Post on 05-May-2018
Learning Objectives 1. Definitions: (Big) Data, Dashboard, Analytics 2. Review effective dashboard principles 3. Understand important differences between
data, analysis and presentation 4. Recognize differences in analytics 5. Review Financial Metrics, Analytics in a
practice setting
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What does this graph tell us?
What will happen in the following scenario? • I am driving 80 mph in my Camaro with the
windows down and sunroof open, the music blaring and my cool sunglasses on.
• A State Trooper has a radar gun. • The radar gun is pointed directly at my
Camaro. • The State Trooper is also wearing cool
sunglasses.
Cam’s Personal Dashboard
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Cam's Life
Cute/Sexy Smart Funny
Agenda • What is (big) data? What is a dashboard? What
is analytics?
• Design Principles of a dashboard – Data and Analysis – Design – Presentation
• Principles of Analytics – Real Time – Predictive – Historical
• Business Intelligence – How to use it…
What is not a dashboard? • Report • Just because it’s on-line
or web based • Just because it’s real time • Balanced Scorecard • Tool to dive into detail • Just because it has
gauges and graphs
A dashboard is …
… a visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a
single screen so the information can be monitored at a glance.
Please count the # of 7’s in this array:
59374304814959730493
70241382710993183453
59474942479103587494
What is the purpose of dashboard? • Mission Statement • Audience • Performance Measures (KPI & Metrics) • What logical groupings will organize the info
best? • What comparisons should be made? • How frequently will it be updated? • What actions might be taken?
Time…understand it in your analysis. Real Time versus Time Trends • Airline Pilot or ICU physician or Call Center • Retail, Financial, Manufacturing • Closer to real time better • Understanding secular/historical trends
important also
Trends • Hour by Hour, Day by Day, Week by Week • Month by Month, Quarter by Quarter • Compare to prior hour, day, week, month,
quarter, year • Month to Date, Quarter to Date, Year to Date
Context/Comparisons add Perspective • Historical
– Last period – Last period 1 year ago
• Benchmark • Goals or Budgets • Relative to other providers • Forecast
You Need Good, Unfiltered Data • Available-Who did you have to toss under the
bus to find this information? • Reliable -Do you believe these #’s? • Relevant • Timely-Last years data is too late. • Simple, simple, SIMPLE
Structured Breakdown with purpose Detailed examination of the elements
– Identifying the elements – Separating the elements into constituent
elements – Measuring the elements
Analytic Continuum
Descriptive- What happened?
Diagnostic- Why did it happen?
Predictive-What will happen?
VALU
E
DIFFICULTY
How does it work in healthcare?
Increase Marketing Dollars during end of season
Have more open scheduling slots during
end of season
Another example
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No
Sho
ws
# of days between Booking Date and Appt Date
No Shows: Days from Booking Date
Group one week follow-up slot times; advertise
walk in availability at same times
Double book one week follow-ups with other appointments to keep
utilization high
Change booking pattern; encourage
patients to self-schedule follow ups
Types of Data
I. Volume A. Productivity (patients,
encounters, days worked)
B. Activities (procedures, labs, x-ray, calls)
C. Hours/Shifts
II. Quality & Clinical A. Clinical Quality B. Patient Satisfaction
III. Billing & Financial A. Charge Capture &
Gross Revenue B. Costs C. Collections D. Profit/Loss
Customer Analysis by Location
• Cary location sees more Millennials than other locations.
• Sanford has the oldest patient population.
Opportunity cost • Compare commercial contract proposals to
other payors; is participating worth the effort?
Variance Analysis • Review payments to ensure payor
compliance with contracted rates, identify denial trends or performance issues
I. Volumes • Patient Encounters per day/provider/group • Procedures per E&M • Year to Year trends/ Seasonality • Admits/Discharges • By Financial Class • Day of Week • By CPT Code • By ICD-9 (soon -10)
II. Quality & Clinical • X-rays per ICD-10 • Scripts per encounter • Volume of Preventative
Well Visits • Vaccinations to total
practice population • Core Measures
• Nurse calls • Lab Response Times • Patient Education
Delivery • Recall rates • Patient satisfaction • Wait Times
III. Billing & Financial • RVU per encounter • Cost per procedure/category • Code Distributions • Patient Responsibility per encounter • Patients by Payer Class • Accounts Receivable > 90 days • PQRS Capture • Insurance Denials
Always Remember: A Dashboard is a Presentation of the Data Analytics is an interpretation of the Data Basics to not forget!!!
– P&L – A/R Analysis – Revenue Flow – Financial Classification
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