Social Media in Primary Care

Social media has become a standard part of the day for the majority of people in the United States, and reciprocally has become an effective platform and tool for patient engagement within health care. This review provides context for its place in patient education, communication, and treatment, combined with a review of general operational and ethical principles for social media platforms within a primary care practice.

The full article can be downloaded below.  

An algorithm strategy for precise patient monitoring in a connected healthcare enterprise

This perspective paper describes the building elements for realizing a precise patient monitoring algorithm to fundamentally address the alarm fatigue problem. Alarm fatigue is well recognized but no solution has been widely successful. Physiologic patient monitors are responsible for the lion’s share of alarms at the bedside, most of which are either false or non-actionable. Algorithms on patient monitors lack precision because they fail to leverage multivariate relationship among variables monitored, to integrate rich patient clinical information from electronic health record system, and to utilize temporal patterns in data streams. Therefore, a solution to patient monitor alarm fatigue is to open the black-box of patient monitors to integrate physiologic data with clinical data from EHR under a four-element algorithm strategy to be described in this paper. This strategy will be presented in this paper in the context of its current status as described in our prior publications.

The full article can be downloaded below.  

What Genetic Testing Teaches About Long-Term Predictive Health Analytics Regulation

The ever-growing phenomenon of predictive health analytics is generating significant excitement, hope for improved health outcomes, and potential for new revenues. Researchers are developing algorithms to predict suicide, heart disease, stroke, diabetes, cognitive decline, future opioid abuse, and other ailments. The researchers include not only medical experts, but also commercial enterprises such as Facebook and LexisNexis, who may profit from the work considerably. This Article focuses on long-term disease predictions (predictions regarding future illnesses), which have received surprisingly little attention in the legal and ethical literature. It compares the robust academic and policy debates and legal interventions that followed the emergence of genetic testing to the relatively anemic reaction to predictions produced by artificial intelligence and other predictive methods. The paper argues that like genetic testing, predictive health analytics raise significant concerns about psychological harm, privacy breaches, discrimination, and the meaning and accuracy of predictions. Consequently, as alluring as the new predictive technologies are, they require careful consideration and thoughtful safeguards. These include changes to the HIPAA Privacy and Security Rules and the Americans with Disabilities Act, careful oversight mechanisms, and self-regulation by health care providers. Ignoring the hazards of long-term predictive health analytics and failing to provide data subjects with appropriate rights and protections would be a grave mistake.

The full article can be downloaded below.  

Is health-care data the new blood? 

We propose that health-care data records are digital specimens and should be treated with the same rigour, care, and caution afforded to physical medical specimens. We advocate that the use of these digital samples be limited to validated and beneficial uses for the donor and that patient privacy be fully protected.

The full article can be downloaded below.  

Role of Artificial Intelligence within the Telehealth Domain

This paper provides a discussion about the potential scope of applicability of Artificial Intelligence methods within the telehealth domain. These methods are focussed on clinical needs and provide some insight to current directions, based on reports of recent advances.

Examples of telehealth innovations involving Artificial Intelligence to support or supplement remote health care delivery were identified from recent literature by the authors, on the basis of expert knowledge. Observations from the examples were synthesized to yield an overview of contemporary directions for the perceived role of Artificial Intelligence in telehealth.

Two major focus areas for related contemporary directions were established. These were first, quality improvement for existing clinical practice and service delivery, and second, the development and support of new models of care. Case studies from each focus area have been chosen for illustration purposes.

Examples of the role of Artificial Intelligence in delivery of health care remotely include use of tele-assessment, tele-diagnosis, tele-interactions, and tele-monitoring. Further developments of underlying algorithms and validation of methods will be required for wider adoption. Certain key social and ethical considerations also need consideration more generally in the health system, as Artificial-Intelligence-enabled-telehealth becomes more commonplace.

The full article can be downloaded below.  

The Cutting-Edge Of AI Cancer Detection

Detecting cancer might be AI’s most altruistic and convoluted challenge yet. Standard screening methods such as radiological imaging can miss signs of cancer, or return a false negative (as it does in 20-30% of cases). The process of scanning images is particularly in need of improvement, as doctors must often visually search for signs of cancer which can leave only the largest, most advanced tumors to be detected. Hereditary testing is another detection method that determines genetic predisposition to cancer, but this does not provide much detail and cannot reveal if a person has cancer now.

AI can not only greatly improve the accuracy of image detection for cancer, but could also open up entirely new fields between genomics and cancer screening. AI faces its own unique challenges in this field - such as the lack of enough data to train neural networks - but companies on the cutting-edge of cancer detection are finding novel ways to get around the problems, and achieving impressive results.

The full Forbes article can be viewed at this link.  

5 Reasons Why Doctors Should Learn Data Science

Data science and artificial intelligence are no longer buzz words in the biomedical research community. Physicians and other caregivers are increasingly being encouraged by hospitals and health insurance companies to utilize continous data captured using wearable medical devices. However classical healthcare heavily relies on high accuracy sparse datasets, i.e. patients are expected to get a thorough medical checkup once a month, as opposed to continuous monitoring of a handful of vital parameters. The most significant impact of data science will be in helping physicians extract clinically relevant information from such dense low-quality data sets.

In this article, we have listed five such reasons why physicians and caregivers should learn about emerging technology such as data science and artificial intelligence .

1. Diagnose using large volumes of data generated from continuous monitoring
2. Diagnose using multiparameter data
3. Diagnose using data visualization
4. Understand AI workflow
5. Understand the statistical significance of clinical studies

The full Forbes article can be viewed at this link.  

The Incredible Ways Artificial Intelligence Is Now Used In Mental Health

We’re experiencing a mental health crisis. Approximately 15.5% of the global population is affected by mental illnesses, and those numbers are rising. Although there are many who require treatment, more than 50% of mental illnesses remain untreated. In the United States, one in five adults suffers from some form of mental illness. Every 40 seconds one person dies from suicideand for every adult who dies from suicide, there are more than 20 others who have attempted to end their life. The ramifications of this go beyond our families and cultures as mental health also has a tremendous economic impact for the cost of treatment as well as the loss of productivity.

There are several reasons why AI could be a powerful tool to help us solve the mental health crisis. Here are four benefits:

1.      Support mental health professionals
2.      24/7 access
3.      Not expensive
4.      Comfort talking to a bot

The full Forbes article can be viewed at this link.  

Electronic Health Records and Doctor Burnout

Electronic health records are here to stay. So, what’s it going to take to relieve providers of burdens imposed on them by the EHR? It will take a real culture change, more than just jiggering a few policies or procedures. Clinicians and clinical concerns need to be placed at the forefront of EHR development and deployment. People on the front lines of patient care must not only be consulted about the EHR, they—not the administrators, accountants or technologists, not even the quality specialists—must be empowered to make most of the decisions about it.

Clinicians must reclaim our identity as healers whose foundation is built on the relationship between patient and provider. Health care information systems should be constructed in a way that facilitates these relationships. Patients’ stories need to be promoted to a position in the EHR that is equal to their data.

The full article from the Scientific American blogs can be viewed at this link.  

Revealing the secret prices insurers pay can save health care

A bold proposal to publish tightly held secrets about health care prices could unleash the power of markets to lower health care costs.

The Department of Health and Human Services has released a request for information on a proposal to create public access to real price information in health care under the regulatory framework of the Health Insurance Portability and Accountability Act (HIPAA). Unlike the mandate earlier this year from the Centers for Medicare and Medicaid Services that requires hospitals to publish their so-called chargemaster prices, the HHS proposal would shed light on the secret negotiated prices insurance companies pay.

Making these prices public would infuse much-needed competition into health care’s bloated $3.5 trillion market.

The full STAT article can be viewed at this link.