Exploring Algorithmic Management Practices in Healthcare – Use Cases along the Hospital Value Chain
Maximilian Kempf, Filip Simić, Maria Doerr, and Alexander Benlian
This study explores how algorithmic management (AM), the use of algorithms for tasks typically done by human managers, is being applied in hospitals. Through nine semi-structured interviews with doctors and software providers, the research identifies and analyzes specific use cases for AM across the hospital's operational value chain, from patient admission to administration.
Problem
While AM is well-studied in low-skill, platform-based work like ride-hailing, its application in traditional, high-skill industries such as healthcare is not well understood. This research addresses the gap by investigating how these algorithmic systems are embedded in complex hospital environments to manage skilled professionals and critical patient care processes.
Outcome
- The study identified five key use cases of algorithmic management in hospitals: patient intake management, bed management, doctor-to-patient assignment, workforce management, and performance monitoring. - In admissions, algorithms help prioritize patients by urgency and automate bed assignments, significantly improving efficiency and reducing staff's administrative workload. - For treatment and administration, AM systems assign doctors to patients based on expertise and availability, manage staff schedules to ensure fairer workloads, and track performance through key metrics (KPIs). - While AM can increase efficiency, reduce stress through fairer task distribution, and optimize resource use, it also introduces pressures like rigid schedules and raises concerns about the transparency of performance evaluations for medical staff.
Host: Welcome to A.I.S. Insights, powered by Living Knowledge. I’m your host, Anna Ivy Summers. Host: Today, we’re looking at where artificial intelligence is making inroads in one of the most human-centric fields imaginable: healthcare. Host: We’re diving into a study called "Exploring Algorithmic Management Practices in Healthcare – Use Cases along the Hospital Value Chain." Host: It explores how algorithms are taking on tasks traditionally done by human managers in hospitals, from the moment a patient arrives to the administrative work behind the scenes. Host: To help us understand the implications, we have our expert analyst, Alex Ian Sutherland. Welcome, Alex. Expert: Great to be here, Anna. Host: Alex, we usually associate algorithmic management with the gig economy – think of an app telling a delivery driver their next route. But this study looks at a very different environment. What’s the big problem it’s trying to solve? Expert: That’s the core question. While we know a lot about algorithms managing low-skill platform work, we know very little about how they function in traditional, high-skill industries like healthcare. Expert: Hospitals are facing huge challenges: complex coordination, staff shortages, and of course, incredibly high stakes where every decision can impact patient outcomes. Expert: The study investigates if these algorithmic tools can help alleviate pressure on overworked staff, or if they just introduce new forms of control and risk in a setting where human judgment is critical. Host: So, how did the researchers get inside the hospital walls to figure this out? Expert: They went straight to the people on the front lines. The research team conducted in-depth interviews with seven doctors from different hospitals, two software providers who actually build these systems, and one domain expert for broader context. Expert: This gave them a 360-degree view of how this technology is actually being designed and used day-to-day. Host: And what did they find? Where are these so-called 'robot managers' actually showing up? Expert: They identified five key areas. The first two happen right at the hospital's front door: patient intake and bed management. Expert: For patient intake, an algorithm helps triage incoming patients by analyzing their symptoms and medical history to rank them by urgency. One doctor described it as a preliminary screening that moves critical cases to the top of the list, using color codes like ‘red for review immediately.’ Host: So it’s about getting the sickest patients seen first, faster. What about bed management? Expert: Exactly. Traditionally, finding a free bed is a manual, time-consuming process. The study found systems that automate this, matching patients to available beds with a single click. Expert: A software provider estimated this could save up to six hours of administrative work per day on a single ward, and eliminate up to nine phone calls per patient transfer. Host: That’s a massive efficiency gain. What happens after a patient is admitted? Expert: The algorithms follow them into treatment and administration. For instance, in doctor-to-patient assignment, the system can match a patient with the best-suited doctor based on their specialization, experience, and availability. Expert: It also helps ensure continuity of care, so a patient sees the same doctor for follow-ups, which is crucial for building trust and effectiveness. Host: And it manages the doctors themselves, too? Expert: Yes, through workforce management and performance monitoring. Algorithms create schedules and personalized task lists to ensure a fair distribution of work. One doctor mentioned it meant they had 'significantly less to do' because they no longer had to constantly cover for others. Expert: And finally, these systems monitor performance by tracking key metrics, like the time it takes from image acquisition to diagnosis in radiology. Host: This brings us to the most important question for our audience: why does this matter for business? This sounds incredibly efficient, but also a bit concerning. Expert: It’s absolutely a double-edged sword, and that’s the key takeaway for any business leader in a high-skill industry. Expert: The upside is undeniable. We're talking about optimized resources, reduced administrative costs, and even direct revenue gains. The study mentioned one hospital increased its occupancy by 5%, leading to an extra €400,000 in annual revenue. Expert: Plus, fairer workloads can reduce employee stress and burnout, which is a critical business concern in any industry. Host: And the downside? The risk of taking the human element out of the equation? Expert: Precisely. The study also found that these systems can create new pressures. Another doctor reported feeling frustrated by the rigid, time-oriented schedules the algorithm imposes. You must finish your task in the defined timeframe, or you work overtime. Expert: There’s also a transparency issue. On performance monitoring, one doctor said, “We are informed by our chief doctors afterward whether everything met the standards... I assume most of this evaluation is conducted by a program.” The algorithm is a black box. Host: So it's a balancing act. You gain efficiency but risk alienating your highly-skilled, professional workforce by reducing their autonomy. Expert: Exactly. The main lesson here is that algorithmic management in professional settings isn’t about replacing managers; it’s about augmenting them. The technology is best used for coordination and optimization, but human oversight, flexibility, and clear communication are non-negotiable. Host: A powerful insight for any leader looking to implement A.I. in their operations. To summarize: algorithmic management is moving into complex fields like healthcare, offering huge efficiency gains in scheduling and resource management. Host: But the key to success is balancing that efficiency with the need for professional autonomy, transparency, and the human touch. Host: Alex, thank you for breaking that down for us. Expert: My pleasure, Anna. Host: And thank you for tuning into A.I.S. Insights, powered by Living Knowledge.
