Was It a Glitch or Human Error? How a Code Made My Hospital Stay Unpayable

Earlier this March, I visited the ER with troubling neurological symptoms. I received treatment and was released without a clear diagnosis. Weeks later, I learned my hospital stay had been coded as non-emergency, and my insurance denied nearly $9,000 in charges.
What followed was a long and frustrating effort to understand the decision — involving calls with hospital billing, ER Department Staff, and insurance reps,. It raised questions not just about how coding is done, but who does it — and whether software or overloaded billing staff may be introducing errors with significant consequences for patients.
I’ve written a detailed account of my experience, supported by medical records, economic data, and recent research on AI in billing and healthcare consolidation. My goal isn’t just to share what happened — it’s to advocate for change.
If you work in health policy, tech, or patient advocacy, I’d welcome your thoughts on how we can build a more transparent and accountable system.

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How to: Parse Android Logs for Analytics and Machine Learning Applications

“Where are the logs? I can’t do anything if there aren’t logs!”

Software Engineers most useful tool for debugging is through the analysis of logs. Logs are the ledger that helps keep track of various states of systems at any single time. It is imperative that this historical record be analyzed properly to get the most out of monitoring performance—from complex to simple systems. In this post, Conaxon goes over how to parse Android LogCat Logs for use in analytics and machine learning applications.

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Keep Analytics, Machine Learning, and Artificial Intelligence to Simple Use-Cases with Collective Vantage

Conaxon takes the reader through a simple sales forecasting project for a retail store. We cover data cleaning (pandas), feature engineering (encoding categorical/cyclical features), building a base GradientBoostedRegressor Model (Sklearn), and hyper-parameter tuning (GridSearchCV, RandomizedSearchCV, KFold).

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Key Struggles with Analytics, Decision Intelligence, and Machine Learning Micro-Business Communities

Conaxon talks about key problems in applying Machine Learning, Artificial Intelligence, Analytics, and Decision Intelligence within Small / Micro Businesses.

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Sentiment Analysis to Drive Content Strategy on your YouTube Marketing Channel (Part 2)

Conaxon uses the YouTube API to extract stats and comments from a YouTube channel as a method of using data to drive content strategy that viewers and listeners like watching. Likewise, finding the content that tends to be engaged with more negatively. Deciding content strategy is not an exact science. But, there are tools out there to create a more efficient decision making process. We dive into a few different topics like using APIs, VADER Lexicon from NLTK, for and while loops, cleaning text data, and a dashboard concept to visualize a channel’s data.

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Sentiment Analysis to Drive Content Strategy on your YouTube Marketing Channel (Part 1)

Conaxon uses the YouTube API to extract stats and comments from a YouTube channel as a method of using data to drive content strategy that viewers and listeners like watching. Likewise, finding the content that tends to be engaged with more negatively. Deciding content strategy is not an exact science. But, there are tools out there to create a more efficient decision making process. We dive into a few different topics like using APIs, VADER Lexicon from NLTK, for and while loops, cleaning text data, and a dashboard concept to visualize a channel’s data.

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