Further interpretability techniques were employed for analyzing prediction which enhanced preprocessing step. In recent years, advanced healthcare systems have gained immense popularity due to demographic growth, and an increase in diseases which, in turn, requires enormous clinical assets and even hospital staff. Advancement in the medical area was initiated in the early 1991s and it was considered a completely advanced area for treatment. Since then, healthcare systems had been revolutionized in several ways, for example, agile treatment, appropriate early patient serving, delivering, and monitoring healthcare services remotely, and quick action towards emergency cases. The key challenge encountered during the advancement of the medical area was its demand for emerging efficient types of equipment to deliver the best services to patients 1.
2. Challenges
Fog computing enables real-time data processing by leveraging clients’ devices for significant storage, communication, control, configuration, and management tasks. Fog/edge enables local decision-making and data analysis, which is performed closer to the data source, thereby conserving bandwidth, reducing delay, improving service delivery, and enhancing overall system efficiency. EHealth encompasses a diverse range of digital health solutions, including Electronic Health Records (EHRs), telemedicine, health informatics, and online health services. EHealth uses digital technology to enhance the quality, accessibility, and efficiency of health services.
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These spreadsheets were frequently deleted when the patient was released from the hospital, offering no long-term advantages. Implementing EHR systems is essential to prevent the loss of patient information, as they enable the storage and retrieval of valuable patient data. This would provide several benefits, including improved patient care, enhanced research opportunities, and data-driven decision-making. Digital medical imaging technologies, such as computed tomography (CT) scans, magnetic resonance imaging (MRI), X-rays, and ultrasounds, can all be stored in an electronic health record (EHR) system. Since hospitals originally introduced EHR for internal administrative purposes, a variety of classification systems and controlled vocabularies have been developed to record relevant medical events and information. Diabetes is an incurable and long-lasting illness caused due to increase in glucose in the blood and it has become increasingly common among people irrespective of sexual orientation, race, age, habits, etc.
Big Data Analytics and Cognitive Computing in Smart Health Systems
The following section synopsizes key areas that warrant consideration and further research before integrating a smart health ecosystem into practice. If a weight measurement was not detected by midday, and if motion sensors detected that he was at home, Herbert would receive a notification on his smartphone reminding him to step on the scale. An activity tracker indicated that Herbert was sitting longer than usual, prompting a symptom questionnaire delivered and answered through a conversational agent (eg, Alexa). System algorithms were https://forestwildwood.com/articles/arizona-prescription-drug-monitoring-program/ used to analyze these parameters in relation to Herbert’s standard measurements (collected by EMA multiple times a day) and prompted an intervention.
For example, the work in Pradhan et al. (2021) investigated the architecture and applications of IoT for efficient RPM in healthcare systems, thereby shifting the focus from hospital-centric (or clinic-centric) to patient-centric care. The work in Mamdiwar et al. (2021) presented several studies on IoT-assisted wearable sensors for RPM systems. The works in Baker et al. (2017); Wu et al. (2018); Khan et al. (2023) presented detailed research on RPM systems based on IoT. An efficient IoT biomedical device based on the Raspberry Pi 3 has been proposed in Garbhapu and Gopalan (2017) for RPM, which can quickly monitor the vital signs of many people and send the collected information wirelessly to doctors. The application of dual-arm motion mapping for telerobotics in RPM was discussed in Zhou et al. (2019).
Such risks, however, can be reduced by applying privacy-by-design principles, such as storing and processing data on edge devices only (without the need to send it over a communication network). Part of this virtual care was the use of medical portal https://labverra.com/articles/understanding-id-now-pcr-testing/ technology to build secure online healthcare portals to help providers and patients track treatment, store health records, and collect and access data from wearable devices or clinic visits. RPM devices, such as continuous glucose monitors and remote cardiac monitors, provide valuable data that aids in personalized treatment plans and timely interventions. By leveraging this data, healthcare providers can adjust medications, recommend lifestyle changes, and take other necessary actions to manage a patient’s health more effectively. This kind of continuous care is especially beneficial in the rehabilitation phase post-surgery or for managing long-term chronic conditions like diabetes or heart disease, where constant vigilance can significantly impact patient outcomes.
AI-driven tools are particularly effective in recognizing patterns that might be missed by the human eye, making them indispensable in the early detection of cancers and other life-threatening diseases. In Spence’s analysis, products such as the Persona IQ can be classified as “digitized” innovations. These initiatives essentially represent “digitized versions of existing analogue products and services.” Other innovations point beyond digitized technologies to a more ambitious approach, which Spence terms “connected care”. “At this stage we start to see a degree of personalized and predictive healthcare with AI-driven insights.” One example of this connected approach is the Sheba Medical Center, a virtual hospital in Israel which treats over 1 million patients every year.
Further discussion about these issues and the role of ML techniques in addressing them can be found in Pham et al. (2020). Smart healthcare systems can manage the rapid growth of connected IoT devices by adopting scalable architectures, leveraging edge and cloud computing for efficient data processing, and implementing robust security protocols. In addition, using AI-based analytics and adaptive network management can ensure consistent performance, reliability, and real-time responsiveness, which is crucial for patient safety and care quality. The emerging cloud computing technologies are considered an appropriate technology to manage the smart grids with many IoT smart devices of healthcare systems in a reliable, secure, and scalable way (Ghorbanian et al. 2019).
- Therefore, continued investigation is needed to address these requirements and guide future implementations.
- The work in Vora et al. (2023) discussed how AI and ML transform drug discovery and pharmaceutical development.
- Smart health systems have their roots in the concept of the “Smart Planet,” initially introduced by IBM back in 2009 in Armonk, NY, USA.
- The chosen sensor networking techniques must be resource-efficient and customized for RPM applications.
- Key innovation techniques are essential for transforming traditional healthcare into smart, efficient, and effective systems.
IoT is a system that comprises devices connected which possess the ability to acquire data and can transfer/exchange data through a wireless network automatically. IoT had already been implemented in various fields like industrial automation, agriculture, transportation, construction, supply chain, retail, smart home applications, smart cities, smart grids, and smart parking. Apart from all these fields, it can even be applied to healthcare to perform different tasks ranging from patient data collection to monitoring patients’ health 56, 57.
- By combining heterogeneous data types, data fusion can enhance diagnostic accuracy, support timely interventions, and provide deeper insights into patient conditions across multiple dimensions.
- Therefore, ensuring a reliable network connection that enables seamless service delivery is crucial.
- In addition, using AI-based analytics and adaptive network management can ensure consistent performance, reliability, and real-time responsiveness, which is crucial for patient safety and care quality.
- Smart health systems involve combining state-of-the-art information technologies such as the internet of Things (IoT) big data cloud computing and artificial intelligence with traditional medical approaches.
- Futhremore, future work should address enhancing the software sophistication challenges to improve the overall metaverse experience.
1.2 Current challenges in cloud computing and future research directions
- By separating edge devices from the central network, edge computing also guarantees device interoperability.
- Furthermore, exploring how electromagnetic nanonetworks can enhance existing applications and services, as well as enable new ones, will be crucial for future work.
- Modu et al. 25 developed a warning system and an android application to analyze malaria outbreaks.
- A cloudlet is a small-scale cloud data center or server cluster situated closer to end-users or devices at the network’s edge.
- Most blockchains are based on public-key cryptosystems, which face issues related to efficiency and security.
- The work in Aceto et al. (2020) presented SLR discussing the impact of IoT in healthcare systems, with a special focus on big data as well as cloud computing and fog computing.
Cloud-assistive treatments can also be used to help medical professionals provide services irrespective of geographical location. It is necessary to conduct further research on the utilization of edge and cloud computing to support big data. Encryption plays a crucial role in securing information, particularly for large volumes of real-time data transfers, which necessitate energy-efficient encryption algorithms. Addressing these challenges will be essential for advancing the security and reliability of IoT systems in the future. Furthermore, several studies have investigated the application of ICT techniques in the healthcare system.
Big Data Analytics
Wearable devices are typically attached to the human body or are integrated into elastic bands, textile fibers, or patient clothes. Wearable devices are used to measure the physiological signals of the patients and their activity. Summary of deep learning-based algorithms applied for various disease detection and classification. In healthcare, blockchain is used for everything from securing patient data to managing the pharmaceutical supply chain.
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RPM can facilitate the efficient transmission of patient data to healthcare providers, enabling timely interventions and necessary actions based on real-time information. RPM has the ability to continually gather information on health conditions, including physiological parameters, metabolic status, and body movements. These real-time data recordings enable close monitoring of health conditions and prompt action recommendations as needed (Vaghasiya et al. 2023). In RPM, sensor fusion can also incorporate data from wearables and mobile apps to facilitate early treatment initiation and improved chronic condition management.