Difference between revisions of "Digital Health"
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− | With the emergence of digital health technologies, there are unintended consequences including misinterpretation of health information. Technological progress has made access to health information on the web easier, while also increasing the possibility of finding unreliable information. This access to medical web data has led to the issue of malicious medical information and with the majority of web users unaware of medical terms, it could lead to misinterpretation of data. For instance, the no-vax movement on the internet has been largely due to the deception regarding vaccines. This proliferates with false propaganda and well-tailored stories as it does not reflect the reality and misleads the general public into making assumptions. Given this, scientists and scholars advice that web users remain suspicious and vigilant when reading medical information on the web. One study found that patients with chronic diseases or disabilities were the most active members of searching the internet for health data, while another study reported that 82% of individuals searching the internet for health information were students. Given that the internet is a place where anyone has the opportunity to create content and publish it, students realize it can be a potential source of biased information. <ref name="ref 19"> Battineni, Gopi, et al. “Factors Affecting the Quality and Reliability of Online Health Information.” DIGITAL HEALTH, vol. 6, 2020, p. 205520762094899., https://doi.org/10.1177/2055207620948996.</ref> | + | With the emergence of digital health technologies, there are unintended consequences including misinterpretation of health information. Technological progress has made access to health information on the web easier, while also increasing the possibility of finding unreliable information. This access to medical web data has led to the issue of malicious medical information, and with the majority of web users unaware of medical terms, it could lead to misinterpretation of data. For instance, the no-vax movement on the internet has been largely due to the deception regarding vaccines. This proliferates with false propaganda and well-tailored stories as it does not reflect the reality and misleads the general public into making assumptions. Given this, scientists and scholars advice that web users remain suspicious and vigilant when reading medical information on the web. One study found that patients with chronic diseases or disabilities were the most active members of searching the internet for health data, while another study reported that 82% of individuals searching the internet for health information were students. Given that the internet is a place where anyone has the opportunity to create content and publish it, students realize it can be a potential source of biased information.<ref name="ref 19"> Battineni, Gopi, et al. “Factors Affecting the Quality and Reliability of Online Health Information.” DIGITAL HEALTH, vol. 6, 2020, p. 205520762094899., https://doi.org/10.1177/2055207620948996.</ref> |
− | Due to the limited awareness | + | Due to the limited awareness on the problem of quality of web health information, scholars have proposed that healthcare professionals assist patients in accessing high quality medical information on the internet. Many healthcare professionals have doubts regarding online health information; hence, it’s advised that they mold their competence to the patient’s needs to help guide them to reliable internet sources. This presents a problem of its own given doctors themselves have difficulty accessing reliable online health information.<ref name="ref 19" /> |
− | The issue of unreliable health information remains unsolved, creating a platform for misinterpretation of data by individuals lacking medical terminology. As a response, experts suggest governments and educational institutions should provide education on the quality of sources as it could lead to the development of new strategies in the field of preventative and personalized medicine. <ref name="ref 19" /> | + | The issue of unreliable health information remains unsolved, creating a platform for misinterpretation of data by individuals lacking medical terminology. As a response, experts suggest governments and educational institutions should provide education on the quality of sources as it could lead to the development of new strategies in the field of preventative and personalized medicine.<ref name="ref 19" /> |
===AI Bias in Healthcare=== | ===AI Bias in Healthcare=== |
Revision as of 21:00, 11 February 2022
Digital health is an emerging field that implements technology within healthcare to provide a wide variety of services ranging from mobile health, health information technology, wearable devices, telehealth and telemedicine, and personalized medicine. Digital health technologies make use of computing platforms, software, and sensors to aid providers and stakeholders in numerous efforts, including reducing costs and inefficiencies and improving access and quality of care through a personalized and precise approach to medicine.[2]
Although digital health technologies enable inexpensive communication and have the potential to prevent disease, the COVID-19 pandemic has made inequities between social and minority groups apparent.[3] Critics also warn of privacy and security concerns regarding personal health information, along with the possibility of misinterpretation of medical results by patients, and artificial intelligence bias in the healthcare system.[4]
Technologies
Mobile Health
The introduction of the smartphone has revolutionized people’s lives in many ways, including how they seek medical information. Specifically, smartphones have led to a popularity in mobile health, or mHealth, apps. These apps are health related applications that can serve a multitude of functions aimed at improving patients' health. The major app stores house over 165,000 apps that allow individuals to track their own health. These apps can provide “low cost, around-the-clock access” to health information to users, allowing them to stay informed on the best processes and techniques when navigating chronic disease management, mental health, and general health concerns.[5]
Mobile health apps can be grouped into five categories.[5]
- Wellness Management - Two-thirds of the mHealth apps in the app store are consumer-facing that aim to encourage a healthy lifestyle. These apps monitor individuals’ diet and physical activity by collecting data through smartphones or external devices like a heart rate monitor.
- Medication Reminders - For individuals struggling with medication compliance, these apps allow patients to enter in their medications, provide a calendar, and send notifications on when to take their prescriptions.
- Disease Management - individuals with chronic diseases like diabetes benefit from mHealth apps given it allows them to keep a symptom diary, enter food diary entries, and ensure compliance with their medications and physical activity.
- Self-Diagnosis - Apps of this nature have become popular among consumers during emergencies as they provide a first step for patients to understand their symptoms and generate possible diagnosis.
- Patient Portals - These apps are created primarily by major electronic health companies and enable patients to access parts of their medical records like laboratory results and serve as a reminder tool for appointments, vaccinations and more.
Wearable Technology
Wearable technology consists of a broad scope of devices including smartwatches. Smartwatches and other fitness tracking devices became prominent starting in 2014 as individuals wanted to track more than just weight loss.[7] Popular fitness brands like Apple Watch and Fitbit are equipped with sensors on bands or patches that allow a user’s data to be logged throughout the day. These technologies allow consumers to self-monitor and regulate their own health practices without the need of a healthcare professional.[8] For instance, these wearable gadgets allow users to track advanced metrics like heart rate, body fat percentage, menstrual cycle, and more.[7] Growth within this area is largely contributed to the growth of the internet of things, a move towards connecting devices to the internet.[8]
In addition to smartwatches, there is ongoing research focusing on associated energy solutions for implantable devices in a healthcare setting. Progress has been made in the study of nanomaterials as research shows that they can allow for seamless interaction with soft tissues and organs. Research also suggests that an advantage of soft, flexible wearable sensors is its capability of tracking physiological parameters such as cardiac activity. These sensors can monitor cardiovascular biomarkers such as hearth rate, blood pressure and more to mitigate incidences and mortality of cardiovascular disease. Monitoring biomarkers through wearable sensors can allow healthcare professionals to deliver personalized medicine to their patients by tailoring medications and therapeutics based on collected data.[9]
Telemedicine
The use of advanced telecommunications and computer technologies have been investigated by clinicians, health service providers, and others to improve health care. These efforts have led to the introduction of telemedicine which combines mainstream and innovative information technologies. Telemedicine encompasses a wide spectrum that includes familiar services like phone consultations on one end and largely experimental innovations like telesurgery on the other end. In between these two ends lies an array of technologies and applications such as video conferencing and “store and forward” technologies that permit digital images and other information to be saved and transmitted efficiently. Along with patient care, these varied technologies have possible uses in other areas such as research, public health, and administration.[10]
There are a multitude of digital health services currently offered to patients.
Remote and Digital Clinics
Access to healthcare has propelled interest in clinical applications of telemedicine.[10] Remote clinics have emerged to provide individuals in remote, typically rural communities, with specialty medical services. In many of these areas, there is also a lack of specialty expertise making it a necessary decision to link health care centers.[11]
Digital clinics include email consultations which can provide rapid communication for non-urgent concerns. Video consultations have emerged as well to save patients time, hospital resources, and to provide care for those who do not have easy access to healthcare.[11]
Online Appointment Booking
Online appointment bookings allow for administrative efficiencies where staff sees a minimized number of missed appointments. The industry is evolving to the technological changes through the use of smartphones by utilizing text alerts for appointments and confirmations. Services like ordering prescription refills online and booking clinical appointments have overall benefits like increasing patient outcome, reducing pharmaceutical risk, and making the patient’s experience efficient and satisfactory.[11]
Digital Doctor
Recently, there has been an information overload due to expansion of the knowledge base and biomedical evidence. Doctors are focused on referrals, x-rays, blood test reports, etc. during appointments, which means staring at computer screens instead of having a conversation with the patient. This hinders the personable component to patient-doctor relationships, but the technology of the time allows for smart access to quality information, allowing for better care and advice from healthcare professionals.[11]
The “E-Doctor” Era
Nowadays, healthcare professionals can google medical information before seeing their patients. They also have access to patients monitors that can be viewed on their devices at any time. Even hospitals have adopted a modern digital service where electronic health records are kept for each patient. Hospital’s libraries have launched new services that facilitate quick access to updated evidence-based medical information that can assist doctors in their decisions. In this digital age, e-prescriptions ensure speedy drug delivery and keep doctors aware of a patient’s drug record changes.[11]
Other
New digital health applications, like artificial intelligence, continue to enter the market as the industry is trying to adapt. Artificial intelligence can be a tool used to assist imaging specialists like radiologists that could help minimize errors in interpretation of images, leading to a more accurate diagnosis. This technology can propel automation using AI algorithms to increase productivity and save time for all stakeholders. Tasks that may have been completed manually before could now utilize AI to accelerate the processes.[12]
Another technology assisting healthcare professionals is augmented reality. Augmented reality leverages 2D images and other patient data to then create a 3D model that can assist in understanding the anatomy of a patient’s body. These images help professionals like surgeons when planning surgical procedures, decreasing the chances of errors in the operating room. During an operation, surgeons and their teams must sort through a clutter of images and data which diverts their attention from the patient, increasing the chance of an error. Augmented reality technology allows for a higher level of precision when navigating the body to locate organs during an operation.[12]
Recent Application: COVID-19
Over the course of history, digital health technologies have played an important role during pandemics. The impact on the public infrastructure caused by these health crises has made the need and development for digital technologies apparent. The current COVID-19 pandemic has challenged health systems in several ways making it critical to focus on early detection and treatment, surveillance, and outbreak control. Healthcare providers have applied different services such as telemedicine, electronic health records, and artificial intelligence to manage care, prevent and control the COVID-19 pandemic. During the pandemic, telemedicine has allowed for an exchange of information, while avoiding close contact to limit disease spread, reducing healthcare exposure, and streamlining decisions made in the first stage care of suspected COVID-19 patients. For instance, the COVID-19 information page has been a resourceful tool updated in real time, while providing readers with symptom information and instructions for quarantine processes. This service has even enabled patients to connect with healthcare providers for remote medical screenings and assessments in efforts to minimize frontline provider exposure. Additionally, with recent travel restrictions imposed, cloud based electronic health records have become critical in documenting recent history of travel to support efforts of contact tracing to control the spread of COVID-19 around the world.[13]
The pandemic has also given rise to certain new digital health technologies like artificial intelligence that have assisted in predicting modeling and trends that can help in creating a framework for the epidemic response. This technology has been aiding epidemiologists and infectious disease professionals develop control strategies along with facilitating an effective COVID-19 vaccine delivery system.[13]
Criticisms
Privacy and Security Concerns
A big concern sparked by digital health is the privacy of big data. With the application of advanced analytics, protecting privacy has become a challenge. Given this, it is difficult to use standard mechanisms of protection because of how stretched they have become in an environment like this.[14]
The collection and storage of personal health information has proliferated with the continuing increase in mHealth apps. Due to this, the Federal Trade Commission has recommended that mHealth apps add privacy policies given that consumers value control over their personal data. However, little attention is paid to these policies. A study revealed that only 183 out of 600 of the most commonly used mHealth apps had privacy policies.[15] A more recent study by the British Medical Journal analyzed nearly 21,000 mHealth apps available on the Google Play store and found that 88% of the apps could access and possibly share personal data. The analyzed data also suggests that 28.1% of mHealth apps offered no privacy policy text and 256% of data transmissions violated stated privacy policies.[16]
Wearable healthcare technologies pose their own privacy issues, however, there is a lower perception of risk towards these technologies as they are seen as low risk versus other devices like smartphones. A study showed how sensors within smartwatches have been able to collect information on keyboards through keystrokes. This is referred to as keystroke inference attacks and many are unaware of this new type of threat to privacy.[17] Additionally, wearable technologies rely heavily on iOS and Android, and vulnerabilities within these systems can be attacked by hackers, resulting in private data breaches. Users have a lack of control over their devices when it comes to data permissions given as they are not able to shut down a sensor; hence it becomes difficult to authorize and view data. Legislation for wearable healthcare technologies is complicated given there is no uniform industry standard for the data format and content collection, leading to difficulties in storing and managing data.[18]
Data security is another concern as cyber-attacks and hacking of databases occur frequently. The Breach Portal of the Health and Human Services states that there have been millions of health care records that have been breached to date, and these incidents continue to rise. Growing public concerns regarding big data along with these incidents have painted a stark picture of the future of privacy. However, these concerns can largely be addressed through enforcement of articulated policies and the adoption of technologies that monitor and evaluate security systems.[14]
Lack of Existing Regulations
As new digital health technologies have been introduced, it has become apparent that there is a lack of existing regulation within the industry. The first framework to be passed was in 1996 called The Health Insurance Portability and Accountability Act (HIPAA) to standardize the use of electronic health information and protect the public’s health information. Since the implementation of HIPAA, critics have argued for stronger protections than are listed in the framework as it has merely reduced, not eliminated, concerns about privacy and security of personal health information.[19] To strengthen the HIPAA rules, in 2009, The Health Information Technology for Economic Health and Clinical Health Act (HITECH) was signed to address the privacy and security concerns related to electronic health information.[20]
In this technological age, the accuracy of health information on mHealth apps is a cause for concern. Given the age of widespread misinformation, a lack of regulations within the digital health space can compromise users’ health and safety. Due to the lack of regulations, healthcare professionals are hesitant to recommend mHealth apps to patients. To help this, the FDA has issued guidance on an approach that focuses on certain apps to transform a mobile platform into a regulated medical device, but critics question whether this can be enforced to make an impact.[21]
Misinterpretation of Data
With the emergence of digital health technologies, there are unintended consequences including misinterpretation of health information. Technological progress has made access to health information on the web easier, while also increasing the possibility of finding unreliable information. This access to medical web data has led to the issue of malicious medical information, and with the majority of web users unaware of medical terms, it could lead to misinterpretation of data. For instance, the no-vax movement on the internet has been largely due to the deception regarding vaccines. This proliferates with false propaganda and well-tailored stories as it does not reflect the reality and misleads the general public into making assumptions. Given this, scientists and scholars advice that web users remain suspicious and vigilant when reading medical information on the web. One study found that patients with chronic diseases or disabilities were the most active members of searching the internet for health data, while another study reported that 82% of individuals searching the internet for health information were students. Given that the internet is a place where anyone has the opportunity to create content and publish it, students realize it can be a potential source of biased information.[22]
Due to the limited awareness on the problem of quality of web health information, scholars have proposed that healthcare professionals assist patients in accessing high quality medical information on the internet. Many healthcare professionals have doubts regarding online health information; hence, it’s advised that they mold their competence to the patient’s needs to help guide them to reliable internet sources. This presents a problem of its own given doctors themselves have difficulty accessing reliable online health information.[22]
The issue of unreliable health information remains unsolved, creating a platform for misinterpretation of data by individuals lacking medical terminology. As a response, experts suggest governments and educational institutions should provide education on the quality of sources as it could lead to the development of new strategies in the field of preventative and personalized medicine.[22]
AI Bias in Healthcare
The adoption of artificial intelligence, or AI, in the healthcare space has the potential to make patient experiences and outcomes better; however, it also can be biased. The term “algorithmic bias” encompasses this idea. AI technologies use algorithms to assess data, make a representation of that data, and then use that information to make an inference. Through this process, bias can enter from a multitude of areas including the study design, data collection and data entry. It is estimated that Caucasians make up 80% of collected data within the field of genetics, making studies more applicable to them versus minority groups. The Framingham Heart Study depicts this disparity given than the cardiovascular risk score performed well for the Caucasian patients, but not the African American patients. As a result, this can lead to inaccurate decisions in regard to the distribution of care. [23]
Brian Powers, a faculty member in Harvard’s Applied Artificial Intelligence for Health Care, found that algorithms used by prominent health systems are racially biased. Based on this information, healthcare professionals make recommendations regarding medical care for certain patients. [23] This issue has been pertinent for the care of women regarding pregnancy and child-birth. According to the CDC, Black women are over 3 times more likely to die in childbirth than white women. A study done in 2016 found that Black women who had college degrees suffered more severe complications compared to white women without high school diplomas. In 2018, when USA Today investigated these racial disparities, it was revealed that there was no national system for tracking complications sustained during pregnancy or childbirth. The lack of maternal data raises the issue of who is valued in the healthcare system. [24]
Due to the lack of inclusion among AI developers, the framing of problems is in the perspective of the majority group which exasperates racial and gender disparities. [25] In 2018, the U.S Bureau of Labor Statistics released that only 26% of women make up STEM occupations and across those women, only 12% are Black or Latinx. The number of women in these roles has been in decline as women computer science graduates peaked in the mid-1980s at 37% compared to 26% today. [24]
Scholars suggest that there will always be some amount of bias, simply due to the inequities that continue to proliferate in society. A collaboration between the private sector, government, academia, and society will be required to make strides towards mitigating this bias.[23]
References
- ↑ “Minsait: The Digital Technologies to Build the Future of Healthcare.” Thehealthcareinsights, 24 Mar. 2021, https://thehealthcareinsights.com/minsait-the-digital-technologies-to-build-the-future-of-healthcare/.
- ↑ “What is Digital Health?” U.S Food and Drug Administration, 22 Sept. 2020, https://www.fda.gov/medical-devices/digital-health-center-excellence/what-digital-health.
- ↑ Özdemir, Vural, OMICS: A Journal of Integrative Biology, Digital Is Political: Why We Need a Feminist Conceptual Lens on Determinants of Digital Health, vol. 25, no. 4, 2021, pp. 249-254., https://doi.org/10.1089/omi.2021.0020.
- ↑ Dhingra, Dhulika, and Aashima Dabas. “Global Strategy on Digital Health.” Indian Pediatrics, vol. 57, no. 4, 2020, pp. 356–358., https://doi.org/10.1007/s13312-020-1789-7.
- ↑ 5.0 5.1 Kao, Cheng-Kai, and David M. Liebovitz. “Consumer Mobile Health Apps: Current State, Barriers, and Future Directions.” PM&R, vol. 9, 2017, https://doi.org/10.1016/j.pmrj.2017.02.018.
- ↑ Research, GlobalData Thematic, et al. “Wearable Technology in Healthcare: What Are the Leading Tech Themes Driving Change?” Medical Device Network, 22 July 2020, https://www.medicaldevice-network.com/comment/wearable-technology-in-healthcare-what-are-the-leading-tech-themes-driving-change/.
- ↑ 7.0 7.1 Greiwe, Justin, and Sharmilee M. Nyenhuis. “Wearable Technology and How This Can Be Implemented into Clinical Practice.” Current Allergy and Asthma Reports, vol. 20, no. 8, 2020, https://doi.org/10.1007/s11882-020-00927-3.
- ↑ 8.0 8.1 Rich, Emma, and Andy Miah. “Mobile, Wearable and Ingestible Health Technologies: Towards A Critical Research Agenda.” Self-Tracking, Health and Medicine, 2017, pp. 84–97., https://doi.org/10.4324/9781315108285-7.
- ↑ Gao, Wei, and Cunjiang Yu. “Wearable and Implantable Devices for Healthcare.” Advanced Healthcare Materials, vol. 10, no. 17, 2021, p. 2101548., https://doi.org/10.1002/adhm.202101548.
- ↑ 10.0 10.1 ELFORD, ROD. “Telemedicine: A Guide to Assessing Telecommunications in Health Care.” Telemedicine Journal, vol. 3, no. 4, 1997, pp. 297–298., https://doi.org/10.1089/tmj.1.1997.3.297.
- ↑ 11.0 11.1 11.2 11.3 11.4 El-Miedany, Yasser. “Telehealth and Telemedicine: How the Digital Era Is Changing Standard Health Care.” Smart Homecare Technology and TeleHealth, Volume 4, 2017, pp. 43–51., https://doi.org/10.2147/shtt.s116009.
- ↑ 12.0 12.1 Das, Reenita. “Top Five Digital Health Technologies in 2019.” Forbes, Forbes Magazine, 4 Feb. 2019, https://www.forbes.com/sites/reenitadas/2019/02/04/the-top-five-digital-health-technologies-in-2019/?sh=25112afc6c0f.
- ↑ 13.0 13.1 Tilahun, Binyam, et al. “Mapping the Role of Digital Health Technologies in Prevention and Control of Covid-19 Pandemic: Review of the Literature.” Yearbook of Medical Informatics, vol. 30, no. 01, 2021, pp. 026–037., https://doi.org/10.1055/s-0041-1726505.
- ↑ 14.0 14.1 Vayena, Effy, et al. “Digital Health: Meeting the Ethical and Policy Challenges.” Swiss Medical Weekly, EMH Media, 16 Jan. 2018, https://smw.ch/article/doi/smw.2018.14571.
- ↑ Kao, Cheng-Kai, and David M. Liebovitz. “Consumer Mobile Health Apps: Current State, Barriers, and Future Directions.” Wiley Online Library, John Wiley & Sons, Ltd, 18 May 2017, https://onlinelibrary.wiley.com/doi/full/10.1016/j.pmrj.2017.02.018.
- ↑ Reynolds, Keith A. “Analysis Finds Serious Privacy Problems in Mobile Health Apps.” Medical Economics, Medical Economics, 21 June 2021, https://www.medicaleconomics.com/view/analysis-finds-serious-privacy-problems-in-mobile-health-apps.
- ↑ Xue, Yukang. “A Review on Intelligent Wearables: Uses and Risks.” Wiley Online Library, John Wiley & Sons, Ltd, 16 Sept. 2019, https://onlinelibrary.wiley.com/doi/full/10.1002/hbe2.173.
- ↑ Jiang, Dawei, and Guoquan Shi. “Research on Data Security and Privacy Protection of Wearable Equipment in Healthcare.” Journal of Healthcare Engineering, Hindawi, 5 Feb. 2021, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884134/.
- ↑ Nass, Sharyl J. “Introduction.” Beyond the HIPAA Privacy Rule: Enhancing Privacy, Improving Health Through Research., U.S. National Library of Medicine, 1 Jan. 1970, https://www.ncbi.nlm.nih.gov/books/NBK9576/.
- ↑ (OCR), Office for Civil Rights. “Hitech Act Enforcement Interim Final Rule.” HHS.gov, 28 June 2021, https://www.hhs.gov/hipaa/for-professionals/special-topics/hitech-act-enforcement-interim-final-rule/index.html.
- ↑ Kao, Cheng-Kai, and David M. Liebovitz. “Consumer Mobile Health Apps: Current State, Barriers, and Future Directions.” PM&R, vol. 9, 2017, https://doi.org/10.1016/j.pmrj.2017.02.018.
- ↑ 22.0 22.1 22.2 Battineni, Gopi, et al. “Factors Affecting the Quality and Reliability of Online Health Information.” DIGITAL HEALTH, vol. 6, 2020, p. 205520762094899., https://doi.org/10.1177/2055207620948996.
- ↑ 23.0 23.1 23.2 Igoe, Katherine J. “Algorithmic Bias in Health Care Exacerbates Social Inequities - How to Prevent It.” Executive and Continuing Professional Education, 12 Mar. 2021, https://www.hsph.harvard.edu/ecpe/how-to-prevent-algorithmic-bias-in-health-care/.
- ↑ 24.0 24.1 ID'Ignazio, Catherine, and Lauren F. Klein. “Chapter 1: The Power Chapter.” Data Feminism, The MIT Press, Cambridge, MA, 2020.
- ↑ “Racial Bias in Health Care Artificial Intelligence.” NIHCM, https://nihcm.org/publications/artificial-intelligences-racial-bias-in-health-care.