Hospitals increasingly use real-time location systems (RTLS) to track patients, staff, and equipment to improve operational efficiencies, generating billions of micro-interactions and data points across the care delivery journey. Most RTLS vendors have a minimal investment in the data they produce, creating all the bricks but on its own doesn’t build a structure.
Picture this: An infusion pump equipped with an RTLS tag sends a location ping to a database every minute of every day so we know where it is at all times. A nurse needing an infusion pump checks the tracking data and discovers it is on the hospital’s second floor. Is this information valuable? Sure, it saves nurses time and effort searching for equipment. But what about the 303 million additional data points for that specific pump that are recorded throughout the day? What can be done with that information?
Much of the data that hospitals generate from care operations remains unused and untapped, representing a missed opportunity to lower costs, boost care quality, and drive operational efficiencies.
By combining GenAI and RTLS with a single-source enterprise data platform, hospital administrators can gain a holistic perspective of the patient journey and quickly obtain novel insights from operational data that improve care delivery and save costs. Specifically, GenAI enables providers to predict patient demand for admissions, optimize resources, improve space utilization, automate discharge planning, and orchestrate care operations, among other operational advancements.
To identify areas where generative AI can yield operational value, begin by identifying a health system’s “known unknowns” and “unknown unknowns.” Stated differently: Seek out answers to the important questions administrators are already aware of, such as bottlenecks that impact patients’ length of stay. Then evolve those newly gained insights to apply them to problems administrators do not yet know exist but should ask about.
For example: historical data show that a laparoscopic appendectomy patient is usually in the hospital for 1 to 2 days. Patients undergoing laparoscopic surgery can sometimes be discharged within 24 hours. When they are discharged 3 days after admission, what caused the delay? How to address it? Another example: How much time do nurses in one department spend bedside versus out of the department? How does this compare to the other hospitals in my system? Now show me the three most frequently visited locations nurses go to in the hospital.
Five ways GenAI drives operational improvement for providers
GenAI offers strong potential to create numerous benefits for provider organizations, including:
- Predicting patient demand for admissions: Armed with data on historical patterns, seasonal trends, and external factors, GenAI can help health system leaders anticipate upcoming bed-capacity needs. By predicting patient demand, leaders can better align staffing and resources with individual patient needs, depending on factors such as diagnosis and treatment plan.
- Optimizing resource allocation: By forecasting when various hospital wards or departments will be busy, administrators can leverage GenAI to optimize staff schedules to ensure adequate on-the-floor coverage. Additionally, GenAI can predict patient demand for important medical equipment like MRI machines and ventilators, reducing the likelihood of bottlenecks.
- Automating discharge planning: Hospital administrators can automate discharge planning by leveraging GenAI to create personalized plans for patients that account for historical patient recovery patterns and post-discharge needs. Automated discharge planning saves resources and enables seamless transitions from hospitals to post-acute providers or other receiving facilities.
- Improving space utilization: GenAI can improve bed management by predicting when beds will be available and then assigning incoming patients to beds in the most appropriate locations based on patient needs and health system capacity. Additionally, GenAI can be used to increase the efficiency of room turnover by creating post-discharge room cleaning and preparation schedules, minimizing the time rooms remain unoccupied.
- Orchestrating care operations: At the heart of care operations is the patient journey. Health systems need to move from data creation to consolidation to recommendations, using AI to uncover patterns and trends that impact the entire patient journey. By training AI to orchestrate care operations, such as automated notifications and scheduling, hospitals can optimize queues, manage capacity, deliver just-in-time services, and recommend the next best course of action.
It’s time for health system leaders to dive into the data they have created to yield fast outcomes, higher efficiency, and greater patient satisfaction. With GenAI, hospital leaders gain the ability to converse with their data, understanding not only what’s happening now but also why it happened and what will happen next, to ultimately generate ROI and improve care quality.
Rom Eizenberg joined Kontakt.io as Chief Revenue Officer (CRO) in 2020. Prior to joining Kontakt.io, Rom was the VP of Global Sales at Bluvision – HID, part of Assa Abloy, where he led sales and marketing following the acquisition of IoT startup Bluvision by HID. Throughout his career, Rom has led enterprise software commercial organizations from Fortune 500 to tech startups, twice as a founder. Educated in computer science and international economics, Rom holds three registered patents.
