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About the Authors:
Andrew Walsh
Managing Director
Andrew Walsh is a managing director and works with entrepreneurs as an investor and as an advisor on growth transactions to help build leading healthcare software businesses. Andrew has over 20 years of experience as a healthcare entrepreneur, executive and operator. Prior to joining First Analysis in 2019, Andrew was part of the senior leadership team of Ciox Health, a large private equity-backed healthcare technology company. Andrew brings a wealth of experience to the First Analysis team and has served in senior executive roles at some of the most respected healthcare companies in the United States including UnitedHealth Group, Optum and Davita. Andrew also founded, grew and sold a successful population health company focused on helping vulnerable, high-risk populations. He saw this company through two exits, ultimately selling a combined entity to Guidewell in 2017. Andrew earned an MBA from the University of Notre Dame and a bachelor's degree in economics from the University of Illinois.
Kenny Guerrero
Senior Analyst
Kenny Guerrero is a senior analyst with First Analysis. Kenny joined the firm in 2021. He previously worked for Prairie Capital Advisors, where he was responsible for developing and reviewing financial models and analyses and preparing valuation reports. Kenny graduated from the University of Illinois at Urbana-Champaign with a bachelor’s degree in accounting and finance.
First Analysis Healthcare Technology Team
Andrew Walsh
Managing Director
Matthew Nicklin
Managing Director
Joseph Munda
Senior Vice President
Tracy Marshbanks
Managing Director
Kenny Guerrero
Senior Analyst
First Analysis Quarterly Insights
Healthcare Technology
Practical and real-world applications of AI in healthcare today
October 3, 2023
  • We explore the transformative effects ethical and clinically appropriate AI can have on patient experience and cost-saving efforts in healthcare, particularly in the administrative functions among payers and providers.
  • Among the administrative functions where AI can have the greatest impact are eligibility verification, prior authorization, and claims adjudication. AI can streamline these often routine and resource-intensive interactions among payers and providers, resulting in reduced administrative burden and significant cost savings for many healthcare stakeholders.
  • We examine AI’s potential in each of these three areas and highlight companies providing AI-based technology that can help payers, providers and other healthcare stakeholders realize this potential.

TABLE OF CONTENTS

Includes discussion of seven private companies

Exploring how AI can transform healthcare

AI and its subcategories

The ethical and clinically appropriate use of AI in healthcare

Supporting clinical decision making with AI

Cost-saving potential from AI in administrative activities

Great potential for thoughtful and careful use of AI in healthcare

Healthcare tech index left behind in broader index recovery

Healthcare tech M&A: Notable transactions include HealthPay24 and TabulaRasa

Healthcare tech private placements: Notable transactions include Sempre Health and NeuroFlow

Exploring how AI can transform healthcare

Integrating artificial intelligence (AI) into healthcare has the potential to revolutionize the industry by supporting and enhancing clinical decision-making by clinicians. AI can also significantly improve healthcare’s administrative activities, streamlining processes and unlocking immense cost savings. But while AI can bring numerous benefits to patient care, it is crucial to approach its implementation with ethical considerations and clinical appropriateness at the forefront. This report explores the application and benefits of ethical and clinically appropriate AI in healthcare and delves into the transformative effects AI can have on cost-saving efforts and patient care, particularly in the administrative functions among payers and providers.

AI and its subcategories

There are many subcategories under the broad umbrella of artificial intelligence that vary in purpose and application. The following four areas play a pivotal role in the application of AI in healthcare: structured machine learning, unstructured machine learning, natural language processing (NLP), and generative AI. Structured and unstructured machine learning differ by the types of data they process and the types of conclusions they help healthcare practitioners draw.

Structured machine learning can use data like height, weight, diagnoses, treatment plans and procedures to predict how patients may respond to certain treatments, or it can use patient inflow data, bed occupancy data and staff availability data to predict how hospitals can best allocate resources.

Unstructured machine learning uses more nuanced data like medical images, handwritten medical notes, and discharge summaries to summarize and highlight years of clinical notes or help with medical image-based diagnosis by detecting abnormalities in magnetic resonance images and X-rays.

NLP manages and interprets unstructured data, bridging the gap between unstructured and structured machine learning models and facilitating more efficient data extraction and research.

Generative AI, likely the least adopted and newest application of AI in healthcare, uses frameworks like generative adversarial networks to generate novel data samples that are similar to input data by using two neural networks, the generator and the discriminator, where the generator has the goal to produce data that is indistinguishable from real data and the discriminator has the goal to distinguish between real and fake data produced by the other model. Over time, as the generator network receives feedback from the discriminator network, it becomes highly effective at generating data like the input data. While the use of generative AI in healthcare is still in its early stages, it could ultimately be used to generate more personalized care pathways, to simulate disease progression, or in treatment regimen simulation to better understand how different regimens may affect a patient.

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