SUMMARY

Proven clinical data analyst helping companies translate their business goals to reality with a demonstrated ability to deliver valuable insights via predictive and statistical analytics. Proven ability to combine vision, ingenuity, technical skills, and strong scientific acumen with well-developed project management and leadership qualities to solve complex problems by leveraging insights obtained from data analysis.

EDUCATION

Master’s in Health Informatics Aug 2019 – May 2021

Indiana University Purdue University, Indianapolis

Doctor of Pharmacy (Pharm D)

Malla Reddy College of Pharmacy, Osmania University, Hyderabad, India Aug 2012 – Aug 2018

TECHNICAL SKILLS

Programming Technologies:

Data Visualization:

Python, R, SQL, HTML, XML, JSON.

Power BI, Tableau, DHIS 2

EHR Systems

Cerner, and Open MRS

Certifications

HIPAA, and CITI

Healthcare Domain Knowledge

Proficient in healthcare policies HITECH, HIPPA, Medicaid, Medicare, insurance providers, and terminologies like SNOMED, LOINIC, CPT, ICD- 9/10, and LIS (Epic)

Knowledge of HEDIS data measures and research design.

Expertise in analyzing quantitative and qualitative data.

Familiar with basic statistics, project management, HL7 and FHIR standards, POSTMAN, API.

PROFESSIONAL EXPERIENCE

Clinical Data Analyst Internship Jan 2021 – Present

Indiana University School of Medicine, IN

Predictive Analytics: Focused on enhancing Radiology Information Systems (RIS) and clinical information system interoperability in the electronic health record of clinicians (EHR).

Data Collection: Successfully collected information from the Cerner database of IU Health that contains 10 million observations and several variables (IU Methodist Hospital EHR data).

Performed patient web view data analysis by retrieving the C-CDA document in XML format using Postman.

Software used: HL7, FHIR, Cerner care technology, Back beach software – C-CDA viewer, Postman, and Visual Studio code.

Graduate Research Assistant Jan 2019 – May 2020

Indiana University Purdue University, School of Informatics and Computing, IN

Retrieved data from Kaggle and evaluated it using a cross-validation technique. Performed data processing and a correlation heat map were plotted using Python.

Linear Regression Model, Random Forest Regressor Model, Decision Tree Regressor Model, and concluded that the Random Forest Regression Model is more accurate than others with our data set.

Clinical Experience: Clinical Pharmacist Internship Aug 2017 – Aug 2019

Osmania University, India

Evaluated findings, analyze patients’ data, and decided treatment plan, and participated in the initial data collection and statistical analysis of the various skull parameters of the research project conducted by the department of forensic odontology.

Performed Comparative Dose Efficacy Study of Atorvastatin on Lipid Profile and Incidence of Adverse Effects in Dyslipidemic Patients.

Assessment of drug-related problems and health-related quality of life in TB patients visiting pulmonology department in a teaching hospital.

ACADEMIC PROJECTS

Analysis of 70,000 Clinical Database Patient Records (Applied Statistics, R)

Conducted a data exploration that helped in revealing the data structure which ultimately provides a better understanding of the problem.

Multiple Logistic Regression Model and MANOVA test is the statistical method conducted to define the severity of the diseased patients and able to predict the readmission of the patients and Spearman Correlation test is used to define the correlation between discharge id and readmission.

Establishing Interoperability between EHR systems (XML, FHIR, XSLT, HL7, Open MRS, RESTAPI)

Created a CDA document using XML and edited it using XML XPath queries and transformed data into JSON to post it to Open MRS through REST commands using a POSTMAN.

Placed x-ray order using REST API commands were given to extract it from EHR using the “GET” function in Postman and data is converted to XML using the POSTMAN function “POST”.

Data was converted to XML and posted to the Libre Health Radiology Information System (RIS) using the Postman function “POST”.

Comprehensive Profiles of Federal Qualified Health Centers and their catchment areas (FQHCs).

Clinical Information System. (Excel worksheet, PowerPoint)

Develop profiles of a subset of FQHCs’ catchment areas based on MPH demographic and economic data for their location. FQHCs practices, service offerings, and stated philosophies are based on data obtained from their respective websites.

Develop a database in Excel to collect this information in a usable form the Excel database will be developed by the BHIRC in conjunction with affected faculty.

Conduct qualitative and descriptive and statistical analysis to provide a comparative profile of their subset of FQHCs.

Diagnosis of COVID 19 using AI algorithm. (Project Management)

The project was to develop the diagnosis of COVID 19 using an AI algorithm.

Through this project, we understood the concept of project management, included project scope statement, work breakdown structure, timeline and its constraints, communication plan, risk analysis, project budget in our project presentation and worked as a team on it.

Business Health Informatics:

Knowledge of the business models for healthcare in the US and the role of Health Informatics in improving financial outcomes. Ability to identify areas in healthcare delivery or administration where HI solutions could improve performance and develop HI solutions to address the identified problems and identify the financial implications of the solution under diverse healthcare payment models.

Biomedical Analytics:

Import data from Electronic Health Record (EHR) systems into data warehousing system and used analytics tools. Design and analytic strategy to frame a potential issue and solution relevant to the health improvement of the patient population.

Introducing the largescale data sets and using the data visualization tools DHIS2 to show the distribution of the disease and health outcomes in relevant populations of interest as well as geographic regions and represent the data on maps and apply the clinical analytics to various contexts of quality improvement.

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