This project presents an interactive Looker Studio dashboard built to visualize hospital admission data.
The dataset includes patient demographics, comorbidities, outcomes, lifestyle habits, and clinical parameters, enabling insights into hospital trends, mortality, and disease patterns.
The dashboard is divided into three key sections to provide a comprehensive understanding of patient trends and hospital outcomes:
- Patient Demographics & Admissions
- Clinical Profile & Disease Burden
- Lifestyle & Risk Behavior Insights
Each section explores different aspects of hospital data — from who the patients are, to what conditions they face, and how lifestyle choices influence outcomes.
You can explore the full Hospital Admission Analytics Dashboard here:
👉 View Live Dashboard on Looker Studio
The dataset includes detailed information for each patient admission:
| Category | Fields |
|---|---|
| Identifiers | SNO, MRD No. |
| Dates | Date of Admission (DOA), Date of Discharge (DOD) |
| Demographics | Age, Gender, Locality (Urban/Rural) |
| Admission Details | Type (Emergency/OPD), Duration of Stay, ICU Duration, Outcome |
| Lifestyle Factors | Smoking, Alcohol |
| Comorbidities | DM, HTN, CAD, CMP, CKD |
| Lab Parameters | HB, TLC, Platelets, Glucose, Urea, Creatinine, BNP, EF |
| Clinical Conditions | STEMI, ACS, Heart Failure, Cardiogenic Shock, Pulmonary Embolism, etc. |
Goal: Understand the hospital’s patient composition and admission trends.
Key Charts:
- Total Admissions, Discharges, Deaths, Mortality Rate, Avg. Stay Duration
- Count of Patients by Gender
- Count of Patients by Age Group
- Patient Location (Urban vs Rural)
- Admission Type (Emergency vs OPD)
- Outcome Funnel (Discharge → Expiry → DAMA)
Insights:
- Majority of admissions were male patients aged 50–69 years.
- Urban patients account for ~75% of total admissions.
- 68% of admissions were emergency cases.
- Average hospital stay: 7 days; Mortality rate: ~9%.
Goal: Assess disease distribution, comorbidity impact, and clinical severity.
Key Charts:
- Comorbidity Prevalence (Mortality Impact) — 100% stacked bar showing discharge vs expiry.
- Mortality by Primary Cardiovascular Condition — Pie chart (Heart Failure, ACS, STEMI, etc.).
- Laboratory Parameters by Outcome — Table comparing averages (HB, EF, BNP, etc.).
- Length of Stay vs Outcome — Expired patients had longer stay durations.
- ICU Stay vs Outcome — Higher ICU time correlates with higher mortality.
- Cardiogenic Shock by Age Group — Visualizes high-risk elderly patients.
Insights:
- Diabetes Mellitus and Hypertension were the most common comorbidities.
- Heart Failure (27%) and ACS (15%) were major contributors to mortality.
- Expired patients showed higher BNP and Urea and lower EF, indicating cardiac strain.
- Older age groups (60–79) exhibited higher mortality and ICU stay rates.
Goal: Examine how smoking and alcohol consumption affect patient outcomes.
Key Charts:
- Smoking Trend in Patients Admitted
- Alcohol Consumption Trend in Patients Admitted
- Death Due to Smoking & Alcohol (Pie Chart)
- Lifestyle-based Admission Category (Emergency vs OPD)
- Admissions by Lifestyle & Age Group
- Lifestyle-based Death per Admission Category
- Admissions based on Lifestyle & Comorbidity
Insights:
- ~90% of patients had no smoking or alcohol habits, yet lifestyle-linked cases showed higher mortality.
- Smokers + Alcoholics had the highest expiry proportion despite lower total admissions.
- Lifestyle risks were more prevalent among middle-aged males (40–69 years).
- Hypertension and Diabetes were most common in lifestyle-linked patients.
- Age, comorbidities, and lifestyle are key predictors of hospital outcomes.
- Patients with multiple comorbidities (DM + HTN + CAD) show higher mortality and ICU stay duration.
- Elevated lab values like BNP, Urea, and Creatinine are strong indicators of poor prognosis.
- Lifestyle factors, though less common, are disproportionately fatal among admitted cases.
- Google Looker Studio (Data Studio) — Dashboard creation and visualization
- Google Sheets / CSV Data Source — Data storage
- Custom Fields & Formulas:
Mortality Rate = (SUM(Death) / COUNT(MRD No.)) * 100Death = CASE WHEN OUTCOME = "EXPIRY" THEN 1 ELSE 0 ENDAge Group = CASE WHEN AGE BETWEEN 0 AND 9 THEN '0-9' ... ENDLifestyle Status = CASE WHEN SMOKING=1 AND ALCOHOL=1 THEN 'Smoker + Alcoholic' ... END
- Add trend analysis over time (month-wise mortality, admissions, lifestyle changes).
- Integrate severity scoring combining comorbidities and lab parameters.
- Include geographical distribution of admissions by locality.
- Enable interactive filtering by gender, admission type, or outcome.
| Section | Screenshot |
|---|---|
| Patient Demographics |  |
| Clinical Profile & Disease Burden |  |
| Lifestyle Insights |  |