Articles

Safeguarding Patient Confidentiality in Telemedicine: A Systematic Review of Privacy and Security Risks, and Best Practices for Data Protection

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The COVID-19 pandemic accelerated telemedicine adoption, showcasing its potential in improving healthcare delivery. However, privacy and security risks pose challenges, impeding widespread acceptance. The aim is to investigate the integration of data analytics, data analysis, and data cleaning in telemedicine, focusing on patient data privacy and security, with the goal of proposing strategies to mitigate risks and uphold confidentiality. Utilizing a qualitative approach, privacy and security challenges in telemedicine were investigated. Multiple databases, including PubMed, Embase, and Cochrane Library, were searched from 2018-2023. Inclusion criteria involved English-language, peer-reviewed empirical studies focusing on telemedicine privacy and security. Out of 770 unique records screened, eight studies were included. Full-text review and risk of bias assessment were conducted using CASP tool.  Privacy and security, technology hurdles for providers, patient trust, professional training, physical assessment challenges, and disparities among special populations were identified. Environmental, technological, and operational factors contribute to privacy and security risks in telehealth. Technology challenges like restricted access to telehealth tools and poor internet hinder adoption. Data analytics in telemedicine facilitates healthcare transformation, addressing privacy and security while optimizing patient outcomes through advanced analytics techniques and structured data analytics lifecycles. The integration of data analytics in telemedicine shows promise for healthcare transformation by providing insights into patient behavior and policy impacts, while ensuring data privacy and security. Addressing barriers, accelerated by the COVID-19 pandemic, requires infrastructure enhancements and global research efforts for inclusive telehealth ecosystems.

Predictive Analysis for Inventory Management of Coconut Warehouse (Case Study: Banio Lahewa)

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Inventory management plays a pivotal role in the coconut farming business, directly influencing sales and income. An essential component of this management is warehousing, which not only affect revenue but also involves suppliers in the coconut storage process. Warehousing management and technology are two elements that can help companies operate more effectively and efficiently. This research focuses on efforts to improve warehouse management efficiency in the agricultural sector, particularly at Banio Lahewa, a company that operates as a coconut supplier in a small village with limited resources. Currently, the company still records data manually and lacks a real-time system to monitor demand patterns, stock rotation, and restocking frequency in the warehouse. This situation is caused by uncertainty about the products entering the warehouse, leading to the company’s focus being more limited to daily operational issues rather than future planning. To address this challenge, this research uses future event prediction methods, specifically forecasting by applying two neural network models: the Feed Forward Neural Network and the Long Short Term Memory. The implementation of this system is expected to provide new insights to the company, enabling them to be more adaptive in efficiently managing warehouse systems. With an understanding of patterns and predictions of future events, it is expected that the company can be more prepared and responsive to changes in customer demand and able to expand products more quickly. The results of this research are expected to make a positive contribution to the company, helping them optimize warehouse management and become more adaptive to market dynamics.

Determining Marketing Mix of CV Nutri Pro by Using Big Data Analytics

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Technological growth supports the acceleration of the health industry. Technology provides an opportunity for business actors to convey product advantages to be disseminated widely through digital media. Apart from providing benefits in the easy dissemination of information, digital media can be a sales media for the health industry. Based on data from Tokopedia (e-commerce with the most users in Indonesia), the biggest sales are dominated by health products. Large amounts of data (big data) available in e-commerce can be extracted using the Web Scraping method. Big data can be processed to gain certain insights in achieving competitive advantage. CV Nutri Pro as a medium-sized business has limited data which causes the marketing mix that has been prepared beforehand to be incomplete. This condition causes out of sync, where there are demands that cannot be fulfilled. Based on the opportunity to utilize big data, CV Nutri Pro can determine a comprehensive renewable marketing mix. Each aspect of the marketing mix (4Ps) will be processed using big data analytics. The methods used include Pivot Data, K-Means, and Multidimensional Scaling (MDS). This research provides new insights for the company to renew marketing mix.