Treatment models have changed & many of these changes are driven by data and the amount of sources to gain insights keeps growing.

The emphasis has shifted from the sheer size of data to its intelligent management through the integration of advanced analytics. This approach enables hospitals to enhance patient care and services.

See use cases Contact us
Data Visualisation
  • hospital

    Visualisation across Power BI, Tableau, Apache Superset

flow-icon

Data Intelligence

Revenue Acceleration Levers
  • 10 – 20%Improvement in ARPP
  • 10% Reduction in disallowance rate
  • 20 – 25% Increase in Doctor productivity
  • 20% Higher patient retention
Margin Improvement Levers
  • 0 – 15% Reduction in purchase costs
  • 0 – 20% Reduction in inventory carrying costs
  • 5 – 20% reduction in average usage of surgical consumables
Clinical Service Improvement Levers
  • 15 – 20% reduction in re-admission rates
  • 20 – 25% reduction in turnaround time of patient facing processes
  • 15 – 20% improvement in clinical and surgical mix
  • Higher clinical compliance
Data Cubes
  • hospital

    Organized data fields for Defined Use Cases

Processing Layer/Logic Layer
  • python icon

    Python

  • Business Logic icons

    Complex Business Logic

Unified Data Platform
Data Building
  • gear icon

    De-duplication Engine

  • gear icons

    Standardisation Engine

  • Dictionary icon

    Investigations Dictionary

  • Dictionary icons

    Diagnosis Dictionary

  • Meta Tags icon

    Drug/Condition Meta Tags

  • gear icons

    De-identification Engine

Data Intelligence

Revenue Acceleration Levers
  • 10 – 20%Improvement in ARPP
  • 10% Reduction in disallowance rate
  • 20 – 25% Increase in Doctor productivity
  • 20% Higher patient retention
Margin Improvement Levers
  • 0 – 15% Reduction in purchase costs
  • 0 – 20% Reduction in inventory carrying costs
  • 5 – 20% reduction in average usage of surgical consumables
Clinical Service Improvement Levers
  • 15 – 20% reduction in re-admission rates
  • 20 – 25% reduction in turnaround time of patient facing processes
  • 15 – 20% improvement in clinical and surgical mix
  • Higher clinical compliance

  • Hospital Information System

  • Lab Information System

  • ERP

  • Quality Management System

  • Any Other System

Automated Modules

  • Patient Suite

  • Doctors Suite

  • Analytics Suite

  • Analytics as a Service

Case Study: Achieved Clinical & Cost efficiency in performing surgical procedures

Dashboard Description: Provides comparison of clinical & operational efficiency parameters across doctors performing the same surgical procedure. Helps hospital management to assess clinical variance, identify areas of waste & streamline surgical performance

Variance for Lap Cholecystectomy, Private Insurance patients
Consumable Usage Variance by Unit/Doctor

Plot diagrams provide the average, highest and lowest amount of consumables used /no. of tests prescribed by surgeons performing the same procedure for the same cohort of patients

Doctors Payout

After comparing the clinical efficiency of surgeons, doctor payout graph can be used to assess if a more efficient doctor is being compensated/incentivized better

Impact/
Advantage

  • ~15-20% reduction in average usage of surgical consumables and no. of investigations through continuous monitoring using the dashboard & defining standard surgical kits for surgeries
  • Improved adherence to best clinical practices
  • Increased patient satisfaction due to rational prescription & optimum length of stay at hospital

Case Study: Enhanced per patient revenue realization across departments

Dashboards Description: ‘Revenue loss’ dashboard provides details of the income and volume which the hospital has lost from OPD patients; ‘What did not work well’ dashboard gives in-depth analysis of the specialties which show de-growth, and the reasons for the same

Revenue Analysis Dashboard
Least performing specialties by Revenue
1 Metric Rev May 2018 YoY YoY Diff YoY % Change Cont to Total Rev
2 Others 198M 192M 6.23M 3% 56%
3 Heart Center 45.8M 51.6M -5.78M -11% 13%
4 Medical Onco 35.9M 41.3m -5.43M -13% 10%
Admission Volume Loss
1 Metric Rev May 2018 YoY YoY Diff YoY % Change Cont to Total Rev
2 Others 198M 192M 6.23M 3% 56%
3 Heart Center 45.8M 51.6M -5.78M -11% 13%
4 Medical Onco 35.9M 41.3m -5.43M -13% 10%

Detailed analysis of specialities which show revenue decline. Helps the leadership team in idenstifying the caused for de growth, and focus on the appropriate payer channels and case mix, rather than a general marketing approach.

ARPOB Loss
1 Metric Rev May 2018 YoY YoY Diff YoY % Change Cont to Total Rev
2 Others 198M 192M 6.23M 3% 56%
3 Heart Center 45.8M 51.6M -5.78M -11% 13%
4 Medical Onco 35.9M 41.3m -5.43M -13% 10%
Payer wise revenue Loss
1 Metric Rev May 2018 YoY YoY Diff YoY % Change Cont to Total Rev
2 Others 198M 192M 6.23M 3% 56%
3 Heart Center 45.8M 51.6M -5.78M -11% 13%
4 Medical Onco 35.9M 41.3m -5.43M -13% 10%
OP Revenue Loss by Speciality

OP Volume Loss by Speciality

Understanding the service and specialty-wise loss of income and volume from out-patients helps in making process and marketing related changes to retain patients and increase the realization from support services such as diagnostics and pharmacy

Impact/
Advantage

  • Improved case mix
  • Increase in ARPP (average revenue per patient) from existing specialties
  • Higher patient retention and higher uptake of diagnostic services
  • Focused marketing approach on specific payer channels and specialties