HARITHA KASARLA

Experienced and highly skilled Health Informatics Specialist with a strong background in data analysis, clinical documentation, and healthcare management. Possessing a Master’s in Health Informatics and a Bachelor’s in Dental Surgery, a unique combination of technical expertise and clinical knowledge is brought to the table. Proficiency in Python, R, SQL, and advanced Excel, combined with hands-on experience in managing Electronic Health Records (EHR) and Health Information Management (HIM), equips one to improve healthcare delivery and data accuracy. Recent roles include successfully streamlining health informatics data, developing scalable databases, and engineering custom algorithms to enhance data retrieval efficiency. Previous experience as a data science intern involved managing healthcare data to significantly improve decision-making processes and developing innovative algorithms for data analysis. As an Associate Drug Safety Physician, over 10,000 individual case safety reports were managed, ensuring precise data entry and timely regulatory submissions. Multiple projects focused on leveraging machine learning and AI to advance health outcomes, such as suicide prevention platforms, fetal health prediction models, and disease identification applications, have been led. Commitment to patient privacy and data security is demonstrated through work on securing clinical EHRs using role-based access control and biometric authentication. Dedicated to improving the quality of medical record documentation, ensuring compliance with CMS guidelines, and educating healthcare teams on best practices. Strong analytical skills, attention to detail, and ability to collaborate with multidisciplinary teams position one to contribute effectively to any healthcare organization.

Mind Safe: Advancing Suicide Prevention through personalized, Data-driven Interventions and Machine learning  (Spring 2024)

• Developed MindSafe, a personalized suicide prevention platform utilizing machine learning to analyze heart rate, speech patterns, sleep trends, and self-reported data for early detection and tailored interventions
• Conducted comprehensive research, including interviews, surveys, and controlled experiments, to evaluate effectiveness, focusing on mindfulness exercises and coping strategies
• Ensured robust encryption and data controls to prioritize user privacy, fostering trust and enabling informed care plans with user consent, aiming for enhanced early detection, increased user engagement, and reduced suicide rates.

Securing Clinical Electronic Health Records: A Comprehensive Approach Through Role-based Access Control And Biometric Authentication In The Era Of Health Informatics   (Fall 2023)

• Developed and implemented a security solution for Electronic Health Records (EHR) systems, integrating Role-
• Based Access Control (RBAC) and Biometric Authentication
• Enhanced data confidentiality and integrity by structuring access based on roles and employing biometric identifiers for user verification
• Ensured continuous monitoring and compliance with Health Informatics principles and regulatory standards, optimizing data accessibility and privacy

Optimizing Fetal Health Prediction: Identifying the Key CTG Measurements Using R  (Spring 2023)

Technologies used: R Studio, Machine Learning Models

• Conducted in-depth data exploration to assess CTG measurements' viability in enhancing fetal health monitoring
• Protocols within a dataset of 2126 participants, alongside a 5-member team
• Executed a series of statistical tests to improve fetal health predictive accuracy by 18% and identified vital CTG
• Measurements through logistic regression analysis, boosting fetal health prognosis effectiveness by 14% in clinical settings

Social Anxiety Prediction in Online Gamers Using Python  (Fall 2022)

Technologies used: SQL, Python, Scikit-Lear

• Led a 6-person team in analyzing behavioral data from 13,000 online gamers, enhancing model precision by 25%
• Developed and validated predictive models that identified risk factors for social anxiety, achieving a 30% improvement in early detection rates.

Disease Identification & Prevention Based on Artificial Intelligence Algorithms  (Fall 2022)

• Led the end-to-end creation of the EarlySymptomDetect application, partnering with healthcare experts to guarantee data precision
• Incorporated AI algorithms that precisely pinpointed disease clusters, resulting in a 50% quicker reaction time from nearby healthcare providers during outbreak situations.