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MicroStrategy World: Optimizing Healthcare with Mobile Analytics

Juliet Silver, Chief Strategist for Healthcare, presented on optimizing healthcare operations with mobile analytics.

Healthcare Market Forces

There are a number of market forces that influence healthcare

  • Healthcare is about 17% of our GDP
  • 25% of healthcare interactions will be exclusively digital by 2022
  • The remaining 75% will gravitate to coordinated real time systems delivering the best value and outcomes – David Allen, Digital Health Director, Perficient
  • 66% of physicians believe digital health will reduce burdens and drive better outcomes
  • 9 out of 10 clinicians expect to be using mobile devices at the bedside by 2022

Quote: The value agenda is designed to force hospitals and other healthcare institutions to compete for patients. Those that provide the highest value care and the best outcomes at the lowest cost will outlast those that don’t. — Michael Porter, Harvard Business Strategist

This evolution will take place in three key domains

  1. Engage: frictionless and compelling engagement across the journey
  2. Deliver: digitally enabled care delivery
  3. Insight: dramatic improvement in health outcomes and operational outcomes

The case: Mobile devices give actionable intelligence  at the bedside by increasing time with patient and reducing errors

Healthcare Operations Opportunities

There are a number of opportunities here.

If you think about this on the contiuum, you see a range or opportunities as healthcare entities mature

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Diving into each element you can see some impact:

Variation analysis: Length of stay represents one of the most common statistics.  Longer stays drive more cost and in the new marketplace, will have adverse impacts on a variety of factors. There exists opportunity to improve this with analytics.

Care Management and Diagnosis Prediction: Care management teams are responsible for managing transitions in care from hospital to home. One team spent their day taking lists of patients and combing through the EHR to determine how they would code out or what the final diagnosis would be.  This was a huge waste of time.  By using natural language processing of this structured and unstructured data, you can significantly decrease the amount of time it takes to determine where to focus.

Patient Volumes and Predictive staffing: It’s a classic problem. Who to staff based on expected volume. Staff too many and you affect costs. Staff too few and you impact quality of care.  Analytics with predictive analysis can give you insight. That said, managers need real time information on a tablet as well to do this job correctly.

Utilization and population health: It’s critical to understand forecast utilization. You can only understand this by understanding your patient population. There’s lots of opportunity to make improvements

Revenue Optimization: When you schedule a visit with a physician, there’s a lot that happens in the background.  Someone needs to determine how many patients can be seen, how many will show up for the appointment, how many will need a last minute appointment, etc. You need to determine the optimal level without compromising patient care. This represents yet another opportunity.

Real Time Monitoring: there’s a huge rise in health at home.  We are starting to monitor these patients using a number of different modalities. This generates a massive amount of data. You have to process it and understand what represents something that requires clinical intervention.  Doctors could far more easily track everything from respiration rate, cardiac output, oxygen and carbon dioxide levels etc.  But this would need to be mobile given the reality doctors face in day to day functions

Patient Engagement: consumer / patient analytics is a top focus in healthcare now.  Think of

  • customer journey analytics
  • emotion detection and speech analytics
  • customer engagement center
  • consumer journey across multiple devices and channels
  • personalize, contextualize, and predict behavior so you can suggest next best action.


Mobile Analytics in Healthcare


Key Considerations

Based on the the maturity curve, there are a number of considerations:

  1. Prioritize Opportunities: vet opportunities and qualify them
  2. Engage execs and ops and clinical leaders.
  3. Ensure value by measuring before and after
  4. Many don’t have centers for enablement. If you don’t have a place to measure genomic data then you have some foundational activities to start
  5. Modern Data Architecture or lack thereof means you may have to start here in order to have a place to even place the data for analysis
  6. EIM Capabilities. You will need to align enterprise information management capabilities


  • Take an agile approach
  • Use POC, POTs, and other working prototypes to prove out the value
  • Iterate to a production solution
  • Be collaborative. it takes business operations, clinical, and IT teams working together to bring these solutions to a production level
  • Remember that you won’t get it right the first time so the iteration is necessary
  • Keep the stakeholder engaged. Sell the value and report on the value



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Michael Porter

Mike Porter leads the Strategic Advisors team for Perficient. He has more than 21 years of experience helping organizations with technology and digital transformation, specifically around solving business problems related to CRM and data.

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