Promising AI Applications in Improving Patient Outcomes and Streamlining Healthcare Operations

With a lot of our attention focused on AI, it seems as if we are getting news every day about a different application or development being discovered for AI. And while this is truly an exciting time for innovation, it can also be a little overwhelming. If everyone is working on ways to invent and improve AI, how can you not only find the applications that hold the most potential that you want to keep an eye out for but also keep track of them in the never-ending sea of new applications?

To help you sift through this sea of AI, we reached out to our incredible Healthcare IT Today Community to help us assemble a list of the most promising AI applications for improving patient outcomes and streamlining healthcare operations. The following is what they had to share with us.

Amy Brown, Founder and CEO at Authenticx
Currently, the focus on AI in healthcare is related to the clinical side of the equation, which leverages data sources that are highly structured, such as diagnostics, labs, and claims data. A missing element is the ability to understand how humans perceive and respond to not only their own healthcare but the healthcare system. The way to understand the patient experience is to leverage conversation data.

As business and healthcare leaders, if we don’t understand what’s being said within conversations, then we’re missing a critical source of intelligence — how customers view and experience the healthcare system. AI enables us to harness the power of conversation data to help employees serve those customers and to identify underlying processes and policies that have downstream impacts on patients and organizations. Even seemingly small administrative changes can significantly impact healthcare outcomes.

John Waters, Director, Revenue Cycle Product at CliniComp
AI can be used in the RCM world to solve two overarching themes: inefficiencies in workflow, and errors that cause delays in payment. For inefficiencies, leveraging AI to do things like reading the clinical chart to suggest codes for billing, or driving next steps for managing denials that come in for insurance, reduces the burden on staff. In terms of reducing errors, checking the accuracy of coding prior to sending claims, leveraging AI to confirm and automate authorizations, and eligibility tracking, or avoiding denials altogether by using machine learning to address the “trigger” of the denial before billing ever begins can greatly reduce manual rework and delays in payment.

Ian Maurer, Vice President of Technology at Veradigm
As companies identify responsible, innovative ways to implement AI in healthcare, improving the daily lives of healthcare providers by streamlining workflow efficiencies remains top of mind. According to the American Medical Association (AMA), almost 50% of providers experience at least one symptom of burnout. We know that time spent focusing on administrative tasks takes away from direct patient care and excessive ‘pajama time’ while working on these tasks outside of working hours. AI-powered scribes that are integrated directly into EHR workflows streamline operations and improve clinical accuracy, while also reducing burnout and allowing providers to reclaim valuable time for optimized patient care. As with all innovation, AI technology holds the most promise when driven by provider needs and utilized responsibility, with a human in the loop for oversight.

Greg Miller, Vice President, Marketing & Business Development at Carta Healthcare
In the coming year and beyond we will see advanced AI algorithms enhance data accuracy and reduce errors in patient records. AI facilitates real-time data analysis, providing clinicians with instant access to patient histories, lab results, and treatment plans. This enables better-informed clinical decisions, which inevitably result in better care as well as better patient outcomes and higher satisfaction. AI-powered tools increasingly will automate routine administrative processes such as scheduling, billing, documentation, and data abstraction. Streamlining administrative tasks is transformative because it frees up healthcare professionals to focus more on patient care. By helping clinicians to practice at the top of their license, healthcare organizations can optimize resources and lower costs.

Navaneeth Nair, Chief Product Officer at Infinx
One of the reasons AI is so attractive, especially in healthcare, is that it has the ability to help revenue cycle management (RCM) teams who are already strapped for time make decisions in a responsible, more efficient manner than they would have been able to otherwise. AI can optimize RCM processes, from specific areas to end-to-end automation. Take for example prior authorization or eligibility verification. These are great starting points for AI because they’re high-volume, repetitive tasks. While they’re essential tasks to be done and be done correctly, they’re known for being time-consuming and riddled with errors. Delays in prior authorizations cause a ripple effect not only in the name of revenue cycle efficiency but more seriously–patient care.

I like to associate RPA as the “worker” and AI as the “thinker.” RPA is great for automating the submission process; think entering data into a patient portal or website. When it comes to a patient’s clinical documentation or answering medically necessary questions such as “Did the patient have conservative treatment done?”, that is when AI comes into play. AI, or Cognitive AI, has the ability to read, understand, rationalize, and answer questions that RPA alone cannot. When AI is plugged into traditional RPA, the combination allows for true E2E automation of an entire RCM process, such as prior authorization.

Joseph Mossel, Chief Executive Officer at Ibex Medical Analytics
One of the most promising applications of AI is its role in enabling greater diagnostic accuracy and efficiency in the pathology lab. When it comes to cancer, minutes and hours matter. The burden of possibly having cancer is hard enough without having to wait days for the test results. As a digital assistant, AI supports pathologists with growing caseloads by automating routine tasks, enhancing efficiency, and aiding in the differentiation between benign and malignant slides. It also helps quantify cells, grade tumors, and identify cancer subtypes, ultimately improving both diagnostic accuracy and overall workflow efficiency. A misdiagnosis is the difference between life and death, and when time is of the essence, it’s increasingly important that AI and the pathologist work together to get accurate results to patients as soon as possible.

Manuela Vecsler, PhD, VP of Clinical and Scientific Affairs at Ibex Medical Analytics
The adoption of AI in clinical settings will have untold benefits for patients and providers alike. In pathology, as more laboratories look to digitize their services, AI will have the potential to completely transform how pathology services are delivered, ultimately improving patients’ access to care. For example, an accurate and timely diagnosis is the cornerstone of improved patient outcomes. Especially when it comes to cancer – one of the leading causes of death worldwide – a misdiagnosis or delayed treatment can be life-threatening. Widespread digitization in pathology will not only advance health equity by giving patients access to the best possible care through cutting-edge AI solutions, but for pathologists, AI will also act as a diagnostic validation tool from the start, enhancing both precision and efficiency.

Deb Jones, Senior Director, Insights Strategy at Tendo
One of the most promising applications of AI in healthcare is in clinical documentation and quality improvement. With the growing integration of risk adjustment into quality metrics and payment models, the industry’s focus on accurately capturing patient acuity and complexity will intensify. Advanced platforms leverage AI tools like clinical algorithms and natural language processing (NLP) to help clinicians and documentation specialists close gaps in documentation and coding. These tools assist users in identifying evidence of chronic and under-documented conditions, enabling organizations to improve risk adjustment scores, optimize reimbursement, and allocate resources effectively. As these technologies evolve, they will play an even greater role in aligning financial stability with quality care delivery.

Chris Darland, CEO at Peerbridge Health
Gaining basic access to cardiac care is extremely challenging for millions of rural Americans who may live hundreds of miles from a cardiologist. And with the cardiologist shortage expected to worsen as our population ages, accessing cardiac care will become even more difficult. Education around new AI-powered remote devices that can be used by patients to monitor their cardiac health conveniently from their homes, and at a fraction of the cost involved in cardiologist appointments and testing, is critical. Early detection of heart disease will improve patient outcomes, lower healthcare costs, and promote health equity.

Caleb Manscill, President at Vyne Medical
AI is transforming healthcare, making it easier to deliver better outcomes for patients. One exciting area is workflow automation, which is rapidly expanding. Tools that significantly reduce data transcription times, streamline processes, and optimize staffing are freeing up healthcare teams to focus more on what really matters—caring for patients. AI is also helping doctors provide more personalized care. By analyzing clinical data quickly and accurately, it supports faster diagnoses and tailored treatments, leading to quicker recoveries and better results. Another big win is in addressing health equity. AI can pinpoint gaps in underserved areas and connect patients to critical resources, helping reduce disparities. Overall, AI is transforming healthcare by improving workflows, making care more accessible, and helping providers deliver better outcomes for patients.

Sandra Hewett, RN, BSN, CCM, Chief Nursing Officer at ZeOmega
Communication applications that help drive answers to members’ questions about their health before transitioning to a “live” person are especially helpful as long as the conversation is part of the record that is transferred. In addition, the translation of conversations into actionable items that can then be automated is such a timesaver for health plans and also shows the member/patient that these conversations are valuable for their learning and health maintenance.

Bob Farrell, CEO at mPulse
AI, such as machine learning and predictive AI, have proven valuable across the healthcare ecosystem to help support patient engagement along their healthcare journey. The mix of AI solutions are a foundational element of AI-powered enterprise engagement strategies. For example, by integrating natural language processing (NLP) and GenAI with predictive analytics, healthcare organizations can personalize engagement—such as reaching out to high-risk patients with automated, but empathetic reminders for preventive screenings or medication adherence that are tailored for the individuals’ needs. Additionally, GenAI-powered virtual assistants can adapt responses dynamically—making conversations more engaging and personalized—and could also provide multilingual support, improving accessibility for diverse patient populations.

However, while AI is foundational to progressing patient engagement strategies when it’s integrated as a point solution versus integrated at an enterprise level, the real potential of AI is not fully realized. This not only propagates fragmented healthcare but also poses a risk burden of managing different advanced technology models across the business. By combining predictive modeling, NLP, and GenAI, healthcare organizations can create a more personalized, proactive, and efficient healthcare ecosystem. AI is not just improving operational workflows—it’s driving better patient outcomes by enabling earlier interventions, smarter interactions, and deeper insights.

George Pappas, CEO at Intraprise Health by Health Catalyst
Healthcare cybersecurity and risk management is a promising field for AI applications. Today’s CISOs (Chief Information Security Officers) must sift through vast amounts of diverse risk data to determine what needs fixing, protecting, and prioritizing. Current solutions often rely on manual effort or rigid, costly systems that are difficult to maintain—creating barriers that leave critical risk management tasks undone. AI enhances risk correlation across disparate data sets, uncovering deeper, prioritized risks that might otherwise be missed. In this role, AI functions like a mid-level risk analyst, saving risk teams hundreds or even thousands of hours. This shift enables security professionals to focus on executing risk programs and refining strategy. Because risk management operates within well-defined boundaries, AI can be highly effective in this domain with minimal risk of hallucination.

So many good insights here! Huge thank you to Amy Brown, Founder and CEO at Authenticx, John Waters, Director, Revenue Cycle Product at CliniComp, Ian Maurer, Vice President of Technology at Veradigm, Greg Miller, Vice President, Marketing & Business Development at Carta Healthcare, Navaneeth Nair, Chief Product Officer at Infinx, Joseph Mossel, Chief Executive Officer at Ibex Medical Analytics, Manuela Vecsler, PhD, VP of Clinical and Scientific Affairs at Ibex Medical Analytics, Deb Jones, Senior Director, Insights Strategy at Tendo, Chris Darland, CEO at Peerbridge Health, Caleb Manscill, President at Vyne Medical, Sandra Hewett, RN, BSN, CCM, Chief Nursing Officer at ZeOmega, Bob Farrell, CEO at mPulse, and George Pappas, CEO at Intraprise Health by Health Catalyst for taking the time out of your day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.

What do you think are the most promising applications of AI in improving patient outcomes and streamlining healthcare operations? Let us know over on social media, we’d love to hear from all of you!