Dr. Deepa Bhide’s Post

I am excited to share a bit about my journey as a Systematic Reviewer with over 5 years of experience. Throughout my career, I have had the privilege of conducting over 115 systematic review studies and developing more than 200 review protocols, aiding organizations in making well-informed clinical decisions. My role has primarily focused on synthesizing meta-analytical evidence to guide clinical practice, ensuring that healthcare providers have access to the most reliable and up-to-date information. In the evolving landscape of healthcare, making data-driven decisions is crucial. My work helps organizations navigate the complexities of medical research, offering clear, evidence-based insights that inform patient care and clinical policies. As a business consultant and writer, my goal has always been to empower healthcare professionals with the tools and knowledge they need to improve outcomes. In addition to my expertise in systematic reviews, I have also embraced the transformative power of artificial intelligence in healthcare research. As an AI consultant and Prompt Engineer, I specialize in leveraging the latest AI technologies to enhance research methodologies. My skills in AI-powered system commands allow me to streamline data analysis processes, identify patterns, and generate actionable insights more efficiently than ever before. By integrating AI into systematic reviews, I help organizations unlock new potentials in their research capabilities. This fusion of systematic review expertise and AI innovation enables me to provide comprehensive, cutting-edge solutions tailored to the unique needs of each client. Let's connect if you’re interested in how systematic reviews and AI can revolutionize your healthcare research and clinical decision-making processes. Together, we can make informed, evidence-based decisions that drive positive change in the healthcare industry. #HealthcareResearch #SystematicReview #MetaAnalysis #AIinHealthcare #ClinicalDecisionMaking #AIConsulting #PromptEngineering #DataDrivenDecisions

Healthcare AI is at a tipping point, with research breakthroughs and innovative business models unlocking unprecedented opportunities that are reshaping both markets and modern medicine. The most successful healthcare AI companies will focus on critical junctures where high volumes of valuable data are generated, upstream of essential workflows. By owning these pivotal points of data creation and workflow initiation, healthcare AI platforms can exert significant influence over a cascade of downstream processes, ideally capturing the attention of multiple stakeholders and delivering on the promise of a multi-faceted ROI. The benefits of this approach are three-fold: 1. Workflow control: Platforms can streamline and optimize core processes across multiple stakeholders, and integrate AI-driven insights into daily operations to facilitate standardization and the creation of best practices across the healthcare ecosystem. These changes can significantly reduce friction in cross-stakeholder interactions and information exchange, leading to more efficient and effective healthcare delivery. 2. Data advantages: By building data density with one stakeholder, platforms create new opportunities with other stakeholders in the ecosystem. By capturing diverse, complementary datasets that are unique and hard-to-replicate, they are able to both develop novel multimodal solutions to address complex healthcare problems and increase the platform's defensibility. 3. Distribution: Platforms can leverage existing stakeholder relationships to rapidly scale adoption. As more users contribute to and benefit from the platform, powerful network effects emerge. The central position of these platforms also facilitates cross-selling of additional AI-powered solutions within the ecosystem, while simultaneously reducing customer acquisition costs through an integrated, multi-stakeholder approach. https://lnkd.in/gyetmccN

A Year of Journal Articles (Day 331/365): Sáez, Carlos, Pablo Ferri, and Juan M. García-Gómez. "Resilient Artificial Intelligence in Health: Synthesis and Research Agenda Toward Next-Generation Trustworthy Clinical Decision Support." Journal of Medical Internet Research 26 (2024): e50295. Summary: 〰 AI in healthcare aims to leverage EHR data for improved clinical decision support. 〰Challenges arise from inherent biases, uncertainty, variability, and quality issues in real-world data (RWD). 〰These issues can affect both training and prediction phases of AI models. 〰Potential consequences include biased medical decisions harming patient safety and rights. 💡 Resilient AI (RAI): 〰Defined as AI that can adapt and react to unforeseen situations in real-world environments. 〰Aims to be trustworthy for clinical decision support by: 〰Addressing data quality issues during training and prediction. 〰Accounting for variability over time, across settings, and overlapping information zones. 〰Enabling interpretable and explainable predictions. 〰Considering user requirements and fairness. 📚 RAI functionalities: 📗 Training: 〰Ensure generalizable knowledge through automated data preparation and cost-effective lifelong learning. 〰Handle missing, implausible, or outlier data. 〰Be resilient against variability over time, across settings, and user requirements. 〰Account for user requirements regarding expected performance and fairness. 〰Enable prospective explainability and interpretability. 📘 Prediction: 〰Balance clinical usefulness with time-efficiency and minimal intrusiveness. 〰Provide explainable and interpretable predictions. 〰Quantify uncertainty in predictions. 〰Enable comparison of predictions across sites and identify potential changes over time or across subpopulations. 📙 Production: 〰Continuously monitor and adapt to changes in data acquisition, target populations, and clinical knowledge. 〰Benchmark model performance and user-defined data quality rules. 📕 Regulations: 〰Existing regulations for medical devices and AI can be adapted to accommodate RAI functionalities. 〰Focus on trustworthiness, patient safety, fairness, data governance, explainability, and self-adaptation. 📔 Available methods and solutions: 〰Data quality assurance methods for labeling, addressing faulty data, and handling missing information. 〰Uncertainty management techniques for quantifying prediction variability and confidence. 〰Continual learning and model transportability methods for adapting to data changes and different settings. 〰Fundamental rights assurance methods to mitigate bias and ensure fairness. 〰Human-computer interaction methods for user-centered design and explainable AI. 📓 Research Agenda: 〰Implementing existing methods for practical RAI development. 〰Addressing challenges like development complexity, regulations, and method validation. 〰Exploring new research areas for continual improvement of RAI functionalities.

The deployment 🌎 of AI in healthcare does not succeed without physicians 💪 . Too many AI-driven solutions are being launched without any assessment, and the great majority will never be used at all 😞 . This team offers a checklist for assessing the value of an AI-driven solution in healthcare 🎉 5 Stakeholders :  1-Clinical Expert group (#collaborativeintelligence) 😷 . 2-AI Solution developper (industry vendors, academic group, organizational research) 3-Organizational Data Lead (data resources and infrastructures, security) 4-Machine Learning Operations Expert  5-Organizational AI Lead group Physicians need to be involved in the 4 main stages of project development. 1)Clinical Need Specification 🏨 : Define the clinical need? Is AI the best solution? Is there an alternative? What about the workflow? 2)Data Exploration 👨❤️💋👨 : -Population characterization: population test vs local population  -Input data : data collection process workflow, ethic -Output data: are they clinically efficient? 3)Development and Performance Evaluation 🤡 -Development process: is there any biaises (groups attributes) -Performance: clinical trial -Explainability: transparency global and local -Regulation, data security and privacy 4)Deployment and monitoring plan Deployment plan : clinical workflow, decision-making Monitoring plan : pro active monitoring, feed back loop Evaluation plan: effectiveness and harms « World is yours », come and exchange on the CAOS-France Officiel stand at the Sofcot Officiel congress from 11 to 13 November 2024 or attend the outstanding CAOS congress in January 2025! Marc-Olivier GAUCI Alexandre Caubère Thomas Neri Cécile Batailler Arnaud Clavé Thomas Chauvet

Beyond FHIR: AI’s Potential to Revolutionize Healthcare Data Insights - HIT Consultant Title: Beyond FHIR: AI's Potential in Healthcare Data Insights #Introduction: The blog post discusses how Artificial Intelligence (AI) can revolutionize healthcare data insights beyond Fast Healthcare Interoperability Resources (FHIR). #Challenges with FHIR: FHIR has limitations in handling complex healthcare data and interoperability issues. #AI Solutions: AI offers advanced analytics, natural language processing, and machine learning to enhance healthcare data insights. #Improving Data Quality: AI can help in cleaning, organizing, and standardizing healthcare data for better accuracy and efficiency. #Enhancing Clinical Decision Making: AI-powered insights can assist healthcare providers in making informed decisions for patient care. #Predictive Analytics: AI can predict patient outcomes, identify trends, and improve ai.mediformatica.com #data #this #healthcare #clinical #fhir #acce #insights #outcomes #scheduling #billing #innovation #healthcaredata #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/3ZC22fi)

🔹 Are you ready to harness the power of healthcare technology platforms for patient and customer engagement, healthcare innovation, and healthcare informatics? 🔹 As a Principal Cloud Solution Architect specializing in healthcare technology, I'm excited about the potential for generative AI, data and analytics, and data science to drive digital transformation and complex advanced analytics in the healthcare industry. 🔹 With the increasing focus on patient and customer engagement, healthcare organizations are seeking innovative solutions to deliver personalized and proactive care. Healthcare technology platforms play a pivotal role in facilitating these engagements, leveraging the power of data and analytics to drive informed decision-making and enhance patient outcomes. 🔹 The integration of generative AI into healthcare technology platforms presents a remarkable opportunity to automate and optimize processes, resulting in more efficient and effective healthcare delivery. From predictive modeling to natural language processing, generative AI has the potential to revolutionize the way healthcare organizations leverage data to improve patient care. 🔹 Additionally, the intersection of data science and healthcare informatics has paved the way for evidence-based decision-making and actionable insights. By harnessing the power of healthcare data, organizations can unlock valuable information that fuels innovation and drives continuous improvement in care delivery. 🔹 As a Principal Cloud Solution Architect, I am committed to supporting healthcare organizations in harnessing the full potential of their data through comprehensive cloud solutions. Whether it's EHR reporting, digital transformation, or the implementation of complex advanced analytics, I am dedicated to providing a successful approach that empowers organizations to navigate the evolving landscape of healthcare technology. 🔹 Have you thought about how healthcare technology platforms can revolutionize patient and customer engagement in your organization? By leveraging the expertise of a dedicated team, you can embrace the educational information and advisory support needed to optimize your healthcare technology initiatives. 🔹 Can we help you with this type of project? As your trusted advisory partner, we are here to provide the informative way forward, empowering you to achieve meaningful advancements in healthcare innovation and patient engagement. Let's embark on this transformative journey together. #HealthcareTechnology #CloudSolutions #HealthcareInnovation #DataScience #AI #HealthcareInformatics

AI in healthcare is projected to grow 20x this year. Part 3. These biotech companies are HIRING NOW: - IQVIA: A global leader in advanced analytics and clinical research services for the life sciences sector, leveraging data to enhance patient outcomes. https://jobs.iqvia.com/ - Change Healthcare: A healthcare technology company focused on data-driven solutions to improve clinical and financial outcomes in the healthcare system. https://lnkd.in/dE7gxfAc - Medtronic: A leading medical technology company that develops innovative therapies and products to improve patient outcomes across various medical fields. https://lnkd.in/dz6RMrW9 - Persistent Systems: A global technology services firm that provides digital transformation solutions, specializing in AI and machine learning for healthcare. https://lnkd.in/dzKYTvFM - Fractal.ai: An AI and analytics company that delivers healthcare solutions using advanced machine learning to improve operational efficiency and patient care. https://lnkd.in/dCcV6-47 Hopefully the resources from the last posts were useful to you, if you feel like you are ready to apply your skill to the task, then check this out. Share if you find this useful ♻ Subscribe for more resources like this!

AI in healthcare is projected to grow 20x this year. Part 3. Check out the list of healthcare companies that are hiring right now: Hopefully the resources from the last posts were useful to you, if you feel like you are ready to apply your skill to the task, then check this out: - IQVIA: A global leader in advanced analytics and clinical research services for the life sciences sector, leveraging data to enhance patient outcomes. https://jobs.iqvia.com/ - Change Healthcare: A healthcare technology company focused on data-driven solutions to improve clinical and financial outcomes in the healthcare system. https://lnkd.in/dE7gxfAc - Medtronic: A leading medical technology company that develops innovative therapies and products to improve patient outcomes across various medical fields. https://lnkd.in/dz6RMrW9 - Persistent Systems: A global technology services firm that provides digital transformation solutions, specializing in AI and machine learning for healthcare. https://lnkd.in/dzKYTvFM - Fractal.ai: An AI and analytics company that delivers healthcare solutions using advanced machine learning to improve operational efficiency and patient care. https://lnkd.in/dCcV6-47 Share if you find this useful ♻ Subscribe for more resources like this!

From Biases to Balance: Fairness in AI-Driven Health-Care 🤖⚖️ Bias in AI systems, particularly in healthcare, can lead to significant disparities in treatment and outcomes for different patient groups. Understanding and addressing these biases is crucial to ensure that AI technologies contribute to equitable and effective healthcare delivery. To gain a deeper understanding of this subject, Griffith College Dublin hosted an insightful online webinar as part of the AI2MED project on November 21st. The event was dedicated to exploring the critical intersection of artificial intelligence (AI) and healthcare, with a specific focus on addressing biases and achieving balance in AI-driven healthcare solutions. The keynote speaker, Dr. Harut Shahumyan, a renowned expert in data science and Director of Data Science at Optum Ireland, delivered a thought-provoking presentation on the fairness of AI in healthcare outcomes. His discussion underscored the importance of tackling biases inherent in AI systems and presented strategies to mitigate these challenges while maintaining an equilibrium between AI automation and human oversight. Bias in AI and its Impact on Healthcare Delivery 🩺📉 One of the key focus areas of this webinar was to address the important issue of bias in AI and its effects on healthcare delivery. It highlighted data bias, which occurs when certain populations are underrepresented in training datasets, leading to unequal outcomes. The discussion also covered algorithmic bias, where flaws in the design of AI systems can lead to unfair treatment or errors, and user bias, where healthcare professionals may misinterpret AI recommendations based on their assumptions. These biases risk creating inequalities in healthcare, particularly for vulnerable groups and reducing the positive impact AI could have on improving healthcare services. Strategies to Mitigate Bias 🛠️📋 The importance of the collaborative approach to addressing bias in AI systems was taken into account. The need for clear regulations was highlighted to ensure fairness and uphold ethical standards. Together, these efforts aim to align AI to deliver fair and equal healthcare outcomes for all. Future Perspective 🚀🌈 To reduce bias in AI and ensure equitable healthcare, it is essential to invest in diverse datasets, promote explainable AI for transparency, foster collaboration across disciplines, and develop regulatory frameworks that prioritise fairness and equity in AI implementation. The webinar highlighted the AI2MED project's role in fostering dialogue to shape a future of inclusive and accessible healthcare.

🌟 RESOURCE ALERT 🌟 As product managers, we may not be data scientists, but with the growing use of AI/ML in health products, many of us are eager to upskill. I recently completed an "AI in Healthcare Specialization" from Stanford via Coursera (https://lnkd.in/g96xY-UG), and here are a few concepts I found valuable for collaborating with data scientists from the Machine Learning (ML) for Healthcare course: 💡 PRECISION & RECALL: Key model performance metrics. • Precision: Ratio of true positives to predicted positives. • Recall: Ratio of true positives to actual positives. 🔍 Example: In predicting cancer, high precision might be prioritized to avoid false positives that could lead to unnecessary procedures. ✅ Assessment: Ensure metrics align with clinical goals. Verify the trade-off between false positives and false negatives matches intended outcomes. 💡 OVERFITTING & UNDERFITTING: • Overfitting: Model too complex, captures noise, poor generalization. • Underfitting: Model too simple, can't capture data complexity. 🔍 Example: A patient readmission model that overfits might perform well on historical data but poorly on new patients. Test on diverse datasets. ✅ Assessment: Compare training and validation loss curves—ideally, both should steadily improve and then level off without diverging. 💡 HYPERPARAMETERS: Settings that determine model behavior before training. 🔍 Example: For a triage model in emergency care, prioritize speed over accuracy for rapid decision-making. ✅ Assessment: Assess hyperparameter tuning by monitoring validation performance and reviewing experiments showing different configurations' impact. 💡 FEATURE ENGINEERING: Selecting, modifying, or creating input variables to improve model performance. 🔍 Example: In a diabetes management model, collaborate with clinicians to include features like diet patterns and exercise frequency. ✅ Assessment: Review feature importance scores to ensure clinically relevant features are represented and contribute to accuracy. 💡 TRAINING, VALIDATION & TEST SETS: Data subsets used to train and evaluate models. • Training Set: Teaches the model. • Validation Set: Tunes the model. • Test Set: Assesses final performance. 🔍 Example: For a model predicting patient no-shows, ensure the test set includes recent data to validate generalization to current behaviors. ✅ Assessment: Review data pipeline to confirm the test set reflects real-world conditions. In health tech, our goal is not just building AI features but understanding the clinical context and creating solutions that improve care. Curious how others are applying these principles in their products. What challenges have you faced integrating ML into health products? Data scientists, what do you wish PMs knew?