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NIX’s modernized data analytics solution empowers internal client departments, healthcare institutions, and insurance agencies with customizable reports, revealing key insights into market trends, consumer behavior, and potential business impacts.
Healthcare
Data Engineering, Data Analytics
Python
The company is a large U.S. provider of multiple software solutions for the healthcare sector that help create new revenue streams, support compliance processes, optimize costs, reduce risk, and enhance customer engagement.
One of the client’s products is a solution that delivers in-depth analyses of past and anticipated market trends and facility patient numbers. It also provides valuable information on physician’s demand, disease prevalence, and overall health status. This solution aids in expediting the utilization of market-specific, highly accurate insights by employing extensive data resources.
The client sought to amplify their predictive models to empower the healthcare industry with more sophisticated analytics and market forecasts.
To help forge ahead, the company enlisted NIX’s data science expertise to enhance and update the existing predictive models, ensuring their effective performance over time.
The healthcare industry is in a perpetual state of flux, with an overarching objective to predict future changes and trends over the next 5 to 10 years. This proactive approach enables informed decision-making.
The existing models for estimates were sharpened for:
Evaluate Statistical Analysis System (SAS) scripts and assess challenges in tracking recent changes.
Make necessary adjustments to the SAS scripts, including updating demographics to reflect the desired timeframe, execute the scripts, and thoroughly analyze the output for anomalies to ensure reliable and smooth analytics.
Filter data using SAS scripts based on medical institutions, age groups, disease types, and relevant criteria, remove irrelevant information, and optimize data loading for enhanced performance.
Ensure the relevance and accuracy of input data by updating it to reflect changes in the healthcare industry, aligning with current practices, standards, and terminology.
Utilize logistic regression formulas to generate insights and forecasts by considering factors like age, gender, and location, and evaluate logs and predictions within a specified timeframe (e.g., 5 and 10 years).
Analyze healthcare industry changes by comparing logs and previous years’ readings, visualize data through graphs, and identify areas requiring attention. Additionally, we developed Python script to investigate significant deviations from previous results and devise appropriate solutions.
Update and enhance the documentation of analytic models and processes, including clear explanations of the underlying logic, providing clients with up-to-date data.
The client received a modernized and precise data analytics solution, which serves as a powerful tool for internal client departments, healthcare institutions, and insurance agencies. This solution offers customizable reports that unlock a wealth of data, including insights into emerging market trends, consumer characteristics, and potential business impacts.
This powerful data analytics solution provides businesses with the necessary support to make strategic and million worth decisions based on comprehensive and well-informed insights.
3 Data Analysts
Python, SQL, Pandas, Informix, Jupyter, SAS
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