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The Health Thread

Wearable health technology

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Written By THT Editorial Team

Reviewed by Astha Paudel, Biomedical Engineering graduate (CBEAS) Nepal, Currently Navigating Bio-Nano Material Science Engineering at AIT, Thailand

Title: Reliability of Wearable Health Technology: Differentiating Fact from Fiction


Wearable health technology, a flourishing domain comprising fitness trackers and smartwatches, is reshaping how individuals engage with their health. These devices, armed with features like step counting, heart rate monitoring, and sleep tracking, hold the promise of enhancing personal well-being. However, a critical examination of their reliability becomes imperative. This article delves into research-based insights on wearable health technology, aiding users in making judicious decisions regarding their use.

Accuracy of Heart Rate Monitoring: Heart rate monitoring stands as a pivotal feature of wearable devices. Research suggests that these devices yield reliable heart rate measurements during periods of rest and moderate-intensity activities (Gillinov et al., 2017; Shcherbina et al., 2017). However, the term “individual differences” requires clarity; these differences may encompass factors such as age, fitness level, and overall health status. Moreover, during high-intensity exercise or rapid changes in heart rate, the accuracy of these devices may fluctuate (Gillinov et al., 2017; Ferguson et al., 2018). Various factors, including device placement, motion artifacts, and physiological diversity, contribute to the variability in heart rate measurements.

Step Counting and Physical Activity Tracking: Wearable devices excel in tracking steps during walking and running (Montoye et al., 2018; Evenson et al., 2015). However, it is crucial to acknowledge their limitations, particularly in activities involving upper body movement or stationary periods. These devices may capture minor body movements that don’t necessarily translate into major physical activity. Wearers should be aware of such nuances and consider the context in which step counts are recorded.

Sleep Tracking: Sleep tracking, while insightful, demands cautious interpretation. Wearable devices offer valuable insights into sleep duration (Matsumoto et al., 2019; Cellini et al., 2020). Yet, the accuracy of sleep stage classification, such as distinguishing light sleep from deep sleep or REM sleep, varies among devices (de Zambotti et al., 2019; Montgomery-Downs et al., 2012). Users should approach sleep data as estimations rather than definitive measures of sleep stages.

Caloric Expenditure Estimation: Estimating caloric expenditure introduces a layer of complexity. Some smartwatches utilize heart rate sensors, but factors like stress, caffeine intake, and individual body composition can impact accuracy (Hall et al., 2013; Montoye et al., 2018). Additionally, inaccuracies may arise from the device’s interpretation of physical activity intensity. Users should exercise caution, recognizing these estimations may not be as precise as laboratory-based measurements.

Factors Affecting Device Accuracy: The reliability of wearable devices is contingent on various factors, including sensor technology, motion artifacts, misalignment between the skin and sensors, and variations in skin color and ambient light. Recognizing these influences is essential for users seeking accurate health data.

Reliability Across Different Brands and Models: Comparative studies reveal significant variability in the performance of wearable devices across brands and models (Evenson et al., 2015; Bai et al., 2016). Potential buyers should conduct independent research or seek reliable sources for comparisons and recommendations before making a purchase.

Wearable health technology holds immense potential for self-monitoring and fostering a healthy lifestyle. While these devices offer valuable insights, understanding their limitations is paramount. Reliability varies across features, activities, and individuals. Users must interpret data judiciously, considering the context and staying informed about research findings on accuracy and limitations. The dynamic landscape of wearable technology requires users to approach it with a discerning mindset.


  • Bai, Y., et al. (2016). Comparing usability and accuracy of wearable devices for calorie expenditure estimation. Journal of Medical Internet Research, 18(9), e253. doi:10.2196/jmir.5669
  • Cellini, N., et al. (2020). Wearable technology for measuring sleep: A systematic review. Sleep Medicine Reviews, 55, 101–116. doi:10.1016/j.smrv.2020.101419
  • de Zambotti, M., et al. (2019). Agreement between a smartwatch and polysomnography for the assessment of sleep across distinct sleep stages. Sleep, 42(2), zsy203. doi:10.1093/sleep/zsy203
  • Evenson, K. R., et al. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12, 159. doi:10.1186/s12966-015-0314-1
  • Ferguson, T., et al. (2018). Validation of consumer-based hip and wrist activity monitors in older adults with varied ambulatory abilities. Journal of Geriatric Physical Therapy, 41(1), 42–50. doi:10.1519/JPT.0000000000000103
  • Gillinov, S., et al. (2017). Variable accuracy of wearable heart rate monitors during aerobic exercise. Medicine & Science in Sports & Exercise, 49(8), 1697–1703. doi:10.1249/MSS.0000000000001284
  • Hall, K. D., et al. (2013). Accuracy of wearable devices for estimating total energy expenditure: Comparison with metabolic chamber and doubly labeled water methods. Journal of the American Medical Association Internal Medicine, 173(8), 672–674. doi:10.1001/jamainternmed.2013.2296
  • Kooiman, T. J. M., et al. (2015). Reliability and validity of ten consumer activity trackers. BMC Sports Science, Medicine and Rehabilitation, 7(1), 24. doi:10.1186/s13102-015-0018-5
  • Matsumoto, M., et al. (2019). Reliability and validity of wearable devices for energy expenditure during a graded exercise test. Journal of Clinical Medicine Research, 11(9), 627–635. doi:10.14740/jocmr3936
  • Montgomery-Downs, H. E., et al. (2012). Insomniacs’ perceptions of nighttime occupational and social activities. Journal of Clinical Sleep Medicine, 8(4), 431–439. doi:10.5664/jcsm.2136
  • Shcherbina, A., et al. (2017). Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort. Journal of Personalized Medicine, 7(2), 3. doi:10.3390/jpm7020003

Blockchain in healthcare


Blockchain technology, originally developed for secure digital transactions in the realm of cryptocurrencies, has emerged as a transformative force across various industries. In recent years, the healthcare sector has recognized the potential of blockchain to revolutionize data management, security, interoperability, and patient-centered care. This article explores the uses and effectiveness of blockchain in healthcare, shedding light on its potential to reshape the industry.

Enhanced Data Security and Privacy:

Blockchain’s distributed ledger system offers enhanced data security and privacy, making it an ideal solution for healthcare. By decentralizing data storage and encrypting transactions, blockchain ensures the integrity, confidentiality, and immutability of healthcare records. It mitigates the risk of data breaches and unauthorized access, enabling patients to have greater control over their personal health information.

A study published in the Journal of Medical Internet Research highlighted blockchain’s potential in preserving the privacy of patients’ sensitive data. It demonstrated how blockchain-based systems can improve security, confidentiality, and data sharing in healthcare (1).

Streamlined Interoperability and Data Exchange:

Interoperability, the seamless exchange of healthcare data across different systems, has long been a challenge in the industry. Blockchain technology provides a decentralized, standardized platform for securely sharing and exchanging healthcare data among different stakeholders, including healthcare providers, researchers, and patients.

Research conducted by the Massachusetts Institute of Technology (MIT) explored blockchain’s role in healthcare data exchange. The study proposed a blockchainbased architecture that enables secure, real-time data sharing and access control across multiple healthcare providers, leading to improved care coordination and interoperability (2).

Efficient Supply Chain Management:

Blockchain technology offers significant potential in optimizing supply chain management in healthcare. It enables end-to-end traceability of pharmaceuticals, medical devices, and healthcare supplies, ensuring transparency, authenticity, and quality control throughout the supply chain. By eliminating counterfeit products and enhancing inventory management, blockchain reduces the risk of medication errors and improves patient safety.

A pilot project conducted by Chronicled and The LinkLab demonstrated the effectiveness of blockchain in supply chain management. The project utilized blockchain to track and verify the origin, authenticity, and movement of medical devices and supplies, resulting in increased efficiency, reduced costs, and improved patient safety (3).

Secure and Efficient Clinical Trials:

Clinical trials are vital for advancing medical research and developing new treatments. However, the process is often burdened by complex data management, lack of transparency, and data integrity issues. Blockchain technology can address these challenges by providing a secure, decentralized platform for managing and verifying clinical trial data.

A study published in the Journal of Clinical Oncology demonstrated the potential of blockchain in improving the efficiency and integrity of clinical trials. The research highlighted how blockchain can enhance patient consent management, data sharing, and auditing processes, ultimately streamlining the research process and accelerating medical breakthroughs (4).

Empowering Patients with Ownership of Health Data:

Traditionally, patients have limited control over their health data, resulting in fragmented records and limited access. Blockchain technology empowers patients by giving them ownership and control over their health data. Through blockchainbased platforms, patients can securely manage and share their medical records, enabling seamless healthcare experiences, second opinions, and improved care coordination.

One example is MedRec, a blockchain-based electronic medical record (EMR) system developed by researchers at MIT. MedRec allows patients to control their medical data, granting access to healthcare providers when needed while maintaining privacy and security (5).


Blockchain technology has the potential to revolutionize healthcare by enhancing data security and privacy, streamlining interoperability and data exchange, optimizing supply chain management, and empowering patients with ownership of their health data. The studies and pilot projects mentioned demonstrate the effectiveness and potential of blockchain in improving various aspects of healthcare. As the technology continues to evolve, it is expected to further transform the healthcare industry, leading to improved patient care, data management, and collaboration among healthcare stakeholders.


  • Ichikawa D, Kashiyama M, Ueno T. Blockchain technology for healthcare: A systematic review. Journal of Medical Internet Research. 2019;21(7):e13583.
  • Ekblaw A, Azaria A, Halamka JD, Lippman A. A case study for blockchain in healthcare: “MedRec” prototype for electronic health records and medical research data. Proceedings of the 2016 2nd International Conference on Open and Big Data. 2016:25-30.
  • Chronicled. Healthcare: An industry in need of transformation. https://chronicled.com/wp-content/uploads/2020/08/Healthcare.pdf
  • Benchoufi M, Ravaud P. Blockchain technology for improving clinical research quality. Journal of Clinical Oncology. 2017;35(7):761-764.
  • Azaria A, Ekblaw A, Vieira T, Lippman A. MedRec: Using blockchain for medical data access and permission management. Proceedings of the 2nd International Conference on Open and Big Data. 2016:25-30.

Telemedicine and virtual healthcare

Telemedicine and virtual healthcare have emerged as transformative solutions in healthcare delivery, especially in recent years. With advancements in technology and the increased availability of digital platforms, telemedicine offers an innovative approach to providing remote medical services, consultation, and monitoring. This article aims to explore the effectiveness and challenges of telemedicine based on recent research findings, highlighting its potential in revolutionizing access to quality care.

Effectiveness of Telemedicine: Recent research findings demonstrate the effectiveness of telemedicine in various aspects of healthcare delivery.

Improved Access to Care: Telemedicine has been shown to enhance access to care, particularly for individuals in remote or underserved areas. Studies indicate that telemedicine can reduce geographical barriers, allowing patients to connect with healthcare providers regardless of their location (Bashshur et al., 2020; Scott et al., 2021). This has resulted in increased healthcare utilization, reduced travel costs, and improved patient satisfaction.

Enhanced Chronic Disease Management: Telemedicine has proven beneficial in managing chronic diseases. Research indicates that remote monitoring and virtual consultations facilitate regular patient-provider communication, leading to improved medication adherence, better symptom management, and early detection of potential complications (Whitten et al., 2020; Polinski et al., 2021). This proactive approach promotes self-management and reduces hospitalizations.

Mental Health Support: Telemedicine has emerged as a valuable tool for delivering mental healthcare services. Recent studies highlight its effectiveness in providing remote therapy, counseling, and psychiatric consultations (Luxton et al., 2020; Sayers et al., 2021). Telepsychiatry has shown positive outcomes in terms of patient engagement, access to specialized care, and improved mental health outcomes.

Emergency Medical Consultations: Telemedicine has proven crucial in emergency situations. Research demonstrates that telemedicine consultations can aid in triaging and providing timely interventions, even in remote areas lacking immediate access to specialized care (Sampson et al., 2021; So et al., 2022). Telemedicine’s ability to connect emergency providers with specialists enhances diagnostic accuracy and facilitates early interventions.

Challenges and Limitations: While telemedicine offers numerous benefits, there are challenges and limitations that must be addressed.

Technological Barriers: Limited internet access, inadequate technological infrastructure, and technological literacy can pose challenges for widespread telemedicine implementation, particularly in underserved areas (Kruse et al., 2020; World Health Organization, 2020). Efforts are needed to bridge the digital divide and ensure equitable access to virtual healthcare services.

Privacy and Security Concerns: The transfer and storage of personal health information raise concerns regarding data privacy and security. Safeguarding patient confidentiality and protecting data from potential breaches are critical considerations in telemedicine (Krupinski et al., 2017; Taylor et al., 2021). Robust security measures and compliance with privacy regulations are necessary to maintain patient trust.

Diagnostic Limitations: Telemedicine encounters may have limitations compared to in-person consultations. Physical examination and diagnostic procedures may be challenging to perform remotely, potentially leading to diagnostic errors or limitations in certain medical conditions (Meyer et al., 2019; Hollander and Carr, 2020). Developing innovative tools and techniques to enable accurate remote assessments is an ongoing area of research.

Unequal Access and Health Disparities: Although telemedicine has the potential to address healthcare disparities, it can also inadvertently exacerbate existing inequities. Limited access to technology, language barriers, and socioeconomic factors can hinder disadvantaged populations from fully benefiting from telemedicine services (Kinchin et al., 2021; Nouri et al., 2021). Efforts must be made to ensure equitable access and promote health equity in telemedicine implementation.

Conclusion: Telemedicine and virtual healthcare have proven to be effective in improving access to care, enhancing chronic disease management, providing mental health support, and facilitating emergency medical consultations. These advancements in healthcare delivery have the potential to revolutionize the way healthcare services are accessed and provided. However, challenges such as technological barriers, privacy and security concerns, diagnostic limitations, and health disparities must be addressed to ensure equitable and widespread adoption of telemedicine.

By leveraging the power of technology and addressing these challenges, telemedicine can play a vital role in expanding access to quality care, particularly for underserved populations and those in remote areas. Continued research and innovation in telemedicine will further enhance its effectiveness, accuracy, and scope, paving the way for a more patient-centered and accessible healthcare system.


  • Bashshur, R. L., et al. (2020). Telemedicine and the COVID-19 pandemic, lessons for the future. Telemedicine and e-Health, 26(5), 571-573.
  • Hollander, J. E., & Carr, B. G. (2020). Virtually perfect? Telemedicine for COVID-19. New England Journal of Medicine, 382(18), 1679-1681.
  • Kruse, C. S., et al. (2020). Barriers to the use of telemedicine: A systematic review of the literature. Journal of Telemedicine and Telecare, 24(1), 4-12.
  • Luxton, D. D., et al. (2020). Recommendations for the ethical use and design of artificial intelligent care providers. Artificial Intelligence in Behavioral and Mental Health Care, 207-227.
  • Meyer, B. C., et al. (2019). Telemedicine quality and outcomes in stroke: A scientific statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 50(1), e3-e25.
  • Nouri, S., et al. (2021). Equity of telemedicine utilization in the COVID-19 pandemic: A systematic review. Journal of Medical Internet Research, 23(2), e24747.
  • Polinski, J. M., et al. (2021). Remote monitoring of high-risk patients during the COVID-19 pandemic: A case series. JMIR Public Health and Surveillance, 7(4), e24331.
  • Sampson, B. M., et al. (2021). A systematic review of telemedicine in acute care: Feasibility of telemedicine and patient satisfaction. Telemedicine and e-Health, 27(7), 747-755.
  • Sayers, S. L., et al. (2021). Telepsychology and the digital divide: COVID-19 and beyond. Psychological Services, 18(3), 349-353.
  • Scott, K. R., et al. (2021). Telemedicine in the context of COVID-19: Changing perspectives in Australia, the United Kingdom, and the United States. Journal of Medical Internet Research, 23(7), e28587.
  • So, C., et al. (2022). Telemedicine in emergency medicine: A scoping review. Journal of Telemedicine and Telecare, 28(1), 3-14.
  • Taylor, P., et al. (2021). Protecting patient privacy in the age of telehealth. Annals of Internal Medicine, 174(2), 256-257.
  • Whitten, P., et al. (2020). Systematic review of telemedicine in acute care: Feasibility of telemedicine and patient satisfaction. Telemedicine and e-Health, 26(5), 558-570.

Want to know an estimation of your biological age ?

Epigenetic clock refers to a method used to estimate biological age by examining changes in DNA methylation patterns. Epigenetics refers to modifications in gene expression patterns that are not caused by changes in the DNA sequence itself but can have a significant impact on gene activity.

Dr. Steve Horvath is a prominent scientist who has made significant contributions to the field of epigenetic clock research. He has developed several epigenetic clocks that accurately estimate an individual’s chronological age based on DNA methylation data from specific sites in the genome. These clocks provide an estimate of an individual’s biological age, which can differ from their chronological age.

The accuracy of the epigenetic clock developed by Dr. Horvath has been extensively validated. It has been shown to be highly precise in predicting age across various tissues and cell types, including blood, brain, and other organs. In numerous studies, the Horvath DNAmAge clock has consistently demonstrated remarkable accuracy, with predictions often closely aligning with an individual’s chronological age.

The epigenetic clock is not only used to estimate chronological age but also serves as a valuable tool in studying age-related processes and diseases. It has been applied in research to investigate factors influencing biological aging, such as lifestyle choices, environmental exposures, and disease states. By comparing an individual’s biological age to their chronological age, researchers can gain insights into the impact of these factors on aging and age-related diseases.

Moreover, the epigenetic clock has shown promise as a biomarker for assessing health status and disease risk. Accelerated aging, as indicated by a higher biological age compared to chronological age, has been associated with an increased risk of age-related diseases, including cardiovascular disease, cancer, and neurodegenerative disorders.

Examples of studies utilizing epigenetic clocks, including those developed by Dr. Horvath, abound in the scientific literature. For instance, research has demonstrated the utility of epigenetic clocks in predicting mortality risk, evaluating the effects of lifestyle interventions on aging, and investigating the relationship between epigenetic age and various health outcomes.


  • Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14(10), R115. doi: 10.1186/gb-2013-14-10-r115.
  • Horvath, S. (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics, 19(6), 371-384. doi: 10.1038/s41576-018-0004-3.
  • Levine, M. E., et al. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging, 10(4), 573-591. doi: 10.18632/aging.101414.
  • Marioni, R. E., et al. (2015). DNA methylation age of blood predicts all-cause mortality in later life. Genome Biology, 16, 25. doi: 10.1186/s13059-015-0584-6.

Health apps and digital therapeutics

In recent years, there has been a surge in health technology, particularly in the form of mobile health apps and digital therapeutics. These tools are aimed at improving healthcare outcomes by enabling patients to monitor and manage their health more effectively. In this article, we will explore the growing field of health apps and digital therapeutics, and how they are transforming the healthcare industry.

Health Apps:

Health apps are mobile applications that are designed to promote health and wellness. These apps can range from simple tools that track fitness goals to more complex apps that monitor and manage chronic conditions such as diabetes or hypertension. Health apps provide users with real-time feedback, personalized recommendations, and data analysis, which can help them make better-informed decisions about their health.

A study by the University of California, San Francisco found that the use of health apps can lead to improved health outcomes in patients with chronic conditions. The study showed that patients who used health apps to monitor their conditions had better medication adherence, better disease management, and improved quality of life (1).

Digital Therapeutics:

Digital therapeutics are a form of health technology that use software to deliver therapeutic interventions. These interventions are designed to prevent, manage, or treat a medical condition. Digital therapeutics are evidence-based, clinically validated, and regulated by the FDA. They can be used alone or in conjunction with traditional medical treatments.

A study by the Journal of Medical Internet Research found that digital therapeutics can be effective in treating a wide range of conditions, including depression, anxiety, and substance abuse. The study found that digital therapeutics were as effective as traditional interventions and were more convenient and accessible for patients (2).

Benefits of Health Apps and Digital Therapeutics:

Health apps and digital therapeutics offer several benefits for patients and healthcare providers. These benefits include:

Improved Patient Outcomes: Health apps and digital therapeutics can lead to improved patient outcomes by providing real-time feedback, personalized recommendations, and data analysis.

Increased Patient Engagement: Health apps and digital therapeutics can increase patient engagement by providing patients with a sense of ownership over their health.

Reduced Healthcare Costs: Health apps and digital therapeutics can reduce healthcare costs by promoting preventive care, reducing the need for hospitalization, and improving medication adherence.

Remote Monitoring: Health apps and digital therapeutics can facilitate remote monitoring of patients, allowing healthcare providers to monitor patients’ health in real-time and make timely interventions.


Health apps and digital therapeutics are transforming the healthcare industry by providing patients with more control over their health and enabling healthcare providers to deliver more personalized care. Research studies have shown that health apps and digital therapeutics can lead to improved patient outcomes, increased patient engagement, and reduced healthcare costs. As health technology continues to advance, we can expect to see even more innovations in this field.


  • Mendiola, M. F., Kalnicki, M., & Lindenauer, P. K. (2018). Valuable Features in Mobile Health Apps for Patients and Consumers: Content Analysis of Apps and User Ratings. JMIR mHealth and uHealth, 6(6), e10723. https://doi.org/10.2196/10723
  • Ventola, C. L. (2018). Mobile devices and apps for health care professionals: uses and benefits. P&T: a peer-reviewed journal for formulary management, 43(5), 286–296.

Artificial intelligence and machine learning in healthcare

Title: Leveraging Artificial Intelligence and Machine Learning to Reduce Healthcare Costs

Introduction: The healthcare industry has always been on the forefront of technology adoption, and Artificial Intelligence (AI) and Machine Learning (ML) are no exception. The application of AI and ML in healthcare has the potential to transform the industry by improving efficiency, accuracy, and patient outcomes while also reducing costs. This article will explore how AI and ML can be leveraged to reduce healthcare costs while improving patient care, providing researchbacked examples and references to support the argument.

AI and ML Applications in Healthcare: AI and ML can be applied to various aspects of healthcare, such as diagnosis, treatment, and research, to name a few. The following are some examples of how AI and ML are being used in healthcare:

Diagnostic Imaging: AI and ML algorithms can analyze medical images, such as Xrays, CT scans, and MRIs, to detect and diagnose diseases with greater accuracy than traditional methods. For example, a study by Google Health demonstrated that an AI algorithm could detect breast cancer with 90% accuracy, compared to 88% for human radiologists (McKinney et al., 2020). By improving the accuracy of diagnosis, AI and ML can reduce the need for follow-up tests and procedures, ultimately reducing healthcare costs.

Personalized Treatment: AI and ML can help personalize treatment plans for patients by analyzing vast amounts of patient data, such as medical history, genetic information, and lifestyle factors. This can result in more effective treatments, reducing the likelihood of treatment failure, and the need for costly hospitalizations or readmissions (Nikolayeva et al., 2021).

Drug Discovery: AI and ML can help researchers identify potential drug candidates more quickly and accurately by analyzing vast amounts of data, such as genetic information, disease biomarkers, and drug interactions. This can reduce the time and cost associated with traditional drug development processes, resulting in more efficient and cost-effective drug discovery (Osheroff et al., 2020).

Reducing Healthcare Costs with AI and ML: The application of AI and ML in healthcare can result in significant cost savings for patients, providers, and insurers. The following are some examples of how AI and ML can help reduce healthcare costs:

Preventive Care: By analyzing patient data and identifying patterns and risk factors, AI and ML can help healthcare providers deliver more effective preventive care. This can reduce the likelihood of patients developing chronic conditions, resulting in lower healthcare costs in the long term (Poon et al., 2018).

Reduced Hospitalizations: AI and ML can help identify high-risk patients who are more likely to require hospitalization. By proactively identifying these patients and providing targeted interventions, such as home monitoring or telehealth services, healthcare providers can reduce the need for costly hospitalizations (Le et al., 2020).

Streamlined Administrative Processes: AI and ML can automate administrative processes, such as billing and coding, scheduling, and record-keeping, reducing the administrative burden on healthcare providers and resulting in cost savings (Liu et al., 2019).

Examples of AI and ML Reducing Healthcare Costs: The following are some realworld examples of how AI and ML are being used to reduce healthcare costs:

Geisinger Health System: Geisinger Health System in Pennsylvania implemented an AI-powered tool that analyzed patient data to predict the likelihood of patients developing sepsis, a potentially life-threatening infection. By identifying high-risk patients and providing early interventions, Geisinger reduced sepsis mortality rates by 53% and saved an estimated $10 million in healthcare costs (Reed et al., 2019).

Optum: Optum, a healthcare services company, implemented an AI-powered tool that analyzed patient data to identify those who were at high risk of readmission. The tool provided targeted interventions, such as home visits and telehealth services, to prevent readmissions. As a result, Optum reduced readmissions by 25% and saved an estimated $4.5 million in healthcare costs (Dixon, 2020).

Northwell Health: Northwell Health, a healthcare provider in New York, implemented an AI-powered tool that analyzed patient data to identify those at high risk of developing pressure ulcers. By providing targeted interventions, such as repositioning and skin care, Northwell reduced the incidence of pressure ulcers by 58%, resulting in an estimated $10 million in healthcare cost savings (Furlan et al., 2020).


AI and ML have immense potential to transform the healthcare industry by improving efficiency, accuracy, and patient outcomes while reducing healthcare costs. From diagnostic imaging to drug discovery and administrative processes, AI and ML can be applied to various aspects of healthcare to achieve cost savings and improve patient care. Real-world examples, such as Geisinger Health System, Optum, and Northwell Health, demonstrate the potential of AI and ML to reduce healthcare costs while improving patient outcomes. As the healthcare industry continues to adopt AI and ML technologies, we can expect to see more cost savings and improved patient care in the future.


  • Dixon, B. (2020). Optum uses AI to reduce hospital readmissions, saving $4.5M. Health IT Analytics. Retrieved from https://healthitanalytics.com/news/optumuses-ai-to-reduce-hospital-readmissions-saving-4.5m
  • Furlan, A., Huguenin, J., & Spires, J. (2020). Reducing pressure ulcers through the application of artificial intelligence. International Journal of Health Care Quality Assurance, 33(3), 209-215.
  • Le, T., Schenck-Gustafsson, K., & Hedenmalm, K. (2020). Using machine learning to predict hospitalizations in patients with heart failure. PloS one, 15(2), e0228978.
  • Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., … & De Fauw, J. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271-e297.
  • McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., … & Topol, E. J. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94.
  • Nikolayeva, O., Robinson, J. G., & Blackwell, T. (2021). Personalized Medicine: The Promise, the Reality, and the Challenges Ahead. Frontiers in Medicine, 8, 615127.
  • Osheroff, J. A., Teich, J. M., Levick, D., Saldana, L., Velasco, F., Sittig, D. F., … & Jenders, R. A. (2020). Improving Outcomes with Clinical Decision Support: An Implementer’s Guide, Second Edition. Healthcare Information and Management Systems Society.
  • Poon, E. G., Wright, A., Simon, S. R., Jenter, C. A., Kaushal, R., Volk, L. A., … & Bates, D. W. (2018). Relationship between use of electronic health record features and health care quality: results of a statewide survey. Medical care, 46(12), 1269-1277.
  • Reed, M. J., Kim, Y., & Karnik, N. S. (2019). Using machine learning to predict sepsis in the ICU. Health Management, Policy and Innovation, 4(1), 15-23.

Electronic health records and patient data privacy

Title: Electronic Health Records and Patient Data Privacy: The Effective Implementation of a Secure Healthcare System

Introduction: Electronic health records (EHRs) have revolutionized healthcare information management, offering numerous benefits such as improved coordination of care, enhanced clinical decision-making, and efficient data exchange. However, ensuring patient data privacy and security is crucial to maintaining trust and compliance with privacy regulations. This article explores research-backed evidence on the effectiveness of EHRs and patient data privacy, providing insights into why and how to implement secure healthcare systems.

Effectiveness of Electronic Health Records:

Improved Coordination of Care: EHRs facilitate seamless communication and information sharing among healthcare providers, resulting in enhanced care coordination. Research demonstrates that EHR use leads to reduced medical errors, improved medication reconciliation, and increased patient safety (AdlerMilstein et al., 2017; Amarasingham et al., 2018).

Enhanced Clinical Decision-Making: EHRs provide comprehensive patient information, including medical history, lab results, and diagnostic reports. Studies have shown that access to complete and accurate data through EHRs supports evidence-based clinical decision-making, leading to improved patient outcomes (Bates et al., 2015; Romano et al., 2020).

Efficient Data Exchange: EHRs enable secure and timely exchange of patient information between healthcare providers, leading to better care transitions and reduced healthcare costs. Research has highlighted the benefits of interoperable EHR systems in improving care continuity and reducing duplicative tests or procedures (Fridsma et al., 2017; Patel et al., 2018).

Importance of Patient Data Privacy:

Maintaining Patient Trust: Patient trust is essential in healthcare delivery. Protecting patient data privacy builds trust and fosters a positive patient-provider relationship. Research indicates that patients are more willing to share sensitive health information when they have confidence in the privacy and security of their data (Makri et al., 2020; Kaya et al., 2021).

Compliance with Privacy Regulations: Healthcare organizations must adhere to privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Research emphasizes the legal and ethical importance of safeguarding patient data, ensuring compliance, and avoiding potential legal repercussions (Halamka et al., 2019; Singh et al., 2020).

Effective Implementation of Secure Healthcare Systems:

Robust Security Measures: Implementing robust security measures, including encryption, access controls, and secure authentication, is crucial to protect patient data. Research suggests that technologies such as blockchain and secure cloud storage can enhance data security and mitigate risks (Dinh et al., 2018; Kuo et al., 2020).

Staff Training and Awareness: Healthcare organizations should provide comprehensive training to staff members regarding data privacy policies, security protocols, and best practices. Research emphasizes the importance of ongoing education and awareness programs to ensure the proper handling and protection of patient data (Koutkias et al., 2018; Kim et al., 2021).

Privacy-Enhancing Technologies: Privacy-enhancing technologies, such as deidentification and anonymization techniques, can be employed to protect patient privacy while enabling data analysis for research purposes. Research highlights the potential of these technologies in striking a balance between data utility and privacy protection (El Emam et al., 2020; Malin and Emam, 2015).

Examples of Effective Implementation:

Estonia’s National Health Information System: Estonia’s secure and interoperable EHR system, known as the X-Road, has demonstrated effective implementation of patient data privacy. The system employs strong data security measures, decentralized storage, and strict access controls, ensuring patient privacy while facilitating efficient healthcare services (T Timpka et al., 2018). The system has gained trust from patients and healthcare providers and serves as an exemplary model for the effective implementation of EHRs with a focus on patient data privacy.

MyChart Patient Portal: The MyChart patient portal, implemented by various healthcare organizations, including the Mayo Clinic and Cleveland Clinic, demonstrates an effective approach to patient data privacy. The portal allows patients to securely access their EHRs, communicate with healthcare providers, and manage their health information. Strict authentication measures, encrypted communication channels, and user-friendly privacy settings ensure patient data privacy while empowering individuals to take an active role in their healthcare (Ancker et al., 2018; Ralston et al., 2016).

Conclusion: Electronic health records (EHRs) have revolutionized healthcare, offering numerous benefits in terms of care coordination, clinical decisionmaking, and data exchange. However, maintaining patient data privacy is crucial for building trust and complying with privacy regulations. Implementing secure healthcare systems involves robust security measures, staff training, and privacyenhancing technologies. Examples such as Estonia’s National Health Information System and the MyChart patient portal showcase effective implementations of EHRs with a focus on patient data privacy.

By prioritizing patient data privacy and adopting best practices in secure EHR implementation, healthcare organizations can leverage the benefits of EHRs while ensuring the confidentiality, integrity, and availability of patient information.


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3D printing in healthcare

The field of healthcare is one of the areas where technology has made significant advancements. One of the latest innovations in healthcare is 3D printing. 3D printing has revolutionized the healthcare industry by allowing the creation of customized, complex, and personalized medical devices, prosthetics, and implants. This essay will explore the benefits of 3D printing in healthcare, including research findings, and how it is transforming the healthcare industry.

Benefits of 3D Printing in Healthcare:

1.Personalized Medical Devices: 3D printing technology has enabled the production of customized medical devices that perfectly fit the patient’s needs. This technology has improved patient care by providing medical devices that fit better, are more comfortable to wear, and function more effectively. A study by the National Institute of Health found that 3D printing technology has the potential to improve prosthetic fit, function, and comfort, thereby increasing patient satisfaction (1).

2.Customized Implants: 3D printing technology has also revolutionized the manufacturing of implants. With the help of 3D printing, implants can be designed and customized to match the patient’s unique anatomy. A study by the University of Michigan found that 3D printing technology is highly effective in producing custom-made implants, which resulted in better clinical outcomes (2).

3.Surgical Planning: 3D printing technology has also helped improve surgical planning by providing a more accurate representation of the patient’s anatomy. Surgeons can use 3D-printed models to plan surgical procedures, practice complex surgeries, and reduce the risk of complications during surgery. A study by the University of California, Los Angeles (UCLA) found that 3D-printed models helped improve the accuracy of surgical planning, resulting in better surgical outcomes (3).

Research Findings:

1.A study conducted by researchers at the University of Michigan found that 3D-printed tracheal splints helped treat three babies with life-threatening tracheobronchomalacia, a rare respiratory disease. The 3D-printed splints were able to maintain the airway and allowed the babies to breathe normally, leading to successful treatment (4).

2.Researchers at the University of British Columbia found that 3D printing technology can be used to produce personalized spinal implants that can improve the surgical outcome and reduce complications. The study concluded that 3D printing technology can improve the accuracy and safety of spinal surgeries (5).

3.Researchers at the University of California, San Diego, found that 3D printing technology can be used to create personalized hearing aids that are more comfortable and effective for patients. The study found that 3D-printed hearing aids resulted in better patient satisfaction and improved sound quality (6).


3D printing technology has revolutionized the healthcare industry by providing personalized medical devices, customized implants, and improving surgical planning. Research studies have found that 3D printing technology can improve patient outcomes, increase patient satisfaction, and reduce healthcare costs. As 3D printing technology continues to advance, we can expect to see even more innovations and benefits in the field of healthcare.


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Gene editing and personalized medicine

Recent developments in gene editing and personalized medicine have been revolutionizing the field of healthcare. Gene editing allows scientists to modify and edit specific genes, which has the potential to treat a variety of diseases and disorders. Personalized medicine, on the other hand, involves tailoring medical treatments to an individual’s unique genetic makeup. In this essay, we will explore the recent developments in gene editing and personalized medicine and their potential to transform healthcare.

Gene Editing:

CRISPR-Cas9 is a gene-editing technology that has been gaining a lot of attention in recent years. This technology allows scientists to precisely modify and edit specific genes. Researchers at the University of California, San Francisco, used CRISPR-Cas9 to edit the genes of mice with sickle cell disease, which is a genetic blood disorder. The study found that the edited cells produced healthy red blood cells, potentially leading to a cure for sickle cell disease (1). Another study by researchers at the University of Pennsylvania used CRISPR-Cas9 to edit genes in Tcells to treat cancer. The study found that the edited T-cells were able to target and kill cancer cells, leading to promising results in cancer treatment (2).

Personalized Medicine:

The field of personalized medicine involves using a patient’s genetic information to tailor medical treatments to their specific needs. Researchers at the Mayo Clinic conducted a study on personalized medicine for patients with Crohn’s disease, a chronic inflammatory bowel disease. The study found that patients who received personalized treatment based on their genetic information had better outcomes and fewer hospitalizations compared to those who received standard treatment (3). Another study by researchers at the University of California, San Diego, used genetic information to predict the risk of adverse drug reactions. The study found that using genetic information to personalize drug treatments could significantly reduce the risk of adverse drug reactions and improve patient outcomes (4).


Gene editing and personalized medicine are rapidly advancing and have the potential to transform the field of healthcare. Gene editing allows for precise modification of specific genes, which has the potential to cure genetic diseases. Personalized medicine involves tailoring treatments to an individual’s unique genetic makeup, which can lead to better outcomes and fewer complications. As research in these fields continues to advance, we can expect to see even more innovative treatments and therapies.


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