Reduce Health Care Costs Research Articles

Implementation of cardiac enhanced recovery after surgery at Lausanne University Hospital, our roadbook to certification.

Enhanced recovery after surgery (ERAS) is a multidisciplinary, patient-centred approach aimed at expediting recovery, improving clinical outcomes, and reducing healthcare costs. Initially developed for colorectal surgery, ERAS principles have been successfully applied across various surgical specialties, including cardiac surgery. This study outlines the implementation and certification process of the ERAS program in a tertiary cardiac surgical centre within the Heart-Vessel Department at Lausanne University Hospital. The implementation involved forming a multidisciplinary team, including cardiac surgeons, anaesthesiologists, intensivists, a cardiologist, clinical nurse specialists and physiotherapists. The ERAS nurse coordinator played a central role in organizing meetings, promoting the program, developing protocols, and collecting data. The certification process required adherence to ERAS guidelines, structured training and external evaluation. Key phases included pre-ERAS data collection, protocol dissemination, inclusion of the 1st patients, followed by analysis and full implementation. Achieving certification required maintaining a compliance rate of over 70% with established protocols. The process involved overcoming various barriers, such as inconsistent practices and the need for multidisciplinary collaboration. In this paper, we provide some solutions to these challenges, including team education, regular meetings and continuous feedback loops. Preliminary data from the initial cohort showed improvements in early mobilization, opioid use, respiratory complications and shorter hospital stays. The successful implementation of the ERAS program at our institution demonstrates the feasibility and benefits of a structured, multidisciplinary approach in cardiac surgery. Continuous self-assessment and adherence to guidelines are essential for sustained improvement in patient outcomes and healthcare efficiency.

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods

Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare. Methods The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility. Results For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214–0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort). Conclusion We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

Gender differences and hypercholesterolemia: real-world evidence from the study WECARE (Women Effective CArdiovascular Risk Evaluation)

Introduction: The therapeutic control of LDL-cholesterol is essential in cardiovascular prevention, as recommended by the recent guidelines. Objective: To evaluate gender differences in terms of demographic and clinical characteristics, treatment pattern, treatment adherence and healthcare costs in patients on lipid-lowering therapy, stratified by cardiovascular risk in the Italian real clinical practice. Methods: An observational analysis was conducted on the administrative databases of healthcare institutions, covering about 6.1 million health-assisted subjects. After inclusion of all patients on lipid-lowering therapy between January 2017 and June 2020, the population was investigated in the period before the first prescription of a lipid-lowering drug and followed-up for at least 12 months. Clinical and demographic variables were compared after stratification by gender and by cardiovascular risk (very high/high/other risk). The main outcome measures were treatment adherence and direct healthcare costs during follow-up. Results: Of the 684,829 patients with high/very high cardiovascular risk, 337,394 were men and 347,435 women, aged on average 69.3 years and 72.1 years, respectively (p &lt; 0.001). Men were characterised by a worse comorbidity profile. Regardless of cardiovascular risk, female subjects were associated with larger utilisation of low-potency statins and lower adherence (p &lt; 0.001). The annual healthcare costs per patient during follow-up were higher in men than in women (p &lt; 0.001). Conclusions: The results highlighted larger utilisation of low-potency statins, a lower adherence and a milder comorbidity profile in women, the latter feasibly explaining the reduced healthcare costs compared to men.

Does an allied health transdisciplinary stroke assessment save time, improve quality of care, and save costs? Results of a pre-/post- clinical study

Purpose: Demand for stroke services is increasing. To save time and costs, stroke care could be reorganised using a transdisciplinary assessment model embracing overlapping allied health professional skills. The study compares transdisciplinary assessment to discipline-specific allied health assessment on an acute stroke unit, by evaluating assessment time, quality of care, and cost implications. Method: The pre-/post- clinical study used non-randomised groups and 3-month follow-up after hospital admission. Patients with confirmed/suspected stroke received usual discipline-specific allied health assessment (pre-implementation phase) or the novel transdisciplinary assessment (post-implementation phase). Staff/student assessment times (primary outcome) and medical record data (secondary outcomes) were collected. Time differences were estimated using multivariable linear regression controlling for confounding factors. Cost minimisation and sensitivity analyses estimated change in hospital resource use. Findings: When the transdisciplinary assessment was used ( N = 116), compared to usual assessment ( N = 63), the average time saving was 37.6 min (95% CI −47.5, −27.7; p &lt; 0.001) for staff and 62.2 min (95% CI −74.1, −50.3; p &lt; 0.001) for students. The median number of allied health occasions of service reduced from 8 (interquartile range 4–23) to 5 (interquartile range 3–10; p = 0.011). There were no statistically significant or clinically important changes in patient safety, outcomes or stroke guideline adherence. Improved efficiency was associated with an estimated cost saving of $379.45 per patient (probabilistic 95% CI −487.15, −271.48). Discussion and conclusion: Transdisciplinary stroke assessment has potential for reorganising allied health services to save assessment time and reduce healthcare costs. The transdisciplinary stroke assessment could be considered for implementation in other stroke services.

Advancing Thrombosis Research: A Novel Device for Measuring Clot Permeability.

Thromboembolism, a global leading cause of mortality, needs accurate risk assessment for effective prophylaxis and treatment. Current stratification methods fall short in predicting thrombotic events, emphasizing the need for a deeper understanding of clot properties. Fibrin clot permeability, a crucial parameter in hypercoagulable states, impacts clot structure and resistance to lysis. Current clot permeability measurement limitations propel the need for standardized methods. Prior findings underscore the importance of clot permeability in various thrombotic conditions but call for improvements and more precise, repeatable, and standardized methods. Addressing these challenges, our study presents an upgraded, portable, and cost-effective system for measuring blood clot permeability, which utilizes a pressure-based approach that adheres to Darcy's law. By enhancing precision and sensitivity in discerning clot characteristics, this innovation provides a valuable tool for assessing thrombotic risk and associated pathological conditions. In this paper, the authors present a device that is able to automatically perform the permeability measurements on plasma or fibrinogen in vitro-induced clots on specific holders (filters). The proposed device has been tailored to distinguish clot permeability, with high precision and sensitivity, between healthy subjects and high cardiovascular-risk patients. The precise measure of clot permeability represents an excellent indicator of thrombotic risk, thus allowing the clinician, also on the basis of other anamnestic and laboratory data, to attribute a risk score to the subject. The proposed instrument was characterized by performing permeability measurements in plasma and purified fibrinogen clots derived from 17 Behcet patients and 15 sex- and age-matched controls. As expected, our results clearly indicate a significant difference in plasma clot permeability in Behcet patients with respect to controls (0.0533 ± 0.0199 d vs. 0.0976 ± 0.0160 d, p < 0.001). This difference was confirmed in the patient's vs. control fibrin clots (0.0487 ± 0.0170 d vs. 0.1167 ± 0.0487 d, p < 0.001). In conclusion, our study demonstrates the feasibility, efficacy, portability, and cost-effectiveness of a novel device for measuring clot permeability, allowing healthcare providers to better stratify thrombotic risk and tailor interventions, thereby improving patient outcomes and reducing healthcare costs, which could significantly improve the management of thromboembolic diseases.

The influence of low-dose s-ketamine on postoperative delirium and cognitive function in older adults undergoing thoracic surgery

BackgroundPostoperative delirium (POD) and cognitive dysfunction (POCD) are common complications following thoracic surgery, particularly in patients aged 65 years and above. These complications can significantly affect recovery and increase healthcare costs. This study investigates the effects of low-dose S-ketamine on reducing POD and POCD in this patient demographic.MethodsIn this retrospective cohort study, medical records of patients aged ≥ 65 years who underwent elective thoracic surgery from January 2019 to August 2023 were reviewed. Patients were categorized into S-ketamine and Control groups based on intraoperative S-ketamine exposure. POD was assessed using the Confusion Assessment Method (CAM), while cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA) at baseline, 1 week, 1 month, and 6 months post-surgery. Intraoperative and postoperative parameters, including hemodynamic stability, blood loss, pain scores, and ICU stay length, were also recorded.ResultsThe study comprised 140 participants, with 70 in each group. The S-ketamine group demonstrated a significantly lower incidence of POD at 7 days post-surgery (12.0% vs. 26.7%, P < 0.001), and reduced POCD at 1 month (18.7% vs. 36.0%, P < 0.05) and 6 months (10.7% vs. 21.3%, P < 0.05). The Ketamine group had a significantly higher median MoCA score compared to the Control group both at 1 month (P = 0.021) and 6 months (P = 0.007). Adverse events, such as infection, bleeding, and respiratory failure, showed no significant differences between the groups, suggesting a safe profile for S-ketamine.ConclusionAdministering low-dose S-ketamine during thoracic surgery in patients aged 65 years and above significantly reduces the incidence of POD and POCD, highlighting its neuroprotective potential. These findings advocate for the inclusion of S-ketamine in anesthetic protocols to improve postoperative outcomes and reduce healthcare costs in this patient population.

The impact of normothermic machine perfusion and acuity circles on waitlist time, mortality, and cost in liver transplantation: A multicenter experience.

Ex situ normothermic machine perfusion (NMP) helps increase the use of extended criteria donor livers. However, the impact of an NMP program on waitlist times and mortality has not been evaluated. Adult patients listed for liver transplant (LT) at 2 academic centers from January 1, 2015, to September 1, 2023, were included (n=2773) to allow all patients ≥6 months follow-up from listing. Routine NMP was implemented on October 14, 2022. Waitlist outcomes were compared from pre-NMP pre-acuity circles (n=1460), pre-NMP with acuity circles (n=842), and with NMP (n=381). Median waitlist time was 79 days (IQR: 20-232d) at baseline, 49 days (7-182) with acuity circles, and 14 days (5-56) with NMP ( p <0.001). The rate of transplant-per-100-person-years improved from 61-per-100-person-years to 99-per-100-person-years with acuity circles and 194-per-100-person-years with NMP ( p <0.001). Crude mortality without transplant decreased from 18.3% (n=268/1460) to 13.3% (n=112/843), to 6.3% (n=24/381) ( p <0.001) with NMP. The incidence of mortality without LT was 15-per-100-person-years before acuity circles, 19-per-100 with acuity circles, and 9-per-100-person-years after NMP ( p <0.001). Median Model for End-Stage Liver Disease at LT was lowest with NMP, but Model for End-Stage Liver Disease at listing was highest in this era ( p <0.0001). The median donor risk index of transplanted livers at baseline was 1.54 (1.27-1.82), 1.66 (1.42-2.16) with acuity circles, and 2.06 (1.63-2.46) with NMP ( p <0.001). Six-month post-LT survival was not different between eras ( p =0.322). The total cost of health care while waitlisted was lowest in the NMP era ($53,683 vs. $32,687 vs. $23,688, p <0.001); cost-per-day did not differ between eras ( p =0.152). The implementation of a routine NMP program was associated with reduced waitlist time and mortality without compromising short-term survival after liver transplant despite increased use of riskier grafts. Routine NMP use enables better waitlist management with reduced health care costs.

Clinical evaluation of droplet digital PCR in the early identification of suspected sepsis patients in the emergency department: a prospective observational study.

Rapid and accurate diagnosis of the causative agents is essential for clinical management of bloodstream infections (BSIs) that might induce sepsis/septic shock. A considerable number of suspected sepsis patients initially enter the health-care system through an emergency department (ED), hence it is vital to establish an early strategy to recognize sepsis and initiate prompt care in ED. This study aimed to evaluate the diagnostic performance and clinical value of droplet digital PCR (ddPCR) assay in suspected sepsis patients in the ED. This was a prospective single-centered observational study including patients admitted to the ED from 25 October 2022 to 3 June 2023 with suspected BSIs screened by Modified Shapiro Score (MSS) score. The comparison between ddPCR and blood culture (BC) was performed to evaluate the diagnostic performance of ddPCR for BSIs. Meanwhile, correlative analysis between ddPCR and the inflammatory and prognostic-related biomarkers were conducted to explore the relevance. Further, the health economic evaluation of the ddPCR was analyzed. 258 samples from 228 patients, with BC and ddPCR performed simultaneously, were included in this study. We found that ddPCR results were positive in 48.13% (103 of 214) of episodes, with identification of 132 pathogens. In contrast, BC only detected 18 positives, 88.89% of which were identified by ddPCR. When considering culture-proven BSIs, ddPCR shows an overall sensitivity of 88.89% and specificity of 55.61%, the optimal diagnostic power for quantifying BSI through ddPCR is achieved with a copy cutoff of 155.5. We further found that ddPCR exhibited a high accuracy especially in liver abscess patients. Among all the identified virus by ddPCR, EBV has a substantially higher positive rate with a link to immunosuppression. Moreover, the copies of pathogens in ddPCR were positively correlated with various markers of inflammation, coagulation, immunity as well as prognosis. With high sensitivity and specificity, ddPCR facilitates precision antimicrobial stewardship and reduces health care costs. The multiplexed ddPCR delivers precise and quantitative load data on the causal pathogen, offers the ability to monitor the patient's condition and may serve as early warning of sepsis in time-urgent clinical situations as ED. Early detection and effective administration of antibiotics are essential to improve clinical outcomes for those with life-threatening infection in the emergency department. ddPCR, an emerging tool for rapid and sensitive pathogen identification used as a precise bedside test, has developed to address the current challenges of BSI diagnosis and precise treatment. It characterizes sensitivity, specificity, reproducibility, and absolute quantifications without a standard curve. ddPCR can detect causative pathogens and related resistance genes in patients with suspected BSIs within a span of three hours. In addition, it can identify polymicrobial BSIs and dynamically monitor changes in pathogenic microorganisms in the blood and can be used to evaluate antibiotic efficacy and survival prognosis. Moreover, the copies of pathogens in ddPCR were positively correlated with various markers of inflammation, coagulation, immunity. With high sensitivity and specificity, ddPCR facilitates precision antimicrobial stewardship and reduces health care costs.

Revolutionizing Healthcare: Qure.AI's Innovations in Medical Diagnosis and Treatment.

Qure.AI, a leading company in artificial intelligence (AI) applied to healthcare, has developed a suite of innovative solutions to revolutionize medical diagnosis and treatment. With a plethora of FDA-approved tools for clinical use, Qure.AI continually strives for innovation in integrating AI into healthcare systems. This article delves into the efficacy of Qure.AI's chest X-ray interpretation tool, "qXR,"in medicine, drawing from a comprehensive review of clinical trials conducted by various institutions. Key applications of AI in healthcare include machine learning, deep learning, and natural language processing (NLP), all of which contribute to enhanced diagnostic accuracy, efficiency, and speed. Through the analysis of vast datasets, AI algorithms assist physicians in interpreting medical data and making informed decisions, thereby improving patient care outcomes. Illustrative examples highlight AI's impact on medical imaging, particularly in the diagnosis of conditions such as breast cancer, heart failure, and pulmonary nodules. AI can significantly reduce diagnostic errors and expedite the interpretation of medical images, leading to more timely interventions and treatments. Furthermore, AI-powered predictive analytics enable early detection of diseases and facilitate personalized treatment plans, thereby reducing healthcare costs and improving patient outcomes. The efficacy of AI in healthcare is underscored by its ability to complement traditional diagnostic methods, providing physicians with valuable insights and support in clinical decision-making. As AI continues to evolve, its role in patient care and medical research is poised to expand, promising further advancements in diagnostic accuracy and treatment efficacy.

Multiscale dilated convolutional neural network for Atrial Fibrillation detection.

Atrial Fibrillation (AF), a type of heart arrhythmia, becomes more common with aging and is associated with an increased risk of stroke and mortality. In light of the urgent need for effective automated AF monitoring, existing methods often fall short in balancing accuracy and computational efficiency. To address this issue, we introduce a framework based on Multi-Scale Dilated Convolution (AF-MSDC), aimed at achieving precise predictions with low cost and high efficiency. By integrating Multi-Scale Dilated Convolution (MSDC) modules, our model is capable of extracting features from electrocardiogram (ECG) datasets across various scales, thus achieving an optimal balance between precision and computational savings. We have developed three MSDC modules to construct the AF-MSDC framework and assessed its performance on renowned datasets, including the MIT-BIH Atrial Fibrillation Database and Physionet Challenge 2017. Empirical results unequivocally demonstrate that our technique surpasses existing state-of-the-art (SOTA) methods in the AF detection domain. Specifically, our model, with only a quarter of the parameters of a Residual Network (ResNet), achieved an impressive sensitivity of 99.45%, specificity of 99.64% (on the MIT-BIH AFDB dataset), and an [Formula: see text] score of 85.63% (on the Physionet Challenge 2017 AFDB dataset). This high efficiency makes our model particularly suitable for integration into wearable ECG devices powered by edge computing frameworks. Moreover, this innovative approach offers new possibilities for the early diagnosis of AF in clinical applications, potentially improving patient quality of life and reducing healthcare costs.

Die räumliche Produktion von Alternsbildern durch Smart-Home-Technologien

Abstract. Promising independence in old age, smart home technologies are increasingly being mobilized as a solution to the care crisis in western industrialized societies. A widely accepted concept, “ageing in place” promotes ageing and care at home, with technologies ensuring implementation and reducing healthcare costs. However, images of ageing that promote active ageing and problematize old age are inscribed in the development process of smart home technologies. Based on an ethnographic investigation of technically mediated promises and use cases and interviews with technology developers and exhibitors, we show how smart home technologies construct images of self-responsible, active age on the one hand, and dependent old age on the other. Drawing on a social constructivist notion of ageing and extending feminist STS influenced conceptions of the co-construction of technology and ageing, we show that images of ageing are also spatial constructions that idealize the home as a place of the “third age” and devalue the nursing home as a place of the “fourth age”.

Transcending Language Barriers: Can ChatGPT Be the Key to Enhancing Multilingual Accessibility in Health Care?

ObjectiveTo explore the capabilities of Chat Generative Pre-trained Transformer (ChatGPT) for the purpose of simplifying and translating radiology reports into Spanish, Hindi, and Russian languages, with comparisons to its performance in simplifying to the English language. MethodsFifty deidentified abdomen-pelvis CT reports were fed to ChatGPT (4.0), instructing it to simplify and translate the report. The processed reports were rated on factual correctness (category 1), potential harmful errors (category 2), completeness (category 3), and explanation of medical terms (category 4). The translated versions were also rated on the quality of translation (category 5). The scores in each category were compared between the translated versions and each translated version was compared with the English version in the first four categories. The original reports and the simplified English reports were rated on the Flesch Reading Ease Score and the Flesch Kincaid Grade Level. ResultsThe Spanish translation outperformed the Hindi and Russian version significantly in categories 1 and 3 (P < .05). All translated versions performed significantly worse compared with the English version in category 4 (P < .001). Notably, the Hindi translated version performed significantly worse in all four categories (P < .05). The Russian translated version was also significantly worse in category 3 (P < .05). In the first three categories, the Spanish translation, and in the first two categories, the Russian translation demonstrated no statistically significant difference from the English version. No statistically significant difference was observed in the Flesch Reading Ease Score and Flesch Kincaid Grade Level of the simplified English reports. Typographical errors in the original reports negatively affected the translation. ConclusionChatGPT demonstrates potential ability in translating reports and communicating pertinent clinical information with limited errors. More training and tailoring are required for languages that are not as commonly used in medical literature. Large language models can be used for translating and simplifying radiology reports, potentially improving access to health care and helping reduce health care costs.

Chemotherapy Care Companion: Enhancing cancer care through digital remote monitoring.

1636 Background: Oncology patients often encounter preventable acute issues prompting the need for proactive monitoring and management. To manage disease and therapy complications, Ochsner Health developed Chemotherapy Care Companion (CCC) for remote monitoring of treatment patients. This abstract highlights the feasibility of digital remote monitoring (DRM) in managing oncologic patient care, while increasing patient satisfaction and reducing patient events and healthcare costs. Methods: Starting in January 2021, patients initiating an oral or intravenous cancer treatment with an active Ochsner portal account and smartphone became eligible. Providers can manually enroll patients via an order set and receive enrollment reminders within the Epic electronic medical record (EMR). Patients receive a digital WiFi-enabled monitoring scale, blood pressure cuff, and ear thermometer, at no cost. Patients are reminded via phone alerts to take vitals at 9am and 3pm, along with a daily questionnaire. The data is transmitted wirelessly to the EMR, where abnormal vital signs or questionnaire responses trigger escalations sent to an Epic in-basket pool. During clinic hours, advanced practice providers (APPs) review and intervene, utilizing embedded urgent care slots for same-day access. In August 2022, a patient satisfaction survey comprising of nine questions was distributed to all enrolled patients. Results: A retrospective analysis was conducted from September 2022 to August 2023 including 722 participants, 7,360 escalations and 668 interventions. Patient compliance reached 62%, defined as submitting at least 50% of daily vitals. The most common interventions were related to hypertension, hypotension, weight changes, and questionnaire responses. Regarding questionnaire responses, diarrhea, pain, fever, and dizziness were the mostly frequently triaged. Data analytics revealed that CCC led to a 5.9% decrease in emergency room visits, a 41.7% decrease in the rate of inpatient hospital admissions, and a 32.3% improvement in the inpatient length of stay compared to non-enrolled patients. Extrapolating this data across 5,500 oncology patients within our hospital system, CCC could prevent 172 ER visits, 914 inpatient hospital admissions and 2,006 hospital days. The survey (131 responses) revealed high satisfaction with CCC: over 80% felt a sense of security, 90% were overall satisfied, and more than 90% would recommend CCC. Conclusions: Findings highlight the value of integrating DRM in cancer care, showcasing its ability to reduce healthcare costs and optimize resource utilization while enhancing patient care and satisfaction. Moreover, the study underscores the high patient satisfaction and engagement with DRM, bolstering its feasibility and acceptance in oncology care. Further investigation is warranted to explore the financial benefits for hospitals, insurance companies, and pharmaceutical companies.

The Development of Artificial Intelligence in Knee Joint MRI Detection

The application of artificial intelligence in the diagnosis and analysis of knee joint MRI has significantly advanced, leveraging technologies like machine learning and deep learning to enhance both accuracy and efficiency. AI models are adept at identifying, classifying, and predicting various pathologies such as osteoarthritis and ligament tears by analyzing complex imaging data. This facilitates more accurate diagnoses by assisting radiologists. Key developments include automation of image segmentation, image resolution enhancement, and noise reduction in MRI scans. Despite these advances, integrating AI into clinical workflows, managing data variability, and ensuring extensive validation pose ongoing challenges. Nonetheless, AI's potential to transform diagnostic processes, improve patient outcomes, and reduce healthcare costs by streamlining workflows remains promising.

The Role of Alternative Medicine in Managing Type 2 Diabetes: A Comprehensive Review.

Diabetes, a chronic metabolic disorder marked by elevated blood glucose levels, is increasingly prevalent globally, significantly impacting health-related quality of life. Type 2 diabetes (T2DM), characterized by insulin resistance and inadequate insulin production, presents a substantial public health challenge, necessitating comprehensive management strategies. Conventional treatments, including lifestyle modifications and pharmacotherapy, are essential for glycemic control and preventing complications. However, adherence to these treatments is often limited, highlighting the need for alternative strategies. Complementary and alternative medicine (CAM) offers potential cost-effective and accessible approaches for managing T2DM.Key herbal remedies like cinnamon, fenugreek, and bitter melon, along with dietary supplements like chromium, magnesium, and vanadium, have shown promise in glycemic control. Mind-body therapies, including yoga, tai chi, and meditation, contribute to improved hemoglobin A1c and fasting blood glucose levels. Research supports the integration of CAM with conventional therapies, demonstrating enhanced clinical efficacy and reduced economic burden. However, challenges such as standardization, quality control, and potential risks of herbal medicines need careful consideration. Regulatory frameworks and ethical considerations are essential to ensure patient safety and informed decision-making.Patient education and effective communication between healthcare providers and patients are crucial for integrating CAM into diabetes management. Empowerment-based interventions and collaborative approaches can enhance self-management skills and clinical outcomes. Overall, integrating CAM with conventional treatments offers a holistic approach to managing T2DM, potentially improving patient outcomes and reducing healthcare costs.

Generative artificial intelligence in healthcare from the perspective of digital media: Applications, opportunities and challenges

IntroductionThe emergence and application of generative artificial intelligence/large language models (hereafter GenAI LLMs) have the potential for significant impact on the healthcare industry. However, there is currently a lack of systematic research on GenAI LLMs in healthcare based on reliable data. This article aims to conduct an exploratory study of the application of GenAI LLMs (i.e., ChatGPT) in healthcare from the perspective of digital media (i.e., online news), including the application scenarios, potential opportunities, and challenges. MethodsThis research used thematic qualitative text analysis in five steps: firstly, developing main topical categories based on relevant articles; secondly, encoding the search keywords using these categories; thirdly, conducting searches for news articles via Google ; fourthly, encoding the sub-categories using the elaborate category system; and finally, conducting category-based analysis and presenting the results. Natural language processing techniques, including the TermRaider and AntConc tool, were applied in the aforementioned steps to assist in text qualitative analysis. Additionally, this study built a framework, using for analyzing the above three topics, from the perspective of five different stakeholders, including healthcare demanders and providers. ResultsThis study summarizes 26 applications (e.g., provide medical advice, provide diagnosis and triage recommendations, provide mental health support, etc.), 21 opportunities (e.g., make healthcare more accessible, reduce healthcare costs, improve patients care, etc.), and 17 challenges (e.g., generate inaccurate/misleading/wrong answers, raise privacy concerns, lack of transparency, etc.), and analyzes the reasons for the formation of these key items and the links between the three research topics. ConclusionsThe application of GenAI LLMs in healthcare is primarily focused on transforming the way healthcare demanders access medical services (i.e., making it more intelligent, refined, and humane) and optimizing the processes through which healthcare providers offer medical services (i.e., simplifying, ensuring timeliness, and reducing errors). As the application becomes more widespread and deepens, GenAI LLMs is expected to have a revolutionary impact on traditional healthcare service models, but it also inevitably raises ethical and security concerns. Furthermore, GenAI LLMs applied in healthcare is still in the initial stage, which can be accelerated from a specific healthcare field (e.g., mental health) or a specific mechanism (e.g., GenAI LLMs’ economic benefits allocation mechanism applied to healthcare) with empirical or clinical research.

Estimating the health care costs associated with receipt of lisocabtagene maraleucel: Insights from adults with mantle cell lymphoma (TRANSCEND NHL 001).

7028 Background: Anti-CD19 CAR T cell therapy is a major advancement for treating R/R B-cell malignancies such as mantle cell lymphoma (MCL). While effective disease management with CAR T cell therapy is expected to reduce patients’ health care resource utilization (HCRU) over the long-term, few studies have examined the short-term impacts of this treatment on HCRU and associated health care costs. Here, we used data from the TRANSCEND NHL 001 (NCT02631044) clinical trial to estimate the HCRU-related costs incurred by patients with R/R MCL over the first 12 months after receipt of lisocabtagene maraleucel (liso-cel). Methods: A retrospective analysis of clinical trial data was conducted to calculate the frequency of HCRU (medications [excluding liso-cel], diagnostics, procedures, ICU and non-ICU hospitalizations) observed over the 12 months immediately after administration of liso-cel. Costs for each HCRU event were derived from public databases and literature, adjusted to 2023 United States (US) dollars, and used to estimate patient-level health care costs. Results: Of the patients treated (N = 88), 76.1% were male, 87.5% were White, and mean ± standard deviation (SD) age was 67.5 ± 9 years. All patients were treated in the US and completed a median (range) of 3 (1–11) prior lines of therapy before receiving liso-cel. Most patients (93.2%) received the recommended dose of 100 × 106 CAR+ T cells. The total mean ± SD per-patient cost (excluding liso-cel) was estimated at $138,413 ± $58,555 over 12 months. Facility use accounted for 77.7% of all costs, primarily owing to non-ICU hospitalizations (98.9%), which had a mean ± SD length of stay of 19.5 ± 16.7 days per eligible patient. Half of all health care costs were incurred within the first month of infusion (53.2%), with each subsequent month contributing a fraction (1.5%–6.3%) of total costs. A total of 13 out of 88 patients received their initial infusion in an outpatient setting and incurred 23% fewer costs compared with those who received their infusion in an inpatient setting ($110,050 vs $143,418), primarily as a function of reduced first-month facility use. Total costs for patients receiving liso-cel in outpatient settings were further reduced to $72,771 upon exclusion of 3 outliers. Conclusions: Non-ICU hospitalizations were the largest driver of liso-cel–related costs in this R/R MCL cohort of clinical trial participants. Most of the cost burden was incurred within 1 month of treatment, with dramatic reductions in HCRU and health care costs observed thereafter. Results suggest that treatment in the outpatient setting, even in part (eg, at initial infusion only), may confer substantial cost savings to the health care system.

More competent informal caregivers reduce advanced cancer patients' unplanned healthcare use and costs.

Patients with metastatic cancer experience high healthcare use and costs, most of which are unplanned. We aimed to assess whether patients with more competent informal caregivers have lower unplanned healthcare use and costs. This study used data from a prospective cohort of patients with solid metastatic cancer. Patients and their informal family caregivers were surveyed every 3 months until patients' death. Patients' unplanned healthcare use/costs were examined through hospital records. Caregivers responded to the 4-item Caregiver Competence Scale. First, in a deceased subsample of patients and their caregivers, we used patients' last 2 years of data (226 dyads) to assess the association between caregivers' competency (independent variable) and patients' unplanned healthcare use/costs (outcomes). Next, in a prospective sample of patient-caregiver dyads (up to 15 surveys), we assessed whether patients' functional well-being and psychological distress moderated the association between caregivers' competency and unplanned healthcare use/costs (311 dyads). In the deceased subsample, during last 2 years of patients' life, caregivers' higher competency lowered the odds of patients' unplanned healthcare use [OR (CI) = 0.86 (0.75, 0.98), p = 0.03], and was associated with a significant reduction in unplanned healthcare costs [Coeff (CI) = -0.19 (-0.36, -0.01), p = 0.03]. In the prospective sample, patients' functional well-being and psychological distress moderated the association between caregivers' competency and patients' unplanned healthcare use/costs. With deterioration in patients' condition and an increase in caregiving demands, improving caregivers' competency can reduce patients' unplanned healthcare use and costs. This should be further tested in future trials.

Outpatient parenteral antibiotic therapy (OPAT) through elastomeric continuous infusion pumps in a real-life observational study: Characteristics, safety, and efficacy analysis

IntroductionThis study aimed to present real-life data on the use, efficacy, and safety of administering antibiotic therapy through portable elastomeric pumps (pEP) in the outpatient setting. MethodsThis retrospective observational cohort study was conducted from January 2020 to May 2023 in a large academic hospital in Rome, Italy. All patients receiving antibiotic therapy via pEP were included up to a follow-up period of 90 days after the end of antibiotic therapy.The primary outcome was the treatment response. Secondary endpoints were adverse events attributable to the drug administered, the vascular catheter, or the infection itself. ResultsOf the 490 patients referred to our outpatient parenteral antibiotic therapy (OPAT) unit, 94 (19.2%) received antibiotic therapy via pEP and were included in the final analysis. The most frequently treated infections were those involving bone and prosthetics, including spondylodiscitis (n=27; 28.8%). Most infections were due to Pseudomonas aeruginosa (n=55; 48.3%). Cefepime (n=32; 34.0%), piperacillin/tazobactam (n=29; 30.9%), ceftolozane/tazobactam (n=7; 7.5%), and oxacillin (n=7; 7.5%) were the most frequently administered antibiotics. The infection cure rate reached 88.3% (n=83). 12 patients (12.8%) reported adverse events, of which half (6.4%) were drug-related and half (6.4%) were line-related. ConclusionsOPAT through portable elastomeric infusion pumps proved to be safe and effective. It also contributed to the reduction of healthcare costs, fully respecting the principles of personalized medicine. This strategy has emerged as a promising tool for antibiotic stewardship and infection control.

Medicare reimbursement trends of biological reference agents and their biosimilars.

11155 Background: Biosimilars have the potential to reduce healthcare costs for payers and provide savings to patients by introducing competition and driving down prices. Centers for Medicare and Medicaid Services’ (CMS) Average Sales Price (ASP) is used as an index price to determine Medicare drug reimbursement (MCR) in the United States and is published on a quarterly basis. We studied the trends of ASP and MCR reimbursement rates for reference and biosimilar agents. Methods: Using publicly available quarterly CMS data, we evaluated and compared the trends of MCR for 7 reference and 25 biosimilar agents for a period of 2 years before and 5 years after launch. We evaluated the MCR for filgrastim, pegfilgrastim, bevacizumab, trastuzumab, rituximab, infliximab, epoetin-alfa, and their respective biosimilars. Results: On average, the published MCR for all reference agents increased by 9.2% (range: 0.6% to 16.8%) over the 2-year period prior to the biosimilar launch. Over the 5-year period following the launch of the first biosimilar, reference agents’ MCR fell 32.7% (range: -8.0% to -78.0%) while biosimilar agents’ MCR fell 50.3% (range: -3.7% to -90.5%) over the same period, with a median follow-up period of 16 quarters. Biosimilar MCR rates fell at an accelerated rate compared to reference agents. 76% (19 of 25) of the biosimilars that entered the market had an initial MCR below, and 20% (5 of 25) above, that of the reference agent. 16 of 18 successive biosimilars for reference agents launched with a MCR above that of the first biosimilar. Despite the fall in MCR, reference agents’ rates in the most recent quarter were 2x higher than their corresponding biosimilar agents, except for pegfilgrastim, which had a precipitous fall in MCR (87%) after biosimilar launch to a price lower than 5 of its 6 biosimilars. Conclusions: Biosimilars, with lower Medicare reimbursement at launch, and an accelerated decline in ASP post-launch, deliver on the goal of driving lower healthcare costs and increased savings. Biosimilar competition breaks the price increase trends with reference agents and contributes to a decrease in reference agent drug reimbursement. The effects of manufacturer rebates, payer policies, 340b programs, and discounts on ASP and Medicare reimbursement are also areas of active investigation.