1. Predicting Tomorrow by Crunching Today’s Numbers
David Hartzband, D.Sc.
Founder & Principal, PostTechnical Research, &
Research Scholar, Institute for Data, Systems & Society, Massachusetts Institute of Technology
Predicting Tomorrow - TACHC 2016

2. Predicting Tomorrow - TACHC 2016
This isn’t in the…
Future
rear view mirror
Predicting Tomorrow - TACHC 2016

3. Predicting Tomorrow - TACHC 2016
What is Prediction?
Prediction = a statement about a future event or state of being
Predictive Analytics = the application of statistical & visualization methods as well as algorithms to data to make a statement about a future event or state of being
In Healthcare = using these techniques to characterize & predict the state of operational & clinical information (for individuals & populations) for use in decision making
Predicting Tomorrow - TACHC 2016

4. Prediction depends on Data!
Use of data depends on “data awareness”
Data Awareness = Knowing:
What data is available (internal & external) to make a decision
Location, accessibility, validity & quality of relevant data
Does validity &/or quality affect its use? Can these be improved if necessary?
What type(s) of visualizations/analyses are relevant to the decision
Can they be done? Interpreted? Used in decision making?
Predicting Tomorrow - TACHC 2016

5. Predicting Tomorrow - TACHC 2016
Types of Analysis…
Aggregation & visualization
Range of algorithms from very simple similarity (pattern matching) to complex higher-level pattern analysis (including machine learning)
Simple graphing to complex visual comparisons
Dashboards present simple to moderately complex data characterization (How many? Related to? etc.)
Statistical Models
Simple models (like correlation) to show relationships among data variables
Higher-level models (principal components, multivaritiate models)
Predictive models (simple & complex regression, simulations)
Algorithmic Analysis
Neural net & other “cognitive” models
Rule-based & other AI techniques
Natural Language analysis
Many others
Predicting Tomorrow - TACHC 2016

6. Predicting Tomorrow - TACHC 2016
What to be aware of?...
Availability – inventory of data & information used & referenced by the organization, who uses it, how, notes on use – ongoing inventory
Location – not only where the data is located, but how to access it
Quality – are there quality issues (missing data? Incomplete data? Unusable data? Etc.) Can the data quality be improved?
Validity – has the data been corroborated (validated)? How can it be? Can data validity be improved?
Usage aligned with users & decisions:
Dashboards – what data to display
Analytic results – what methods to use
Interpretation – relevance to decision-making, suggesting other questions (analyses)
Predicting Tomorrow - TACHC 2016

7. Predicting Tomorrow - TACHC 2016
Usage Issues…
What data should be analyzed/displayed?
Aligned with operational, clinical or strategic decision(s)
Work back from what needs to be decided, then you’ll know what data to use
How should data (& results) be organized?
Who will be using the data/results? What are their priorities?
Decisions drive data & analysis
What decisions do you need to make?
Random analysis & pattern matching rarely drive decisions (but may drive further analysis)
Predicting Tomorrow - TACHC 2016

8. Patient Cycle Time – Rural Hospital (TN)
Courtesy of Tableau Software, Inc., academic collaboration program
Predicting Tomorrow - TACHC 2016

9. ED Dashboard – Piedmont Healthcare
Courtesy of Tableau Software, Inc., academic collaboration program
Predicting Tomorrow - TACHC 2016

10. Predicting Tomorrow - TACHC 2016
Analytics in Use…
& now… A real-life example of a dashboard currently in use: Community Health Service Agency Greenville, TX
Predicting Tomorrow - TACHC 2016

11. Predicting Tomorrow - TACHC 2016
Questions? ?
David Hartzband, D.Sc.
Founder & Principal
PostTechnical Research
&
Research Scholar
Institute for Data, Systems & Society
Massachusetts Institute of Technology
Predicting Tomorrow - TACHC 2016