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Three Reasons Augmented Intelligence Is the Future of AI in Healthcare

Jason Jones, PhD

Chief Analytics and Data Science Officer

Health systems increasingly turn to AI to help all team members make more informed decisions in a shorter time frame. Instead of an artificial-intelligence approach that threatens the critical role healthcare experts play in decision making, organizations should define AI as augmented intelligence. In his first podcast, Dr. Jason Jones, our Chief Analytics and Data Science Officer, explains how augmented intelligence can help health systems accelerate progress toward achieving the Quadruple Aim. The three unique opportunities augmented intelligence offers health systems include the following:1. Augmented—not artificial—intelligence.2. Think “change management.”3. Address and overcome healthcare disparities.

Three Ways AI Can Earn Clinicians’ Trust

Ed Corbett, MD

Medical Officer

Over the last decade, many health systems have found that augmented intelligence (AI) technologies have overpromised and underdelivered. The promises of AI in clinical care were grand—to ease physicians’ burdens and deliver the most relevant information at the point of decision making. However, more technology has increased the demand on providers along with clinicians’ doubt of AI’s capabilities.Organizations can still deliver valuable AI-derived patient insight to providers at the front lines of care by taking a collaborative approach to AI that enlists clinicians in three key areas:1. Development.2. Implementation.3. Results.

Your AI Journey Starts Here: A Four-Step Framework for Predictive Analytic Success

COVID-19 has highlighted the imperative for health systems to proactively prepare for future scenarios. One way organizations can ready themselves is by using artificial intelligence (AI), such as predictive analytics, to forecast clinical, operational, and financial needs. While many health systems have the historical and current data they need for predictive modeling, they often lack the requisite analytics foundation and knowledge to begin any AI project, let alone predictive analytics journey.Data and analytics technology lay the foundation to support a health system for a successful AI pursuit, including predictive analytics. With the right tools in place, health systems are ready to follow the four-step framework:1. Project intake and prioritization.2. Project kickoff.3. Model development.4. Operationalizing the predictive model.

Predicting Denials to Improve the Healthcare Revenue Cycle and Maximize Operating Margins

Marlowe Dazley

Senior Vice President and Managing Director of Financial Advisory Services

Healthcare financial leaders are constantly brainstorming ways to increase operating margins through better revenue cycle performance. These efforts often lead revenue cycle leaders to denied claims—when a payer doesn’t reimburse a health system for a service rendered. Although denials are a common reason for lost revenue, experts deem nearly 90 percent avoidable.Effective denials management starts with prevention. Organizations can use revenue cycle performance data, combined with artificial intelligence, to predict areas within each claim’s lifecycle that are likely to result in a denial. With denial insight, health systems can optimize revenue cycle processes to prevent denials and increase operating margins.

2021 Asia-Pacific Healthcare Trends: Growing Digitization, Universal Health Coverage, and More

Farhana Nakhooda

SVP Health Catalyst, Asia Pacific (APAC)

Larry Lofgreen

Asia Pacific Sales and Solutions Consulting, VP

Along with the rest of the globe, 2021 healthcare trends across Asia-Pacific (APAC) countries will center on COVID-19 recovery and resuming the healthcare improvement journey. In the APAC region, however, a mix of developed and developing countries poses unique challenges, as healthcare access and basic infrastructure vary widely between urban and rural populations and economic levels. To shepherd healthcare out of the pandemic and enhance delivery overall in 2021, APAC nations will focus on increasing investment in digital health (including virtual care, machine learning, and EMR adoption), achieving universal health coverage, shifting more towards value, and improving payer-provider relationships.

Expanding AI in Healthcare: Introducing the New Healthcare.AI™ by Health Catalyst

Tarah Neujahr Bryan

Chief Marketing Officer

As healthcare leaders continue to face unprecedented decisions around revenue, cost, and quality, they turn to augmented intelligence (AI) to maximize their analytics. However, leaders struggle to implement AI into existing business intelligence workflows, demonstrate ROI, and move AI efforts beyond predictive models.Health systems can overcome AI’s implementation challenges with the New Healthcare.AI™ offering by Health Catalyst. As a suite of AI products and expert services, Heatlhcare.AI integrates transparent, cutting-edge technology into existing workflows, allowing analysts to produce high-quality insights in minutes. The AI offering dramatically broadens the use and use cases of AI for any healthcare organization with a mix of self-service products and expert services:1. Analytics integration.2. Choosing/building predictive models.3. Optimizing predictive models.4. Retrospective comparisons.5. Prescriptive optimization.

Data Science Reveals Patients at Risk for Adverse Outcomes Due to COVID-19 Care Disruptions

One of the biggest challenges health systems have faced since the onset of COVID-19 is the disruption to routine care. These care disruptions, such as halted routine checkups and primary care visits, place some patients at a higher risk for adverse outcomes. Health systems can rely on data science, based on past care disruption, to identify vulnerable patients and the short- and long-term effects these care disruptions could have on their health. Data science can also inform the care team which care disruptions to address first. With comprehensive information about care disruption on patients, health systems can apply the right interventions before it’s too late.

Safeguarding the Ethics of AI in Healthcare: Three Best Practices

As artificial intelligence (AI) permeates the healthcare industry, analytics leaders must ensure that AI remains ethical and beneficial to all patient populations. In absence of a formal regulatory or governing body to enforce AI standards, it’s up to healthcare professionals to safeguard ethics in healthcare AI.The potential for AI’s use in support of the pandemic response can have enormous payoffs. However, ensuring its ethical implementation may prove challenging if healthcare professionals are not familiar with the accuracy and limitations of AI-generated recommendations. Understanding how data scientists calculate algorithms, what data they use, and how to interpret it is critical to using AI in a meaningful and ethical manner to improve care delivery. By adhering to best practices for healthcare AI, health systems can guard against bias, ensure patient privacy, and maximize efficiencies while assisting humanity.

Artificial Intelligence and Machine Learning in Healthcare: Four Real-World Improvements

As COVID-19 has strained health systems clinically, operationally, and financially, advanced data science capabilities have emerged as highly valuable pandemic resources. Organizations use artificial intelligence (AI) and machine learning (ML) to better understand COVID-19 and other health conditions, patient populations, operational and financial challenges, and more—insights that are supporting pandemic response and recovery as well as ongoing healthcare delivery. Meanwhile, improved data science adoption guidelines are making implementation of capabilities such as AI and ML more accessible and actionable, allowing organizations to achieve meaningful short-term improvements and prepare for an emergency-ready future.

Four Strategies Drive High-Value Healthcare Analytics for COVID-19 Recovery

COVID-19 response and recovery is pushing healthcare to operate at an unprecedented level. To meet these demands and continue to improve outcomes and lower costs, healthcare analytics must perform more actionably and with broader organizational impact than ever. Health systems can follow four strategies to produce high-value analytics to withstand the pandemic and make healthcare better in the long term:1. Minimize benchmarking.2. Outsource regulatory reporting.3. Grow risk-based stratification capabilities.4. Run activity-based costing plus at-risk contracting.