Explore the value it delivers.
Artificial intelligence has a lot of hype surrounding it — and for good reason. Questions abound about the practical value for AI in today’s health care world. Click through our interactive infographic to learn more about what AI is and how it is making an impact. Explore the opportunities where its applications can truly make a difference for both health care organizations and consumers.
Is artificial intelligence smart for health care?
Where is AI today?
Artificial intelligence (AI) has left behind its sci-fi legacy to become a transformative technology in a modern digital age. A range of possibilities exists for AI in health care. However, we need to get past the hype, such as robots fully replacing the expertise and services of doctors, and show how AI allows us to reimagine the status quo and sheds new light on real solutions.
60% of health professionals selected artificial intelligence as a top or high adoption priority among various technology categories. – Forrester study on behalf of Intel 2017
Let’s start with defining what it’s not. AI isn’t a singular technology, but an umbrella term that includes using deep learning, machine learning and natural language processing, among other methods, to perform ‘smart’ tasks we often associate with the human mind such as learning and reasoning.
Deep Learning operates similar to how the brain’s neural synapses strengthen with repeated action becoming more efficient in adjusting for errors to modify its approach when faced with new data. – The Rise of Machine Learning, The Advisory Board, June 2017
Natural Language Processing refers to the interpretation of speech and text. – The Rise of Machine Learning, The Advisory Board, June 2017
Machine Learning uses advanced statistical techniques to identify patterns in data and then make predictions. – The Rise of Machine Learning, The Advisory Board, June 2017
Is health care ready?
Al is actually already at work in health care. AI helps people with diabetes regulate their blood sugar. It automates prescription refills. It matches call center customers to the person most qualified to assist them. With the convergence of algorithmic advances, data proliferation and tremendous increases in computing power and storage, the opportunities for AI in health care will only just keep growing.
While only 4.7% of health care respondents are already using AI technologies, about 35% plan to leverage AI within two years – and more than half intend to do so within five years. – Adapted from a Healthcare IT News and HIMSS Analytics survey 2017.
What is the value?
Better performance: AI automates repetitive or tedious tasks and quickly helps discover patterns and anomalies to lower the total cost of care and help individuals work at the top of their field.
- Clinical Documentation Improvement
- Payment Integrity
- Prior Authorization
Better outcomes: AI can quickly process data, personalized for each patient, to provide appropriate medications, identify health issues and alert us to take preventative action.
- Early Diagnosis and Treatment
- Prescription Benefit Management
- Risk Adjustment
- Simplified Population Analysis
Better patient experience: AI simplifies and connects complex data to break down care silos, translates the complicated into easy-to-understand information and helps improve the patient care experience.
- Call Centers
- Care Coordination
- Employee Benefits
Better performance. Increase operational efficiency, speed and consistency
Clinical documentation: Natural language processing can identify clinical indicators across a patient’s entire electronic medical record, including lab results, notes and prescriptions. NLP unlocks this unstructured (free form) data to understand each case and identify those where information may be missing. The result is more complete and accurate documentation to support better coding and appropriate quality measures.
Coding: NLP can understand clinical documentation to capture comprehensive diagnosis and procedure codes, eliminating tedious, manual case reviews. Coders don’t have to start from scratch to provide more thorough, accurate coding results that lead to more appropriate revenue capture.
Payment integrity: Payment integrity is all about ensuring payments made from one organization to another are done correctly and appropriately. Data analysis and predictive modeling identifies problem areas and improper claims faster and more accurately than ever before.
Prior authorization: Physicians have all relevant prior authorization information, insurance coverage, formulary rules and potential drug-to-drug interactions in real time. This gets patients their medicines faster and with less uncertainty when they get to the pharmacy counter.
Better outcomes. Increase early disease identification and preventative treatment
Early disease identification: Predictive models using regression techniques and newer machine learning approaches are helping in the early identification and preventative treatment of specific conditions. As an example, current models can identify signals of dementia five to eight years earlier than the first diagnosis.
Prescription benefit management: Predictive models are used to identify the right intervention at the right time. Algorithms use pharmacy data, lab results and other electronic health data to get to the right outcome.
Risk adjustment: NLP and AI are increasingly fueling prospective risk adjustment analytics that identify undiagnosed conditions and support the correct interventions leading to enhanced quality of care.
Simplified Population Analysis: Advanced analytics can be presented in a graphical or pictorial format helping quickly define and profile populations, simplifying and speeding time to analysis and evidence generation to lower risk and improve outcomes.
Better patient experience. Personalize, simplify and deliver patient information at the right time
Call centers: AI helps call center agents make actionable, data-driven decisions at an individual member level. Interventions are prioritized according to clinical value and an individual’s propensity to take action.
Employee benefits: AI provides reliable benefits data that is integrated and easy to understand to help translate health care data into actionable information.
Care coordination: With intuitive, configurable workflow designs, providers get the information critical to coordinating care, including key patient identifiers, gaps in care, and complications with chronic and complex populations.
What data does AI need?
If the foundational data is unorganized or limited, even the most advanced data analytic tools may simply get you to the wrong outcome faster. AI needs data that is standardized, tested and, in most cases, collected from multiple sources. For example, Optum Performance Analytics® uses longitudinal data and a range of sources from claims to clinical and across care silos. AI not only needs the right type of data, it needs the human component to understand the interrelationship of the varied data sets and how they can be applied to deliver real-world solutions.
Predictive accuracy of a system increases from below 1% to over 90% accuracy by including health care information from a variety of sources. – Artificial Intelligence and Advanced Analytics in Health Care, Frost and Sullivan, 2017
Delivering on the promise
Delivering on the promise of AI, we infuse our products and services with OptumIQ™, a unique combination of data, analytics and applied expertise. At Optum, our over 24,000 data, analytics and technology experts focus on pragmatic applications of AI in health care with data that includes 5 billion medical procedures, 11 billion lab results and 4 billion diagnoses.
Optum invests approximately $3.3 billion annually in technology and innovation.
Planning for AI
Growing pains are inevitable. Set your AI project up for success using these fundamental steps:
- Identify use cases. Understand what problems you want AI to solve before you begin.
- Convene the right people. You’ll need data scientists to translate and expedite results. You’ll also need people in the field who can and will apply them.
- Curate data. Data from disparate sources needs to be standardized and organized so it can uncover patterns and validate repeatable solutions.
- Set benchmarks for success. Identify key benchmarks or milestones and be sure to document and share the results.
Date: April 09, 2019