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Author: Medha Nair

Expericia > Articles by: Medha Nair
Two people signing the deal to procure an equipment

Cut Equipment Approval Time by 55% with Automated Workflows

Case Study: Accelerating Capital Equipment Procurement in a Hospital in Singapore   Executive Summary A 500-bed acute care hospital in Singapore was experiencing significant delays in procuring critical medical equipment, leading to operational and financial inefficiencies. By implementing Microsoft Power…

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Infographic titled 'Smarter Bed Management in Hospitals' displayed in a 2x2 grid. Top left shows 'Admit' with patient info logged digitally. Top right shows 'Assign Bed' with app checking availability and notifying nurses. Bottom left shows 'Discharge & Clean' with automatic cleaning triggered. Bottom right shows 'Mark Ready' with bed marked vacant and reception updated.

Reduce Bed Downtime. Improve Patient Flow. No New HMS Needed.

One missed update can delay care. A receptionist assigns a bed – but the system doesn’t alert housekeeping.The patient arrives, but the room’s not ready.10 minutes lost. Satisfaction lowered. Staff frustrated. This isn’t a people problem.It’s a process problem. You…

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A visual metaphor of complexity - tangled wires symbolizing disconnected hospital systems and workflow chaos in traditional Hospital Management Systems (HMS)

Tired of Clunky Hospital Systems? There’s a Smarter Way

We’ve all experienced it – waiting endlessly for a discharge summary, a doctor running late, or an appointment that keeps getting pushed. And when you’re finally done, you still have to settle bills separately at the hospital and the pharmacy.…

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Snowflake and Databricks Compared: Architecture, Performance, and More

Introduction As organizations increasingly adopt cloud-based data platforms to manage and analyze large volumes of data, two major players have emerged at the forefront of this transformation: Snowflake and Databricks. Both platforms offer advanced data solutions but cater to different…

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Scalability with data lakehouse

Data Lakehouse Explained: 5 Ways It Outperforms Legacy Systems

Introduction In today’s data-driven world, organizations are handling an ever-growing volume of data from diverse sources. Traditional data warehouses have long served as the foundation for business intelligence and reporting, offering structured storage with robust query performance. Meanwhile, data lakes…

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Sandra

HR Myths Debunked: Expert Insights from Sandra Hasseer

We’ve all had our fair share of experiences with HR, some positive, some… not so much. From being left in the dark after an interview to wondering whether anyone ever reads cover letters, these experiences have led to a variety…

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Data warehouse

Data Lake vs. Data Warehouse: A Comparative Overview

1. Definition and Purpose Data Lake:A data lake is a centralized repository designed to store raw data in its native format—structured, semi-structured, or unstructured. It allows organizations to collect and store data without the need to structure it first. This…

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Nikhil

Expericia’s Internship Culture Through the Eyes of a New Joiner

Giving an interview can often be a daunting task , especially when you don’t know what to expect or how to prepare. Many companies keep their interview process under wraps, hoping to test your spontaneity and presence of mind. While…

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Brainstorming on marketing

7 Strategic Ways Sitefinity Enhances Marketing Productivity and Drives Enterprise Growth

For modern enterprises, marketing speed and agility are no longer optional—they’re a competitive advantage. Sitefinity, an enterprise-grade Digital Experience Platform (DXP), empowers marketing teams to move faster, reduce costs, and deliver personalized experiences at scale. But Sitefinity doesn’t just benefit…

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Bid data, AI

Why Data Quality Matters: The Key to Trustworthy and High-Performance AI

Introduction Artificial Intelligence (AI) systems are rapidly transforming sectors like healthcare, finance, transportation, and customer service. But their effectiveness depends on one critical factor—data quality. Just as a recipe fails with bad ingredients, flawed data leads to faulty AI outcomes.…

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