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Three practical use cases for AI and automation in health care

Key considerations and tips for your AI-powered transformation

August 01, 2024
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Microsoft Data & digital services Health care Artificial intelligence Digital transformation Machine learning
Digital evolution Innovation Predictive analytics Generative AI

Health care organizations are facing a new reality, one that is more digitally driven, efficient, patient-centric and responsible. Factors driving this new reality, and the organizational transformation needed to support it, include the cost and availability of labor, physician burnout, declining margins, changing reimbursement models and the growing demand for value-based care.

And while it’s clear that key technology solutions like artificial intelligence and automation can address many of these challenges, some health care organizations struggle with how to even begin this transformational journey.

Where to start?

The following outline describes a strategic pathway for your health care organization to harness the potential of AI and automation, providing the right infrastructure and support to ensure success and significant transformation.

If you need more help, check out our Where to start FAQ about generative AI and how to support your business goals.

Health Care AI and automation use cases

AI and automation solutions can be a game changer by addressing some of the most nagging and critical challenges in your health care organization. In many cases, these solutions can leverage existing systems, such as Microsoft Azure, avoiding the need for costly new technological investments. In addition, for some applications, funding for certain AI and automation assessments is available to get you started.

A trusted advisor can help you evaluate the potential benefits, impact and feasibility of implementing improved data, analytics, AI and automation within specific areas of your organization, as well as build the architecture and strategy to implement solutions tailored to your challenges.

Physician messaging

Managing physician inboxes is a significant challenge due to the overwhelming volume of electronic communications from patients and others. Physicians must navigate numerous messages, emails and consultation requests while balancing administrative tasks and patient care.

Challenge: Patient communications and needs will continue to increase, but health care organizations will be challenged to fill physician, nursing and supportive staff roles to address them. The right technology can help bridge the gap.

Utilizing machine learning, natural language processing and other types of AI technology for patient messaging can help streamline operations by providing:

  • Assessment of the organization’s current and future messaging needs
  • Analysis of the business value of an AI solution for real-time read and write connectors, and implementation of a selected solution
  • Evaluation of performance and message triage using sample messages​
  • Development and assessment of strategic release gates for all releases to production
  • Ongoing monitoring, assessment and optimization

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Patient movement and throughput

Streamlining processes ranging from emergency department admissions to discharge orders and preparations can ease a patient’s journey through the entire health care system, improving care quality and accuracy, addressing costs, and enhancing patient satisfaction.

Challenge: Patient throughput efficiency remains an ongoing issue for today’s providers. Length of stay is the single largest factor affecting cost and margin.

Tracking patient movement throughout the hospital can identify bottlenecks of inpatient flow, along with unnecessary processes, orders and utilization. The use of predictive analytics and biostatistics, along with discrete event models (digital twins), can enable timely alerts and effective responses to high-occupancy events, facilitating assignment of resources where they provide the most value. Potential benefits include improved scheduling and resource utilization for surgeries and appointments.

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Revenue cycle

As the world continues to witness the rapid evolution and adoption of AI and automation, it has become increasingly apparent that organizations not at the forefront of this technological revolution will be left behind. Health care providers are witnessing this in all aspects of their operations—but most vitally, in their margins and revenue cycle management.

Challenge: Claim denials are on the rise as payers deploy AI around claim reviews, prior authorization requirements and more. The results have been devastating to hospitals and health systems already operating on tight margins and lacking the AI tools to address and manage payer demands.

Enhancing existing platforms and electronic health record systems with AI tools can optimize the revenue cycle, resulting in:

  • A more level playing field with payers, counteracting algorithms, streamlining complex processes and optimizing financial performance
  • More  prior authorizations, cost estimates, claim status updates, appeals and other revenue cycle functions

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