Blockchain

by Jolly Nanda | Jul 30, 2020 | Uncategorized

Blockchain Technology in Data Management

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Big data in healthcare is a hot topic right now – we constantly hear about how analytics can predict health conditions, the health of a population, the spread of disease, support the efforts of wearable devices and more. There are a lot of cutting-edge developments in this field, however, they will all be undermined if data management in healthcare does not also catch up with these new tools and technologies. In this article we will explore the role blockchain technologies play in healthcare.

Blockchain

One use of blockchain and crypto-technology in healthcare is to increase security and safety of data. Patient records are highly sensitive information that have been exposed to data breaches in the past and that is unacceptable but was hard to prevent in the era before blockchain technologies. Now, highly randomized and privatized algorithms can be used to increase the security surrounding these files and ensure a patient’s safety and right to privacy. This effort has been highly prioritized by nearly every healthcare organization including the United States government. A company working in this area, called Factom, has received $200,000 in grants from the Department of Homeland Security to further research and development in this area (Daley).

Another area in which blockchain can make great strides in healthcare is in communication networks. Currently, miscommunication costs the healthcare industry about $11 billion USD per year mainly due to the current process of coordinating medical records – that is a significant amount of money that could be much better spent elsewhere (Daley). Utilizing blockchain technology to create a means of transferring information privately and accurately should absolutely be a priority when using blockchain technology.

Blockchain can also help protect information in the healthcare supply chain by providing secure and reliable processes to guarantee the authenticity of medicine. This can be done by creating a distribution system that marks a point of origin and keeps track of a shipments location and who it came in contact with. This ensures the medicines delivered to patients are from a legitimate supplier and have not been tampered with. In relation to recent developments in the field of medicine, this can also be useful in tracking the safety of medications and patients if their medication happened to come in contact with an employee who contracted Covid-19 – the tracking ledger can ensure that anyone who came into contact with contaminated products after the infected employee is notified and recommended to be tested.

Conclusion

Blockchain technology is popping up in nearly every industry as a means to accurately and privately transfer information. This type of technology is extremely useful in the healthcare space and has many applications in disease control and prevention. However, another area in healthcare it can impact significantly is in data collection and management. By securing the data of patient’s, healthcare organizations not only ensure the peace of mind of patients but also better diagnoses by ensuring the accuracy of the data communicated. Blockchain technologies can also prevent delays in diagnosis caused by slow-moving communication which continues to support the notion that blockchain in data management is a powerful tool that can greatly benefit patients.

Works Cited

Daley, Sam. “15 Examples of How Blockchain Is Reviving Healthcare.” Built In, 2020, builtin.com/blockchain/blockchain-healthcare-applications-companies.

Discussing Recent Changes in the Availability to Covid-19 Data

by Jolly Nanda | Jul 23, 2020 | Uncategorized

Discussing Recent Changes in the Availability to Covid-19 Data

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Just as we were beginning our weekends last week, our news channels were dominated by headlines detailing the story of how the Trump Administration has stripped the CDC of its access to Covid-19 data. Throughout the Covid-19 Pandemic the CDC has been an integral part of getting key information out to the public through press conferences, visual education material, data reports and more. For those reasons, this development is incredibly surprising and begs the question of who the public should look to for trusted information regarding the spread of and how to protect themselves from Covid-19. This also brings up several issues for hospitals and resource allocation, as well as many other issues for organizations relying on accurate data to make decisions.

Discussion

The first question that you might ask in this regard is – how does the CDC get its Covid-19 data? The CDC was getting its data by hospitals directly sending them information. This move by the Trump Administration tells hospitals to bypass the CDC in data reporting and report information regarding all Covid-19 cases, as well as that hospitals available number of beds and ventilators, to the Department of Health and Human Services. This is incredibly problematic because many of the available models and projections made for Covid-19 have come from private and university researchers who rely on the fact that the CDC makes its data public, but the Department of Health and Human Services does not operate the same way. In fact, this has already impacted many researchers who claim that their models and prediction algorithms began underperforming when access to data was quietly taken away. This begs the question of whether or not who this data will be made available to and if this is simply a method to politicize the information the public is being given regarding Covid-19. Many scientists, physicians and leaders in the field have “long expressed concerns that the Trump administration is politicizing science and undermining its health experts [and feel that] the data collection shift reinforced those fears.”

Michael Caputo, a spokesperson for the Department of Health and Human Services, has said that his department and the CDC would share data so that it remained available to the public, however, many states, including Kansas and Missouri, have not received timely access to relevant data, with some hospitals saying they’ve experienced up to a week of lag time in receiving requested data. As hospitals across the nations are seeing new peaks in their Covid-19 cases, hospital workers have also said that this change in reporting of data is highly disruptive to their usual processes and taking time away from more pressing matters. Some workers also express a sense of frustration knowing that this increase in workload will only lead to less access to data in the future. Additionally, the Trump Administration has repeatedly made claims that the way testing is reported in the United States is leading to confusing reports and information regarding Covid-19. This brings up serious concerns for many physicians who worry that this thought process will lead to false or inaccurate reporting of Covid-19 cases in an attempt to support the goals of the current Administration.

Conclusion

The recent changes in the reporting of Covid-19 cases and relevant information has been widely criticized by leading officials, physicians and the general public as an unnecessary stunt by the Trump Administration. Most have concurred that the decision puts many people at risk by complicating a process that has been in place throughout the pandemic and has supported the public access to and research of Covid-19 data. As we move forward in the battle against Covid-19, we must analyze how we can better access reliable data and provide hospitals with the tools necessary to prepare and succeed in their endeavors.

Works Cited

[1] Stolberg, Sheryl Gay. “Trump Administration Strips C.D.C. of Control of Coronavirus Data.” The New York Times, The New York Times, 14 July 2020, www.nytimes.com/2020/07/14/us/politics/trump-cdc-coronavirus.html.

What Role Does Analytics Play in Mental Health Research?

by Jolly Nanda | May 20, 2020 | Analytics, Health and Wellness, Uncategorized

What Role Does Analytics Play in Mental Health Research?

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

May is Mental Health Awareness Month, an especially important topic this year as we as a society continue to navigate the coronavirus pandemic. Mental illness is incredibly prevalent in the United States with 1 in 5 adults (43.8 million people) experiencing mental illness and 1 in 25 (9.8 million people) experiencing mental illnesses that limit their ability to live a normal life (Coleman). Furthermore, as people across the country and world currently face the struggles of social isolation and job uncertainty, they report significantly higher incidents of negative mental health effects (Panchal). Clearly, there are a great number of people who would benefit from further developments in mental health research. While the study of analytics as it pertains to mental health is a fairly new field, there are many promising reports and developments that we will discuss here.

Key Points

When looking at mental health from a data standpoint, there are several approaches to a mental health study. Unlike the monitoring and analysis of conditions like coronary artery disease or diabetes, many current mental health studies focus on factors outside of the metabolic panel such as tracking a patients’ actions, words, facial expressions and non-verbal cues to make predictions about behavior.

While many of the studies detailed here do not look at physical health as a factor in their research, it is important to know that depression and mental illness is more common amongst those with chronic illnesses such as: cancer, coronary artery disease, diabetes, epilepsy, multiple sclerosis, stroke, Alzheimer’s, HIV/AIDS, Parkinson’s, lupus, and rheumatoid arthritis (NIMH).

Research Studies

The Crisis Text Line is a crisis counseling center that receives text messages from people experiencing mental health instability and those who may be considering self-harm or suicide. It then connects them to counselors via text message – a form of communication that can be more comfortable than a phone conversation for many people. Crisis Text Line has collected and analyzed the language patterns over 30 million text messages to analyze trends in those who were more likely to self-harm or commit suicide. What they found was a wealth of key words, such as the word “Advil,” that indicated a person’s risk of committing suicide. Interestingly, none of the key words included those that were previously considered high risk (DDS).

Another fascinating study came out of the University of Southern California where researchers created a virtual therapist called “Ellie.” Ellie captures and analyzes facial expressions and non-verbal cues and uses artificial intelligence to learn to detect the presence of mental illness. In the study, Ellie was more effective than a routine health assessment at detecting Post Traumatic Stress Disorder in military personnel returning from tours in Afghanistan (DDS).

Kaiser Permanente has also conducted research in this area. They successfully built an analytics model that predicts the 90-day suicide risk of patients visiting a mental health professional. The model took in behavioral patterns such as prior suicide attempts, substance use, emergency room incidents and a questionnaire, as well as medical and mental health diagnoses and prescribed medication, as variables for the model. The model was able to identify the top 5% of those with the highest risk of committing suicide. It has created a great foundation for tracking and protecting patients with mental health issues (DDS).

Where Can Analytics Take This Field?

Many mental illnesses are the manifestation of both natured and nurtured inputs. Consequently, the study of data science as it relates to mental health will continue to see a synergy of biological and behavioral inputs that are factored into predictive algorithms as variables. We will likely see more studies take on a biostatistical approach for many of the biological factors related to mental illness including those discussed above. Other factors that show promising abilities to predict and track mental illness include neurobiological mechanisms such as biomarkers from brain imaging, neurocognitive task assessment and psychometrics as they relate to biological aging (Wall). Artificial intelligence can do a lot of the heavy lifting in determining which factors, biological or behavioral, carry the most weight in prediction, prevention, and management. Given the fact that artificial intelligence tools are becoming more and more mainstream, we can likely expect to see many exciting developments in this field.

How You Can Look After Your Mental Health During the Pandemic

The World Health Organization has listed the following items as methods to cope with the stress and anxiety surrounding the COVID-19 pandemic:

Stay informed by checking the news once or twice a day
Keep a routine by maintaining your previous routine or creating a new routine
Maintain a healthy lifestyle be eating healthy meals, exercising regularly, getting enough sleep, and maintaining personal hygiene
Maintain social contact by checking in on and catching up with friends and family
Limit screen time in terms of video games and social media
Limit alcohol and drug use

Making sure that you are checking in with yourself and monitoring your mental health is always important, but it is even more so as we all face the struggles of a pandemic. By taking care of your body and ensuring you have enough time to rest, you can set yourself up to adapt to a trying situation. Additionally, be sure to reach out to love ones and check in on them as well.

Free Tools Available

There are several free tools for mental health available. We have consolidated few resources below to help you navigate to these resources.

Works Cited

“Chronic Illness & Mental Health.” National Institute of Mental Health, U.S. Department of Health and Human Services, www.nimh.nih.gov/health/publications/chronic-illness-mental-health/index.shtml.

Coleman, Madeline. “Mental Health and Big Data: A Step in the Right Direction.” RxDataScience Inc. – Data Science for Healthcare, 6 May 2020, www.rxdatascience.com/blog/mental-health-and-big-data-a-step-in-the-right-direction.

Panchal, Nirmita, et al. “The Implications of COVID-19 for Mental Health and Substance Use.” The Henry J. Kaiser Family Foundation, 21 Apr. 2020, www.kff.org/coronavirus-covid-19/issue-brief/the-implications-of-covid-19-for-mental-health-and-substance-use/.

“Using Data Science to Help Tackle Mental Health Issues.” DiscoverDataScience.org, 16 Mar. 2020, www.discoverdatascience.org/social-good/mental-health/.

Wall, Melanie. “Mental Health Data Science.” Columbia University Department of Psychiatry, 3 Mar. 2020, www.columbiapsychiatry.org/mental-health-data-science.