Health Management Approach Research Articles

Validating and Improving Adjusted Clinical Group's Future Hospitalization and High-Cost Prediction Models for Dutch Primary Care.

The rise in health care costs, caused by older and more complex patient populations, requires Population Health Management approaches including risk stratification. With risk stratification, patients are assigned individual risk scores based on medical records. These patient stratifications focus on future high costs and expensive care utilization such as hospitalization, for which different models exist. With this study, the research team validated the accuracy of risk prediction scores for future hospitalization and high health care costs, calculated by the Adjusted Clinical Group (ACG)'s risk stratification models, using Dutch primary health care data registries. In addition, they aimed to adjust the US-based predictive models for Dutch primary care. The statistical validity of the existing models was assessed. In addition, the underlying prediction models were trained on 95,262 patients' data from de Zoetermeer region and externally validated on data of 48,780 patients from Zeist, Nijkerk, and Urk. Information on age, sex, number of general practitioner visits, International Classification of Primary Care coded information on the diagnosis and Anatomical Therapeutic Chemical Classification coded information on the prescribed medications, were incorporated in the model. C-statistics were used to validate the discriminatory ability of the models. Calibrating ability was assessed by visual inspection of calibration plots. Adjustment of the hospitalization model based on Dutch data improved C-statistics from 0.69 to 0.75, whereas adjustment of the high-cost model improved C-statistics from 0.78 to 0.85, indicating good discrimination of the models. The models also showed good calibration. In conclusion, the local adjustments of the ACG prediction models show great potential for use in Dutch primary care.

Embedding a multiple health condition approach across NHS Greater Manchester

Here, we present two real-world examples of how a prioritised, person-centred, multiple health condition approach - also termed a multimorbidity approach - has been applied to long-term condition management in general practice in Greater Manchester. The first example is the implementation of targeted multiple health condition reviews via a population health management approach across general practice in the City of Manchester. The second example is the development of a person-centred risk stratification tool, focused on unmet cardiovascular need, called 'CVNeed'. This tool provides a unique approach to highlighting the highest unmet need and, thus, the largest return on investment to the system from proactive efforts. These examples demonstrate how a person-centred, multiple health condition approach to long-term condition management, enabled by data intelligence to drive prioritisation of clinical need, can help to address longstanding health inequalities and unwarranted variation in health outcomes. This work also highlights the potential for integrated care systems (ICS) to work collaboratively to tackle health inequalities at a system, locality and neighbourhood level, thus making significant strides toward achieving the vision set out in the Greater Manchester Integrated Care Partnership (ICP) Strategy.

Association Between COVID-19 Pandemic and Serious Mental Illness: Systematic Review within Salutogenesis Model for Public Health Management

Background: The outbreak of the COVID-19 pandemic, the constant transformation of the SARS-COV-2 virus form, exposure to substantial psychosocial stress, environmental change, and isolation have led to the inference that the overall population's mental health could be affected, resulting in an increase in cases of psychosis. Objective: We initiated a systematic review to determine the impact of the SARS-COV-2 virus and its long-term effects - in both symptomatic and asymptomatic cases - on people with or without psychosis. We envisioned that this would give us an insight into effective clinical intervention methods for patients with psychosis during and after the pandemic. Method: We selected fifteen papers that met our inclusion criteria, i.e., those that considered participants with or without psychiatric illness and exposed to SARS-COV-2 infection, for this review and were retrieved via Google, Google Scholar, MEDLINE, PubMed, and PsychINFO Database. Key Gap: There is a dearth of research in understanding how COVID-19 affects people with or without a prior personal history of psychosis. Results: The systematic review summary provides insight into the state of knowledge. Insights from the systematic review have also been reviewed from the salutogenesis model's perspective. There is moderate evidence of new-onset psychosis during the COVID-19 pandemic in which some antipsychotics treated the psychotic symptoms of patients while treating for COVID-19. Suggestions and recommendations are made for preventive and promotive public health strategies. Conclusion: The Salutogenesis model and Positive Psychology Interventions (PPI) provide another preventive and promotive public health management approach.

Grey-box Approach for the Prognostic and Health Management of Lithium-Ion Batteries

The Lithium-Ion Batteries (LIB) industry is rapidly growing and is expected to continue expanding exponentially in the next decade. LIBs are already widely used in everyday life, and their demand is expected to increase further, particularly in the automotive sector. The European Union has introduced a new law to ban Internal Combustion Engines from 2035, pushing for the adoption of electric vehicles and increasing the need for more efficient and reliable energy storage solutions such as LIBs. As a result, the establishment of Gigafactories in Europe and the United States is accelerating to meet the growing demand and partially reduce dependencies on China, which is currently the main producer of LIBs.
 To fully realize the potential of LIBs and ensure their safe and sustainable use, it is crucial to optimize their useful life and develop reliable and robust methodologies for estimating their state of health and predicting their remaining useful life. This requires a comprehensive understanding of LIB behavior and the development of effective prognostic and health management approaches that can accurately predict battery degradation, plan for maintenance and replacements, and improve battery performance and lifespan.
 This work, funded by the GREYDIENT project, a European consortium aiming to advance the state of the art in the grey-box approach, combines physical modeling (white box) and machine learning (black box) techniques to demonstrate the grey-box effectiveness in the Prognostic and Health Management. The grey-box approach here proposed consist in a combination of a physical battery model whose degradation parameters are estimated online at every cycle by a Multi-Layer Perceptron Particle Filter (MLP-PF).
 An electrochemical degradation model of a Lithium-Ion battery cell has been derived by use of Modelica. The model simulates the output voltage of the cell, while the degradation over time is simulate through the variation of 3 parameters: qMax (maximum number of Lithium-Ions available), R0 (Internal Resistance) and D (Diffusion Coefficient). To validate the model we resorted to the well-known NASA Battery Dataset, which has also been used to infer the optimal values of the three hidden degradation parameters at every cycle, to obtain their Run-to-Failure history. Then, the physical model is combined the MLP-PF: a MLPArtificial Neural Network is firstly trained on the Run-to-Failure degradation processes of the model parameters, allowing the propagation of the parameters in the future and the corresponding estimation of the battery Remaining Use ful Life (RUL). The MLP is then updated online by a Particle Filter every time a new measurement is available from the Battery Management System (BMS), providing flexibility to this method, needed for the electrochemical nature of the batteries, and allowing the propagation of uncertainties.

Predictive Analytics for Hydropower Fleet Intelligence

A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.

How can public health agencies support the adoption of a population health management approach in PHC? The case of SVN

Abstract Slovenia has a longstanding tradition of providing accessible and comprehensive PHC services. Established in 1927, Slovenian PHC strongly emphasizes addressing health equity and reaching out to vulnerable populations in local communities to identify environmental, social and behaviour-related risk factors and diagnose disease in its early stage of development. Through a cross-sectoral cooperation based on a community based approach and a strong collaboration of public health and primary care services, Slovenia has improved health and reduced health inequality over the last decades. Registered nurses in family medicine teams conduct regular screening and follow-up for people with noncommunicable diseases to reduce family doctors’ growing burden and workload. Each family medicine practice has a list of registered patients (a capitation list), managed in defined groups of healthy individuals; those with risk factors; and those with the most common chronic diseases. Financial incentives are given for meeting target values in screening check-ups. The National Public Health Institute manages and leads the noncommunicable disease prevention and control programme at the national level.

Implementing population health management in England. A GP perspective

Abstract PHC professionals often already have deep understanding of the population that they serve, their needs and the wider determinants of their health. A data-driven population health management approach does not replace this understanding but rather complements it, quantifies it, enables even deeper understanding and, at times, can challenge pre-existing assumptions. PHC professionals’ understanding of their community (in which they often live) naturally facilitates population health management as an effective approach within PHC. There are resultant benefits when clinicians participate in leading this transformational change, even though most of the day-to-day work required is often not done by the clinicians themselves and, in fact, often reduces their workload. This presentation will describe the implementation of a PHM approach in Berkshire West (England) and will provide examples of their practical applications before, during and in the wake of the COVID-19 pandemic.

Using big data and Population Health Management to assess care and costs for patients with severe mental disorders and move toward a value-based payment system

BackgroundMental health (MH) care often exhibits uneven quality and poor coordination of physical and MH needs, especially for patients with severe mental disorders. This study tests a Population Health Management (PHM) approach to identify patients with severe mental disorders using administrative health databases in Italy and evaluate, manage and monitor care pathways and costs. A second objective explores the feasibility of changing the payment system from fee-for-service to a value-based system (e.g., increased care integration, bundled payments) to introduce performance measures and guide improvement in outcomes.MethodsSince diagnosis alone may poorly predict condition severity and needs, we conducted a retrospective observational study on a 9,019-patient cohort assessed in 2018 (30.5% of 29,570 patients with SMDs from three Italian regions) using the Mental Health Clustering Tool (MHCT), developed in the United Kingdom, to stratify patients according to severity and needs, providing a basis for payment for episode of care. Patients were linked (blinded) with retrospective (2014–2017) physical and MH databases to map resource use, care pathways, and assess costs globally and by cluster. Two regions (3,525 patients) provided data for generalized linear model regression to explore determinants of cost variation among clusters and regions.ResultsSubstantial heterogeneity was observed in care organization, resource use and costs across and within 3 Italian regions and 20 clusters. Annual mean costs per patient across regions was €3,925, ranging from €3,101 to €6,501 in the three regions. Some 70% of total costs were for MH services and medications, 37% incurred in dedicated mental health facilities, 33% for MH services and medications noted in physical healthcare databases, and 30% for other conditions. Regression analysis showed comorbidities, resident psychiatric services, and consumption noted in physical health databases have considerable impact on total costs.ConclusionsThe current MH care system in Italy lacks evidence of coordination of physical and mental health and matching services to patient needs, with high variation between regions. Using available assessment tools and administrative data, implementation of an episodic approach to funding MH could account for differences in disease phase and physical health for patients with SMDs and introduce performance measurement to improve outcomes and provide oversight.

Soil Texture Prediction Using Machine Learning Approach for Sustainable Soil Health Management

Soil in the earth acts as a foothold for all crops. Soil texture is the most important soil health indicator being used for the selection of crops, mechanical manipulation, irrigation management, and fertilizer management. The texture of the soil influences the storage and flow of air and water within the soil, as well as root development, the accessibility of plant nutrients, and the activities of different microorganisms. These factors collectively impact the soil's fertility, quality, and soil health. A conventional method of soil texture analysis is cumbersome, time-consuming, and labor-intensive. Machine learning (ML) is a newly emerging technique being used to assess the soil's physical, chemical, and biological properties quickly in real-time. This is an eco-friendly approach since it does not involve any hazardous chemicals. Machine learning can learn complex features and predict nonlinear properties. Convolutional Neural Networks (CNN) employs convolutional layers to automatically learn features from the input data and is widely used in image classification, object detection, and image generation tasks in a short time. Soil texture images are given as input dataset after the completion of image subsetting, data preprocessing, and Image augmentation. This gives a CNN-based soil texture predictive model with a reliable accuracy of 87.50% at a lower cost.

A Novel Model of Multi-level Linkage Health Management in “Hospital-community-home-individual” in the Era of Internet of Things

This paper aims to explore the construction of the Internet of Things (IoT) enabled “Hospital-community-home-individual” multi-level linkage health management model. By establishing a framework for constructing the health management model, a combination of theoretical research and practical exploration is utilized to integrate the resources and advantages of hospitals and communities. Hierarchical and graded health management approaches and measures are investigated. The proposed framework for health management model construction in this study can be effectively applied to model development, demonstrating the feasibility of multi-level linkage health management. It provides a reference for the development of IoT-enabled health management models.

Population health management in Belgium: a call-to-action and case study

BackgroundAlthough there are already success stories, population health management in Belgium is still in its infancy. A health system transformation approach such as population health management may be suited to address the public health issue of atherosclerotic cardiovascular disease, as this is one of the main causes of mortality in Belgium. This article aims to raise awareness about population health management in Belgium by: (a) eliciting barriers and recommendations for its implementation as perceived by local stakeholders; (b) developing a population health management approach to secondary prevention of atherosclerotic cardiovascular disease; and (c) providing a roadmap to introduce population health management in Belgium.MethodsTwo virtual focus group discussions were organized with 11 high-level decision makers in medicine, policy and science between October and December 2021. A semi-structured guide based on a literature review was used to anchor discussions. These qualitative data were studied by means of an inductive thematic analysis.ResultsSeven inter-related barriers and recommendations towards the development of population health management in Belgium were identified. These related to responsibilities of different layers of government, shared responsibility for the health of the population, a learning health system, payment models, data and knowledge infrastructure, collaborative relationships and community involvement. The introduction of a population health management approach to secondary prevention of atherosclerotic cardiovascular disease may act as a proof-of-concept with a view to roll out population health management in Belgium.ConclusionsThere is a need to instill a sense of urgency among all stakeholders to develop a joint population-oriented vision in Belgium. This call-to-action requires the support and active involvement of all Belgian stakeholders, both at the national and regional level.

Factors Associated With Receipt of Diabetes Self-Management Education and Support for Type 2 Diabetes: Potential for a Population Health Management Approach.

Population health management approaches can help target diabetes resources like Diabetes Self-Management Education and Support (DSMES) to individuals at the highest risk of complications and poor outcomes. Little is known about patient characteristics associated with DSMES receipt since widespread uptake of telemedicine for diabetes care in 2020. In this retrospective cohort study, we used electronic medical record (EMR) data to assess patterns of DSMES delivery from May 2020 to May 2022 among adults who used telemedicine for type 2 diabetes (T2D) endocrinology care in a large integrated health system. Multilevel regression models were used to evaluate the association of key patient characteristics with DSMES receipt. Of 3530 patients in the overall cohort, 401 patients (11%) received DSMES. In adjusted multivariable logistic regression, higher baseline HbA1c (odds ratios [OR] 3.10 [95% confidence interval 2.22-4.33] for HbA1c ≥9% vs <7%), insulin regimen complexity (OR 3.53 [2.59-4.80] for multiple daily injections vs no insulin), and number of noninsulin medications (OR 1.17 [1.05-1.30] per 1 additional medication) were significantly associated with receipt of DSMES, whereas rurality and area-level deprivation of patient residence were not. Diabetes Self-Management Education and Support remains underutilized in this cohort of adults using telemedicine to access endocrinology care for T2D. Factors contributing to clinical complexity increased the odds of receiving DSMES. These results support a potential population health management approach using EMR data, which could target DSMES resources to those at higher risk of poor outcomes. This risk-stratified approach may be even more effective now that more people can access DSMES via telemedicine in addition to in-person care.

Food Safety Risk Analysis in East African Community: A Cornerstone for an Effective Regional Risk-Based Food Safety System

Food risk analysis remains a major cornerstone that provides a framework for the risk-based food safety system. This paper aimed at evaluating the effectiveness of the food safety system, and the foundational role and opportunities of the risk analysis concept in the East Africa Community (EAC). The study was done through the explorative review of the literature as the best approach to getting broad information on the concept of risk analysis in global food safety and its impact on food safety systems. Through analysis of the relevant literature and previous studies related to global food safety and food safety systems, the approach helped to draw on challenges and opportunities and to reformulate the potential key role of the risk analysis concept in the EAC food safety system. Challenges were identified through the evaluation of the effectiveness of the EAC food safety system with reference to the US- effective food safety model. The role and opportunities of the risk analysis in the EAC food safety system were evaluated against identified challenges, and its implementation feasibility and assets in the EAC food safety system. As results, the EAC food safety system showed many gaps and challenges, and was ineffective. While the risk analysis concept was found a key factor in attributes of the effective food safety system, it could help the EAC food safety system to become effective, and to respond to the current and future food safety needs of both the African continent and the Globe. With many opportunities and potentials in EAC, the risk analysis concept can be integrated into the EAC food safety system. Therefore, a useful and strategic approach as a model of the integration of the risk analysis concept was designed for EAC. It is the establishment of an EAC- Regional Centre of Expertise in Food Risk Analysis (EAC-CREFRA). In addition, the EAC food safety system needs to anchor One Health and multi-factorial food safety management approaches to food safety.&#x0D;

The introduction of a data-driven population health management approach in the Netherlands since 2019: The Extramural LUMC Academic Network data infrastructure

Improving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional cross-domain partnerships have emerged in several countries, which aim to achieve better population health, quality of care and a reduction in the per capita costs. These cross-domain partnerships aim to have a strong data foundation and are committed to continuous learning in which data plays an essential role. This paper describes our approach towards the development of the regional integrative population-based data infrastructure Extramural LUMC (Leiden University Medical Center) Academic Network (ELAN), in which we linked routinely collected medical, social and public health data at the patient level from the greater The Hague and Leiden area. Furthermore, we discuss the methodological issues of routine care data and the lessons learned about privacy, legislation and reciprocities. The initiative presented in this paper is relevant for international researchers and policy-makers because a unique data infrastructure has been set up that contains data across different domains, providing insights into societal issues and scientific questions that are important for data driven population health management approaches.

A spatial data integration and visualization approach for occupational health and safety risks management: Application to Algerian electricity and gas company

AbstractIn the safety 4.0 era, occupational health and safety (OHS) related data has become one of the most valuable assets for organizations. Risk Management in the OHS field is a data‐driven process. However, geospatial data has not been fully considered and few works have used visualization for decision‐making purposes. Our research was conducted using a design science research methodology. It is a methodology for creating and evaluating IT artifacts to solve identified organizational problems. We used Wang's safety intelligence framework and the recommendations of the ISO 45001 model from extant literature to inform the Design. While creating our artifact, we chose to make an inventory of occupational risk prevention practices in Algeria. A qualitative study using interviews with experts in the field consolidated the requirements for such a system. The key findings from the analysis of collected data state that the development of safety data‐driven decision‐making requires top management support and involvement, which is in line with previous studies. A second concern is the availability of spatial data. The designed artifact is an approach called spatial data integration and visualization approach for occupational health and safety management systems (SDIVA‐OHSMS). SDIVA‐OHSMS provides a practical approach for organizations in charge of OHS services to implement OHS data‐driven decision‐making solutions highlighting spatial and semantic dimensions. The evaluation was performed through a representative case study within the Algerian electricity and gas company (Sonelgaz), employing expert opinion and software implementation. As a result, experts concluded that such systems could provide a more comprehensive and accurate view of occupational health issues and guide further prevention policies.

Community-Based Disaster Health Management Approaches: A Systematic Literature Review

Background: Community-based interventions can be effective due to the active participations of communities in pre- and post-disaster planning and management. Effective community-based disaster management for health promotion can be achieved by learning from the successful local and international experiences of community participations. Thus, this study is aimed to identify the community active participation approaches which were effective in promoting the health of disaster-affected communities.&#x0D; Methods: This systematic review was conducted and reported based on PRISMA guideline. Web of Sciences, PubMed and Scopus were searched using the selected keywords. The inclusion criteria were all forms of published articles which studied the approaches of community active participations for improving their health in disasters. The selection of articles was conducted using the titles and abstracts. Data analysis was done based on the categorization of different community-based disaster management approaches.&#x0D; Results: 10 articles were finally selected out of 2843 documents generated by initial search. Asian countries considered community involvements in disaster management the most (40%). The most community active participations in disaster management were conducted in the recovery phase (80%). Approaches of community-based disaster management for the health promotion were two-way university-community participation, focusing on empowering specific community groups, governance, and localizing community participation as well as strengthening social networks.&#x0D; Conclusion: Considering the capacities of academic institutions for active participation in community-based disaster management according to the Yokohama, Hyogo and Sendai Framework is highly suggested. Further research is needed to explore the challenges of community communication and information in disasters.

Pilot study of operational health management approach for transformers in nuclear power plant

Power transformer is one of the most important electrical equipment for safe and reliable operation of Nuclear Power Plant. For trouble free operation of transformer, it is necessary to monitor its health during the lifecycle. There are clear needs for the development of prognostics and health management techniques so that smooth operation of transformer as well as safe operation of nuclear power plant can be ensured. This paper focuses on transformer asset management using Health Index approach. According to actual available data from a nuclear power plant, this paper revises the referenced HI model (Kinectrics Inc.) to consider actual test results of transformer performed in the field, dissolved gas analysis and other test of oil, maintenance work orders on various components of transformer, health of tap changer etc. The final value of health index is associated with the predicted life and condition of transformer according to pre-defined limits. Revised health index model studied is tested by the actual test data collected from a real nuclear power plant. It shows that the improved method is practically feasible for nuclear power plant. Introduction, revised health index model, results, discussion and recommendations are described in detail in following sections.

A lunar surface scenario simulation applying adaptive operating systems (revised version)

There is much discussion about the further exploration of the moon and its implementation by autonomous robots and rovers. Prognostics and health management approaches are considered for autonomous systems to assure reliable operation. In this context, the reliability-adaptive systems approach deals with the prediction of the remaining useful life (RUL) to avoid maintenance conflicts. The proposed paper introduces a multi-system scenario on the lunar surface consisting of six rovers maintained by a single base. The rovers drill into the lunar surface and bring soil samples back to the base. To estimate the RUL as precisely as possible, the wear of the drill is monitored. Soil density, radiation, and soil versus rover temperature are just a few random aspects which are considered in the calculation of the drill failure rate. The simulation applies different values to the Weibull function shape parameter b > 1. This modeling represents the pessimistic assumption of an increasing soil density per drilling depth on the Moon. The base can maintain only one rover at a time. To avoid maintenance conflicts and to maximize the total scenario soil output, some rovers are derating their performances in order to extend their RUL so that all rovers reach the base preferably one after the other. When predicting the RUL, all previous failures, the current performances, and RUL prognoses of all rovers are considered. The paper discusses the operation algorithm including the rover procedures. The proposed contribution compares the efficiency of systems operated either conventionally in a non-derating mode or in a reliability-adaptive mode. The scenario-wide workload performed by all rovers is the evaluation measure in this approach. The following results have been obtained: Reliability-adaptive systems are operating more efficiently in the given lunar context than the conventional systems operate as the adaptive rovers continuously achieve higher up times in total. The longer the simulation time, the higher is the efficiency of reliability-adaptive operation. It has also been shown that reliability-adaptive systems have a significant influence on reducing delay times when returning to the drilling sites.

Primary care patients' and providers' perspectives about an online weight management program integrated with population health management: Post-intervention qualitative results from the PROPS study.

To assess patients' and providers' attitudes about the online weight management program and population health management approach in the PROPS Study, which examined the effectiveness of these strategies in primary care. We conducted semi-structured interviews with 22 patients and nine providers. Using thematic analysis, we analyzed transcripts of the interviews to identify key themes. Most patients found the online program to be well-structured and easy to use, although a few noted that the information was overwhelming or could be more personalized. Patients mentioned that the support from the population health managers was critical for their success, and several reported that they would have liked more involvement from their primary care provider or a dietitian. Providers also were satisfied with the interventions, and several stated that the population health management support was helpful because it added accountability. Providers suggested that the interventions could be improved by tailoring the information and integrating the online program with the electronic health record. Most patients and providers were satisfied with the interventions, with several recommendations for improvements. These findings give additional information about patients' and providers' experience with this innovative approach for managing overweight and obesity in primary care.

P102 Sleep health management practices in Australian residential aged care facilities: The perspective of registered nurses

Abstract Background Sleep disturbances are commonly experienced by older people living in residential aged care facilities. Non-pharmacological approaches have a strong evidence base, however research shows that medications (sedatives, antidepressants, antipsychotics) are often used inappropriately. However, little is known about healthcare professionals’ current sleep health management approaches in residential aged care facilities. Aims and objectives This study aimed to explore the current sleep health management practices in Australian residential aged care facilities and investigate potential ways to improve non-pharmacological and pharmacological sleep health practices in the future. Design Qualitative inductive thematic analysis of semi-structured interviews guided by COREQ guidelines. Methods Semi-structured interviews were to be conducted with registered nurses and aged care pharmacists recruited using a convenience-sampling and snowballing approach. Interviews were audio-recorded, transcribed verbatim and inductively analysed for emergent themes. Results Fourteen interviews were conducted with registered nurses in New South Wales. Thematic analysis derived three main themes: 1) Awareness and observations of sleep health, 2) Healthcare approach to sleep health management in residential aged care facilities and, 3) Organisational constraints to evidence-based sleep health care provision. Conclusion Registered nurses assess residents’ sleep health and attempt to manage sleep health through both non-pharmacological and pharmacological strategies. Organisational culture and staffing ratios negatively impact the delivery of best evidence-based practice but effective communication between the multidisciplinary team appears to facilitate management of sleep disturbances. Participants emphasized the need for sleep health educational programs and the development of national sleep health management guidelines across aged care in Australia.