Reduce Health Care Costs Research Articles

Integrating informal and formal care: An innovative, scalable program blueprint.

People have infinite needs, including illness prevention, wellness, self-care, practical support, and quality of life. This article describes community-based, informal care programs that help people identify their needs, set goals, and organize networks of care to address their needs holistically in a way that can also significantly reduce healthcare costs. Approaches can be customized for primary care, home and community, hospice, and other care sectors to facilitate low-cost, high impact adoption. We provide a blueprint for programs that integrate informal and formal care across social, physical, and mental health domains as a key part of healthcare system transformation.

The crowding-out effect of physical fitness activities on medical expenditure in the aged group

IntroductionChina is facing the challenge of “deep aging”, and promoting healthy aging has become a key research topic. Both medical care and physical exercise are important for health, but while medical services focus on treating illness, physical fitness activities focus on prevention, making them a more effective approach for promoting healthy aging.MethodsThis study uses data from the China Health and Retirement Longitudinal Study (CHARLS), focusing on individuals aged 60 to 80, to investigate the relationship between physical fitness activities and medical expenditures. A Tobit model was employed to analyze the data.Results(1) Active participation in physical fitness activities significantly improves the health of older adults, making physical activity an essential pathway to achieve healthy aging. (2) Participation in sports and fitness activities leads to a crowding-out effect on medical expenditures, significantly reducing healthcare costs for participants. (3) High-intensity physical activities are most suitable for individuals aged 60-65, moderate-intensity activities for those aged 66-70, and low-intensity activities for those aged 71–80.Discussion(1) Policies should focus on raising awareness of physical fitness benefits among older adults, encouraging regular physical activity to improve health and reduce medical costs. (2) A shift from treatment to prevention in health management is needed, promoting exercise as a cost-effective way to reduce healthcare spending. (3) Age-specific fitness guidelines should be developed to provide tailored exercise recommendations for different older aldult age groups.

Traumatic Brain Injury as a Public Health Issue: Epidemiology, Prognostic Factors and Useful Data from Forensic Practice

Traumatic brain injury (TBI) represents a major public health problem, being a leading cause of disability and mortality among young people in developed countries. Head trauma occurs across all age groups, each experiencing consistently high rates of mortality and disability. This review aims to present an overview of TBI epidemiology and its socioeconomic impact, alongside data valuable for prevention, clinical management, and research efforts. Methods: A narrative review of TBI was performed with a particular focus on forensic pathology and public health. In fact, this review highlighted the economic and epidemiological aspects of TBI, as well as autopsy, histology, immunohistochemistry, and miRNA. Results: These data, together with immunohistochemical markers, are crucial for histopathological diagnosis and to determine the timing of injury onset, a fundamental aspect in forensic pathology practice. There is compelling evidence that brain injury biomarkers may enhance predictive models for clinical and prognostic outcomes. By clarifying the cause of death and providing details on survival time after trauma, forensic tools offer valuable information to improve the clinical management of TBI and guide preventive interventions. Conclusions: TBI is one of the most common causes of death today, with high costs for health care spending. Knowing the different mechanisms of TBI, reduces health care costs and helps improve prognosis.

Reducing healthcare costs by timely diagnosis and management in functional motor disorders.

Functional motor disorders (FMDs) are prevalent and highly disabling conditions among young adults, leading to reduced independence. Despite advancements in diagnosis and treatment, the economic burden of FMDs remains largely unknown. This study evaluates the impact of accurate FMDs diagnosis on direct healthcare costs within the Italian National Health System by comparing healthcare utilization and costs before and after diagnosis. This before-after study included 40 patients with a definite diagnosis of FMDs. Retrospective data on healthcare utilization, including diagnostic tests, specialist visits, hospitalizations, Emergency Room (ER) visits, and rehabilitation services, were collected from diaries and documents two years before and after diagnosis. Healthcare direct costs decreased by 67%, from an average of €4,467 [95% CI 3,604-5,329] to €1,461 [95% CI 945-1,976] after diagnosis. Hospitalization costs fell from €2,618 [95% CI 1,899-3,336] to €492 [95% CI 162-821], and ER costs dropped from €403 [95% CI 177-628] to €43 [95% CI 6-78]. Diagnostic procedure costs decreased significantly, from €403 [95% CI 177-628] to €43 [95% CI 6-78]. Specialist visit costs remained unchanged. Rehabilitation costs increased from €371 [95% CI 194-547] to €635 [95% CI 318-915], but this rise was not statistically significant. Accurate diagnosis of FMDs significantly reduces healthcare costs by minimizing unnecessary tests, hospitalizations, and ER visits while emphasizing rehabilitation. This highlights the economic and clinical benefits of improved diagnostic accuracy and specific multidisciplinary intervention. Investing in cost-effective diagnostic tools is crucial for earlier diagnosis and reducing delays.

Minimizing Indoor Allergen Exposure: What Works?

Allergic rhinitis and asthma morbidity has been linked to indoor allergen exposure. Common indoor allergens include dust mites, cats, dogs, rodents, and cockroaches. These allergens are ubiquitous and often difficult to remove from the home, making long-lasting reduction strategies difficult to achieve. Identifying strategies for reducing the presence of indoor allergens in homes could be utilized to decrease allergic disease burden, improve symptomology, reduce healthcare costs, and improve patients' quality of life. Studies have yielded mixed results with regard to specific environmental control measures in reducing indoor allergen levels and in improving clinical outcomes of allergic disease. In this review, we assess the available evidence of the effectiveness of environmental control measures in reducing indoor allergens and the potential clinical impact of these measures.

Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.

Accurate prediction of anticancer drug responses is essential for developing personalized treatment plans in order to improve cancer patient survival rates and reduce healthcare costs. To this end, we propose a drug sensitivity prediction model based on multi-stage multi-modal drug representations (ModDRDSP) to reflect the properties of drugs more comprehensively, and to better model the complex interactions between cells and drugs. Specifically, we adopt the SMILES representation learning method based on the deep hierarchical bi-directional GRU network (DSBiGRU) and the molecular graph representation learning method based on the deep message-crossing network (DMCN) for the multi-modal information of drugs. Additionally, we integrate the multi-omics information of cell lines based on a convolutional neural network (CNN). Finally, we use an ensemble deep forest algorithm for the prediction of drug sensitivity. After validation, the ModDRDSP shows impressive performance which outperforms the four current industry-leading models. More importantly, ablation experiments demonstrate the validity of each module of the proposed model, and case studies show the good results of ModDRDSP for predicting drug sensitivity, further establishing the superiority of ModDRDSP in terms of performance.

Utilizing C-Reactive Protein (CRP) and CRP Ratios for Early Detection of Postoperative Complications Following Rectal Cancer Surgery

Background: Surgical treatment for rectal cancer may cause complications that exacerbate the condition, lengthen hospital stays, and raise healthcare costs. Rectal cancer surgery complications can be detected immediately with postoperative C-reactive protein (CRP) monitoring. Elevated levels of CRP indicate the presence of inflammation and can serve as a predictive factor for future outcomes. Methods: A retrospective cohort analysis was performed on 67 rectal cancer surgery patients. Prior to and after surgery, CRP levels were measured on days 1–3 and 4–7. Postoperative complications were linked to CRP, surgical approach (open, laparoscopic, conversion), and patient characteristics. This study included t-tests, chi-square tests, Mann–Whitney U tests, ANOVA, Pearson and Spearman correlation analyses, and logistic regression. Results: There was a significant correlation between high levels of CRP on postoperative days 4–7 and the development of problems (p < 0.001). The ratios of CRP/albumin and CRP/platelet were highly predictive of problems over this period (p = 0.000033). Patients who encountered problems had a notably greater estimated blood loss (p = 0.0086). Logistic regression analysis demonstrated a statistically significant relationship between higher Charlson Comorbidity Index (CCI) scores and an elevated probability of experiencing problems (p = 0.0078). Moreover, patients who underwent laparoscopic surgery but had to be converted to an open method saw a notably greater incidence of complications (p = 0.0022). From a financial standpoint, the average cost per patient with complications was EUR 1128.75, resulting in a total cost of EUR 44,021.25 for all 39 patients. Conversely, patients who did not experience any difficulties had a cost of EUR 731.25 per patient, resulting in a total of EUR 20,475.00 for all 28 patients. Conclusions: Regularly monitoring CRP, particularly between days 4 and 7 following surgery for rectal cancer, can promptly identify any complications. Monitoring CRP levels and promptly managing any abnormalities can enhance surgical outcomes and reduce healthcare costs.

Unlocking the potential of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) in revolutionizing type 2 diabetes management: A comprehensive review

Diabetes mellitus (DM) is a long-term metabolic disorder caused by inadequate production and resistance to insulin. The prevalence of DM is rapidly increasing, with type 2 diabetes (T2D) accounting for more than 90% of cases. Despite new treatments, many patients with T2D do not meet their glycemic targets due to clinical inertia. This review provides an overview of glucagon-like peptide 1 receptor agonists (GLP-1 RAs) in the management of T2D. The review synthesizes data from clinical trials and meta-analyses on the efficacy, safety, and cost-effectiveness of GLP-1 RAs. It also discusses the mechanisms of action, classification, and barriers to adherence and persistence in therapy. GLP-1 RAs improve glycemic control by lowering A1C levels and promoting weight loss. They have cardioprotective effects and may reduce endothelial inflammation, oxidative stress, and blood pressure. Adherence to GLP-1 RAs is better with once-weekly injections, though gastrointestinal side effects and cost can affect persistence. Semaglutide and liraglutide have shown significant weight reduction, with semaglutide being particularly effective. GLP-1 RAs are cost-effective due to reduced healthcare costs associated with fewer hospitalizations and lower mortality rates. Safety concerns include gastrointestinal issues, pancreatitis, and rare cases of diabetic retinopathy and thyroid C-cell tumors. For clinical practice, GLP-1 RAs represent a valuable option not only for glycemic control but also for weight management and cardiovascular protection. Incorporating GLP-1 RAs into treatment plans can improve patient outcomes, and optimizing dosing regimens and addressing barriers such as cost and side effects are crucial to enhancing patient adherence and long-term treatment success.

Patient-Centered Care Models in Primary Healthcare: A Systematic Review of Implementation and Outcomes

This systematic review evaluates the implementation and outcomes of patient-centered care (PCC) models in primary healthcare settings. Patient-centered care is a foundational approach in primary healthcare, emphasizing patient preferences, needs, and values to improve the quality of care and health outcomes. A comprehensive literature search was conducted across PubMed, Cochrane, and MEDLINE databases, focusing on studies that assessed the impact of PCC models such as the Patient-Centered Medical Home (PCMH) and shared decision-making. Forty studies met the inclusion criteria, covering a range of patient populations and healthcare settings. Findings indicate that PCC models enhance patient satisfaction, improve health outcomes, and increase patient engagement in care. Additionally, these models positively impact provider satisfaction and may reduce healthcare costs by lowering emergency visits and hospitalizations. However, challenges in implementation, including provider workload and resource needs, were commonly reported. This review underscores the effectiveness of PCC models in primary care and highlights the need for structured, resource-supported implementation strategies. Further research is recommended to optimize PCC approaches across diverse populations and healthcare systems.

INNV-02. BRAINMETS: A CLINICIAN-FRIENDLY SOFTWARE TO MANUALLY SEGMENT BRAIN METASTASES AND DETECT PRIMARY TUMOR ORIGIN USING DEEP LEARNING

Abstract Estimates suggest that around 20%-40% of cancer patients are affected by brain metastases. A subset of these patients first present with central nervous system manifestations with an unknown primary. The issue then arises regarding which region of the body should be imaged, if not the entire body, exposing the patient to more radiation. To address this issue, streamline workflows, and save precious time, we developed a software clinicians can use to identify primary tumor locus. “BrainMets” is a user-friendly software that handles a DICOM input, allows the user to segment the region(s) of interest manually, and uses a deep learning model to predict the most likely tumor origin. The model is a mixed-effects neural network pre-trained on radiomic features from open-source data by Ocaña-Tienda et al. (2023) using 637 high-resolution imaging studies from 10 different medical centers with an accuracy of 90.5%, precision of 92.7%, recall of 87.8%, and F1 score of 90.2%. The software leverages the open-source software 3D-Slicer to help segment regions of interest, extract radiomic features, and make predictions. It is built using Flask, a Python package for backend development. The frontend user interface is created using HTML/CSS and Bootstrap. The “BrainMets” software thus strives to enhance diagnostic workflow significantly, reduce appointment times for scans, and improve patient experience. Future enhancements include integrating more AI models, improving healthcare IT system compatibility, and updating datasets to boost predictive accuracy and generalizability. This tool sets a new standard in personalized medicine, optimizing treatment strategies for cancer patients with brain metastases. As adoption increases, it could substantially improve patient outcomes and reduce healthcare costs associated with misdiagnosis and overtreatment. Further research and development will focus on expanding its applicability across various types of cancers.

The impact of health analytics on reducing healthcare costs in aging populations: A review

The impact of health analytics on reducing healthcare costs in aging populations: A review

Enhanced Recovery After Craniotomy: Global Practices, Challenges, and Perspectives.

The global demand for hospital care, driven by population growth and medical advances, emphasizes the importance of optimized resource management. Enhanced Recovery After Surgery (ERAS) protocols aim to expedite patient recovery and reduce health care costs without compromising patient safety or satisfaction. Its principles have been adopted in various surgical specialties but have not fully encompassed all areas of neurosurgery, including craniotomy. ERAS for craniotomy has been shown to reduce the length of hospital stay and costs without increasing complications. ERAS protocols may also reduce postoperative nausea and vomiting and perioperative opioid requirements, highlighting their potential to enhance patient outcomes and health care efficiency. Despite these benefits, guidelines, and strategies for ERAS in craniotomy remain limited. This narrative review explores the current global landscape of ERAS for craniotomy, assessing existing literature and highlighting knowledge gaps. Experts from 26 countries with diverse cultural and socioeconomic backgrounds contributed to this review, offering insights about current ERAS protocol applications, implementation challenges, and future perspectives, and providing a comprehensive global overview of ERAS for craniotomy. Representatives from all 6 World Health Organization geographical world areas reported that barriers to the implementation of ERAS for craniotomy include the absence of standardized protocols, provider resistance to change, resource constraints, insufficient education, and research scarcity. This review emphasizes the necessity of tailored ERAS protocols for low and middle-income countries, addressing differences in available resources. Acknowledging limitations in subjectivity and article selection, this review provides a comprehensive overview of ERAS for craniotomy from a global perspective and underscores the need for adaptable ERAS protocols tailored to specific health care systems and countries.

Examining the sustainable leisure and business values in sporting events and lottery analyses development

Purpose: This study aims to clarify the meaning of sport analysis, explore the contributions derived from sporting event analysts, and highlights the importance of responsible sport gambling. It also investigates how sustainable practices can be integrated into sports analysis to enhance social well-being. Design/methodology/approach: Secondary text data from government documents, news articles, and website information were extracted by searching keywords such as sports lottery and sports analysis in traditional Chinese, and then analyzed to establish the research framework and scope. Subsequently, 18 interviews were conducted with stakeholders to gain deeper insights. Findings: The content analyses reveals that sport analysis tends to be sport data science. Sporting event analysts may contribute to improving the performance of players or a team, enhancing spectator sports, and increasing sports lottery revenues. In the leisure aspect, the professionalism of sporting event analysts not only increases epistemic and entertainment values in spectator sports but also boosts engagement with sport lotteries. To ensure these enhancements remain beneficial, it is vital to emphasize responsible sport gambling and sustainable practices that protect vulnerable groups and promote long-term health benefits for those involved in sports. The integration of sustainable practices in sport analysis and the expertise of sporting event analysts can significantly advance economic and social development by generating funds through sport lottery industry for athlete programs, sports infrastructure, and educational initiatives, aligning with multiple Sustainable Development Goals. Additionally, the professionalism of these analysts may enhance public understanding and engagement of sports, promoting increased participation in sports, reducing healthcare costs, and contributing to the development of a healthier and more resilient society. Originality: Emphasizing responsible sports gambling is essential to the sustainability of sports lotteries and the role of sporting event analysts.

Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms

Diabetes mellitus is considered one of the main causes of death worldwide. If diabetes fails to be treated and diagnosed earlier, it can cause several other health problems, such as kidney disease, nerve disease, vision problems, and brain issues. Early detection of diabetes reduces healthcare costs and minimizes the chance of serious complications. In this work, we propose an e-diagnostic model for diabetes classification via a machine learning algorithm that can be executed on the Internet of Medical Things (IoMT). The study uses and analyses two benchmarking datasets, the PIMA Indian Diabetes Dataset (PIDD) and the Behavioral Risk Factor Surveillance System (BRFSS) diabetes dataset, to classify diabetes. The proposed model consists of the random oversampling method to balance the range of classes, the interquartile range technique-based outlier detection to eliminate outlier data, and the Boruta algorithm for selecting the optimal features from the datasets. The proposed approach considers ML algorithms such as random forest, gradient boosting models, light gradient boosting classifiers, and decision trees, as they are widely used classification algorithms for diabetes prediction. We evaluated all four ML algorithms via performance indicators such as accuracy, F1 score, recall, precision, and AUC-ROC. Comparative analysis of this model suggests that the random forest algorithm outperforms all the remaining classifiers, with the greatest accuracy of 92% on the BRFSS diabetes dataset and 94% accuracy on the PIDD dataset, which is greater than the 3% accuracy reported in existing research. This research is helpful for assisting diabetologists in developing accurate treatment regimens for patients who are diabetic.

The impact of a payer-provider joint venture on healthcare value.

To examine how a novel payer-provider joint venture (JV) between one payer and multiple competitive delivery systems in New Hampshire (NH), which included value-based payment, care management, and non-financial supports, impacted healthcare value and payer and provider group experiences. We conducted a mixed-methods study. We used a quasi-experimental longitudinal difference-in-differences design to examine the impact of the JV (which started in January 2016 and ended in December 2020) on healthcare utilization, quality, and spending, using members in Maine (ME) as a control group. We also analyzed patient uptake of the JV's care management program using routinely collected administrative data and assessed payer and provider group leaders' perspectives about the JV via semi-structured interviews. We used administrative and claims data from 2013 to 2019 in a commercially insured population under 65 years in NH and ME. We also used administrative data on care management eligibility and uptake and conducted semi-structured interviews with payer and provider group leaders affiliated with the JV. The JV was associated with no sustained change in medical utilization, quality, and spending throughout the study period. In the first year of the JV, there was a $142 (95% confidence interval: $41, $243) increase in pharmaceutical spending per member and a 13% (4.4%, 25%) relative increase in days covered for diabetes medications. Only 15% of eligible members engaged in care management, which was a key component of the JV's multi-pronged approach. In a disconnect from the empirical findings, payer and provider group leaders believed that the JV reduced healthcare costs and improved quality. Our findings provide evidence for future payer-provider JVs and demonstrate the importance of having a valid control group when evaluating JVs and value-based payment arrangements.

The impact of a lifestyle behaviour change program on healthcare costs: Quasi-experimental real-world evidence from an open-access mobile health app in the Netherlands

ObjectiveRecognizing the substantial role of modifiable risk factors such as physical inactivity, unhealthy diets, and substance use in driving global healthcare expenses, this study aims to assess the impact of a lifestyle behaviour change mobile health (mHealth) app on healthcare costs within a real-world setting. MethodsEmploying a difference-in-difference approach, this research compared healthcare costs between users of an mHealth app (treatment group) and a propensity score-matched control group of non-users. The study included data from 15,506 participants in the Netherlands, covering healthcare cost records collected from 2015 to 2019. ResultsThe implementation of the mHealth app was linked to a steady reduction in healthcare costs, achieving a 4.9 % decrease (β=−0.05,95%CI:−0.10to0.00) during the first year following the program's implementation, and a further 5.3 % decrease (β=−0.05,95%CI:−0.10to−0.00) in the second year. This healthcare costs reduction is associated with lower expenses for general practitioner services as well as diminished specialist visits. Additionally, increased app use led to further cost reductions, with frequency of use proving more important than intensity. ConclusionsThis study demonstrates that a behaviour change mHealth app is linked to significant reductions in healthcare costs, with more frequent and intense use leading to greater savings. Given their relatively low cost and ability to reach diverse population groups, such apps not only enhance individual health outcomes but can also serve as effective policy tools for large-scale health promotion, supporting broader societal health improvements.

Utilizing a Defined Multiphase, Multispecialty Perioperative Pathway to Achieve Functional Recovery after Surgery

This article emphasizes the need for a personalized approach in managing older cancer patients, highlighting the importance of a multidisciplinary team that tailors treatment to the patient's specific health conditions and goals. The objective is to balance positive oncological outcomes with the restoration of independence, reduction of disabilities and enhancement of quality of life. Functional recovery is central to treatment decisions as disability can impact cancer patients more than the prognosis itself. The paper stresses the value of frailty screening tools, nutritional assessment and physical evaluation which help predict postoperative outcomes and guide clinical management. It advocates for comprehensive preoperative evaluations, including geriatric assessments, to prevent both undertreatment and overtreatment. Multimodal prehabilitation can improve postoperative outcomes, reduce healthcare costs, and help older patients remain engaged in society. The article concludes by recommending a patient-centered, individualized approach to optimize functional recovery and alleviate the economic burden on healthcare systems.

Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.

Bloodstream infections (BSIs) are a severe public health threat due to their rapid progression into critical conditions like sepsis. This study presents a novel eXplainable Artificial Intelligence (XAI) framework to predict BSIs using historical electronic health records (EHRs). Leveraging a dataset from St. Olavs Hospital in Trondheim, Norway, encompassing 35,591 patients, the framework integrates demographic, laboratory, and comprehensive medical history data to classify patients into high-risk and low-risk BSI groups. By avoiding reliance on real-time clinical data, our model allows for enhanced scalability across various healthcare settings, including resource-limited environments. The XAI framework significantly outperformed traditional models, particularly with tree-based algorithms, demonstrating superior specificity and sensitivity in BSI prediction. This approach promises to optimize resource allocation and potentially reduce healthcare costs while providing interpretability for clinical decision-making, making it a valuable tool in hospital systems for early intervention and improved patient outcomes.

Gatekeeping in primary care: Analysing GP referral patterns and specialist consultations in the NHS

This study investigates the impact of increasing the number of gatekeeper General Practitioners (GPs) on referral rates and specialist treatments. Gatekeeping is a supply-side strategy implemented to control health expenditure and improve efficiency by limiting patient access to services below marginal cost. It aims to address specialist moral hazard by reducing the overuse of expensive diagnostics and replacing them with more cost-effective GP diagnostic information. Using administrative data from 2004 to 2011, we examine whether the availability of gatekeeper GPs in local areas is associated with changes in outpatient referrals and elective admissions. Our findings reveal that increasing GP supply in socioeconomically disadvantaged areas leads to a decrease in both outpatient referrals and elective admissions. However, these effects are less pronounced in prosperous areas or regions with high GP referral rates. Interestingly, we observe that having more GP practices in a specific area implies higher referral rates and elective admissions. These findings offer valuable insights that can assist policymakers in crafting targeted policies to effectively reduce healthcare costs and enhance the overall efficiency of the health system.

Unsafe termination of pregnancy where abortion is illegal: A Botswana context-specific study

Unsafe pregnancy termination contributes to morbidity and mortality among women globally. Despite Botswana's restrictive abortion laws, women continue to use unsafe abortion methods. This study aimed to explore factors that contributes to unsafe termination of pregnancies in Ngami sub-district, Botswana. A qualitative descriptive and exploratory research approach, using a phenomenological design, was applied. Semi-structured in-depth interviews were conducted with women admitted to the gynecological ward from June to August 2021. Data saturation was reached with the 12th participant. Data was analyzed thematically. The study revealed two major themes: factors contributing to unsafe abortions, and interventions to reduce unsafe abortions. The study underscores the global issue of unsafe abortion that leads to high morbidity and mortality, and long-term complications. An overall recommendation is for Botswana to reconsider its abortion law and aim to prevent unsafe abortions and reduce healthcare costs.