Vijay Venkatesan on health data governance | CDO Magazine posted on the topic | LinkedIn (2024)

Discover the power of big data in healthcare transformation: Turning data into actionable insights. #bigdata #healthcare #healthcareanalytics #analytics #dataanalytics #bigdataandanalytics

17

How often do you think about data governance? You should be, especially if increasing data quality (to enable, say, data generation for #RWE) is something you’re working on. https://lnkd.in/gRR3vrha“Data is omnipresent in healthcare organizations. Governance ensures that the right people can use data at the right time for the right reasons — all within the business systems they use every day.”#specialtypharmaceuticals #Canada #RWE #RWD #realworlddata #realworldevidence #healthoutcomes #data #quality #data #healthcare

35

1 Comment

Unlocking Insights and Transforming Healthcare with Data AnalyticsWith data being king, the healthcare business is using data in ways that have never been done before. The area of healthcare data analytics, which is growing quickly, is at the front of this revolution. Data analytics is becoming a game-changer as we try to figure out how to practice modern medicine. It gives us insights that help us provide better care to patients, lower costs, and make healthcare systems work better. https://lnkd.in/gCFbZu7r

3

Payers are increasingly turning to advanced actuarial models to enhance their decision-making processes and optimize patient outcomes. By integrating consumer data into these models, we're not just predicting costs; we're reshaping how healthcare is delivered and financed. Here are some key use cases where advanced actuarial modeling stands out:1. **Personalized Premium Setting**: Leveraging consumer behavior data allows for more accurate and individualized premium calculations. By analyzing lifestyle choices, purchasing behaviors, and wellness activities, payers can tailor premiums that reflect the true risk and potential healthcare costs of individuals.2. **Enhanced Risk Assessment**: Consumer data provides deeper insights into the social determinants of health, offering a broader perspective on risk factors. Actuarial models that incorporate this data can better predict high-cost events, leading to more effective risk management strategies.3. **Targeted Health Interventions**: With a richer dataset, models can identify at-risk populations and guide the deployment of targeted intervention programs. This proactive approach not only improves patient health but also reduces unnecessary expenditures by preventing adverse health events before they occur.4. **Improved Member Engagement**: Understanding consumer preferences and behaviors helps payers design more engaging and effective communication strategies. This results in higher member satisfaction and retention rates, as interventions and communications are more likely to resonate and encourage positive health behaviors.5. **Fraud Detection and Prevention**: Advanced modeling techniques can detect anomalies in claims data that may indicate fraudulent activities. By integrating consumer data, these models become more adept at spotting irregular patterns, safeguarding against losses and ensuring financial stability.The integration of consumer data into actuarial models is not without challenges, particularly around data privacy and security. However, with robust data governance frameworks, we can harness the full potential of this data while upholding the highest standards of consumer protection.Let's connect and discuss how we can further advance the role of actuarial science in healthcare. The future is data-driven, and together, we can lead the way.#ActuarialScience #HealthcareData #DataAnalytics #HealthcareInnovation #ConsumerData

10

Nowadays we are surrounded by tons of data, collected for various purposes, e.g., at health insurances, hospitals, during surveys or by personal health apps. So, it is quite tempting to use this big data (#bigdata) for research – which is absolutely fine. But big data needs to be understood before it is analyzed. A large amount of data does not necessarily mean that this data is of high quality, i.e., without errors. Also, potential biases (#bias) must be investigated e.g., by checking the representativeness of the data collected. And these are just the very basics on the data side. When it comes to the analysis of the data, it is essential that a clear purpose of the analyses is determined before. What are the hypotheses to investigate? Or is it just an explorative exercise to create some new hypotheses? If this is the case, the next necessary step is to check these new hypotheses with a new and independent data set. Because finally it should be about creating valid insights from big data. And this is where Biostatisticians are trained for. So, never forget to involve Biostatisticians right from the beginning of any big data exercise!What are your experiences / learnings from this exciting exercise?

25

1 Comment

Revolutionizing Healthcare: The Unstoppable Rise of Data Analytics in the Industry#HealthCare #DataAnalytics #Data #RangHealthcare #RangLifeciences #RangTechnologieshttps://lnkd.in/d9zJ-_Zm

17

3 Comments

Big Data in Healthcare Analytics continues to prove valuable from preventing human error to informed strategic planning, however obstacles are still present.#BigData#HealthcareAnalytics

2

I love seeing a #DataGovernance discussion in healthcare, butone of the most important aspects isn’t mentioned: maintaining a division between clinical data and business data. The authors mention challenges with “regulatory compliance or a lag time to get ‘business-ready data’ to the teams that need it”, but I have found that concerns with that regulatory compliance often keep many organizations from utilizing the acquired data to drive marketing and strategy decisions. Smarter approaches to governance can ensure that data-driven decision making can thrive in both clinical care and non-clinical operations. By having the proper process guidelines and data architecture in place, more organizations can and should use data provided to learn more about their customers while keeping this separate from the data provided as a patient. #DataDoneRight, Marcello Cracolici, #HealthcareMarketing

7

Hello LinkedIn Community 👩💻🌐,I'm thrilled to share my recent project: "Predicting Medical Costs Billed by Health Insurance - EDA & Regression" 🏥💸. This initiative is a fusion of data science and healthcare, focusing on predicting health insurance costs with precision.The project leverages a comprehensive dataset of 1338 entries, encompassing key attributes like age, sex, BMI, number of children, smoking status, region, and individual medical charges. It aims to dissect how these factors significantly impact medical costs. The dataset presents a diverse range, from various age groups to body mass indices, with an average of about one child per individual, and showcases significant variability in medical expenses.Utilizing advanced machine learning models, including Neural Networks, Gradient Boosting, Linear Regression, Random Forest, and Ensemble methods, the project delves deep into the data, unveiling intricate patterns and correlations. This endeavor is not just about data analysis; it's geared towards developing robust predictive models that enable more precise estimations of health insurance costs based on specific individual attributes.This comprehensive analysis offers a window into how data science can be instrumental in transforming healthcare analytics, aiding insurers and policyholders in managing health-related expenses.Dive into the details of this project on my GitHub: https://lnkd.in/eDDHkDvNI'd love to discuss this further and explore any collaborative ideas or feedback you might have! 💡🤝#DataScience #HealthcareAnalytics #MachineLearning #PredictiveModeling #RegressionAnalysis

2

BLOG ALERT!Struggling with predictive analytics in healthcare? Data silos, technical gaps, and budget constraints holding you back? Unlock the power of data and transform patient care.#HealthcareAnalytics #DataOS #Datamesh #DataEngineering

8