Allocation Of Health Care Resources Research Articles

Granulicatella adiacens: An uncommon pathogen in prosthetic joint infections

Granulicatella adiacens is an uncommon culprit in prosthetic joint infections, especially when predisposing factors are absent. Here, we present the case of a 78-year-old female who developed an acute surgical site infection shortly after undergoing left total knee arthroplasty. Despite initial negative cultures, subsequent analysis identified Granulicatella adiacens in the knee fluid, marking only the second reported instance in the United States and the seventh globally. This singular case highlights the considerable challenges associated with isolating this organism, emphasizing its atypical presentation and diagnostic elusiveness. Typically associated with endocarditis and bacteremia, its involvement in isolated surgical site infections is particularly unusual. Our case underscores the necessity for heightened clinical suspicion and the adoption of specialized diagnostic methodologies to accurately detect Granulicatella adiacens infections. Given its fastidious nature and nutritional requirements, standard culture methods may fail to identify this pathogen, necessitating alternative diagnostic approaches. As such, the utilization of specialized media becomes paramount in clinical diagnostics to facilitate appropriate management and optimize healthcare resource allocation. By enhancing our ability to detect and treat infections caused by Granulicatella adiacens, we can mitigate patient morbidity and reduce the burden of unnecessary interventions, thereby improving patient outcomes and healthcare efficiency.

Clinical prediction model for MODY type diabetes mellitus in children

BACKGROUND: MODY (maturity-onset diabetes of the young) is a rare monogenic form of diabetes mellitus, the gold standard of diagnosis is mutations detection in the genes responsible for the development of this form diabetes. Genetic test is expensive and takes a lot of time. The diagnostic criteria for MODY are well known. The development of clinical decision support system (CDSS) which allows physicians based on clinical data to determine who should have molecular genetic testing is relevant.AIM: Provided a retrospective analysis of clinical data of the patients with T1DM and MODY, from 0 to 18 years old, regardless of the duration of the disease to develop the model. Based on clinical data, a feedforward neural network (NN) was implemented - a multilayer perceptron.MATERIALS AND METHODS: Development of the most effective algorithm for predicting MODY in children based on available clinical indicators of 1710 patients with diabetes under the age of 18 years using a multilayer feedforward neural network.RESULTS: The sample consisted of 1710 children under the age of 18 years with T1DM (78%) and MODY (22%) diabetes. For the final configuration of NS the following predictors were selected: gender, age at passport age, age at the diagnosis with DM, HbA1c, BMI SDS, family history of DM, treatment. The performance (quality) assessment of the NN was carried out on a test sample (the area under the ROC (receiver operating characteristics) curve reached 0.97). The positive predictive value of PCPR was achieved at a cut-off value of 0.40 (predicted probability of MODY diabetes 40%). At which the sensitivity was 98%, specificity 93%, PCR with prevalence correction was 78%, and PCR with prevalence correction was 99%, the overall accuracy of the model was 94%.Based on the NN model, a CDSS was developed to determine whether a patient has MODY diabetes, implemented as an application.CONCLUSION: The clinical prediction model MODY developed in this work based on the NN, uses the clinical characteristic available for each patient to determine the probability of the patient having MODY. The use of the developed model in clinical practice will assist in the selection of patients for diagnostic genetic testing for MODY, which will allow for the efficient allocation of healthcare resources, the selection of personalized treatment and patient monitoring.

Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction

This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques—Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection—play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts.

Resisting the heat wave: Revealing inequalities in matching between heat exposure risk and healthcare services in a megacity

Climate warming and the growing urban heat island effect are exacerbating the unequal public health crisis in megacities, and thus it is important to assess the equity of matching relationship between the healthcare services and the heat exposure risk. We selected 255 census units in Tianjin city for the study, and calculated a heat exposure index based on the universal thermal climate index (UTCI) and constructed a comprehensive urban healthcare services evaluation system to measure the degree of mismatch between the two. The results showed the following. (1) The heat exposure risk and healthcare services followed an increasing pattern from the Outer suburb area (OSA) to the Metropolitan development area (MDA) and then to the Main urban area (MUA). (2) The overall imbalance in matching was low in the OSA, whereas the healthcare services were relatively insufficient in the MDA, and the heat exposure risk and healthcare services were relatively balanced in the MUA. (3) The degree of matching between the core and peripheral parts of the MUA was severely polarized, with an overall pattern of excess healthcare in the core area and a high risk of heat exposure in the peripheral areas. The results can provide effective guidance for rationalizing the allocation of healthcare resources from a heat exposure perspective, and provide a policy basis for risk management and fund investment.

Comparative evaluation of postoperative outcomes and expenditure between robotic and conventional single-level lumbar fusion surgery: a comprehensive analysis of nationwide inpatient sample data.

In this study, we investigate the evolution of lumbar fusion surgery with robotic assistance, specifically focusing on the impact of robotic technology on pedicle screw placement and fixation. Utilizing data from the Nationwide Inpatient Sample (NIS) covering 2016 to 2019, we conduct a comprehensive analysis of postoperative outcomes and costs for single-level lumbar fusion surgery. Traditionally, freehand techniques for pedicle screw placement posed risks, leading to the development of robotic-assisted techniques with advantages such as reduced misplacement, increased precision, smaller incisions, and decreased surgeon fatigue. However, conflicting study results regarding the efficacy of robotic assistance in comparison to conventional techniques have prompted the need for a thorough evaluation. With a dataset of 461,965 patients, our aim is to provide insights into the impact of robotic assistance on patient care and healthcare resource utilization. Our primary goal is to contribute to the ongoing discourse on the efficacy of robotic technology in lumbar fusion procedures, offering meaningful insights for optimizing patient-centered care and healthcare resource allocation. This study employed data from the Nationwide Inpatient Sample (NIS) spanning the years 2016 to 2019 from USA, 461,965 patients underwent one-level lumbar fusion surgery, with 5770 of them having the surgery with the assistance of robotic technology. The study focused primarily on one-level lumbar fusion surgery and excluded non-elective cases and those with prior surgeries. The analysis encompassed the identification of comorbidities, surgical etiologies, and complications using specific ICD-10 codes. Throughout the study, a constant comparison was made between robotic and non-robotic lumbar fusion procedures. Various statistical methods were applied, with a p value threshold of < 0.05, to determine statistical significance. Robotic-assisted lumbar fusion surgeries demonstrated a significant increase from 2016 to 2019, comprising 1.25% of cases. Both groups exhibited similar patient demographics, with minor differences in payment methods, favoring Medicare in non-robotic surgery and more private payer usage in robotic surgery. A comparison of comorbid conditions revealed differences in the prevalence of hypertension, dyslipidemia, and sleep apnea diagnoses-In terms of hospitalization outcomes and costs, there was a slight shorter hospital stay of 3.06days, compared to 3.13days in non-robotic surgery, showcasing a statistically significant difference (p = 0.042). Robotic surgery has higher charges, with a mean charge of $154,673, whereas non-robotic surgery had a mean charge of $125,467 (p < 0.0001). Robotic surgery demonstrated lower rates of heart failure, acute coronary artery disease, pulmonary edema, venous thromboembolism, and traumatic spinal injury compared to non-robotic surgery, with statistically significant differences (p < 0.05). Conversely, robotic surgery demonstrated increased post-surgery anemia and blood transfusion requirements compared to non-robotic patients (p < 0.0001). Renal disease prevalence was similar before surgery, but acute kidney injury was slightly higher in the robotic group post-surgery (p = 0.038). This is the first big data study on this matter, our study showed that Robotic-assisted lumbar fusion surgery has fewer post-operative complications such as heart failure, acute coronary artery disease, pulmonary edema, venous thromboembolism, and traumatic spinal injury in comparison to conventional methods. Conversely, robotic surgery demonstrated increased post-surgery anemia, blood transfusion and acute kidney injury. Robotic surgery has higher charges compared to non-robotic surgery.

Primary care clinic visits in formerly evacuated areas due to radiation disaster following the Great East Japan Earthquake: A retrospective descriptive study.

Radiation disasters pose distinctive medical challenges, requiring diverse care approaches. Beyond radiation exposure assessment, addressing health impacts due to lifestyle changes, especially among vulnerable populations, is vital. Evacuation orders issued in radiation-affected areas introduce unique healthcare dynamics, with their duration significantly influencing the recovery process. Understanding evolving patient demographics and medical needs after lifting evacuation orders is crucial for post-disaster care planning. Minamisoma Municipal Odaka Hospital, located 13 to 20 km from Fukushima Daiichi Nuclear power plant in a post-evacuation zone, was greatly affected by the Great East Japan Earthquake and subsequent radiation disaster. Data were retrospectively collected from patient records, including age, gender, visit date, diagnoses, and addresses. Patient records from April 2014 to March 2020 were analyzed, comparing data before and after the July 2016 evacuation order lift. Data was categorized into pre and post-evacuation order lifting periods, using International Classification of Diseases, Tenth Edition codes, to identify the top diseases. Statistical analyses, including χ-square tests, assessed changes in disease distributions. Population data for Odaka Ward and Minamisoma City fluctuated after lifting evacuation orders. As of March 11, 2011, Odaka Ward had 12,842 residents (27.8% aged 65+ years), dropping to 8406 registered residents and 2732 actual residents by April 30, 2018 (49.7%). Minamisoma City also saw declines, with registered residents decreasing from 71,561 (25.9%) to 61,049 (34.1%). The study analyzed 11,100 patients, mostly older patients (75.1%), between 2014 and 2020. Post-lifting, monthly patient numbers surged from an average of 55.2 to 213.5, with female patients increasing from 33.8% to 51.7%. Disease patterns shifted, with musculoskeletal cases declining from 23.8% to 13.0%, psychiatric disorders increasing from 9.3% to 15.4%, and trauma-related cases decreasing from 14.3% to 3.9%. Hypertension (57.1%) and dyslipidemia (29.2%) prevailed post-lifting. Urgent cases decreased from 1.3% to 0.1%. This study emphasizes the importance of primary care in post-evacuation zones, addressing diverse medical needs, including trauma, noncommunicable diseases, and psychiatric disorders. Changing patient demographics require adaptable healthcare strategies and resource allocation to meet growing demands. Establishing a comprehensive health maintenance system tailored to these areas' unique challenges is crucial for future disaster recovery efforts.

TEMPORAL TRENDS AND REGIONAL VARIATION OF HIP ARTHROSCOPY AND PELVIC OSTEOTOMY IN ENGLAND FROM 2010 TO 2023

Hip arthroscopy (HA) and pelvic osteotomy (PO) are surgical procedures used to treat a variety of hip pathology affecting young adults, including femoroacetabular impingement and hip dysplasia respectively. This study aimed to investigate the trends and regional variation in the provision of HA and PO across England from 2010 to 2023 to inform healthcare resource allocation.We analysed the National Hospital Episode Statistics database for all HA and PO procedures in NHS England using specific OPCS-4 codes: HA: ‘W83+Z843’ or ‘W84+Z843’; PO: ‘X222+Z75’. We collected patient demographics, age, sex, and region of treatment. We performed descriptive and regression analyses to evaluate temporal trends in PO volume, age, sex and regional variation.22,401 HAs and 1,348 POs were recorded between 2010 and 2023. The annual number of HAs declined by 28.4%, whilst the number of POs increased by 64% (p&lt;0.001). Significantly more females underwent PO vs HA (90% vs 61.3%) and were older than males undergoing the same procedure (PO: 29.0±8.7 vs 25.8±9.2 years; HA: 36.8±12.0 years vs 35.8±11.2 years, p&lt;0.001). For HA, the mean age of both sexes decreased by 3.3 and 2.9 years respectively (p&lt;0.001), whereas the age of PO patients did not change significantly over the study period. There were significant regional variations with a mean incidence of 1.60/100,00 for HA (ranging from 0.70–2.66 per 100,000) and 0.43/100,000 for PO (ranging from 0.08–2.07 per 100,000).We have observed a decline in HA volume in England, likely due to improved patient selection and the impact of COVID-19, whilst PO volume has significantly increased, with regional variation persisting for both procedures. These trends highlight the need for equitable HA and PO access to improve patient outcomes and call for strategic healthcare planning and resource allocation to reduce disparities and improve training opportunities.

Spatial distribution and influencing factors of CDC health resources in China: a study based on panel data from 2016-2021.

Measuring the development of Chinese centers for disease control and prevention only by analyzing human resources for health seems incomplete. Moreover, previous studies have focused more on the quantitative changes in healthcare resources and ignored its determinants. Therefore, this study aimed to analyze the allocation of healthcare resources in Chinese centers for disease control and prevention from the perspective of population and spatial distribution, and to further explore the characteristics and influencing factors of the spatial distribution of healthcare resources. Disease control personnel density, disease control and prevention centers density, and health expenditures density were used to represent human, physical, and financial resources for health, respectively. First, health resources were analyzed descriptively. Then, spatial autocorrelation was used to analyze the spatial distribution characteristics of healthcare resources. Finally, we used spatial econometric modeling to explore the influencing factors of healthcare resources. The global Moran index for disease control and prevention centers density decreased from 1.3164 to 0.2662 (p < 0.01), while the global Moran index for disease control personnel density increased from 0.4782 to 0.5067 (p < 0.01), while the global Moran index for health expenditures density was statistically significant only in 2016 (p < 0.1). All three types of healthcare resources showed spatial aggregation. Population density and urbanization have a negative impact on the disease control and prevention centers density. There are direct and indirect effects of disease control personnel density and health expenditures density. Population density and urbanization had significant negative effects on local disease control personnel density. Urbanization has an indirect effect on health expenditures density. There were obvious differences in the spatial distribution of healthcare resources in Chinese centers for disease control and prevention. Social, economic and policy factors can affect healthcare resources. The government should consider the rational allocation of healthcare resources at the macro level.

Academic Productivity in Ophthalmology and Its Correlation to National Economic Indicators Among the OECD Countries: A Bibliometric Analysis

ABSTRACT Purpose Prompted by the clinical concern that limited healthcare resources allocation affects physicians’ research productivity, this study examines the association between bibliometric indices of ophthalmologic research and national economic indicators in Organisation for Economic Co-operation and Development (OECD) countries. Methods The Scimago Journal and Country rank source was searched for research productivity data in ophthalmology among OECD countries between 1996 and 2019. Bibliometric indices included: documents number, number and percent of citable documents, citations number, citations per document, and H-index. The updated economic indicators of each country (gross domestic product [GDP] per capita, health spending as percent of GDP (health expenditure), gross domestic expenditure on research, and development as percent of GDP [GERD]) were collected from the World Bank and the OECD websites. Correlation between economic and bibliometric metrics and multivariate linear regression analyses were performed. Results Among 267,444 documents analyzed, correlation analysis found a strong correlation between health expenditure and H index (r = 0.711, p < 0.001); a moderate correlation between health expenditure and documents number (r = 0.589, p < 0.001), number of citable document (r = 0.593, p < 0.001) and citations number (r = 0.673, p < 0.001); and a moderate correlation between GERD and H index (r = 0.564, p < 0.001). Multivariate regression analysis controlling for economic factors, population and language showed the independent association of these parameters with bibliometric indices. Conclusions This study demonstrates a positive correlation between bibliometric indicators of ophthalmology research and economic factors, particularly health expenditure, among the OECD countries. Our results suggest an advantage of domestic investment in health to expand academic productivity in the field of ophthalmology.

Effectiveness and Satisfaction with Telemedicine in Geriatric Patients at High Risk of Fragility Fractures.

Background: Telemedicine has increasingly widespread to improve the monitoring of patients with chronic diseases. Secondary prevention of fragility fractures is an urgent matter to be addressed by means of available technology, although supported by little evidence so far. We investigated the feasibility, efficacy, and satisfaction of managing older adults at high risk of fragility fractures during the COVID-19 lockdown. Methods: During the period January to July 2021, a prospective observational study for safety and adherence purposes was conducted among older adults (n = 407) with ongoing treatments for secondary prevention of fragility fractures. The study procedures comply with national and regional resolutions related to telemedicine service (TS), including equipment, staff behaviors, and patient reports. Results: A majority (86.48% [n = 352]) of the eligible patients joined the remote visits, mainly women (88.2%), 81.4 ± 8.8 years of age, 49.6% independent in 5 out of 6 BADL, despite high comorbidity (4.9 ± 1.5), and polypharmacy (4.9 ± 3.1). Almost all were on second-line antifracture treatments (95.58%) due to previous major (84.03%) and minor (42.5%) fragility fractures. About 58% reported good and very good reliability of the internet network, allowing easy access to the TS platform, and 54% declared the degree of satisfaction with TS as good and very good. About 75% of clinicians acknowledged the efficacy of TS and expressed willingness to recommend the use of TS to colleagues. Ultimately, 68% of specialists defined the time allocated for patients' remote visits as acceptable. Conclusion: TS may be an opportunity to improve the availability of appropriate health care services to satisfy patients' needs and optimize health care resource allocation.

Healthcare Resource Allocation Optimization

Assuring high-quality patient care and making the best use of the resources at hand requires the efficient allocation of healthcare resources. This study uses a thorough examination of patient admission data, treatment plans, and staff availability to tackle the complex problem of optimizing healthcare resource allocation. We want to reduce wait times, optimize resource use, and eventually improve patient outcomes in healthcare facilities by integrating these essential elements. Our strategy makes use of cutting-edge data analytics tools, such as optimization algorithms and predictive modelling, to find underlying patterns and trends in patient demand, treatment regimens, and staffing needs. Our goal is to create prediction models that anticipate patient admissions, treatment requirements, and staffing levels by analysing historical data and real-time inputs. This will allow for proactive resource allocation techniques. The study's conclusions have important ramifications for healthcare administration since they provide practical advice on how to improve patient care delivery, cut wait times, and make the most use of the resources at hand. Healthcare institutions can enhance resource allocation procedures to meet the changing demands of patient care while guaranteeing the optimal use of resources by integrating technology, data-driven decision-making, and evidence-based practices. In summary, this study lays the groundwork for future investigations and innovations in hospital administration and operations while also supporting current initiatives to enhance healthcare resource allocation procedures. Healthcare organizations can improve patient outcomes and achieve operational excellence while providing high-quality, patient-centred care by adopting data-driven approaches and utilizing technology.

A cost-effectiveness analysis of fluoride varnish application in preventing Root caries in elderly persons: a Markov simulation study

BackgroundRoot caries are prevalent issues that affect dental health, particularly among elderly individuals with exposed root surfaces. Fluoride therapy has shown effectiveness in preventing root caries, but limited studies have addressed its cost-effectiveness in elderly persons population. This study aimed to evaluate the cost-effectiveness of a fluoride treatment program for preventing root caries in elderly persons within the context of Chinese public healthcare.MethodsA Markov simulation model was adopted for the cost-effectiveness analysis in a hypothetical scenario from a healthcare system perspective. A 60-year-old subject with 23 teeth was simulated for 20 years. A 5% sodium fluoride varnish treatment was compared with no preventive intervention in terms of effectiveness and cost. Tooth years free of root caries were set as the effect. Transition probabilities were estimated from the data of a community-based cohort and published studies, and costs were based on documents published by the government. The incremental cost-effectiveness ratio (ICER) was calculated to evaluate cost-effectiveness. Univariate and probabilistic sensitivity analyses were performed to evaluate the influence of data uncertainty.ResultsFluoride treatment was more effective (with a difference of 10.20 root caries-free tooth years) but also more costly (with a difference of ¥1636.22). The ICER was ¥160.35 per root caries-free tooth year gained. One-way sensitivity analysis showed that the risk ratio of root caries in the fluoride treatment group influenced the result most. In the probabilistic sensitivity analysis, fluoride treatment was cost-effective in 70.5% of the simulated cases.ConclusionsRegular 5% sodium fluoride varnish application was cost-effective for preventing root caries in the elderly persons in most scenarios with the consideration of data uncertainty, but to a limited extent. Improved public dental health awareness may reduce the incremental cost and make the intervention more cost-effective. Overall, the study shed light on the economic viability and impact of such preventive interventions, providing a scientific basis for dental care policies and healthcare resource allocation.

EQ-5D-3L and EQ-5D-5L population norms for Thailand

BackgroundThe previous Thai norm-based scores for the EQ-5D-5L were established with Thai general population samples aged 20–70 years in 2019. Nevertheless, these values need to be updated after the COVID-19 pandemic because of its effects on both physical and mental health. This study therefore aimed to establish population norms of the Thai EQ-5D-3L, EQ-5D-5L and EQ-VAS scores as well as to estimate disutility values associated with self-reported main diseases.MethodsIndividual face-to-face interviews were conducted with 2000 adult (age ≥ 18 years) members of the general Thai population to estimate norm-based scores. Each participant completed the EQ-5D-3L and EQ-5D-5L as well as questions related to their sociodemographic factors and self-reported main diseases. Responses to the two instruments were converted to health utility (HU) scores on the basis of available value sets. Descriptive statistics were used to report the norm-based scores stratified by age and sex categories. Response redistribution determining the response consistency between EQ-5D versions was investigated. The HU score agreement from those two instruments was investigated using intraclass correlation coefficient (ICC). Tobit regression models were employed to investigate the relationships between sociodemographic factors and HU and EQ-VAS scores. Moreover, it was used to estimate the disutility values associated with self-reported main diseases.ResultsThe means (percentage of ceiling effects) of EQ-5D-3L, EQ-5D-5L, and EQ-VAS scores were 0.845 (57.80%), 0.923 (49.05%), and 79.83 (3.20%), respectively. The average percentage of inconsistent response was 1.09%. A good agreement level was found between both EQ-5D versions with the ICCs of 0.789 (95% CI: 0.558–0.878). Female, older, and unemployed participants and those with BMI ≥ 30 reported lower EQ-5D-3L and EQ-5D-5L than their counterparts. Bone/Joint disorder and stroke contributed to the largest disutility value for those two instruments.ConclusionsThe Thai norm-based scores from those two instruments were diminished when advancing age and among female, unemployed, and obese (BMI ≥ 30) participants. It is expected to provide information to policy makers to better allocate health care resources to those with diminished norm-based scores.

Targeted education for clinicians and clinical coding staff improves the accuracy of clinical coding: A quality improvement project

The allocation of healthcare resources is reliant upon accurate information generated through clinical coding. Several factors contribute to coding inaccuracies, one of which is interpreting medical documentation. A lack of awareness among medical staff of the clinical coding process and the importance of detailed documentation exacerbates this problem. To investigate this further, 1 month of inpatient clinical coding data from a single hospital ward was reviewed by clinicians experienced in the coding and auditing process. If the reviewing clinician identified inaccuracies in the initial clinical coding, Healthcare Resource Group (HRG) codes were changed. Education sessions were then provided both to junior clinicians working on the hospital ward and to clinical coding staff and a further month of clinical coding data was again reviewed to assess for any difference after the sessions. HRG changes were made in 58.5% of 94 cases initially. Following the educational sessions, 20.5% of HRGs changed in 73 cases (p<0.0001), indicating more accurate initial clinical coding. There were also statistically significant reductions in the extent to which the primary and secondary diagnoses were changed. This study demonstrates that targeted education sessions for both junior clinicians and clinical coding staff can improve the accuracy of inpatient clinical coding.

Development and validation of machine learning models to predict frailty risk for elderly.

Early identification and intervention of the frailty of the elderly will help lighten the burden of social medical care and improve the quality of life of the elderly. Therefore, we used machine learning (ML) algorithm to develop models to predict frailty risk in the elderly. A prospective cohort study. We collected data on 6997 elderly people from Chinese Longitudinal Healthy Longevity Study wave 6-7 surveys (2011-2012, 2014). After the baseline survey in 1998 (wave 1), the project conducted follow-up surveys (wave 2-8) in 2000-2018. The osteoporotic fractures index was used to assess frailty. Four ML algorithms (random forest [RF], support vector machine, XGBoost and logistic regression [LR]) were used to develop models to identify the risk factors of frailty and predict the risk of frailty. Different ML models were used for the prediction of frailty risk in the elderly and frailty risk was trained on a cohort of 4385 elderly people with frailty (split into a training cohort [75%] and internal validation cohort [25%]). The best-performing model for each study outcome was tested in an external validation cohort of 6997 elderly people with frailty pooled from the surveys (wave 6-7). Model performance was assessed by receiver operating curve and F2-score. Among the four ML models, the F2-score values were similar (0.91 vs. 0.91 vs. 0.88 vs. 0.90), and the area under the curve (AUC) values of RF model was the highest (0.75), followed by LR model (0.74). In the final two models, the AUC values of RF and LR model were similar (0.77 vs. 0.76) and their accuracy was identical (87.4% vs. 87.4%). Our study developed a preliminary prediction model based on two different ML approaches to help predict frailty risk in the elderly. The presented models from this study can be used to inform healthcare providers to predict the frailty probability among older adults and maybe help guide the development of effective frailty risk management interventions. Detecting frailty at an early stage and implementing timely targeted interventions may help to improve the allocation of health care resources and to reduce frailty-related burden. Identifying risk factors for frailty could be beneficial to provide tailored and personalized care intervention for older adults to more accurately prevent or improve their frail conditions so as to improve their quality of life. The study has adhered to STROBE guidelines. No patient or public contribution.

Submental Island Flaps for Lateral Reconstruction: Technical Refinements for Optimal Outcomes and Resource Efficiency.

To describe our modifications to the submental island flap (SMIF) in a case series that demonstrates improved reproducibility, shortened length of stay (LOS),and reduced utilization of hospital resources. This retrospective case series with chart review included adult patients who underwent resection of malignant or benign tumors resulting in lateral facial, parotid, or temporal bone defects, which were reconstructed with SMIF. A tertiary-care academic referral center. Retrospective case series included all adult patients who underwent SMIF reconstruction between March 2020 and August 2021. Patient demographic and clinical data were collected. Primary outcomes were measures of hospital utilization including duration of surgery, LOS, and postoperative outcomes. Twenty-eight patients were included with a mean age of 71.7 years. Eighty percent were male. All patients underwent parotidectomy, and the mean operative time was 347minutes. The median LOS was 2.5 days (range 0-16 days). Seventy-five percent of the flaps drained into the internal jugular vein, and 25% drained into the external jugular vein. No patients required reoperation or readmission. All flaps survived. SMIFs are a safe and effective option for reconstruction of lateral facial, parotid, and temporal bone defects. Compared to free flap reconstruction, SMIFs offer reduced length of surgery, decreased use of health care resources, and lower rate of reoperation. As health care resource allocation is increasingly important, the SMIF offers an excellent alternative to free flap reconstruction of lateral defects.

Entrectinib as first-line vs. second-line therapy in ROS1 fusion-positive non-small cell lung cancer: a cost-effectiveness analysis.

The concept of cost-effectiveness is crucial for the optimal allocation of scarce healthcare resources. However, the cost-effectiveness of entrectinib in ROS1 fusion-positive non-small cell lung cancer (NSCLC) has not been evaluated. We aim to evaluate the cost-effectiveness of entrectinib as a first-line treatment compared to its reservation for second-line therapy or the exclusive use of chemotherapy in ROS1 fusion-positive advanced NSCLC. A Markov model was created to assess the clinical outcomes and healthcare costs associated with these three treatment approaches. Cost and utility values were obtained from established literature and cost databases. To test model robustness, probabilistic and univariate sensitivity analyses were conducted. In the first-line setting, where entrectinib was administered as the initial therapy, it yielded an extra 0.07 quality-adjusted life years (QALYs) at an incremental cost of $73,453, leading to an incremental cost-effectiveness ratio (ICER) of $1,090,594.30 per QALY compared to chemotherapy. Conversely, in the second-line setting, when entrectinib was used as a second-line therapy following chemotherapy, it provided an extra 0.11 QALYs at an incremental cost of $53,480, resulting in an ICER of $494,290.39 per QALY compared to chemotherapy. Furthermore, the analysis revealed that the cost of entrectinib and utility values of progressed disease were the most influential factors for the ICER. Considering the current pricing of entrectinib, it is not deemed cost-effective as a first-line or second-line therapy for patients with ROS1 fusion-positive advanced NSCLC when compared to chemotherapy. Alternatively, reserving entrectinib exclusively for second-line therapy might strike a balance between healthcare expenditures and patient outcomes.

Development and comparison of machine-learning models for predicting prolonged postoperative length of stay in lung cancer patients following video-assisted thoracoscopic surgery

ObjectiveThis study aimed to develop models for predicting prolonged postoperative length of stay (PPOLOS) in lung cancer patients undergoing video-assisted thoracoscopic surgery (VATS) by utilizing machine-learning techniques. These models aim to offer valuable insights for clinical decision-making. MethodsThis retrospective cohort study analyzed a dataset of lung cancer patients who underwent VATS, identifying 25 numerical features and 45 textual features. Three classification machine-learning models were developed: XGBoost, random forest, and neural network. The performance of these models was evaluated based on accuracy (ACC) and area under the receiver operating characteristic curve (AUC), whereas the importance of variables was assessed using the feature importance parameter from the random forest model. ResultsOf the 6767 lung cancer patients, 1481 patients (21.8%) experienced a postoperative length of stay of > 4 days. The majority were male (4111, 60.8%), married (6246, 92.3%), and diagnosed with adenocarcinoma (4145, 61.3%). The Random Forest classifier exhibited superior prediction performance with an area under the curve (AUC) of 0.792 and ACC of 0.804. The calibration plot revealed that all three classifiers were in close alignment with the ideal calibration line, indicating high calibration reliability. The five most critical features identified were the following: surgical duration (0.116), age (0.066), creatinine (0.062), hemoglobin (0.058), and total protein (0.054). ConclusionsThis study developed and evaluated three machine-learning models for predicting PPOLOS in lung cancer patients undergoing VATS. The findings revealed that the Random Forest model is most accurately predicting the PPOLOS. Findings of this study enable the identification of crucial determinants and the formulation of targeted interventions to shorten the length of stay among lung cancer patients after VATS, which contribute to optimize the allocation of healthcare resources.

A comprehensive value-based method for new nuclear medical service pricing: with case study of radium [223Ra] bone metastases treatment

ImportanceInnovative nuclear medicine services offer substantial clinical value to patients. However, these advancements often come with high costs. Traditional payment strategies do not incentivize medical institutes to provide new services nor determine the fair price for payers. A shift towards a value-based pricing strategy is imperative to address these challenges. Such a strategy would reconcile the cost of innovation with incentives, foster transparent allocation of healthcare resources, and expedite the accessibility of essential medical services.ObjectiveThis study aims to develop and present a comprehensive, value-based pricing model for new nuclear medicine services, illustrated explicitly through a case study of the radium [223Ra] treatment for bone metastases. In constructing the pricing model, we have considered three primary value determinants: the cost of the new service, associated service risk, and the difficulty of the service provision. Our research can help healthcare leaders design an evidence-based Fee-For-Service (FFS) payment reference pricing with nuclear medicine services and price adjustments.Design, setting and participantsThis multi-center study was conducted from March 2021 to February 2022 (including consultation meetings) and employed both qualitative and quantitative methodologies. We organized focus group consultations with physicians from nuclear medicine departments in Beijing, Chongqing, Guangzhou, and Shanghai to standardize the treatment process for radium [223Ra] bone metastases. We used a specially designed ‘Radium Nuclide [223Ra] Bone Metastasis Data Collection Form’ to gather nationwide resource consumption data to extract information from local databases. Four interviews with groups of experts were conducted to determine the add-up ratio, based on service risk and difficulty. The study organized consultation meeting with key stakeholders, including policymakers, service providers, clinical researchers, and health economists, to finalize the pricing equation and the pricing result of radium [223Ra] bone metastases service.Main outcomes and measuresWe developed and detailed a pricing equation tailored for innovative services in the nuclear medicine department, illustrating its application through a step-by-step guide. A standardized service process was established to ensure consistency and accuracy. Adhering to best practice guidelines for health cost data analysis, we emphasized the importance of cross-validation of data, where validated data demonstrated less variation. However, it required a more advanced health information system to manage and analyze the data inputs effectively.ResultsThe standardized service of radium [223Ra] bone metastases includes: pre-injection assessment, treatment plan, administration, post-administration monitoring, waste disposal and monitoring. The average duration for each stage is 104 min, 39 min, 25 min, 72 min and 56 min. A standardized monetary value for medical consumables is 54.94 yuan ($7.6), and the standardised monetary value (medical consumables cost plus human input) is 763.68 yuan ($109.9). Applying an agreed value add-up ratio of 1.065, the standardized value is 810.19 yuan ($116.9). Feedback from a consultation meeting with policymakers and health economics researchers indicates a consensus that the pricing equation developed was reasonable and well-grounded.ConclusionThis research is the first study in the field of nuclear medicine department pricing methodology. We introduce a comprehensive value-based nuclear medical service pricing method and use radium[223Ra] bone metastases treatment pricing in China as a case study. This study establishes a novel pricing framework and provides practical instructions on its implementation in a real-world healthcare setting.

Determinants of Hospital Length of Stay in Respiratory Infections: Insights from Cheras, Malaysia

Respiratory virus infections pose a significant public health concern, contributing to increase morbidity and mortality rates. This study investigated the determinants of hospital length of stay (LOS) for patients with Influenza-like illness (ILI) and Severe Acute Respiratory Infection (SARI). A quantitative study was conducted in Cheras, Kuala Lumpur, Malaysia, from 1 September 2022 until 28 February 2023. Demographic data, viral infection status (influenza A, influenza B, and COVID-19), and environmental factors (temperature, humidity, wind speed, and particulate matter levels) were collected for 632 patients. The data were analysed using ordinal logistic regression in Statistical Package for Social Sciences (SPSS) version 22.0. The results revealed that older age and higher body mass index (BMI) were associated with longer hospital stays, highlighting their significance as risk factors during respiratory outbreaks. Unexpectedly, patients tested positive for the investigated viruses experienced shorter LOS, indicating potential comorbidities or co-infections. Environmental factors played a critical role, with temperature, humidity, and wind speed significantly influencing LOS. These findings provided valuable insights for healthcare resource allocation during epidemics and underscored the need for comprehensive diagnostic tools and further research to enhance prediction accuracy.