The mobile healthcare device market is rapidly changing with the development of smartphones, wearable watches, patient monitors, and even rings and bracelets that monitor patients’ vital signs such as heart rate, body temperature, sleep cycles, exercise, and even electrolytes and metabolites in the skin. But how does the data from these devices get into the patient’s electronic medical record? Healthcare portals allow patients to access their medical records and lab results, as well as notify doctors, schedule appointments and renew prescriptions online.
However, much of the data from mobile healthcare devices remains in corporate databases and is never transferred to other healthcare records. With newer computer algorithms and artificial intelligence, data from millions of patients offers the opportunity to detect changes in health status even before symptoms appear and offers the opportunity to personalize healthcare based on vital statistics. But the storage and transmission of health data also poses a privacy and security risk for patients. In this manuscript, the current concepts of e-health and m-health are reviewed and their potentials and risks in laboratory medicine are examined.
Converting data into information, knowledge and wisdom requires systems thinking. Data (symbols and numbers) are processed into information that gives meaning and relational context to the data. Heart rate data of 50 beats per minute (bpm) alone has no meaning, but in the context of a child, 50 bpm conveys information to the doctor, while in an adult context the information may be different. Knowledge is the application of data and information and the provision and understanding of patterns. It can also be explicit through written guidelines that can be easily passed on to others, or implicit and internalized through experience or intuition. Wisdom evaluates understanding and places knowledge in an ethical and moral framework.
The amount of data stored by organizations, such as B. laboratory data, makes traditional analytical methods impracticable. Big data requires computer algorithms to query data for information and relationships that may not be obvious. Data mining is the term for the analysis and extraction of interesting, previously unknown relationships and patterns. Some examples of big data are deriving reference intervals by data mining all laboratory test results, or using moving averages from patient data for continuous quality control.
eHealth is an emerging field at the intersection of medical informatics, public health and business, relating to health services and information provided or enhanced via the Internet and related technologies. eHealth does not stand for ‘electronic’ but implies a number of other ‘e’ such as efficiency in healthcare, improvement in quality of care, evidence-based interventions, empowerment of consumers and patients, fostering the relationship between patient and healthcare professional, education of physicians through online -Resources, enabling standardized communication, geographical expansion of the scope of healthcare, ethics in patient-physician interaction and equity in healthcare services.
m-health is an acronym for Mobile Health, a term used for the practice of medicine and public health supported by mobile devices. m-health most commonly refers to mobile communication devices such as cell phones, tablet computers, personal digital assistants (PDAs) and wearable devices such as smart watches for health services, information and data collection. It has evolved into a sub-segment of e-health and m-health applications, which involve the use of mobile devices to collect community and clinical health data, and to provide or share health information for physicians, researchers, and patients.
Recent US law changes grant patients the right to access personal health information under the Health Insurance Portability and Accountability Act (HIPAA). A 2014 amendment to CLIA ’88 allows laboratories to provide access to completed test reports to a patient or their representative. This has opened up the opportunity for patients to have easy access to personal health information and lab test results through institutional patient portals.
Because physicians are the laboratory’s traditional “customers,” test results are recorded for professional interpretation. Comments such as “amount insufficient”, “hemolysis”, “heterophilic interference”, “icteric” or “lipemic” may not be understood by the general patient consumer. The laboratory would need to consider the written form of comments in the formal test result and ensure that they are open to interpretation in a future medical record for the public, as patients can often access the test result before the physician in the patient portal.
Medical knowledge has historically been shared by experts through peer-reviewed publications and lectures. The growth of the internet and ease of access has led to a tremendous increase in the information and knowledge available. The danger is that nobody needs very little expertise or financial support; Professionals, patients or “interested” to make their experiences, knowledge and opinions available through websites, blogs and internet forums. This has led to the spread of “fake news” on the internet. How can patients differentiate between medical fact and fiction online without consulting a doctor?
Opportunities are emerging for eHealth, where personal experience rather than data is driving discussions on social media. Special interest groups or “communities of practice” have evolved to bring together people with common interests who can share information and learn from each other. Social media platforms also encourage patients with the same disease to engage in discussions about the disease state to share their experiences. These social media groups are also an opportunity for healthcare professionals to drive awareness and educate patients about clinical trials.
Security and privacy are eHealth concerns. Despite firewalls and encryption, hospitals and even data companies have been hacked. Personal data can be compromised, altered or even blackmailed. How do we ensure the integrity of the data once posted and how do we ensure the validity that what the reader sees is what was originally posted on the internet? The concern for social media is that personal health information can become public. Any time someone posts something on social media, the posts are public and the patient’s identity can be revealed. Even if an individual is not directly named, any personally identifiable protected health information (PHI) can compromise a patient’s privacy. Once information is posted it can never be withdrawn as posts are often read and resent by others.
Wearable wireless devices collect and transmit personal health information and data, allowing trends to evolve over time. These devices connect to health apps that consolidate data from phones, watches, and other devices. However, this data must not be linked to a patient’s electronic medical record or visible to the patient’s doctor. Device manufacturers can collect big data from many patients and combine it with health status data for vital statistics such as heart rate and body temperature, and further tie it to today’s well-being or illness, exercise and meals. Using artificial intelligence algorithms, data can be sorted by disease progression to formulate prediction modes to improve personalized medicine and prevent chronic disease progression.
Patients want access to their medical records and health information. m-health is changing healthcare by making it more accessible, affordable and available. E-Health and M-Health offer patients new ways to personalize their healthcare and connect with their doctors remotely and more continuously.
References available upon request.
James H. Nichols, PhD, DABCC, FAACC is Professor of Pathology, Microbiology and Immunology and Medical Director of Clinical Chemistry and POCT at Vanderbilt University Medical Center.
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This article appears in the latest issue of Omnia Health Magazine. Read the full issue online today.
