Promoting Precision Medicine with a Facilitated Network

Marshall Ruffin, MD, MPH, MBA, CPE, FAAPLPresident and CEO, Progknowse, Inc.

Marshall.Ruffin@Progknowse.comApril, 2019

Progknowse

Progknowse is a precision medicine company and a Facilitated Network that produces predictive analytics

with state-of-the-art deep learning algorithms.Progknowse is teamed with Premier, Inc., to help health

care systems manage value-based and population health contracts with a shared data lake, shared

predictive algorithms and data-science-as-a-service.

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Accurate Prediction of the Medical Future for Patients

• Reduces their dependence on unreliable intuition & wishful thinking• Clarifies their options• Makes choices of diagnosis and treatment easier• Permits closer relationships with family and clinicians• Relieves some of the burden of prediction from the physician• Reduces unrealistic expectations of patients• Allows for more careful planning and fewer surprises• May well help to reduce the intrusion of technology at the end of life

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Predictive Algorithms Already Assist Many Experts

• Airline auto-pilots• Self-driving automobiles• Language translation• Trading stocks and bonds• Searching data and knowledge bases• Weather patterns and forecasts• Network security & penetration detection• Predicting voting patterns and purchasing patterns• Predicting hemorrhage, respiratory failure and sepsis in ICU• Radiology image interpretation

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Predicting Health Care Outcomeswith Large Amounts of Data

• Sources of Data for Predictive Models in Health Care• Business Data

• Charge master detail and claims data• Clinical Data

• Electronic Medical Records, Theradoc and Quality Advisor• Genetic Data

• Pharmacogenomics, Whole Exome and Genome Sequencing• Socioeconomic & Geographic Data

• Business data and Experian, Credit Cards, etc.• Patient Status Data

• Functional Status and Preferences• Organizational Data

• Quality and Safety Metrics, Volumes and Outcomes of Procedures

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Deep Learning Vs. Machine Learning

• Deep learning is a subset of machine learning for messy data.• Deep learning is a third generation of artificial intelligence• First generation: expert systems – rational rule-based logic• Second generation: fitting statistical models to data, regression equations• Third generation: connectionist intuition machines: layer models, artificial

neural networks, convolutional layers, autoregressive layers, long short term memory and residual layers: predicting the future with messy, heterogenous data

• “Deep learning is a disruptive technology taking over the operations of the most advanced technology companies in the world.”• The Deep Learning AI Playbook, Carlos Perez, Intuition Machines, 2017, pg. 6

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Significance of Deep Learning

• “The thing that’s exciting today is that there’s actually a new source of knowledge on the planet and that’s computers. Computer discovering knowledge from data. I think this emergence of computers as a source of knowledge is meant to be every bit as momentous as the previous three (evolution, experience, culture) were, and also, notice that each one of these sources of knowledge produces far greater quantities of knowledge far faster than all the previous ones.”• The Master Algorithm, How the Quest for the Ultimate Learning Machine Will

Remake Our World, Pedro Domingos, Basic Books, 2015

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• Since no health care system has enough data by itself to build the most accurate predictive models, they must share data.• Curse of Dimensionality drives health systems into collaboration

• Produce deep learning algorithms for prediction of outcomes.• Standardize means of putting predictions into clinicians’ and managers’

work flows.• Incorporate socioeconomic, genetic and organizational data into

predictions of patients’ outcomes.• Promote continuous improvement and evolution of algorithms.• Help manufacturers of medical products predict outcomes from their drugs

and devices and identify patients for clinical trials of their products.

Importance of a Facilitated Network

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Curse of Dimensionality

• As the number of predictors (dimensions) in predictive equations increases, the number of separate patient records needed to build accurate predictive equations grows geometrically• Hundreds of dimensions requires records of tens of millions of patients

• How could we have equations with hundreds of dimensions?• Demographic details; diagnoses; procedures; medications, laboratory results;

genomic test results; volume, quality and safety measures of providers, etc.• Building equations with too many dimensions and too few patients

leads to “over fitting” and reduced accuracy of predictive models

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Health Systems;ACOs

Employers

Universities

Manufacturers of Pharmaceuticals and

Medical Devices

Some Key Concepts

• Risk adjustment – indispensable for comparing performance of health systems and individual physicians and medical products.• Risk adjustment means comparing expected to actual outcomes.• The better the predictive models, the more accurate the expected outcomes

and the more informative the comparison of expected to actual outcomes.

• “Pharmacogenomics can play an important role in identifying responders and non-responders to medications, avoiding adverse events, and optimizing drug dose.” FDA Table of Pharmacogenomic Biomarkers in Drug Labeling. More than 250 drug-gene interactions important for physicians to consider before prescribing certain drugs.

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High Performance Medicine:

• “Perhaps the greatest long-term potential of AI in health systems is the development of a massive data infrastructure to support nearest-neighbor analysis, another application of AI used to identify ‘digital twins.’ If each person’s comprehensive biologic, anatomic, physiologic, environmental, socioeconomic, and behavioral data, including treatment and outcomes, were entered, an extraordinary learning system would be created.”• Eric Topol, MD, “High-performance medicine: the convergence of human and

artificial intelligence,” Nature Medicine, Vol 25, January 2019, 44-56.

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Total Joint Arthroplasty

• 1,010,646 procedures from 2015-2017 (hold out 2018 as test set)• Regression Model – R2 = 0.24• Neural Network – R2 = 0.63

• Quality (e.g. prolonged length of stay – 4 days or more)• AUC = 0.736

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Maternity - Delivery

• 2.2 million deliveries between Q4 2015 and current data for 2018• 675,000 of these have at least one prior visit to delivery

• ~220,000 have pregnancy supervision (>= 2 visits with pregnancy supervision code prior to delivery visit)

• Performance –• Regression Model – R2 = 0.44

• Neural Network – R2 = 0.728

• ~20% of predictive power from hospital + physician

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Importance of Accurate Risk Adjustment

• The more accurate the risk adjustment, the more focused and effective can be the measures to prevent bad outcomes• The fewer false positive (low risk treated as high risk) and false

negative (high risk treated as low risk) predictions among patients.

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Unplanned Readmissions (30 days) NOTE: Input values into yellow shaded cells ONLY.Do not change any other cells.

Outcome cost 12,500$ Total cost of this outcome, compared to the outcome not occurringTaking action/intervention: BMP Intense (Action only taken for test positives)

Cost of this action 500$ 800$ % of Outcome Cost saved 20% 30% Examples of savings: outcomes prevented, reduced average cost of the outcomes, etc.

(all times the proportion of outcomes affected by the action)Your population characteristics:

Population size 57,106 Resulting "Confusion Matrices"Proportion of positives 20% Std. Predict. Progknowse

Test characteristics: Actual Pop'n Actual Pop'nStd. Predict. Progknowse YES NO TOTAL YES NO TOTAL

Sensitivity 56% 70% 6,396 13,705 20,101 7,995 6,853 14,848 Specificity 70% 85% True Pos. False Pos. Test Pos. True Pos. False Pos. Test Pos.

5,025 31,979 37,005 3,426 38,832 42,258 False Neg. True Neg. Test Neg. False Neg. True Neg. Test Neg.

11,421 45,685 57,106 11,421 45,685 57,106 Act. Pos. Act. Neg. TOTAL Act. Pos. Act. Neg. TOTAL

SAVINGS ANALYSIS: Std. Predict. ProgknowseBMP Intense

Outcome costs avoided:Cost of a single outcome 12,500$ 12,500$ 12,500$

Cost saved by action 2,500$ 2,500$ 3,750$ x No. of TRUE Positives 6,396 7,995 7,995

Total outcome savings ($000) 15,990 19,987 29,981

Action costs incurredCost of a single action 500$ 500$ 800$ # of TEST Positives 20,101 14,848 14,848

Total action costs 10,051$ 7,424 11,878

Net Overall Savings ($000) 5,939$ 12,563$ 18,103$ Progknowse improvement 6,624$ 12,164$

Test YES Test YES

Test NO Test NO

TOTAL TOTAL

How Do Health Care Organizations Benefit from Predictive Analytics?• Access to lots of data – which includes access to charge level detail and

clinical detail on > 200 million people• Cost avoidance by sharing a database, data scientists and predictive models• Improve the quality and accuracy in predictions of clinical and financial

outcomes that affect your patients and your health system• Influence the selection of outcomes to model and predict and have insight

into model dimensions and coefficients• License predictive models at the lowest possible cost and have an

opportunity to invest in the facilitated network and its products.• Participate in governance of the shared data and predictive models.• Become more efficient and effective at precision medicine, population

health management, and value-based contracting.Proprietary and Confidential 17

Contact Information:Marshall Ruffin, MDPresident and Chief Executive OfficerProgknowse, Inc.1775 Tysons BoulevardFifth FloorTysons, VA, 22102-4285Office: 877-474-0236Mobile: 434-825-4450Email: Marshall.Ruffin@Progknowse.comWeb Page: www.Progknowse.com

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