The Four Eras of Analytics. Thomas H. Davenport

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1 SESSION #2 The Four Eras of Analytics Thomas H. Davenport Babson/MIT/Deloitte/International Institute for Analytics

3 Poll Question #1 On a scale of 1-5, how important to you believe analytics are to the future of healthcare and population health? 1) Not at all important 2) Somewhat important 3) Moderately important 4) Very important 5) Extremely important 6) Don t know / not applicable 3

4 Analytics and Data in Health Care Where Are We? Still mastering small data EMR Cost Operations Big data s already here Genomic Medical device Activity and behavior tracking Clinical notes Mired in descriptive analytics, when we desperately need predictive, prescriptive, and autonomous 4

5 Four Analytical Eras Accelerating! Artisanal Big data Data economy Cognitive analytics analytics analytics analytics 1975-? 2001-? 2013-? 2017-? 5

6 Analytics 1.0 Artisanal Analytics 1.0 Artisanal Analytics Primarily descriptive analytics and reporting Internal, small, structured data Back office teams of analysts Internal decision support focus Predictive models based on human hypotheses Still the norm in healthcare Thomas H. Davenport All Rights Reserved

7 Analytics 2.0 The Big Data Era 1.0 Artisanal Analytics 2.0 Big Data Complex, large, unstructured data New computational capabilities required Data Scientists emerge Online firms create data products Healthcare examples rare Thomas H. Davenport All Rights Reserved

8 Analytics 3.0 The Data Economy 1.0 Artisanal Analytics 2.0 Big Data 3.0 The Data Economy Seamless blend of traditional analytics and big data Analytics core to the business Data and analytics-based products in every business Industrialized decision-making at scale Healthcare leaders here Thomas H. Davenport All Rights Reserved

GE 3.0 $2B initiative in software and analytics, with $7B in digital revenues in 2017 Primary focus on data-based products and services from things that spin Reshaping service agreements for

9 GE 3.0 $2B initiative in software and analytics, with $7B in digital revenues in 2017 Primary focus on data-based products and services from things that spin Reshaping service agreements for locomotives, jet engines, turbines Marketing new industrial data platforms and brands like Predicity and Predix Moving to 4.0 with machine learning-based digital twins and business data unification Thomas H. Davenport All Rights Reserved

10 United Healthcare 3.0 Health in Numbers ad campaign Converts call center speech to text and mines it for indications that Medicare patients might attrit Real-time fraud intervention stops claims payment before it happens Scoring of all UHC members in terms of predictive disease profiles, propensity to enroll and succeed in health management OptumHealth, a $67B business, sells healthcare data, analytics, and technology 10

11 Intermountain Healthcare 3.0 From Brent James science experiments to perfecting the clinical work process Cost of treatment can be monitored by clinicians along with other variables 70 informatics researchers in Homer Warner Center for Informatics Research Intermountain offers software and services (with Deloitte) on health outcomes analysis based on 90 million EHR records over 40 years Using Hadoop to store and analyze clinical notes, machine and sensor data in ICUs, and other pressing issues Modest 4.0 activities involving juvenile diabetes 11

12 Analytics 4.0 The Cognitive Era 1.0 Artisanal Analytics 3.0 The Data Economy 4.0 Cognitive 2.0 Big Data Analytics used to make automated decisions Emergence of cognitive technologies Replacement of human tasks digital/physical Augmentation is human focus Thomas H. Davenport All Rights Reserved

13 (Cumulative) Skills Across the Eras 1.0 Artisanal Analytics Data integration and curation Storytelling with data Business acumen Statistics Iterative exploration 2.0 Big Data Experimentation Data restructuring Open source coding Product development Visual analytics 3.0 The Data Economy Machine learning Agile methods Change management 4.0 Cognitive Natural language processing Event stream processing Work design Neural networks/deep learning Thomas H. Davenport All Rights Reserved

14 Poll Question #2 As a whole, where would you assess your current healthcare organization is within the 4 Eras of Analytics? a) Artisanal analytics b) Big data analytics c) Data economy analytics d) Cognitive analytics e) Don t know / not applicable 14

A Constellation of Cognitive Technologies Machine learning Artificial intelligence/deep learning Natural language processing Rule engines Robotic

15 A Constellation of Cognitive Technologies Machine learning Artificial intelligence/deep learning Natural language processing Rule engines Robotic process automation Text-based cognitive computing, e.g., Watson Custom integrations and combinations of these Thomas H. Davenport All Rights Reserved

Ten Automatable Jobs in Health Care 1. Radiologist automated breast and colon cancer detection and treatment 2. Pathologist automated Pap smears 3.

16 Ten Automatable Jobs in Health Care 1. Radiologist automated breast and colon cancer detection and treatment 2. Pathologist automated Pap smears 3. Anesthesiologist automated Propofol administration 4. Oncologist automated cancer diagnosis and treatment recommendations 5. Surgeon autonomous robotic surgery 6. Nurse robo-nurses in Japan 7. Health insurance prior authorization at Anthem and others 8. Clinical coder automated medical coding 9. EMR system integrators robotic process automation for data integration, conversion 10.Pharmaceutical scientist cognitive computing for new drugs Thomas H. Davenport All Rights Reserved

17 People: Automation or Augmentation? Augmentation smart humans helping smart machines, and vice-versa People augment by aiding automated systems that are better at particular tasks, or by focusing on tasks at which humans are still better The classic example: freestyle chess Better than humans or automated chess systems acting alone Humans can choose among multiple computerrecommended moves Humans know strengths and weaknesses of different programs Augmentation is what most organizations are doing Automation is a race to the bottom Thomas H. Davenport All Rights Reserved

Five Steps to Surviving Automation Step in humans master the details of the system, know its strengths and weaknesses, and when it needs to be modified Step up humans examine the results of

18 Five Steps to Surviving Automation Step in humans master the details of the system, know its strengths and weaknesses, and when it needs to be modified Step up humans examine the results of computerdriven decisions and decide whether to automate new decision domains Step aside humans focus on areas that they do better than computers, at least for now Step narrowly humans focus on knowledge domains that are too narrow to be worth automating Step forward humans build the next generation of automated systems Thomas H. Davenport All Rights Reserved

Prescription for Surviving Data Disruption Continue to evolve your capabilities for data management and analytics Adopt some pilots of cognitive tech Make sure you and

19 Prescription for Surviving Data Disruption Continue to evolve your capabilities for data management and analytics Adopt some pilots of cognitive tech Make sure you and your management team understand the opportunities and threats Pick the low-hanging fruit (e.g., M.D. Anderson) Hire some great quants Close the gap with other industries! 19

20 Poll Question #3 How soon can you envision your healthcare organization moving to where analytics is at the core of your business? a) 0-2 years b) 3-5 years c) More than 5 years d) I doubt we ll ever get to that point e) Don t know / not applicable 20

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