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Matthew Eng

About Me

Hello! I'm a data engineer, scientist, and healthcare advocate. My work supports making healthcare in the US more affordable and a better consumer experience. Through the use of automation, I empower teams to leverage their data to make business decisions and discover insights across the healthcare, marketing, and pharmaceutical industries. I'm passionate about putting data to work for to reduce healthcare costs and create a better healthcare experience.

I have a B.S. in biological sciences from Binghamton University and PhD in molecular biology from the University of the Notre Dame.

Portfolio

xray health
The rising costs of healthcare in the US is an economic and public health problem that strains the economy, prevents patients from seeking needed care, and produces undue financial burden on the consumers. Healthcare spending makes up 17.8% of the US GDP, compared to 11% for similar countries. This means the average American the will spend $10,224 annually in healthcare, compared to $5,280 in comparable countries, despite having lowest life expectancy of top 11 nations and using the health system less. This program is meant to increase the ease of identifying fair healthcare prices in order to give consumers shopping power in the marketplace
 xray health
The rising costs of healthcare in the US is an economic and public health problem that strains the economy, prevents patients from seeking needed care, and produces undue financial burden on the consumers. Healthcare spending makes up 17.8% of the US GDP, compared to 11% for similar countries. This means the average American the will spend $10,224 annually in healthcare, compared to $5,280 in comparable countries, despite having lowest life expectancy of top 11 nations and using the health system less. This program is meant to increase the ease of identifying fair healthcare prices in order to give consumers shopping power in the marketplace
med mapper
Med Mappr is a data product that provides intelligence to help patients get the best care possible by helping pharmaceutical companies determine how to disperse their field medical teams to educate physicians. This product identifies doctors who are likely to be early adopters of new therapies and are located in underserved areas. By pinpointing physicians who may be receptive to learning about new medications and targeting underserved areas, Med mappr's mission is to democratize education about therapeutics and ensure doctors have the latest information to treat their patients.
med mapper
Med Mappr is a data product that provides intelligence to help patients get the best care possible by helping pharmaceutical companies determine how to disperse their field medical teams to educate physicians. This product identifies doctors who are likely to be early adopters of new therapies and are located in underserved areas. By pinpointing physicians who may be receptive to learning about new medications and targeting underserved areas, Med mappr's mission is to democratize education about therapeutics and ensure doctors have the latest information to treat their patients.

On My Mind

The current healthcare system is dominated by the Fee-for-service (FFS) payment model. Briefly, physicians and healthcare organizations are paid for each service they render to the patient. If the patient has symptoms for anemia, a physician might choose to prescribe lab work be conducted, and would get paid for the service. If a patient came in with no symptoms but blood work might reveal that for 1% of patients they are anemic, they might also prescribe blood work. The provider would get reimbursed for this procedure as well. The problem, as demonstrated above is that FFS incentivizes over use of healthcare services. As there is greater benefit for providers to risk over treatment (satisfied consumers, preventing malpractice negligence, financial payments) than under treatment, this can lead to overprescribing of treatments and inflation of healthcare costs.

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On July 1, 2021 the Biden Administration signed an Executive Order prohibiting surprise hospital bills for care provided out of network. Surprise medical bills occur when a doctor or healthcare provider who is not covered by a patient’s insurance plan provides care unknowing to the patient. This can happen for instance when a patient goes to the Emergency Room (ER) at a hospital that accepts their insurance, but are provided care by an anesthesiologist who isn’t employed by the hospital and doesn’t accept the patient’s coverage plan and has negotiated contracts with insurers separate from the hospital’s. The anesthesiologist then directly bills the patient for the out of network cost. The costs for these unexpected bills can be absorbent, ranging from hundreds to thousands of dollars.

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Searching through large datasets can be time-intensive depending on the applications you are running. Luckily, one of the primary methods for running more performant queries is built right into Postgres.

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As the volume of email has surged in the past decade, machine learning approaches can help to classify email content. There are a variety of techniques can be used for natural language processing of email content. Here, I will use both supervised and unsupervised techniques to classify and describe email groupings.

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