Markovchart: an R package for cost-optimal patient monitoring and treatment using control charts

Control charts originate from industrial statistics, but are constantly seeing new areas of application, for example in health care (Thor et al. in BMJ Qual Saf 16(5):387–399, 2007. https://doi.org/10.1136/qshc.2006.022194; Suman and Prajapati in Int J Metrol Qual Eng, 2018. https://doi.org/10.1051/ijmqe/2018003). This paper is about the package, an implementation of generalised Markov chain-based control charts with health care applications in mind and with a focus on cost-effectiveness. The methods are based on Zempléni et al. (Appl Stoch Model Bus Ind 20(3):185–200, 2004. https://doi.org/10.1002/asmb.521), Dobi and Zempléni (Qual Reliab Eng Int 35(5):1379–1395, 2019a. https://doi.org/10.1002/qre.2518, Ann Univ Sci Budapestinensis Rolando Eötvös Nomin Sect Comput 49:129–146, 2019b). The implemented ideas in the package were motivated by problems encountered by health care professionals and biostatisticians when assessing the effects and costs of different monitoring schemes and therapeutic regimens. However, the implemented generalisations may be useful in other (e.g., engineering) applications too, as they mainly revolve around the loosening of assumptions seen in traditional control chart theory. The package is able to model processes with random shift sizes (i.e., the degradation of the patient’s health), random repair (i.e., treatment) and random time between samplings (i.e., visits) as well. The article highlights the flexibility of the methods through the modelling of different disease progression and treatment scenarios and also through an application on real-world data of diabetic patients.

The Health Care Sector’s Experience of Blockchain: A Cross-disciplinary Investigation of Its Real Transformative Potential

Background Academic literature highlights blockchain’s potential to transform health care, particularly by seamlessly and securely integrating existing data silos while enabling patients to exercise automated, fine-grained control over access to their electronic health records. However, no serious scholarly attempt has been made to assess how these technologies have in fact been applied to real-world health care contexts. Objective The primary aim of this paper is to assess whether blockchain’s theoretical potential to deliver transformative benefits to health care is likely to become a reality by undertaking a critical investigation of the health care sector’s actual experience of blockchain technologies to date. Methods This mixed methods study entailed a series of iterative, in-depth, theoretically oriented, desk-based investigations and 2 focus group investigations. It builds on the findings of a companion research study documenting real-world engagement with blockchain technologies in health care. Data were sourced from academic and gray literature from multiple disciplinary perspectives concerned with the configuration, design, and functionality of blockchain technologies. The analysis proceeded in 3 stages. First, it undertook a qualitative investigation of observed patterns of blockchain for health care engagement to identify the application domains, data-sharing problems, and the challenges encountered to date. Second, it critically compared these experiences with claims about blockchain’s potential benefits in health care. Third, it developed a theoretical account of challenges that arise in implementing blockchain in health care contexts, thus providing a firmer foundation for appraising its future prospects in health care. Results Health care organizations have actively experimented with blockchain technologies since 2016 and have demonstrated proof of concept for several applications (use cases) primarily concerned with administrative data and to facilitate medical research by enabling algorithmic models to be trained on multiple disparately located sets of patient data in a secure, privacy-preserving manner. However, blockchain technology is yet to be implemented at scale in health care, remaining largely in its infancy. These early experiences have demonstrated blockchain’s potential to generate meaningful value to health care by facilitating data sharing between organizations in circumstances where computational trust can overcome a lack of social trust that might otherwise prevent valuable cooperation. Although there are genuine prospects of using blockchain to bring about positive transformations in health care, the successful development of blockchain for health care applications faces a number of very significant, multidimensional, and highly complex challenges. Early experience suggests that blockchain is unlikely to rapidly and radically revolutionize health care. Conclusions The successful development of blockchain for health care applications faces numerous significant, multidimensional, and complex challenges that will not be easily overcome, suggesting that blockchain technologies are unlikely to revolutionize health care in the near future.

Artificial Intelligence in Orthodontic Smart Application for Treatment Coaching and Its Impact on Clinical Performance of Patients Monitored with AI-TeleHealth System

Background: Treatment of malocclusion with clear removable appliances like Invisalign® or Spark™, require considerable higher level of patient compliance when compared to conventional fixed braces. The clinical outcomes and treatment efficiency strongly depend on the patient’s discipline. Smart treatment coaching applications, like strojCHECK® are efficient for improving patient compliance. Purpose: To evaluate the impact of computerized personalized decision algorithms responding to observed and anticipated patient behavior implemented as an update of an existing clinical orthodontic application (app). Materials and Methods: Variables such as (1) patient app interaction, (2) patient app discipline and (3) clinical aligner tracking evaluated by artificial intelligence system (AI) system—Dental monitoring® were observed on the set of 86 patients. Two 60-day periods were evaluated; before and after the app was updated with decision tree processes. Results: All variables showed significant improvement after the update except for the manifestation of clinical non-tracking in men, evaluated by artificial intelligence from video scans. Conclusions: Implementation of application update including computerized decision processes can significantly enhance clinical performance of existing health care applications and improve patients’ compliance. Using the algorithm with decision tree architecture could create a baseline for further machine learning optimization.

Design and Implementation of a Wearable Accelerometer-Based Motion/Tilt Sensing Internet of Things Module and Its Application to Bed Fall Prevention

Accelerometer-based motion sensing has been extensively applied to fall detection. However, such applications can only detect fall accidents; therefore, a system that can prevent fall accidents is desirable. Bed falls account for more than half of patient falls and are preceded by a clear warning indicator: the patient attempting to get out of bed. This study designed and implemented an Internet of Things module, namely, Bluetooth low-energy-enabled Accelerometer-based Sensing In a Chip-packaging (BASIC) module, with a tilt-sensing algorithm based on the patented low-complexity COordinate Rotation DIgital Computer (CORDIC)-based algorithm for tilt angle conversions. It is applied for detecting the postural changes (from lying down to sitting up) and to protect individuals at a high risk of bed falls by prompting caregivers to take preventive actions and assist individuals trying to get up. This module demonstrates how motion and tilt sensing can be applied to bed fall prevention. The module can be further miniaturized or integrated into a wearable device and commercialized in smart health-care applications for bed fall prevention in hospitals and homes.

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as `machine learning’, blockchain, `Internet of Things or IoT’, and keywords conjoined with `healthcare’ and `health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.

Carbon Dots: Classification, Properties, Synthesis, Characterization, and Applications in Health Care—An Updated Review (2018–2021)

Carbon dots (CDs) are usually smaller than 10 nm in size, and are meticulously formulated and recently introduced nanomaterials, among the other types of carbon-based nanomaterials. They have gained significant attention and an incredible interest in the field of nanotechnology and biomedical science, which is merely due to their considerable and exclusive attributes; including their enhanced electron transferability, photobleaching and photo-blinking effects, high photoluminescent quantum yield, fluorescence property, resistance to photo-decomposition, increased electrocatalytic activity, good aqueous solubility, excellent biocompatibility, long-term chemical stability, cost-effectiveness, negligible toxicity, and acquaintance of large effective surface area-to-volume ratio. CDs can be readily functionalized owing to the abundant functional groups on their surfaces, and they also exhibit remarkable sensing features such as specific, selective, and multiplex detectability. In addition, the physico-chemical characteristics of CDs can be easily tunable based on their intended usage or application. In this comprehensive review article, we mainly discuss the classification of CDs, their ideal properties, their general synthesis approaches, and primary characterization techniques. More importantly, we update the readers about the recent trends of CDs in health care applications (viz., their substantial and prominent role in the area of electrochemical and optical biosensing, bioimaging, drug/gene delivery, as well as in photodynamic/photothermal therapy).