Small Communities Case Study

by Jolly Nanda | Sep 9, 2020 | Health and Wellness, Health Plans

How Analytics Can Impact and Improve the Health of Small Communities (+ Case Study)

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Community health information can be very revealing; there is a lot to learn from the data of a specified community whether that specification be location, race, gender, occupation, age, or income bracket. However, these are relatively large communities and while that data is incredibly important, small community analytics can be even more targeted and actionable due to the ability to better communicate information to smaller groups.

Discussion

The biggest struggle in studying small communities is validity – many statisticians have argued that small community studies do not meet the benchmarks of a sample size to be referenced in other studies and this is a valid concern. However, if you don’t approach smaller studies as ones to be distributed, they can be especially impactful for their communities. Below, we’ve attached a case study from Cerner to demonstrate the importance of small community studies:

Cerner Case Study

Every year, approximately 735,000 Americans have a heart attack. There’s great interest in improving this number — and one of the ways we can contribute to that goal is by quickly identifying symptoms of and treating heart attacks. Troponin tests are commonly used in the emergency department (ED) to identify if a patient is experiencing a heart attack. In an ideal setting, the turnaround for a troponin test is about 35 minutes; most hospitals have a protocol setting of 60 minutes or less.

We recognized an opportunity for improvement with some of our clients around their troponin test rates. We pulled data on individual clients and compared it to industry wide data, and found that while some of our clients had fantastic numbers, others hadn’t had a focus group around this topic and there was room for improvement. If a hospital’s median turnaround time for a troponin test is 45 minutes, for example, that still means that approximately half their tests are taking longer than that.

Though there is currently no troponin test standard mandated by the Centers for Medicaid and Medicare Services (CMS), the turnaround time clearly impacts patient care. Think of it this way: The 25-minute difference in test results is akin to an ambulance arriving to pick up an individual with heart attack symptoms and then simply waiting in the driveway for over half an hour.

Conclusion

The study of the health of small communities can seem a little slow but actually has the potential to be extremely interesting! Small community studies can identify several localized issues ranging from issues in infrastructure to specified malnutrition to minor disease networks. Here at Altheia Predictive Health, we are specifically trying to make the research and benefits of big data accessible to small communities through our app. If you know a small company or organization that could benefit from our research, please send them our way!

The Most Personalized and Precise Form of Healthcare: a Discussion of the Genome

by Jolly Nanda | Sep 3, 2020 | Analytics, Health and Wellness, Technology

The Most Personalized and Precise Form of Healthcare: a Discussion of the Genome

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Our genomes are essentially a personalized index or library of everything we are – they are the combination of genes and DNA that hold all of our genetic information. The field of genomics is relatively new with most studies citing its roots in the 1980’s. In fact, The Human Genome Project only began development in 1990 and was declared complete in 2003. Though new, the field of genomics, like many other fields of study touched by technology, has evolved rapidly. For context, processing a human genome would have cost $20-25 million in 2006 compared to a cost of well below $1,000 today and its market has seen growth from $1 billion to $4.5 billion in the last 8 years alone. Furthermore, the first time a human genome was sequenced took 3 years of processing power while today a human genome can be processed in less than 3 days. The increased accessibility to genomic information is an incredibly important development in terms of preventative care and can be a life-saving step for many people.

Discussion

The Precision Medicine Initiative “is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person [whose] approach will allow doctors and researchers to predict more accurately which treatment and prevention strategies for a particular disease will work in which groups of people.” This is where genomics finds its highest level of applicability and importance. Genomics and the power that we now have in machine learning can provide incredible insights into which genes are relevant to certain diseases. With that insight, patients can adjust their lifestyle to their risk factor and have a much better understanding of where they stand in terms of their health. Doctor’s also have a much clearer idea of what tests may need to be run a lot earlier in a patient’s life and what tests may never need to be run unless an event occurs prompting questions outside of the norm. Overall, genomics can save a lot of time and money for both providers and patients alike.

One of the struggles with sequencing an individual’s entire genomic profile is the sheer processing and holding power needed to execute algorithms on an entire sequence – massive database space is necessary to perform these types of analytics. However, one consideration that can be factored into account to make genomics even more accessible is isolated sequencing. For example, if someone already knows their family has a high risk of a certain disease, they may choose to only sequence parts of their DNA, such as the BRCA1 and BRCA2 genes sequenced for those individuals with a higher risk of breast cancer. This methodology can be applied to any genetically passed disease. However, the ultimate hope and goal for many is that genome sequencing becomes accessible enough so anyone can sequence their entire genome. This could then be utilized by healthcare providers who can provide a much more personalized approach to a patient’s diagnosis and care plans with that information.

Conclusion

In comparison to many other fields of study, genomics is very new, however, that hasn’t stopped it from catching up (and even outrunning and outshining) many other fields in terms of accessibility. When we look at communities, whether that be by location, ethnicity, age or gender, we get a much clearer picture of how the health of a population is influenced. As accessibility to the technology used to support genomics increases for patients, we can expect that picture to get even clearer and to see an even more personalized approach to healthcare.

TeleTracking Technologies

by Jolly Nanda | Aug 21, 2020 | Health and Wellness, Technology

Concerns Regarding the Trump Administrations Contract with TeleTracking Technologies

Authored by Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

A few weeks ago, when the Trump Administration took Covid-19 reporting responsibilities away from the CDC, there were several questions about how data would be processed and whether or not the public could trust the accuracy of new data. Not long after that development, the Trump Administration awarded a company called TeleTracking Technologies a multi-million dollar contract to collect and report on Covid-19 data. However, inconsistencies in reporting and a lack of transparency in collection methods has raised a lot of questions regarding teletracking as a process and TeleTracking Technologies as a company.

Discussion

One of the biggest causes for concern of TeleTracking Technologies is that they have refused to answer questions regarding Coronavirus data from United States senators due to a nondisclosure agreement with the Trump Administration. This is heavily concerning because it limits the scope of power of other branches of government outside of the executive branch. Lawyers for the company refuse to disclose how TeleTracking Technologies collects and shares its information; in our last article, we heard from physicians and hospital administrators who are extremely concerned that the process in which Covid-19 data will be skewed towards supporting the Trump Administration’s political goals and, given President Trump’s close ties to the founder and CEO of TeleTracking Technologies, this notion does not seem outside of the realm of possibility. This move has been highly criticized by researchers and academics who cannot accurately conduct their own research without transparent data collection and reporting practices.

Finally, a huge concern is that these policy and process changes are coming abruptly and at an awful time. Carrie Kroll, with the Texas Hospital Association, says that “Up until the switch, we were reporting about 70 elements and we’re now at 129… clearly we’re in the middle of a pandemic… this isn’t the type of stuff you try to do in the middle of a pandemic.” Hospitals have been reporting to the CDC with standard practice for over 15 years which means that these changes are a painfully challenging process to push onto hospitals while the Covid-19 Pandemic continues to plague the United States.

Conclusion

The transfer of Covid-19 data reporting responsibilities from the CDC to TeleTracking Technologies is ultimately an irresponsible move on the part of the Trump Administration who have put physicians and patients at a disadvantage by pursuing a path that limits the transparency of data to the general public. However, it is our new reality and if we cannot rely on our government to provide reliable data then we can hope that efforts from private companies, such as IBM, can provide researchers and physicians with trustworthy data.

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.

Use of Analytics in Prediction and Prevention of Coronary Artery Disease

by Jolly Nanda | May 12, 2020 | Analytics, Health and Wellness

Use of Analysis in Prediction and Prevention of Coronary Artery Disease

Ayesha Rajan, Research Analyst at Altheia Predictive Health

Introduction

Coronary artery disease (CAD) is a chronic, comorbid condition that is usually the result of plaque buildup leading to limited blood flow. CAD is the leading cause of death and loss of productivity in the United States due to an aging population and globalization/ urbanization. It is expected to be responsible for over 20 million global deaths by 2030 (World Health Organization). As a result, CAD is clearly an issue that needs attention in terms of preventative care. While there currently exist many different prediction tools, most of them are severely lacking in the use of data points that are correlated with the presence of CAD.

Key Data Points

The table in Figure 1 shows normal, at risk, high risk and highest risk (when possible) ranges for key points in a typical metabolic panel. If you could only look at a limited number of factors to predict and prevent CAD, these would be among the strongest points and are the basic data points included in most current predictive models. However, looking specifically at LDL cholesterol, as well as presence of diabetes or chest pain, cigarette-use, sex, race, and age can provide even more context and accuracy. One could cast an even wider net for more data points and include the results of a resting electrocardiograph, existing exercise induced angina or hyperlipidemia maximum heart rate, ST depression, slope of peak ST segment, and the total number of major vessels colored in fluoroscopy (Saxena).

	Diastolic Blood Pressure	Systolic Blood Pressure	Total Cholesterol	Fasting Blood Sugar	BMI
Normal	<80	<120	<200	<100	12-24.9
At Risk	80-89	120-129	200-239	100-125	25-29.9
High Risk	90+	130-179	240+	>125	<30
Highest Risk	120+	180+

Figure 1: Key CAD Data Points in Metabolic Panel

Existing Analysis

The most prominent example of analytics in relation to CAD is the Framingham Risk Score which is a result of the Framingham Heart Study. The score is an algorithm that estimates a person’s 10-year risk of developing CAD in terms of low, intermediate, and high risk. It takes into account age, sex, presence of diabetes, smoking habits, systolic blood pressure, total cholesterol, HDL cholesterol and BMI or lipids. While the Framingham Equation is intensive and detailed, there are still a great number of factors that could improve its accuracy. For example, the prevalence of CAD varies in race populations: the corresponding age-adjusted prevalence of heart disease among whites, blacks, Hispanics, and Asians was 11.0%, 9.7%, 7.4%, and 6.1%, respectively (Virani). This is a data point with significant variance and using it as a prediction tool could improve the accuracy of the Framingham Equation or any other formula. Adding in other risk factors could also mean that the Framingham risk score could expand to include those who fall outside of the targeted 30-79 year age range or could include patients with diabetes, two groups the current scoring algorithm leaves out. The complexity in analyzing the symptoms and comorbid conditions related to CAD means that one in five patients are victims of misdiagnosis, further confirming the necessity to improve the existing analytics tools (Foote).

Where Can Analytics Take Us Next?

A topic that many people have heard of but equally as many people have not fully grasped is artificial intelligence. Artificial intelligence is the use of predictive models to forecast future events; in terms of CAD, computer programs look at and analyze all the data points available and come up with algorithms that have the strongest correlation variable possible. Currently, a company called Ultromics houses the EchoGo Core system which is an artificial intelligence technology that utilizes ultrasound images to identify disease. In its trials, its diagnostic performance yielded over 90% accuracy and halved the number of misdiagnoses compared to traditional clinical analysis (Foote).

Another direction we may see CAD prediction go into is genomics. It may not be a surprise to many people that CAD often runs in families; this fact indicates there may be data points in genomic profiles that can indicate the risk of having the disease. A study published by the Journal of the American Heart Association investigated the possibility that DNA could hold answers to predicting heart disease and concluded DNA methylation data could, in fact, aid in discovering high-risk individuals who were not classified as “at risk” by other studies, such as those with lower Framingham Risk Scores, which used metabolic panel data (Westerman).

Based on the preceding analysis, it appears the increased use of AI combined with access to a very broad data set is our best path to creating much more robust and accurate predictive models which can lead to earlier and more targeted interventions and better outcomes.

Notes on Prevention and Management

In a study published on NCBI, individuals who changed the following things about their lifestyle and diet also showed a decreased risk of contracting CAD: avoid smoking, increase physical activity, avoid being overweight, using healthy fats, eating fruits and vegetables, using whole grains, reducing, sugar and reducing sodium (Razzak). All of these things are also well-known components of a generally healthy lifestyle and mitigating factors for many other chronic conditions and disease other than CAD.

Citations

“About Cardiovascular Diseases.” World Health Organization, World Health Organization, 29 Sept. 2011, www.who.int/cardiovascular_diseases/about_cvd/en/.

Foote, Natasha. “Artificial Intelligence Technology Developed to Predict Heart
Disease.” Www.euractiv.com, EURACTIV.com, 30 Apr. 2020, www.euractiv.com/section/health-consumers/news/artificial-intelligence-technology-developed-to-predict-heart-disease/.

Razzak, Muhammad Imran, et al. “Big Data Analytics for Preventive Medicine.” Neural Computing & Applications, NCBI, 16 Mar. 2019, www.ncbi.nlm.nih.gov/pmc/articles/PMC7088441/.

Saxena, Kanak. “Efficient Heart Disease Prediction System.” ScienceDirect, 2016, www.sciencedirect.com.

Virani, Salim. “Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association.” AHA Journals, 2020, www.ahajournals.org/doi/10.1161/CIR.0000000000000757.

Westerman, Kenneth. “Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics.” Journal of the American Heart Association, 2020.