Anupam Sharma

With 5 years of analytics expertise and 2 years specifically in healthcare, as an Assistant Manager, I possess adeptness in data analysis and visualization. My GCP certification alongside SQL and Python proficiency has facilitated successful project leadership across startups and MNCs. Refined through healthcare exposure, my analytical acumen has enabled the creation of informative dashboards using PowerBI ,Voila and Streamlit, aiding stakeholders in data-informed decision-making. I bring significant value to teams aiming to harness data for organizational advancement.

Associate Google Cloud Platform Engineer

Best Innovation Award: Enhancing process on several ends (2020)

• Transition Fill Rate of Non-Formulary Drugs (SQL, Python, Voila)- developed an SQL pipeline in a dev environment to target members who were using non-formulary drugs and recommended alternative drugs suggested by the government to reduce the transition fill rate. I suggested the client opt for an IVR mode of communication for outreach and created a dashboard using Python and Voila to evaluate performance. The results showed a 95% success rate in changing members' drug regimes, an 87% outreach rate through IVR, a reduction in transition fill rate from 41% to 27%, and savings of $365K. This approach not only achieved the desired goal but also resulted in significant cost savings.
• Application of DME for decrease in Disease progression (SQL)- analyzing the data of identified members suffering from arthritis, I found that using durable medical equipment (DME) after diagnosis can significantly delay the need for surgery by up to 91 days. This means that patients who use DME can potentially delay surgery from 364 days to 455 days, or in some cases, even prevent surgery altogether. This finding suggests that encouraging the use of DME, such as knee braces or assistive devices, can be an effective solution to reduce the progression of arthritis and potentially avoid the need for surgery.
• Preventive Outreach for Sinusitis: Identifying High-risk and Low-risk Cohorts, Minors, and Preventing Surgery (ML, SQL, Python, Cron Job)-Identified members with potential symptoms of Sinusitis and reached out to them based on a priority model created using statistical and Machine learning algorithms. Additionally, you automated the process of creating a monthly refresh of a tracker (YTD/MTD) using Python and cron job. To evaluate the success of the project, you created a dashboard on Voila using Python packages, which helped in reducing surgery expenses from approximately $1 million to $800K. This reduction in expenses was likely due to early identification and treatment of Sinusitis, which prevented the need for more costly surgical interventions.
• Blue Collar Workforce Management (SQL, PowerBI)- team was experiencing 30% monthly attrition, causing the ops team to continuously source new employees every 2.5 months. To address this issue, biometric machines were installed at each location, and data was fetched using SQL db. A dashboard was created using PowerBI to monitor the alerting sites, resulting in closer monitoring and a 15% reduction in attrition.
• Process Automation of ERP- As the company began, onboarding blue-collar staff was manual with papers and excel sheets. I automated the process using vendors for onboarding, payrolling, attendance, and invoicing, resulting in 37 hours of efficient working daily. The new system streamlined departments and eliminated the need for hard copies.