Artificial Intelligence in Health Care - National Academy of Medicine
Artificial Intelligence in Health Care
Reducing Administrative Burden
Paul Bleicher, MD, PhD, CEO
November 30, 2017
Types of Machine Learning and AI
A range of solutions developed over decades
Rules-based Decision Making
Statistical Reasoning
fuzzy boundaries
Machine Learning
if condition fulfilled then activity 1
activity 2
Boolean Data (yes or no)
Health care examples: ? Grouping claims into episodes of care ? Identifying gaps in care ? Identifying fraud
y
simple regression
Numerical Data allowing for curve fitting
Health care examples: ? Estimating costs to serve a population ? Predicting medical spending for members
?
classification tasks
Arbitrary Data that needs to be abstracted into numbers
Health care examples: ? Identifying patients at risk for readmission ? Identifying patients who are at risk for using the ED inappropriately ? Determining prior authorization for medications
Image Credit: Dr. Boris Adryan
Artificial Intelligence
input set of methods complex behavior
dynamic adaptation to novelty
Arbitrary Data autonomous selection of best methodology when presented with arbitrary data
Health care examples: ? Recommend "best fit" provider for a member ? Making diagnosis from patient symptoms, physical exam and laboratory values
2
Deep Learning
A type of Machine Learning transforming AI today
Deep Learning Neural Networks (DLNNs) are enabled by:
? Massive amounts of labeled data ? Hardware advances (GPUs for gaming) ? Image and text data
Deep Learning is driving most of the recent breakthroughs in AI in other industries:
? Face recognition ? Self-driving cars ? Language translation (Google) ? Credit card fraud detection (FICO Falcon) ? Terrorism flight risk
3
Deep Learning in Health Care
Recent publications indicate promising opportunity
Benign
Malignant
Kalouche S. Vision-Based Classification of Skin Cancer Using Deep Learning. Stanford University.
Figure 1. Our trained convolutional neural network correctly detecting the sinus rhythm (SINUS) and Atrial Fibrillation (AFIB) from this ECG recorded with a single-lead wearable heart monitor
Rajpurkar P, Hannun A, et al. Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, arXiv:1707.01836v1 [cs.CV] 6 Jul 2017.
Researchers have successfully reused trained neural networks
? A Deep Learning Neural Network (DLNN) trained to recognize cats and dogs can be repurposed to distinguish pathology in medical images
Recent work has shown promising results in image classification:
? Skin lesions ? Pathology images ? Retinal hemorrhage ? Signal processing ? EEG, ECG data
But work continues to make results more interpretable
? Classifiers today are best suited to scan data for skilled professionals
4
Administrative Processes
Well-suited for Deep Learning
KEY INGREDIENTS OF ADMINISTRATIVE WORKFLOWS
Set steps in a process
Large amounts of unstructured text data
Decisions recorded in workflow systems
Complex manual review process TYPES OF DATA MANUAL PROCESS DECISIONS
Many administrative processes for claims rely on analyzing text data
? Charts ? Notes ? Comments
Often, sequences of events are critical to determine an outcome
? Groups of claims ? A progression of care
The results of these decisions are well-suited to train a neural network
5
Deep Learning
The model is not the solution
Assess
Problem, available data, and solution
Gather data and implement model
Train
Refine
Infer
Measure
Implement Solution
Regularly measure benefit
Modify Solution/Model
6
Applying Deep Learning Neural Networks(DLNNs)
Use cases underway
Problem
Model
Solution
Measure/adjust
Avoidable ED visits
Unnecessary medical claim reviews
Use labeled ED visits and EHR data /claims data to predict patients at risk for these visits
Proactively reach out to priority patients to educate them about alternative care options
Audit/monitor outcomes of patients contacted to identify appropriate ED visits
Use labeled claims data to predict claims that should be automatically paid
Change claims review process to include automated approvals and review claims flagged for review
? Audit/monitor false positives
? Evaluate policy change
Untimely prior authorizations
Use historical prior authorization data to predict which requests should be automatically approved
Change prior authorization process to include automated approvals and requests requiring review
? Audit/monitor false positives ? Evaluate policy change
7
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