Healthcare Tasks Research Articles

FAMILY CAREGIVER FACTORS AND HOSPITALIZATION IN DISABLED OLDER ADULTS WITH AND WITHOUT DEMENTIA

Older adults with disabilities commonly rely on family caregivers’ help, yet effects of caregiver factors on patient outcomes are poorly understood. Within this population, dementia is common. Our objective was to evaluate the association between caregiver factors and risk of hospitalization in disabled older adults with and without dementia. We examined 2,589 community-living older adults with mobility/self-care disability and their primary family caregiver in four waves of the National Long-Term Care Survey and National Health and Aging Trends Study. We used Cox proportional hazards models to examine risk of one-year, Medicare claims-derived, all-cause hospitalization as a function of caregiver factors, adjusting for older adult characteristics (sociodemographics, comorbidities, healthcare utilization) and survey year, considering dementia a characteristic of interest. Among disabled older adults, 38% were hospitalized over one year, and 31% had probable dementia. Hospitalization rates were similar for older adults with and without dementia (39.5% and 37.3% respectively); dementia was not associated with hospitalization risk (HR 1.09, 95% CI 0.95-1.26). Older adults demonstrated greater risk of hospitalization if their caregiver was male (HR 1.31, 95% CI 1.10-1.56), new to caregiving (HR 1.61, 95% CI 1.27-2.04 for < 1 year versus ≥ 4 years), or helped with healthcare tasks (HR 1.21, 95% CI 1.04-1.41). The association between most caregiving factors and hospitalization risk did not differ by dementia status. Results suggest that strategies to reduce hospitalization in older adults with disabilities could target select caregivers using similar strategies in populations with and without dementia.

Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma

BackgroundPredictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. Recently, deep learning models have achieved state-of-the-art performance for many healthcare prediction tasks. However, deep models lack interpretability, which is integral to successful decision-making and can lead to better patient care. In this paper, we build upon the contextual decomposition (CD) method, an algorithm for producing importance scores from long short-term memory networks (LSTMs). We extend the method to bidirectional LSTMs (BiLSTMs) and use it in the context of predicting future clinical outcomes using patients’ EHR historical visits.MethodsWe use a real EHR dataset comprising 11071 patients, to evaluate and compare CD interpretations from LSTM and BiLSTM models. First, we train LSTM and BiLSTM models for the task of predicting which pre-school children with respiratory system-related complications will have asthma at school-age. After that, we conduct quantitative and qualitative analysis to evaluate the CD interpretations produced by the contextual decomposition of the trained models. In addition, we develop an interactive visualization to demonstrate the utility of CD scores in explaining predicted outcomes.ResultsOur experimental evaluation demonstrate that whenever a clear visit-level pattern exists, the models learn that pattern and the contextual decomposition can appropriately attribute the prediction to the correct pattern. In addition, the results confirm that the CD scores agree to a large extent with the importance scores generated using logistic regression coefficients. Our main insight was that rather than interpreting the attribution of individual visits to the predicted outcome, we could instead attribute a model’s prediction to a group of visits.ConclusionWe presented a quantitative and qualitative evidence that CD interpretations can explain patient-specific predictions using CD attributions of individual visits or a group of visits.

Adverse drug event detection and extraction from open data: A deep learning approach

Drug prescription is a task that doctors face daily with each patient. However, when prescribing drugs, doctors must be conscious of all potential drug side effects. In fact, according to the U.S. Department of Health and Human Services, adverse drug events (ADEs), or harmful side effects, account for 1/3 of total hospital admissions each year. The goal of this research is to utilize novel deep learning methods for accurate detection and identification of professionally unreported drug side effects using widely available public data (open data). Utilizing a manually-labelled dataset of 10,000 reviews gathered from WebMD and Drugs.com, this research proposes a deep learning-based approach utilizing Bidirectional Encoder Representations from Transformers (BERT) based models for ADE detection and extraction and compares results to standard deep learning models and current state-of-the-art extraction models. By utilizing a hybrid of transfer learning from pre-trained BERT representations and sentence embeddings, the proposed model achieves an AUC score of 0.94 for ADE detection and an F1 score of 0.97 for ADE extraction. Previous state of the art deep learning approach achieves an AUC of 0.85 in ADE detection and an F1 of 0.82 in ADE extraction on our dataset of review texts. The results show that a BERT-based model achieves new state-of-the-art results on both the ADE detection and extraction task. This approach can be applied to multiple healthcare and information extraction tasks and used to help solve the problem that doctors face when prescribing drugs. Overall, this research introduces a novel dataset utilizing social media health forum data and shows the viability and capability of using deep learning techniques in ADE detection and extraction as well as information extraction as a whole. The model proposed in this paper achieves state-of-the-art results and can be applied to multiple other healthcare and information extraction tasks including medical entity extraction and entity recognition.

Family Nursing as an Improvement Strategy of Family Health Index in Indonesia: A Literature Review

The occurrence of health issues in the family affected by the ability of the family itself to carry out the role and task of family health care. Family nursing has been developed in various countries as a solution to solve individual, family, and community health issues in a region. The family health status in Indonesia is measured by the Family Health Index (FHI) value, comprising the category of unhealthy families (FHI &lt; 0.50), pre-healthy families (FHI value of 0.50 to 0.80), and healthy families (FHI &gt; 0.8). This study aims to generate a literature review on strategies that can be used to improve the Family Health Index. The search database includes CINAHL, MEDLINE, and Google Scholar with several keywords: 'family nursing', 'family health', and 'family health status'. 102 articles published between the year of 2000 until 2019 have been obtained. The results of the literature review indicate that family nursing is an innovative and efficient strategy to achieve a preferable health status as well as a better quality of human life. Family nursing has a positive correlation with the increased ability of families to carry out their task in maintaining, preventing and overcoming family health issues independently. Research on the families' ability to carry out health care tasks to improve the health status by measuring FHI has never been done previously. Future research is required to discuss the relationship of families' ability in carrying out the five health care tasks to improve FHI independently in Indonesia.

Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics.

Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks. As the deployment of artificial intelligence technologies becomes ubiquitous, it is unsurprising that adversaries have begun developing methods to manipulate machine learning models to their advantage. While the visual analytics community has developed methods for opening the black box of machine learning models, little work has focused on helping the user understand their model vulnerabilities in the context of adversarial attacks. In this paper, we present a visual analytics framework for explaining and exploring model vulnerabilities to adversarial attacks. Our framework employs a multi-faceted visualization scheme designed to support the analysis of data poisoning attacks from the perspective of models, data instances, features, and local structures. We demonstrate our framework through two case studies on binary classifiers and illustrate model vulnerabilities with respect to varying attack strategies.

Hearing care across the life course provided in the community.

Untreated hearing loss is recognized as a growing global health priority because of its prevalence and harmful effects on health and well-being. Until recently, little progress had been made in expanding hearing care beyond traditional clinic-based models to incorporate public health approaches that increase accessibility to and affordability of hearing care. As demonstrated in numerous countries and for many health conditions, sharing health-care tasks with community health workers (CHWs) offers advantages as a complementary approach to expand health-service delivery and improve public health. This paper explores the possibilities of task shifting to provide hearing care across the life course by reviewing several ongoing projects in a variety of settings – Bangladesh, India, South Africa and the United States of America. The selected programmes train CHWs to provide a range of hearing-care services, from childhood hearing screening to management of age-related hearing loss. We discuss lessons learnt from these examples to inform best practices for task shifting within community-delivered hearing care. Preliminary evidence supports the feasibility, acceptability and effectiveness of hearing care delivered by CHWs in these varied settings. To make further progress, community-delivered hearing care must build on established models of CHWs and ensure adequate training and supervision, delineation of the scope of practice, supportive local and national legislation, incorporation of appropriate technology and analysis of programme costs and cost–effectiveness. In view of the growing evidence, community-delivered hearing care may now be a way forward to improve hearing health equity.

Peranan Dukungan Keluarga Penderita Hipertensi Dalam Penggunaan Aplikasi Kesehatan

Hypertension is often found in primary health care facilities and if not followedup can develop into a more serious disease such as myocardial infarction, stroke,kidney failure and can cause death. Health services for non-communicablediseases such as hypertension depend not only on diagnoses and medicalinterventions but also require environmental support and collaboration betweenhealth workers and patients and their families. The effort to fulfill family healthcare tasks in the current era of modernization is one of them by utilizinginformation and communication technology which is the media to bridgecommunication between health workers and the community. The purpose of thisstudy was to determine the family support of hypertensive patients. The numberof samples in this study was 100 families of hypertensive patients. Samplingusing purposive sampling technique. This research was conducted in theSukoharjo Community Health Center area. Analysis of this study using univariateand frequency distribution. The results showed that the average family supportfor hypertensive patients was emotional support of 14.62, award support 20.03,instrumental support 25.67 and informational support 14.57. Based on the results of the study, it can be concluded that the majority of family support forhypertensive patients is in a fairly good category so it needs to be increased again to carry out family empowerment activities. The results of this study can be used as a reference in determining appropriate interventions in increasing family support for hypertensive patients.

Mapping the territory of renal care: a formative analysis of the cognitive work involved in managing acute kidney injury

The design and evaluation of healthcare work systems requires an understanding of the cognitive work involved in healthcare tasks. Previous studies suggest that a formative approach would be particularly useful to examine healthcare activities for this purpose. In the present study, methods from cognitive work analysis and cognitive task analysis are combined in a formative examination of managing acute kidney injury, an activity that occurs across primary and secondary healthcare settings. The analyses are informed by interviews with healthcare practitioners and a review of practice guidelines. The findings highlight ways in which the task setting influenced practitioners’ activity, and ways in which practitioners approached the activity (for example, how they used data to make decisions). The approach taken provided a rich understanding of the cognitive work involved, as well as generating suggestions for the design of work systems to support the clinical task.Practitioner summary: Healthcare tasks often require decision-making in complex and dynamic circumstances, potentially involving collaboration across different practitioner roles and locations. We demonstrate the use of a formative analysis to understand the cognitive work in managing a clinical syndrome across primary and secondary care settings, and consider the implications for work design.

Home Health Care For Children With Medical Complexity: Workforce Gaps, Policy, And Future Directions.

With the medical and surgical advances of recent decades, a growing proportion of children rely on home-based care for daily health monitoring and care tasks. However, a dearth of available home health care providers with pediatric training to serve children and youth with medical complexity markedly limits the current capacity of home health care to meet the needs of patients and their families. In this article we analyze the workforce gaps, payment models, and policy challenges unique to home health care for children and youth with medical complexity, including legal challenges brought by families because of home nursing shortages. We propose a portfolio of solutions to address the current failures, including payment reform, improved coordination of services and pediatric home health training through partnerships with child-focused health systems, telehealth-enabled opportunities to bridge current workforce gaps, and the better alignment of pediatric care with the needs of adult-focused long-term services and supports.

Community-Focused Health Care For The Seriously Ill.

Experts offer a prescription for a health system that is centered on the community rather than traditional health care institutions.

Abstract OT2-10-01: Treatment burden and capacity to manage care among patients with breast cancer

Abstract Patients with breast cancer spend significant time1, effort, and financial resources2 to combat the disease for years after their diagnosis. The large volume of healthcare tasks can cause patients to become overburdened, leading to reduced adherence with care plans and worse outcomes3. On the other hand, certain patient characteristics such as physical resilience, financial well-being, and supportive family environments increase patients' capacity to manage care4. Assessing treatment burden and capacity when prescribing care has been applied to populations such as diabetes patients5. We are investigating this paradigm in treatment of patients with breast cancer. The goal of this preliminary study is to identify significant factors that contribute to treatment burden, capacity to manage care, and outcomes of overburden for patients with breast cancer. Examples of treatment burden, capacity to manage care, and outcomes of overburden in patients with breast cancerTreatment burdenCapacity to manage careOutcomes of overburdenTraveling long distances for careAccess to reliable transportationReduced spending on food, utilities, or other necessitiesPaying for child care during chemotherapyFlexibility in informal caregivers' schedulesMissed appointment with medical oncologistRemembering to take medications with mealsMedical understanding or knowledgeWorse than expected side effectsReporting adverse eventsProficiency with mobile deviceTrip to emergency room Through literature review, interviews with survivors, and expert panels of navigators and providers, we will develop a survey instrument given to patients at the time of diagnosis. The survey will assess patient capacity and help providers give treatment options based on attributes of the patient. Additionally, we will attempt to correlate survey results with treatment burden measures derived from electronic health record data at a population level1. With treatment personalized for patient capacity, patients should be better able to adhere to care plans leading to improved quality of life during treatment and beyond. Acknowledgements: The authors would like to thank Cheryl Jernigan, our patient advocate mentor, for her guidance in this project. We would also like to thank the Susan G. Komen Foundation for their support of this research.

An organisational participatory research study of the feasibility of the behaviour change wheel to support clinical teams implementing new models of care

BackgroundHealth and social care organisations globally are moving towards prevention-focussed community-based, integrated care. The success of this depends on professionals changing practice behaviours. This study explored the feasibility of applying a behavioural science approach to help staff teams from health organisations overcome psychological barriers to change and implement new models of care.MethodsAn Organisational Participatory Research study was conducted with health organisations from North West England, health psychologists and health workforce education commissioners. The Behaviour Change Wheel (BCW) was applied with teams of professionals seeking help to overcome barriers to practice change. A mixed-methods data collection strategy was planned, including qualitative stakeholder interview and focus groups to explore feasibility factors and quantitative pre-post questionnaires and audits measuring team practice and psychological change barriers. Qualitative data were analysed with thematic analysis; pre-post quantitative data were limited and thus analysed descriptively.ResultsFour clinical teams from paediatrics, midwifery, heart failure and older adult mental health specialties in four organisations enrolled, seeking help to move care to the community, deliver preventative healthcare tasks, or become more integrated. Eighty-one managers, medical doctors, nurses, physiotherapists, midwives and other professionals contributed data. Three teams successfully designed a BCW intervention; two implemented and evaluated this. Five feasibility themes emerged from the thematic analysis of qualitative data. Optimising the BCW in an organisational change context meant 1) qualitative over quantitative data collection, 2) making behavioural science attractive, 3) co-development and a behavioural focus, 4) effective ongoing communication and 5) support from engaged leaders. Pre-post quantitative data collected suggested some positive changes in staff practice behaviours and psychological determinants following the intervention.ConclusionsBehavioural science approaches such as the BCW can be optimised to support teams within health and social care organisations implementing complex new models of care. The efficacy of this approach should now be trialled.

Abstract WP81: Initial Experiences With Artificial Neural Networks in Detection of CT Perfusion Deficits

Background: Head CT with perfusion imaging has become crucial in the selection of patients for treatment for mechanical thrombectomy. In recent years machine learning has rapidly evolved and found applications in a wide variety of health care tasks. Objective: We report our initial experiences with training a neural network pipeline to predict the presence and sidedness of a perfusion deficit in patients with acute ischemic stroke, based on imaging alone. Methods: CT perfusion images of patients with suspicion for acute ischemic stroke were retrospectively obtained. Each study was preprocessed into 3 dimensional arrays of Hounsfeld Units stacked through time, segmented to exclude bone, and then normalized. Labels were derived from the attending neuroradiologists’ read. The data was split into training and validation sets. A long term, recurrent convolutional (LRCN) network was constructed consisting of a convolutional neural network stacked on top of a long short term (LSTM) layer. Results: 139 (35.1%) patients had a right sided perfusion deficit, while 199 (50.3%) had a left sided deficit, and 58 (14.6%) had no evidence of a deficit. The best model was able to achieve an accuracy of 85.8% on validation data. Receiver Operating Characteristic (ROC) curves were generated for each class and an Area Under the Curve (AUC) was calculated for each class. For a right sided deficit, the AUC was 0.90, for left sided deficit 0.96, and for no deficit the AUC was 0.93. Conclusion: The field of machine learning, powered by convolutional neural networks for the task of image recognition and processing, has quickly developed in recent years. We have constructed an artificial neural network that can identify and classify the presence and sidedness of a perfusion deficit CT perfusion imaging.

How is professionals’ information seeking shaped by workplace procedures? A study of healthcare clinicians

Professional work is often regulated by procedures that shape the information seeking involved in performing a task. Yet, research on professionals’ information seeking tends to bypass procedures and depict information seeking as an informal activity. In this study we analyze two healthcare tasks governed by procedures: triage and timeouts. While information seeking is central to both procedures, we find that the coordinating nurses rarely engage in information seeking when they triage patients. Inversely, the physicians value convening for timeouts to seek information. To explain these findings we distinguish between junior and expert professionals and between uncertain and equivocal tasks. The triage procedure specifies which information to retrieve but expert professionals such as the coordinating nurses tend to perform triage, which is an uncertain task, by holistic pattern recognition rather than information seeking. For timeouts, which target an equivocal task, the procedure facilitates information seeking by creating a space for open-ended collaborative reflection. Both junior and expert physicians temporarily suspend patient treatment in favor of this opportunity to reflect on their actions, though partly for different reasons. We discuss implications for models of professionals’ information seeking.

Family Caregiver Factors Associated With Emergency Department Utilization Among Community-Living Older Adults With Disabilities.

Background: Older adults with disability are frequent users of the emergency department (ED) and often rely on family caregiver support. We identify whether and which caregiver characteristics are associated with older adults’ ED use. Methods: We use Cox proportional hazards regression to model the likelihood of all-cause ED use (defined as 1 or more visits within 12 months of survey) as a function of caregiver characteristics after adjusting for older adult sociodemographic and health characteristics. We draw from linked older adult and caregiver surveys and administrative claims, creating a sample of 2521 community-living older adults with mobility/self-care disability receiving care from a family or unpaid caregiver. Results: About half (52.5%) of older adults receiving mobility or self-care help incurred 1 or more ED visits within 12 months of interview. Adjusting for year of data collection, sociodemographic characteristics, and health status, these older adults were at greater risk of all-cause ED use if their primary caregiver provided greater than 40 hours of care per week (hazard ratio [HR] 1.22, 95% CI 1.04-1.43; P = .02), helped with health care tasks (HR 1.26; 95% CI 1.08-1.46; P < .01), or experienced physical strain (HR 1.18; 95% CI 1.03-1.36; P = .02). Conclusion: Caregiver strain, helping with health care tasks, and greater hours of help per week are associated with heightened risk of ED use among older adults receiving mobility or self-care help. Study findings suggest the potential benefit of caregiver assessment and support.

Scheduling IoT Healthcare Tasks in Fog Computing Based on their Importance

The Internet-of-Things (IoT) is the future of the Internet and one of the most important technologies used in recent times in all areas, where everything will be connected to the Internet. One of them is using IoT in healthcare area such as mobile health and remote patient monitoring with various diseases such as heart disease, blood pressure, diabetes, and other chronic diseases. The real-time for remote health monitoring applications is very important; any delay caused by transferring data to the cloud and back to the application is unacceptable. So, we propose to use fog computing between sensors and cloud computing to collect and process data in an efficient manner, reduces the amount of data that is transported between the cloud and the sensors, and increase the entire system efficiency. Wireless sensors networks (WSNs) that used in the area of health monitoring send a huge number of tasks with different importance levels and lengths simultaneously to fog computing. Therefore, we need to implement an appropriate task scheduling algorithm with an ability to give priority for tasks accurately and make the main factor in giving priority to tasks is their importance regardless of their length. In this research, we attempt to improve static task scheduling algorithms performance by using a new method called Tasks Classification and Virtual Machines Categorization (TCVC) based on tasks importance. Tasks that received by IoT classified based on their importance into three classes: high importance tasks, medium importance tasks, and low importance tasks based on the patient’s health status. In order to measure the performance achieved by these methods, they will be applied on MAX-MIN scheduling algorithm. The cloudsim simulator has been used to measure their impact on algorithm complexity, resource availability, Total Execution Time (TET), Total Waiting Time (TWT), and Total Finish Time (TFT).

Initial Experiences with Artificial Neural Networks in the Detection of Computed Tomography Perfusion Deficits

Head computed tomography (CT) with perfusion imaging has become crucial in the selection of patients for mechanical thrombectomy. In recent years, machine learning has rapidly evolved and found applications in a wide variety of health care tasks. We report our initial experiences with training a neural network to predict the presence and laterality of a perfusion deficit in patients with acute ischemic stroke. CT perfusion images of patients with suspicion for acute ischemic stroke were obtained. The data were split into training and validation sets. A long-term, recurrent convolutional network was constructed, which consisted of a convolutional neural network stacked on top of a long short-term memory layer. Of the 396 patients, 139 (35.1%) had a right-sided perfusion deficit, 199 (50.3%) had a left-sided deficit, and 58 (14.6%) had no evidence of a deficit. The best model was able to achieve an accuracy of 85.8% on validation data. Receiver operating characteristic curves were generated for each class, and an area under the curve (AUC) was calculated for each class. For right-sided deficits, the AUC was 0.90, for left-sided deficits, the AUC was 0.96, and for no deficit, the AUC was 0.93. The field of machine learning, powered by convolutional neural networks for the task of image recognition and processing, has quickly developed in recent years. We constructed an artificial neural network that can identify and classify the presence and laterality of a perfusion deficit on CT perfusion imaging.

Occupation and task as risk factors for asthma-related outcomes among healthcare workers in New York City.

Previous studies have suggested an association of asthma onset and exacerbation with cleaning and disinfecting activities in a number of industries, including healthcare. The objective of the current study was to investigate the association of asthma and related outcomes with occupations and tasks in urban healthcare workers in the United States. A questionnaire was implemented in a sample of workers from nine healthcare occupations in New York City. We used regression models to examine the association of post-hire asthma, current asthma, exacerbation of asthma, a symptom algorithm for bronchial hyper-responsiveness (BHR-related symptoms), a symptom-based asthma score, and the symptom wheeze with occupation and four healthcare tasks, while adjusting for other risk factors and potential confounders. A total of 2030 participants completed the questionnaire. The task of cleaning fixed surfaces was significantly associated with most outcome variables, including current asthma (odds ratio (OR) = 1.84, 95% confidence interval (CI) 1.26-2.68), moderate exacerbation (OR = 3.10, 95% CI 1.25-7.67), and BHR-related symptoms (OR = 1.38, 95% CI 1.08-1.77). In comparison to nursing assistants, the occupations environmental service workers and registered nurses were at higher risk for current asthma, and licensed practical nurses were at higher risk for moderate exacerbation. Other tasks associated with outcomes were administering aerosolized medications with current asthma and moderate exacerbation, and sterilizing medical equipment with BHR-related symptoms. These findings add to the growing body of evidence for the association of asthma with cleaning and other activities in healthcare. Further research is especially needed to investigate the association of asthma-related outcomes with exposure metrics based on tasks, products, and chemical exposures in healthcare.

Interventions commonly used to prevent work-related musculoskeletal disorders among healthcare workers

Purpose The purpose of this paper is to review the prevalence and risk factors of work-related musculoskeletal disorders (WMSDs) among healthcare workers (HCWs) in order to ascertain the occupation with the highest susceptibility to WMSD in the health sector. This paper will also review the effective interventions which have been used to prevent WMSDs among HCWs. Design/methodology/approach This study is a literature review of 11 papers related to the prevalence and risk factors of WMSDs and 12 papers about the interventions being used to prevent WMSDs among HCWs. The papers were retrieved from respectable databases such as PubMed, Science Direct, Google Scholar and E-Thesis. Findings Nurses belong to the major group of HCWs who had the highest prevalence of WMSDs compared with other health professionals and other hospital workers. Although there are several interventions being commonly used to prevent WMSD risk factors, some interventions were unsuccessful in the prevention of WMSDs in healthcare tasks. Therefore, it is necessary that future research focuses on the tasks of HCWs that are WMSD risk factors and tries to innovate or redesign ergonomic workstations to prevent those risk factors. Originality/value The expected benefit of this study is to motivate ergonomists to provide appropriate and innovative interventions to ensure health and safety for nurses and other HCWs.

Nurses' perceptions of the root causes of community-acquired pressure ulcers: Application of the Model for Examining Safety and Quality Concerns in Home Healthcare.

To explore how the context of care influences the development of community-acquired pressure ulcers from the perspective of nurses working in home healthcare settings, to identify and categorise the factors perceived as contributing to the development of these ulcers using the Model for Examining Safety and Quality Concerns in Home Healthcare, and to explore how these risks are managed in practice. Pressure ulcer reduction is a priority in both hospital and community settings. Evidence suggests the factors affecting safety and performance in community settings are not the same as in hospital. However, research pertaining to pressure ulcer risk management has predominantly been undertaken in hospital settings. The study was framed by a qualitative exploratory design. Semistructured interviews were conducted with a purposive sample of 19 registered nurses recruited from an independent regional tissue viability network and five community nursing provider organisations in London. The experiences and perceptions of participants mapped onto the components of the Model for Examining Safety and Quality Concerns in Home HealthCare: patient characteristics, provider characteristics, nature of home healthcare tasks, social and community environment, medical devices and new technology, physical environment, and external environment. Four strategies to address identified risks were established: behavioural interventions, technical interventions, safeguarding interventions and initiatives to promote better integration between health, local authorities and families. Understanding the complex interplay between people and other elements of the healthcare system is critical to the prevention, management and investigation of pressure ulcers. This study has illuminated these elements from the perspective of nurses working in community settings. Further consideration should be given to the importance of place when both developing risk management strategies for pressure ulcer prevention and learning the lessons from failure.