemployment neighborhoods, and had a higher number of hospitalizations, obesity, prediabetes, and type 2 diabetes than those without a Z‐code. Z‐codes were not frequently used in the EHR (<1% of patients), and there was an overall lack of congruence between quantitative and qualitative results related to the prevalence of social needs. Providers faced barriers at multiple levels (e.g., individual‐level: discomfort discussing social needs; organizational‐level: limited time, competing priorities) for documenting SDOH and identified strategies to improve documentation. Conclusions Providers recognized the impact of SDOH on patient health and had positive perceptions of screening for and documenting social needs. Implementation strategies are needed to improve systematic documentation.

Improving the hospital discharge process to prevent readmission requires a focus on the coordination and communication between interprofessional team members in and outside of the hospital as well as with patients and their caregivers. Yet little is known about how these actors currently communicate and coordinate during the discharge process. Network analysis allows for a direct look at this communication and coordination. This network analysis study utilized retrospective chart review to identify the individuals involved in the discharge planning and their communication with each other for 205 patients. Using this abstracted data, a network was created for each patient wherein a node was any individual involved in the patient's discharge planning process and a tie was any communication documented in the chart related to discharge planning between individuals. Graphical and structural network analyses were used to compare the networks of readmitted patients and non-readmitted patients. Networks of patients not readmitted were more hierarchical, unidirectional, streamlined compared to those readmitted. These findings demonstrate the feasibility and usefulness of conceptualizing discharge planning as a network. Future efforts to understand discharge planning and create interventions to improve the process may benefit by considering network patterns of communication.

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