Global Deaths - dual language
30 Anos de Histórias, 202 Países, 1 Relatório: Acabei de finalizar meu novo projeto de Power BI sobre as Causas de Morte Globais (1990-2019), agora bilíngue! Este relatório não é apenas um dashboard; é uma análise profunda de 6.160 linhas de dados, construído para ser a ferramenta mais dinâmica e intuitiva possível para explorar a mortalidade mundial. O que você vai encontrar: ✅Visão de Longo Prazo: Explore a evolução das mortes e o crescimento populacional ao longo de 30 anos (1990-2019). ✅Métricas Chave: Analise o Total de Mortes, Taxa de Mortalidade, População, Crescimento Anual (YoY) e Densidade Demográfica por país e continente. ✅Análise Profunda: Dados categorizados em 31 causas e 7 grupos principais (DCNT, Lesões, Infecciosas, etc.). ✅Modelo Multi-Idioma: Built-in com a função USERCULTURE(), permitindo que o relatório se ajuste automaticamente ao formato de números e datas do usuário (pt-BR, en-US, etc.). Este projeto também foi um exercício em otimização de dados: usei Power Query para transformar e o DAX para criar as medidas dinâmicas, garantindo um arquivo Power BI o mais enxuto possível. Interessado em ver como a saúde global evoluiu nas últimas três décadas? #PowerBI #DataAnalysis #GlobalHealth #CausesOfDeath #DataViz #DAX #BusinessIntelligence eyJrIjoiYTAwOGE3MjUtYTI5OC00NTE3LWE4YTUtYzM1N2Y3NzkxMjhiIiwidCI6ImNmNzJlMmJkLTdhMmItNDc4My1iZGViLTM5ZDU3YjA3Zjc2ZiIsImMiOjR9
Where do you stand?
Where do you stand, against 8.3 billion people? A 30-year-old man reaches about 83 in Japan. About 65 in Sub-Saharan Africa. Same person, same age — eighteen years, decided mostly by where he was born. Closer to home: swap 7–8 hours of sleep for short nights, and about a year quietly disappears. Your odds of reaching 80, your healthy years, the risks that matter at your age, where your income lands globally — it's all in there, and it all moves when you change one thing. Worth exploring your own numbers. These are population averages from UN, WHO and SSA data — not predictions. Nobody's life is decided by a table. If it made you pause, a like would mean a lot. eyJrIjoiZTgzNzViMjgtNzE2OS00MjNlLWE3YWUtMmJlMWJhNzNmMWI4IiwidCI6IjExYjJhMTMyLTI2YzYtNDJjNy05N2IxLWVlNGY2YTU2NjNlYiIsImMiOjEwfQ%3D%3DHealthcare Analytics Dashboard - Power BI Template
In hospitals, delays and inefficiencies don’t just cost money, they risk lives. This Healthcare Analytics Dashboard (Power BI Template) turns complex health data into clear, actionable insights that empower teams to act quickly and precisely. Designed from real hospital use cases, this tested solution helps clinical and operational leaders improve care delivery, resource planning, and financial performance. Challenges This Dashboard Solves 1. Unpredictable Length of Stay (LOS) Hospitals often struggle with forecasting patient discharge times. This leads to bottlenecks in bed availability, overcrowded wards, and delayed admissions. Without visibility into LOS trends, resource allocation becomes guesswork. This dashboard provides historical and real-time LOS analysis by department, condition, and demographic, helping hospitals predict and plan bed usage accurately. 2. Hidden Billing Inefficiencies Revenue leakages are common when billing data is scattered or inconsistently tracked across departments. Costs tied to specific treatments, medications, or admission types often go unnoticed. The financial dashboard breaks down billing data by condition, hospital wing, and treatment category, helping analysts pinpoint cost spikes and optimize spend. 3. Readmissions Due to Missed Clinical Patterns Chronic conditions like hypertension can lead to repeated ER visits if treatment effectiveness isn’t monitored. Without automated alerts, early warning signs are often missed. This dashboard flags abnormal blood pressure cases, tracks medication response rates, and alerts clinical teams for timely interventions,reducing readmission risks. 4. Workforce Planning Gaps Admission surges during weekends or specific times of year catch teams unprepared, stretching staff thin. Weekend vs. Weekday admission trends allow administrators to align staffing with actual demand patterns, not assumptions. 5. Manual and Delayed Reporting Relying on Excel sheets and outdated reports can delay crucial decisions and lead to inconsistent care quality. Automated Power BI reports eliminate manual effort, providing stakeholders with real-time, exportable, and easy-to-understand insights. Key Benefits – Why This Dashboard Matters Live Monitoring of Admissions and Bed Utilization Stay on top of patient intake, identify overcrowded departments, and reallocate beds in real time to improve flow and reduce wait times. Length of Stay (LOS) & Discharge Trend Analysis Track average stay durations by department, gender, or diagnosis. Use historical trends to forecast bed demand, avoid overcrowding, and improve discharge efficiency. Hypertension Alerts & Medication Effectiveness Tracking Get automatic alerts for patients with abnormal BP, analyze which treatments work best, and proactively manage chronic conditions to reduce complications. Billing Insights by Condition, Department, and Admission Type Quickly uncover which procedures or admission types generate the highest costs. Identify revenue opportunities or overspending areas across service lines. Demographic-Based Performance Insights Segment KPIs by age group, gender, and medical condition. This helps tailor treatment plans and improve patient outcomes across different populations. Who Needs This? Hospital Admins – Optimize resources and costs Clinical Teams – Enhance treatment protocols Revenue Analysts – Fix billing inefficiencies Healthcare Analysts – Ditch manual reports for live data Data Source: Synthetic healthcare data for illustrative use cases. eyJrIjoiZTQzNDYzYjAtNWI4MC00MDJkLWI0NGEtNTFjMjAyYWU3ZGRiIiwidCI6ImE5NDUyNTg2LTJmNGMtNGNiMS04ZDJlLTI2ODkyODllZTcyNiIsImMiOjl9
CityCare Analysis
An end-to-end Power BI analysis of a multi-national hospital trust (4,000 admissions across 8 departments, 36 facilities and 24 countries). The raw data arrived as a single flat file, which I normalised into a star schema. One fact table with dimension tables for departments, facilities and dates, before building the DAX measures behind every KPI: recovery rate, readmission rate, net margin, average length of stay and death rate. The dashboard spans three pages (Overview, Service Quality, Finance) with synced slicers and a page-navigation rail. Beyond the headline numbers, the analysis surfaced findings that reframed the surface story: Oncology's poor recovery and high death rates are explained by case-mix severity rather than care quality, and a facility-level review revealed the revenue-to-margin gap is volume-driven across satellite sites, not a case of any single loss-making location. Built with Power Query, a star-schema data model and DAX. Data is synthetic, created for analytical practice. eyJrIjoiMGZiYjFlYzktZWZhZS00YjU0LTlkOTEtMzZhODFlZDYyMWRhIiwidCI6ImZmMGYzZTNhLTNlNTMtNDU0Zi1iMmI1LTZjNjg3NTNiOGVlNCJ9Healthcare Demographic and Patient Analytics | DataFlip
Healthcare organizations often manage large amounts of patient, billing, and operational data. When this data is scattered across different systems, it becomes difficult to understand patient trends, costs, and service demand. This dashboard brings demographic, operational, and financial insights together in one clear view to support better healthcare decisions. Business Impact This dashboard helps healthcare teams: Understand patient demographics such as age, gender, and population trends Analyze patient visits, repeat encounters, and service usage Track claims, billing, and payer coverage insights Monitor procedure costs and overall healthcare spending Improve operational efficiency through encounter and utilization analysis Support better financial and planning decisions with clear data insights It helps healthcare leaders move from reactive reporting to proactive planning. Who Can Use This Template Hospital Administrators Public Health Analysts Clinical Operations Leaders Insurance and Payer Analysts Healthcare Strategy and Planning Teams Discover more insight-driven templates built for real business decisions. Visit DataFlip eyJrIjoiMGZmZDJiNzMtMTU2Yi00NzhhLTg5MDMtMDA2N2IxOTI2NjIxIiwidCI6ImE5NDUyNTg2LTJmNGMtNGNiMS04ZDJlLTI2ODkyODllZTcyNiIsImMiOjl9
Massachusetts General Hospital Analytics 2011–2022 | Power BI
A 4-page Power BI dashboard analysing 11 years (2011–2022) of synthetic hospital data from Massachusetts General Hospital, covering admissions, patient demographics, and financial performance. Key findings: - 27,891 encounters across 974 patients - 64.68% of patients are seniors (65+) - $101.51M total revenue; only 30.6% covered by insurers - Electrical cardioversion is the highest-cost procedure ($36M) Data Source: Synthea synthetic data via Maven Analytics Tools: Power BI | DAX | Power Query GitHub: https://github.com/orevaagba-coder eyJrIjoiNzA0NmVlYjQtYTQ4ZS00OTcwLTgxNjktZTRjYjc1N2FhYzIyIiwidCI6IjNiYmQ3N2E0LTJhNjItNDkzNS04MmY1LTEwMjMwOWJmMDY2MCJ9&pageName=b3051b070043e388284bGLOBAL HEALTH INDICATORS
This dashboard provides a comprehensive analysis of retail sales performance, helping users understand key business trends through interactive visualizations. It brings together important metrics such as total sales, profit, order volume, customer segments, product categories, and regional performance in a single, easy-to-navigate report. The report enables users to explore sales trends over time, compare performance across different regions and product categories, identify top-performing products, and evaluate customer purchasing behavior. Interactive filters and drill-down capabilities allow decision-makers to investigate specific business scenarios and uncover actionable insights. Designed with a clean and intuitive layout, the dashboard transforms raw business data into meaningful information that supports strategic planning, sales optimization, and data-driven decision-making. It demonstrates how Power BI can be used to create engaging, insightful, and interactive business intelligence solutions for retail analytics. eyJrIjoiNGZlMmVlYTAtZDBiNi00YTJkLTk2MmItOWY2MTBmMGNiYzg0IiwidCI6IjAwMWExYWNmLWJhOTItNDJiZS04MmZkLTZjNjMxZWZmNTc1NCJ9
Emergency Operations & Patient Flow Analytics
🚨 From 9,994 patient visits to actionable insight built an end to end Emergency Operations & Patient Flow Analytics dashboard in Power BI. 3 pages. 11 hospitals (NHS + private). One goal: turn raw ER data into decisions that save time and lives. 🔍 What the data revealed: → Treatment is the #1 bottleneck in the patient journey - 74 min avg delay before clinical intervention begins → Nuffield Health Woking Hospital has the longest wait (108 min); Sandwell General the highest mortality rate (4.02%) - both flagged for priority staffing support → Private hospitals (Spire, Nuffield) run a 1.8x higher staff ratio than NHS trusts, driving stronger efficiency scores → Wait time and patient satisfaction are directly linked - the longer the wait, the lower the experience score 🛠️ Built with: ✅ Custom DAX measures for risk scoring, readmission %, and efficiency index ✅ ZoomCharts visuals for interactive drill downs ✅ A 3 page narrative arc: Overview → Patient Journey → Clinical Outcomes. eyJrIjoiMzIzYjJjYWEtZmRhYi00MmJmLWE1ZjctZGExNzkxNTJkOTY0IiwidCI6IjQ2NTRiNmYxLTBlNDctNDU3OS1hOGExLTAyZmU5ZDk0M2M3YiIsImMiOjl9Hospital Emergency Room| Dashboard
Project Description: This project involved the end-to-end development of a comprehensive Hospital ER Performance Dashboard using Power BI. The primary goal was to transform raw ER operational and patient data into a dynamic, interactive analytical tool. This tool empowers stakeholders with clear visibility into key performance indicators (KPIs), operational efficiencies, patient demographics, and emerging trends, ultimately enabling data-driven decision-making to enhance ER services and patient care. Business Need & Project Objectives: The core business need was to move beyond static reporting and gain deeper, actionable insights from the wealth of data generated by the Emergency Room. Key objectives driven by this need included: Enhanced Operational Efficiency: To provide a clear view of ER operations, including patient volume, throughput, waiting times, and admission rates, to identify bottlenecks and areas for improvement. Improved Patient Management & Care Quality: To track patient satisfaction scores, timeliness of care (e.g., percentage of patients seen within 30 minutes), and analyze referral patterns to ensure optimal patient journeys. Data-Driven Decision Making: To equip management and ER staff with reliable, up-to-date data and synthesized insights to support strategic planning, resource allocation, and proactive problem-solving. Comprehensive Performance Monitoring: To establish a system for ongoing monitoring of key ER metrics through daily, monthly, and consolidated views, comparing performance against previous periods and identifying significant deviations. Discovering and Presenting Meaningful Insights (The Process): The journey from raw data to actionable insights followed a structured analytical process: Requirement Analysis & Data Understanding: The process began with a thorough review of business requirements and an in-depth exploration of the available ER dataset. This ensured a clear understanding of the project's scope and the data's potential and limitations. Data Cleaning & Preparation: Rigorous data cleaning and preparation were performed to ensure data accuracy, consistency, and suitability for analysis. This involved handling missing values, standardizing formats, and structuring the data effectively within Power BI. Iterative Dashboard Development & DAX Implementation: Based on initial wireframes and defined requirements, interactive dashboards were developed. This involved: Creating robust DAX (Data Analysis Expressions) measures to calculate key metrics, such as Referral Patient counts (excluding "None" referrals for departmental focus), percentage changes, and moving averages. Designing intuitive visualizations (KPI cards with sparklines, bar charts for comparisons like timeliness and departmental referrals, heatmaps for patient volume, etc.) that clearly communicate performance. Iteratively refining these visuals and dashboard layouts based on analytical best practices and feedback to enhance clarity and user experience, such as separating "None Referrals" into its own distinct KPI. Insight Generation through Multi-faceted Analysis: Meaningful insights were discovered by: Trend Analysis: Examining KPIs like Total Patients, Admission Rate, Average Waiting Time, and Patient Satisfaction over time (daily trends via sparklines, monthly comparisons, and consolidated long-term views). Comparative Analysis: Comparing current performance (e.g., October 2024) against previous periods (PM) and long-term averages (e.g., 578-day trend) to highlight significant shifts. For instance, noting a 3.55% increase in admission rate in October 2024 compared to the prior month. Pattern Identification: Analyzing data across different dimensions (e.g., patient volume by day/time, referrals by department, patient demographics) to identify consistent operational patterns, such as General Practice and Orthopedics being primary referral destinations and specific peak periods for ER visits. Anomaly Detection: Pinpointing significant deviations from expected trends, such as the notable decrease in departmental referral volume in October 2024 despite a higher admission rate, or average wait times consistently exceeding benchmarks. Synthesizing and Presenting Insights & Recommendations: The discovered insights, patterns, and anomalies were then synthesized into a narrative presented on the "Highlights" page of the report. This page articulates: Key performance summaries. Observed operational and demographic patterns. Clearly identified anomalies requiring attention. Specific, actionable recommendations aimed at optimizing ER operations and patient care, such as implementing fast-track lanes, optimizing staff scheduling, and investigating referral volume drops eyJrIjoiYTJlZjI1YjctZDJkNy00NGE0LTk5YTktN2EzODllZDExODU2IiwidCI6ImJlYjhjMzZmLWNhZDYtNDU5YS05MzBhLWZjYWUwMzAxODAxOCIsImMiOjEwfQ%3D%3D&pageName=5484f2e01bd5a20a9f54📅 Powerful interactive calendar in Power BI 📅
Did you know how powerful the native Power BI 𝗦𝗰𝗮𝘁𝘁𝗲𝗿 𝗖𝗵𝗮𝗿𝘁 is? What if I told you that, 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝗮 𝗠𝗮𝘁𝗿𝗶𝘅, it could create a 𝗳𝘂𝗹𝗹𝘆 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗰𝗮𝗹𝗲𝗻𝗱𝗮𝗿? Back in 2019, I created a 𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹 to show how to build a fully interactive calendar in Power BI by using the Matrix visual. While it worked well, I knew I could improve it. This year, I wanted to explore a new technique by 𝗰𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗠𝗮𝘁𝗿𝗶𝘅 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗦𝗰𝗮𝘁𝘁𝗲𝗿 𝗖𝗵𝗮𝗿𝘁, and here is the result. 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘄 𝗶𝗻 𝘁𝗵𝗶𝘀 𝘃𝗲𝗿𝘀𝗶𝗼𝗻? • A full-year view with statutory holidays for Sénégal, France, and Québec • A refined layout leveraging Scatter and Matrix visuals (no custom visuals) • A dynamic title built with a simple SVG – no external images required • Interactive filters to switch between years, regions, and 𝗰𝗼𝗹𝗼𝗿 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝗶𝘁 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳? I’m making the .𝗽𝗯𝗶𝘅 file available for free so you can dive in, customize it, see the logic behind it, and let me know your thoughts. eyJrIjoiOTQyY2JmMjQtZTIxOS00ZTI1LTgwOTMtYTU1Yjg5ZGZkZjI2IiwidCI6ImE5ZTRlYjg3LWY4OTAtNGE3My05YTYwLTRiMmVjZGI3ZWY4YiJ9&embedImagePlaceholder=true&pageName=75869a7237688a6574a1
