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Study Design.: An observational multicenter study. Summary of Background Data.: Return to work (RTW) is increasingly used to assess the standard, benefit, and quality of health care. Objective.: The aim of this study was to evaluate sick leave patterns among patients undergoing surgery for cervical radiculopathy and identify predictors of successful RTW using two nationwide databases. Materials and Methods.: Data from the Norwegian Registry for Spine Surgery (NORspine) and the Norwegian Labour and Welfare Administration were linked on an individual level. We included patients between 18 and 60 years of age registered in NORspine from June 2012 through December 2019 that were temporarily out of the labor force for medical reasons at the time of surgery. We assessed types and grades of sickness benefits before and after surgery and conducted logistic regression analyses. Results.: Among 3387 patients included in the study, 851 (25.1%) received temporary benefits one year before surgery. The proportion of recipients increased steadily towards surgery. Postoperatively the medical benefit payment decreased rapidly, and half of the patients had already returned to work by four months. The rate of RTW reached a plateau at one year. By the end of the third year, 2429 patients (71.7%) had returned to work. The number of sick days, categorized as 90 or less, during the year before surgery had the most powerful association with RTW at two years (odds ratio: 4.54, 95% CI: 3.42–6.03, P <0.001). Improvement in neck-related disability was the second strongest predictor (odds ratio: 2.17, 95% CI: 1.69–2.78, P <0.001). Conclusion.: RTW after cervical radiculopathy surgery occurs primarily during the first year. The strongest predictor of RTW was fewer sick days before surgery. The clinical improvement after surgery had a lesser impact. Level of Evidence.: 2. [ABSTRACT FROM AUTHOR]

This study examines the ethical permissibility of biomedical moral enhancement (BME) for psychopaths, considering both coercive and voluntary approaches. To do so, I will first briefly explain what psychopaths are and some normative implications of these facts. I will then ethically examine three scenarios of BME for psychopaths: (1) coercive BME for non-criminal psychopaths, (2) coercive BME for psychopathic offenders, and (3) voluntary BME for psychopathic offenders. I will argue that coercive BME for non-criminal psychopaths is ethically problematic due to issues of cost, invasion of privacy, and stigmatic effects of compulsory diagnosis. Similarly, I will argue that coercive BME for criminals is impermissible due to violations of the rights to bodily and mental integrity. However, I will show that voluntary BME for offenders may be ethically permissible under certain conditions, challenging the critique that the consent of vulnerable prisoners cannot be considered fully voluntary. I argue that when an offender is provided with sufficient medical and legal information, incentives such as the possibility of parole review based on BME results do not preclude the voluntariness of consent. Ultimately, I aim to advance the debate on BME for psychopaths by delineating and defending conditions for the ethical permissibility of voluntary BME., (© 2024 The Author(s). Bioethics published by John Wiley & Sons Ltd.)

A feature of much recent medical research has been to offer a range of treatment choices for a given condition. It is important therefore to understand patient preferences among the non-medical as well as medical features of these options. An instructive example is a variety of rice currently under development with recombinant DNA technology as an alternative to the vaccine injection conventionally used against cedar pollen allergy as immunotherapy treatment. We use a survey-based discrete choice experiment to examine and decompose the attitudes to this medical food, along with their likely net effects on final demand. Restricting the survey to allergy sufferers allows focus on the rice's maximum perceived welfare benefits, in contrast to the ambiguous conclusions of previous studies. Patient characteristics, such as allergy severity, GM technology familiarity, and opportunity costs of hospital visits necessary for the injection treatment strongly influence the vaccine rice's appeal. A successful marketing program for the new rice will require appealing to these direct benefits. [ABSTRACT FROM AUTHOR]

Abstract Background Widespread biomedical applications of nanomaterials (NMs) bring about increased human exposure risk due to their unique physicochemical properties. Autophagy, which is of great importance for regulating the physiological or pathological activities of the body, has been reported to play a key role in NM-driven biological effects both in vivo and in vitro. The coexisting hazard and health benefits of NM-mediated autophagy in biomedicine are nonnegligible and require our particular concerns. Main body We collected research on the toxic effects related to NM-mediated autophagy both in vivo and in vitro. Generally, NMs can be delivered into animal models through different administration routes, or internalized by cells through different uptake pathways, exerting varying degrees of damage in tissues, organs, cells, and organelles, eventually being deposited in or excreted from the body. In addition, other biological effects of NMs, such as oxidative stress, inflammation, necroptosis, pyroptosis, and ferroptosis, have been associated with autophagy and cooperate to regulate body activities. We therefore highlight that NM-mediated autophagy serves as a double-edged sword, which could be utilized in the treatment of certain diseases related to autophagy dysfunction, such as cancer, neurodegenerative disease, and cardiovascular disease. Challenges and suggestions for further investigations of NM-mediated autophagy are proposed with the purpose to improve their biosafety evaluation and facilitate their wide application. Databases such as PubMed and Web of Science were utilized to search for relevant literature, which included all published, Epub ahead of print, in-process, and non-indexed citations. Conclusion In this review, we focus on the dual effect of NM-mediated autophagy in the biomedical field. It has become a trend to use the benefits of NM-mediated autophagy to treat clinical diseases such as cancer and neurodegenerative diseases. Understanding the regulatory mechanism of NM-mediated autophagy in biomedicine is also helpful for reducing the toxic effects of NMs as much as possible.

Background: This study aimed to analyze the rapidly evolving ecosystem of digital health applications (Digitale Gesundheitsanwendung; DiGAs) in Germany, spurred by the 2019 Digital Healthcare Act. With over 73 million people in Germany now having access to DiGAs, these prescribable digital health apps and web-based applications represent a substantial stride in health care modernization, supporting both patients and health care providers with digital solutions for disease management and care improvement., Objective: Through a data-driven approach, this research aimed to unpack the complexities of DiGA market dynamics, economic factors, and clinical evidence, offering insights into their impact over the past years., Methods: The analysis draws from a range of public data sources, including the DiGA directory, statutory health insurance reports, app store feedback, and clinical study results., Results: As of July 1, 2024, there are 56 DiGAs listed by the Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte), divided into 35 permanently and 21 preliminarily listed applications. Our findings reveal that a majority of DiGAs extend beyond the intended 1-year period to achieve permanent listing, reflecting the extensive effort required to demonstrate clinical efficacy. Economic analysis uncovered a dynamic pricing landscape, with initial prices ranging from approximately €200 to €700 (€1=US $1.07), averaging at a median of €514 for a 3-month DiGA prescription. Following negotiations or arbitration board decisions, prices typically see a 50% reduction, settling at a median of €221. Prescription data offer valuable insights into DiGA acceptance, with total prescriptions jumping from around 41,000 in the first period to 209,000 in the latest reporting period. The analysis of the top 15 DiGAs, representing 82% of the total prescriptions, shows that these best-performing apps receive from a minimum of 8 to a maximum of 77 daily prescriptions, with native apps and early market entrants achieving higher rates. Clinical evidence from all 35 permanently listed DiGAs indicates a uniform preference for randomized controlled trials to validate primary end points, with no noteworthy use of alternative study designs encouraged in the Digital Healthcare Act and related regulations. Moreover, all evaluated DiGAs focused on medical benefits, with health status improvement as a key end point, suggesting an underuse of patient-relevant structural and procedural improvement in demonstrating health care impact., Conclusions: This study highlights the growth and challenges within the DiGA sector, suggesting areas for future research, such as the exploration of new study designs and the potential impact of patient-relevant structural and procedural improvements. For DiGA manufacturers, the strategic advantage of early market entry is emphasized. Overall, this paper underscores the evolving landscape of digital health, advocating for a nuanced understanding of digital health technology integration in Germany and beyond., (©Moritz Goeldner, Sara Gehder. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.08.2024.)

The paper presents a novel methodology based on machine learning to optimize medical benefits in healthcare settings, i.e., corporate, private, public or statutory. The optimization is applied to design healthcare insurance packages based on the employee healthcare record. Moreover, with the advancement in the insurance industry, it is rapidly adapting mathematical and machine learning models to enhance insurance services like funds prediction, customer management and get better revenue from their businesses. However, conventional computing insurance packages and premium methods are time-consuming, designation specific, and not cost-effective. During the design of insurance packages, an employee's needs should be given more importance than his/her designation or position in an organization. The design of insurance packages in healthcare is a non-trivial task due to the employees' changing healthcare needs; therefore, using the proposed technique employees can be moved from their existing package to another depending upon his/her need. This provides the motivation to propose a methodology in which we applied machine learning concepts for designing need-based health insurance packages rather than professional tagging. By the design of need-based packages, medical benefit optimization which is the core goal of our proposed methodology is effectively achieved. Our proposed methodology derives insurance packages that are need-based and optimal based on our defined criteria. We achieved this by first applying the clustering technique to historical medical records. Subsequently, medical benefit optimization is achieved from these packages by applying a probability distribution model on five years employees' insurance records. The designed technique is validated on real employees' insurance records from a large enterprise.The proposed design provides 25% optimization on medical benefit amount compared to current medical benefits amount therefore, gives better healthcare to all the employees. [ABSTRACT FROM AUTHOR]

Marijuana (cannabis) remains a controversial drug in the 21st century. This review considers current research on use of cannabis sativa and its constituents such as the phytocannabinoids. Topics reviewed include prevalence of cannabis use, other drugs consumed with pot, the endocannabinoid system, use of medicinal marijuana for acute and chronic pain as well as other conditions, medical adverse effects of cannabis, and psychiatric adverse effects of cannabis use. Treatment of cannabis use disorders and withdrawal is difficult and remains mainly based on psychological therapy; current research on pharmacologic management of problems related to cannabis consumption is also considered. The potential role of specific cannabinoids for medical benefit will be revealed as the 21st century matures. However, potential dangerous adverse effects from smoking marijuana are well known and should be clearly taught to a public often confused by a mediadriven, though false message and promise of benign pot consumption. [ABSTRACT FROM AUTHOR]

The paper presents a novel methodology based on machine learning to optimize medical benefits in healthcare settings, i.e., corporate, private, public or statutory. The optimization is applied to design healthcare insurance packages based on the employee healthcare record. Moreover, with the advancement in the insurance industry, it is rapidly adapting mathematical and machine learning models to enhance insurance services like funds prediction, customer management and get better revenue from their businesses. However, conventional computing insurance packages and premium methods are time-consuming, designation specific, and not cost-effective. During the design of insurance packages, an employee’s needs should be given more importance than his/her designation or position in an organization. The design of insurance packages in healthcare is a non-trivial task due to the employees’ changing healthcare needs; therefore, using the proposed technique employees can be moved from their existing package to another depending upon his/her need. This provides the motivation to propose a methodology in which we applied machine learning concepts for designing need-based health insurance packages rather than professional tagging. By the design of need-based packages, medical benefit optimization which is the core goal of our proposed methodology is effectively achieved. Our proposed methodology derives insurance packages that are need-based and optimal based on our defined criteria. We achieved this by first applying the clustering technique to historical medical records. Subsequently, medical benefit optimization is achieved from these packages by applying a probability distribution model on five years employees’ insurance records. The designed technique is validated on real employees’ insurance records from a large enterprise.The proposed design provides 25% optimization on medical benefit amount compared to current medical benefits amount therefore, gives better healthcare to all the employees.

Since 2020, digital health applications (DiGA) can be prescribed at the expense of the German statutory health insurance (SHI) system after undergoing an approval procedure by the Federal Institute for Drugs and Medical Devices (BfArM). DiGA can be approved provisionally for 1 year (with the option of extension) or permanently. The latter is dependent on scientific evidence of a positive effect on care, which can be a medical benefit or a patient-relevant structural and procedural improvement in care. However, it is apparent that the investigation of DiGA in scientific studies is challenging, as they are often complex interventions whose success also includes user and prescriber factors. In addition, health services research data underpinning the benefits of DiGA are lacking to date. In the current article, methodological considerations for DiGA research are presented, and a selection of internal medicine DiGAs is used to critically discuss current research practice., (© 2022. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)