In 2024, two in three physicians reported using AI – representing a wider shift in the healthcare industry to drive efficiency and generate better outcomes for patients with new technology advancements. But many of these use cases have been limited to administrative tasks like voice transcription.
Given all the data that healthcare systems, hospitals and clinics are already gathering as part of their everyday work, the opportunity for AI to reimagine the way they operate–from optimized patient care to elevated staff output (minus the burnout)–is far greater than automating single-step administrative tasks.
With many healthcare systems adopting holistic cloud platforms that centralize all patient and operational data in one place, providers can begin thinking beyond one-off AI applications and toward automating entire workflows that stand between them and patient care.
Consider the multi-step process of monitoring cardiac data from remote and in-person patients. Clinics receive streams of information from implantables, ambulatory monitors, heart-failure devices, and consumer ECGs including heart rhythms, alerts of varying urgency, and device health checks. This once required logging into a dozen different portals, but most clinics now use a cloud-based monitoring platform to centralize their data, reducing manual workload and paving the way for AI automation.
Each patient can generate hundreds of additional data points including device data, medications, anticoagulants, demographics, hospitalizations, diagnosis codes, and more. Reviewing and logging all this manually is time-consuming, often involving 150 clicks per patient while critical insights still remain buried. This makes the workflow an ideal candidate for AI.
Handled completely manually, this workflow–from start to finish–can involve hundreds of clicks for each individual patient. And even with all of these clicks, the critical data they need for taking clinical action may not be brought to the forefront. It’s these steps that makes this process one of many ideal candidates for AI automation in healthcare.
Many healthcare providers might not know exactly where to start when it comes to AI, and some even fear that its role will interfere with patient care. Instead, here are three ways AI can apply to their operations to free up time that could be better dedicated to patient care.
Automatically surface the insights that matter most, with the necessary urgency.
In 2024 alone, there were nearly 4,000 different drugs and medical devices recalled. With each one of these recalls, clinicians need to read through the recall, determine the urgency and quickly identify which of their patients are affected. For a clinic that serves thousands of patients at any given point, manually going through every patient to see who is affected is extremely time consuming – cutting into time that can be better spent on patient care. Additionally, these delays in notifying affected patients can lead to negative health effects.
Instead, AI systems can automatically pull the charts of every patient that could be affected by the recall for the clinician to review. As a result, clinicians only need to go through a handful of charts for patients who are directly affected, and create a treatment plan that takes the recall into account. Then, upon their next remote or in-clinic visit, clinicians can take the appropriate action.
Going back to the cardiac clinic example, every morning doctors need to look at remote device monitoring data from overnight to attend to any cardiac concerns. Rather than needing to manually prioritize cardiac incidents, AI can prioritize alerts based on patient specific data and their health history. This will ultimately help practitioners decrease the amount of time they spend assessing false positive alerts and spending more time on the patients with critical alerts.
Communicate directly with data with natural language to quickly identify trends.
When most professionals have a question about an ongoing trend in their industry, they often turn to Google or an AI like Chat GPT, but because of the highly sensitive nature of data in healthcare, most clinicians do not have the same luxury. With purpose-built AI, though, healthcare professionals can turn their in clinic data into a knowledge engine where they can ask questions in natural language and surface insights based on their own clinic’s data.
For example, practitioners might want to know what device batteries are actually lasting the longest to impact the future implantable devices they recommend to patients. AI allows them to ask these types of questions and gain valuable data-driven insights quickly – rather than sifting through multiple charts. Additionally, the AI tools can help clinicians trace the data, so they can refer to the sources within their own database and make decisions based on the AI outputs while minimizing the likelihood of AI hallucinating insights.
Asking these types of questions and generating responses allows clinicians to provide better care to patients in the future. In the case of device battery length, if there are two different devices that may be a fit for the patient, the clinician may opt for the one that is proven to have a longer battery life based on data from their own patient pool.
Optimize clinics’ operational efficiency and care.
Behind the scenes of patient care in health clinics is a significant amount of administrative and operational workflows that are essential but cut into valuable man-hours.
Take billing, for instance. Rather than having administrators go through each patient’s chart, check which services were performed that are billable, and then bill them accordingly, AI can automatically go through the chart and alert administrators about which services need to be billed. The administrator still needs to manually validate that the billing information is correct, but AI significantly cuts down the timeline.
Additionally, clinics can use AI to uncover business trends and drive profitability. By examining billing patterns and financial reports, AI can offer recommendations to improve performance and guide future planning. For example, if physicians consistently bill more over three months and appointment slots remain full, it may signal the need to hire additional staff. These insights enable senior administrators to make data-driven decisions that strengthen long-term strategy.
By uniting patient, device, and clinic data in centralized platforms, healthcare organizations unlock a foundation for AI that goes far beyond simple task automation. The clinics that embrace these tools will streamline operations and reduce administrative burdens so they can deliver faster, more precise care. Those who act now to integrate AI into both clinical and business workflows will set the new standard for efficiency, profitability, and patient outcomes in a rapidly evolving healthcare landscape.
About Sean Shoffstall
Sean Shoffstall is the Head of AI, Innovation and Data at PaceMate, the remote cardiac monitoring platform that leading healthcare providers trust.
