Background: Disruptive behavior can harm high-quality care and is prevalent in many Western public health systems despite increasing spotlight on it. Comparatively less knowledge about it is available in Asia, a region commonly associated with high-power distance, which may limit its effectiveness in addressing disruptive behavior. Purpose: The aim of this study was to develop a comprehensive framework for tackling disruptive behavior among health care professionals in a public health system. Methodology: A nationwide cross-sectional study relying on the Nurse–Physician Relationship Survey was conducted in Singapore. Four hundred eighty-six public health care professionals responded. Results: Two hundred ninety-eight doctors (95.5%) and 163 nurses (93.7%) had witnessed a form of disruptive behavior. Doctors observed disruptive behavior committed by other doctors and nurses much more frequently than did nurses. Doctors made stronger associations between disruptive behavior and negative employee outcomes and between disruptive behavior and negative patient outcomes. Qualitative analyses of participants’ open-ended answers produced a multipronged three-dimensional approach for tackling disruptive behavior: (a) deterrent measures, (b) development of knowledge and skills, and (c) demonstration of organizational commitment through proper norms, empathizing with staff, and structural reforms. Practice Implications: Disruptive behavior is a multifaceted problem requiring a multipronged approach. Our three-dimensional framework is a comprehensive approach for giving health care professionals the capability, opportunity, and motivation to address disruptive behavior effectively.

Background: Hypertension affects 31% of the global adult population. Artificial intelligence–based chatbots may support self-management of hypertension and other chronic disorders. Chronic Disease Management Program (CHAMP) is a digital health intervention designed to support chronic disease self-management, comprising a patient-facing chatbot and an artificial intelligence–augmented clinical decision support system linked to electronic medical records. Objective: This project aims to optimize the deployment of CHAMP across primary care centers by developing implementation strategies and pilot-testing their appropriateness and effectiveness. Methods: We report 3 interlinked studies. Study 1 is a rapid evidence review to evaluate the factors influencing the implementation of mobile health and chatbot interventions in health care settings and the strategies and processes involved. We will follow the Cochrane Rapid Reviews Methods Group methodology and use the Consolidated Framework for Implementation Research and the unified theory of acceptance and use of technology frameworks to analyze the data. Study 2 is a formative, mixed methods study to inform the state of CHAMP deployment to date, the organizational structure of primary care centers, and the barriers and facilitators influencing the implementation and scale-up of CHAMP in primary care centers. We will interview members of the CHAMP development and initial implementation team, health care providers (HCPs), primary care centers’ leadership, and patient users and nonusers of CHAMP; conduct 1-day on-site visits to primary care centers; and assess the readiness to change among HCPs using validated questionnaires. Study 3 involves the development of implementation strategies for the implementation of CHAMP in primary care centers. We will develop a process map informed by the findings of studies 1 and 2 to outline the patient journey; map the barriers and facilitators influencing the implementation of CHAMP; and develop a set of implementation strategies to effectively implement CHAMP using the Expert Recommendations for Implementing Change taxonomy to define the implementation strategies and the action, actor, context, target, and time framework to define the strategy implementation processes. In parallel, we will use evidence-based behavioral science to test variations of the chatbot’s messages to increase patient engagement. Results: To date, the evidence review protocol (study 1) has been registered on PROSPERO. We have completed title and abstract screening and are in the full-text screening phase. For study 2, we obtained ethics approval and conducted the semistructured interviews with HCPs, primary care centers’ leadership, and members of the CHAMP development and initial implementation team. We are awaiting institutional review board approval to start the interviews with patients. Conclusions: The study results will inform the further implementation and scale-up of CHAMP across primary care centers in Singapore. Successful implementation of digital health interventions to support self-management of chronic disorders may improve health care delivery without further straining health care systems. Trial Registration: PROSPERO CRD42024613653; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024613653 International Registered Report Identifier (IRRID): DERR1-10.2196/72942

Decision making in elderly care is complex and multifaceted. No one can accurately predict whether they will have care needs or what these may be later in life. But if they do find they need it, most will learn that, unlike health care, they have to manage many of these needs themselves in often challenging circumstances.The benefits of enhancing autonomy and choice in elderly care must be weighed against the risks of consumers making unsuitable choices without support, often in stressful circumstances. How can we best implement a consumer-oriented or person-centered social care system without compromising the needs and health of the population? The answer lies largely in how people plan and make good decisions about elderly care, as well as how others (e.g., policymakers and providers) can help them in these.In line with other publications in this area (e.g., the Behavioural Insights Team), we regard good decisions as generally those:• Which are made under relatively little time pressure or emotional stress;• Which use accurate information about elderly care services; and• Which incorporate consumers’ needs and preferences.Evidence suggests that decisions can often be taken in response to unplanned crises, where individual abilities may matter more in effective decision making (e.g., emotion regulation; stress management). However, these alone are unlikely to produce good decisions if consumers fail to possess accurate knowledge and understanding over what it is they want from their care and what their available options are.Thus, the aim of this report is to use predominantly academic literature to highlight key cognitive, emotional and situational variables that influence decision making and planning in the context of elderly care. However, throughout our review, we found a consistent lack of academic literature related to these areas. Where this was the case, we usually supplemented our findings with publicly available publications, from both governmental and non-governmental agencies (e.g., charities). In addition, we emphasised the importance of planning in our report as early engagement is likely to bring more informed and person-centered decisions.Our review identifies three overarching themes, each containing a subset of several factors that may serve as barriers or facilitators of decision making in elderly care:1. Individual factors.2. Task and contextual factors.3. Cognitive and emotional factors.

The last decade has seen numerous policy reforms to emplace person-centered social care. Consequently, the public has been given more information, choice, and autonomy to decide how best they want to be cared for later in life. Despite this, adults generally fail to plan or prepare effectively for their future care needs. Understanding the behavioral antecedents of person-centered decision-making is thus critical for addressing key gaps in the provision of quality social care. To this end, we conducted a literature review of the psychological and health sciences with the aim of identifying the aspects that influence person-centered decision-making in social care. Using an established theoretical framework, we distilled nine behavioral factors―knowledge, competency, health, goal clarity, time discounting, familiarity, cognitive biases, cognitive overload, and emotion―associated with “Capability,” “Opportunity,” “Motivation,” and “Behavior” that explained person-centered decision-making in social care. These factors exist to different degrees and change as a person ages, gradually impacting their ability to obtain the care they want. We discuss the role of carers and the promise of shared decision-making and conclude by advocating a shift from personal autonomy to one that is shared with carers in the delivery of quality social care.