{"id":82,"date":"2026-10-15T05:59:51","date_gmt":"2026-10-15T05:59:51","guid":{"rendered":"https:\/\/www.datascientist.ca\/blog\/?p=82"},"modified":"2026-10-15T06:03:45","modified_gmt":"2026-10-15T06:03:45","slug":"power-bi-healthcare-analytics-tracking-rising-treatment-costs-referral-spikes","status":"publish","type":"post","link":"https:\/\/www.datascientist.ca\/blog\/power-bi-healthcare-analytics-tracking-rising-treatment-costs-referral-spikes\/","title":{"rendered":"Power BI Healthcare Analytics: Tracking Rising Treatment Costs &amp; Referral Spikes"},"content":{"rendered":"\n<iframe loading=\"lazy\" title=\"Hospital Dashboard 11 Oct 2026_14_Oct\" width=\"600\" height=\"373.5\" src=\"https:\/\/app.powerbi.com\/view?r=eyJrIjoiOWQ1Njg1NTEtOTI0ZS00OTUzLTkxMzMtOWQxYWE0MmY2NjNjIiwidCI6IjRhNjk1YWI3LWViNzktNDViZS05NTg2LWQ4NTA4ODUwYTY1NSJ9\" frameborder=\"0\" allowFullScreen=\"true\"><\/iframe>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Power BI Healthcare Analytics: Tracking Rising Treatment Costs &amp; Referral Spikes\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/46fUhkigp6E?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Hospital leaders need to know three things: what is driving cost, where the clinical budget is slipping, and when referrals will surge. This end-to-end Power BI healthcare analytics dashboard answers all three in one place, built from scratch in a single tutorial.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">You will see how inpatient and outpatient costs are separated, how seasonal referral peaks are detected to plan staffing and bed capacity, and how DAX measures and executive-level visuals turn raw healthcare data into decisions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Interactive Dashboard<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">[EMBED: Power BI dashboard iframe goes here]<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Explore the finished dashboard: filter by department, year and month to see how cost, referrals and budget variance change.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Watch the Full Tutorial<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">[EMBED: YouTube video (Smart Data Learners) goes here]<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The full build is about three hours long. Use the chapter list below to jump to the part you need.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What You Will Learn<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Treatment cost breakdown:<\/strong> isolate inpatient vs. outpatient cost drivers and track variance trends over time.<\/li>\n\n\n\n<li><strong>Seasonal referral analysis:<\/strong> detect seasonal peak patterns so clinical staffing and bed allocation can be planned ahead.<\/li>\n\n\n\n<li><strong>Data modeling and DAX:<\/strong> build time intelligence measures, moving averages and dynamic variance calculations.<\/li>\n\n\n\n<li><strong>Executive visualization:<\/strong> design clear KPIs, trend cards and alert matrices that healthcare leaders can read at a glance.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Dataset and Project Files<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The dashboard is built on a sample healthcare dataset (2024\u20132025). The design is based on a sample dashboard by Mautik Patel, and the underlying data was originally sourced from Kaggle.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a target=\"_blank\" rel=\"noopener\" href=\"https:\/\/drive.google.com\/drive\/folders\/1Uds0q50V9rZWihD2d246eQT9cNDpGBB4?usp=sharing\">Download the dataset and related project files<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Download the files first and follow along in Power BI Desktop to get the most from the tutorial.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Video Chapters<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>00:00:00 Introduction to the Power BI Healthcare Analytics Dashboard Project<\/li>\n\n\n\n<li>00:07:34 Reviewing the Completed Healthcare Analytics Dashboard<\/li>\n\n\n\n<li>00:13:12 Building the Executive Summary Dashboard from Scratch<\/li>\n\n\n\n<li>00:16:58 Importing, Transforming, and Reviewing Healthcare Data<\/li>\n\n\n\n<li>00:22:41 Creating DAX Measures for the Executive Summary Dashboard<\/li>\n\n\n\n<li>00:39:34 Developing KPIs for the Executive Summary Dashboard<\/li>\n\n\n\n<li>00:52:43 Creating Executive Summary Dashboard Visualizations<\/li>\n\n\n\n<li>01:00:15 Building a Departmental Referrals Heatmap: YOY and Monthly Comparison<\/li>\n\n\n\n<li>01:22:52 Building the Clinical Analytics Dashboard in Power BI<\/li>\n\n\n\n<li>01:24:47 Creating DAX Measures for Clinical Analytics<\/li>\n\n\n\n<li>01:28:37 Developing Clinical Analytics KPIs<\/li>\n\n\n\n<li>01:39:52 Creating Clinical Analytics Charts and Visualizations<\/li>\n\n\n\n<li>01:56:41 Building the Operational Analytics Dashboard: Measures and KPIs<\/li>\n\n\n\n<li>02:09:53 Creating the Operational Analytics Heatmap and Visualizations<\/li>\n\n\n\n<li>02:38:09 Building the Financial Analytics Dashboard in Power BI<\/li>\n\n\n\n<li>02:43:48 Developing Financial Analytics KPIs and Visualizations<\/li>\n\n\n\n<li>02:57:00 Building Patient Analytics KPIs and Visualizations<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Case Study: Giving Hospital Leadership a Single View of Cost, Budget and Demand<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The challenge.<\/strong> A hospital management team reviewed treatment costs, budget performance and referral volumes in separate reports. Cost overruns surfaced late, and seasonal referral surges caught departments short on staff and beds.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The approach.<\/strong> One Power BI model brought clinical, operational, financial and patient data together. DAX measures handled time intelligence, moving averages and variance against budget. Four dashboards covered the Executive Summary, Clinical Analytics, Operational Analytics and Financial Analytics, with Patient Analytics KPIs added alongside.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>What the dashboard reveals.<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Cost drivers:<\/strong> inpatient vs. outpatient treatment costs, and which departments push variance above budget. [Add your top finding and figure from the dashboard.]<\/li>\n\n\n\n<li><strong>Seasonality:<\/strong> the months when departmental referrals peak, shown in a year-over-year and monthly heatmap. [Add the peak months and departments you found.]<\/li>\n\n\n\n<li><strong>Budget variance:<\/strong> where actual spend departs from plan, flagged by an alert matrix. [Add the largest variance and its department.]<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Recommended actions.<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Review the departments with the largest cost variance each month, starting with the highest-cost treatment categories.<\/li>\n\n\n\n<li>Plan staffing and bed allocation ahead of the seasonal referral peaks the heatmap shows.<\/li>\n\n\n\n<li>Use the alert matrix as a standing agenda item in monthly leadership reviews.<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The result.<\/strong> Leaders get one place to see where money is going, where the budget is slipping and when demand will rise, so they can act before a problem grows.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Key Takeaways<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Separating inpatient and outpatient costs shows where cost variance really comes from.<\/li>\n\n\n\n<li>Seasonal referral patterns let hospitals plan staffing and beds before demand peaks.<\/li>\n\n\n\n<li>Time intelligence and moving-average DAX measures make trends stable and easy to read.<\/li>\n\n\n\n<li>Executive KPIs, trend cards and alert matrices turn a data model into decisions.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Download the project files, build the dashboard alongside the video, and adapt it to your own data. If it helps, subscribe to Smart Data Learners on YouTube for more Power BI and data analytics tutorials.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">#PowerBI #HealthcareAnalytics #DataVisualization #BusinessIntelligence #HealthcareData #DataAnalytics #DAX<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Hospital leaders need to know three things: what is driving cost, where the clinical budget is slipping, and when referrals [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[20,21,11],"tags":[],"class_list":["post-82","post","type-post","status-publish","format-standard","hentry","category-claude-ai","category-power-bi-projects","category-power-bi-tutorials"],"_links":{"self":[{"href":"https:\/\/www.datascientist.ca\/blog\/wp-json\/wp\/v2\/posts\/82","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.datascientist.ca\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.datascientist.ca\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.datascientist.ca\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.datascientist.ca\/blog\/wp-json\/wp\/v2\/comments?post=82"}],"version-history":[{"count":2,"href":"https:\/\/www.datascientist.ca\/blog\/wp-json\/wp\/v2\/posts\/82\/revisions"}],"predecessor-version":[{"id":85,"href":"https:\/\/www.datascientist.ca\/blog\/wp-json\/wp\/v2\/posts\/82\/revisions\/85"}],"wp:attachment":[{"href":"https:\/\/www.datascientist.ca\/blog\/wp-json\/wp\/v2\/media?parent=82"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.datascientist.ca\/blog\/wp-json\/wp\/v2\/categories?post=82"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.datascientist.ca\/blog\/wp-json\/wp\/v2\/tags?post=82"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}