Hot spotting is a healthcare strategy that identifies patients who cycle through emergency rooms and hospitals repeatedly, then connects them with support services to address the root causes of their health crises. The term comes from the same logic used in crime mapping: find the geographic and demographic clusters where problems concentrate, then focus resources there. While the concept has expanded into epidemiology and even medical imaging, most people encounter it in the context of healthcare delivery and cost reduction.
The Healthcare Model
Healthcare hotspotting starts with a simple observation: a small number of patients account for a disproportionate share of hospital visits and costs. These patients, often called “super-utilizers,” typically aren’t people with a single complex disease. They’re people dealing with overlapping medical conditions, unstable housing, food insecurity, mental health challenges, or substance use disorders. Their repeated hospital visits are symptoms of deeper problems that a standard ER discharge can’t fix.
The model was popularized by Dr. Jeffrey Brenner and the Camden Coalition of Healthcare Providers in Camden, New Jersey. Brenner used billing data to map which city blocks generated the most emergency department visits, then built care teams that worked directly with those patients after discharge. The teams helped with everything from scheduling follow-up appointments to navigating benefits, securing stable housing, and connecting people to community organizations. The goal was to break the cycle of admission, discharge, and readmission by treating the whole person rather than just the immediate medical complaint.
How Super-Utilizers Are Identified
Programs use hospital billing records, insurance claims, and emergency department data to flag patients who may benefit from intensive support. A CDC review of 180 different classification systems found that 89% defined super-utilizers based on high levels of utilization, most commonly emergency department visits or inpatient admissions. The most frequently used thresholds were four or more ER visits in a year, four or more hospital admissions in a year, or total costs landing in the top 10% of all patients.
Some programs layer in additional criteria, looking at specific patterns of care, the presence of risk factors tied to preventable costs, or referrals from clinicians who recognize a patient is struggling. A few systems exclude patients whose high costs come from conditions that aren’t realistically preventable, like certain cancers requiring ongoing treatment, to focus resources where intervention is most likely to help.
What the Evidence Shows
Hotspotting gained widespread attention as a promising solution to runaway healthcare costs, but its track record is more complicated than the early enthusiasm suggested. The most rigorous test came in a 2020 randomized controlled trial published in the New England Journal of Medicine, which evaluated the Camden Coalition’s own program. The study found that 62.3% of patients who received the intervention were readmitted to the hospital within 180 days, compared to 61.7% of patients who received no special support. The difference was not statistically significant.
The study also revealed an important methodological lesson. When researchers looked only at the intervention group’s admissions before and after enrollment, it appeared the program had produced a dramatic 38-percentage-point drop in readmissions. But the control group experienced a nearly identical decline on its own, without any intervention. This pattern, called regression to the mean, happens because people tend to be enrolled in programs at their worst point, and their utilization naturally drops afterward regardless of what you do. Without a control group, it’s easy to mistake that natural decline for a program effect.
That doesn’t mean the broader concept is worthless. A large federal initiative called the Accountable Health Communities Model, run by CMS from 2018 to 2023, took a related approach. Thirty-two organizations partnered with clinical sites to screen over 1.1 million Medicare and Medicaid beneficiaries for unmet social needs, then provided navigation services to connect them with community resources. The final evaluation found the model generated more than $200 million in net savings, demonstrating that screening for social needs at scale is both feasible and financially viable. The key difference may be scope: rather than targeting a small group of super-utilizers with intensive coaching, the AHC model cast a wider net and addressed social needs across a larger population.
Hot Spotting in Public Health
Outside of individual patient care, hot spotting also refers to the epidemiological practice of mapping where diseases cluster geographically. Public health agencies use spatial analysis to identify neighborhoods or regions where rates of a particular illness are significantly higher than expected. The CDC uses statistical methods like the Getis-Ord Gi* test, which identifies areas where high values cluster together in a way that’s unlikely to be random. Another approach, the spatial scan statistic, moves a virtual window of increasing size across a map to find zones where observed cases exceed what you’d predict based on the surrounding population.
These tools help health departments decide where to direct screening programs, vaccination campaigns, or environmental investigations. Setting the right parameters matters: the scanning window typically shouldn’t cover more than 50% of the study area, or it risks flagging clusters that are too large to be actionable. The method has been applied to cancer surveillance, infectious disease outbreaks, and chronic disease prevention planning.
Hot Spots in Medical Imaging
In a completely different context, a “hot spot” is a bright area on a nuclear medicine scan, like a bone scan or PET scan. It appears where tissues absorb more of a radioactive tracer than the surrounding area. On a bone scan, hot spots show up at sites of active bone turnover, which could signal a fracture, infection, or tumor. On a PET scan, which uses a sugar-based tracer, hot spots indicate tissues with higher-than-normal metabolic activity.
A hot spot on imaging is not automatically a sign of cancer. Malignant tissues do tend to have increased metabolism compared to normal surrounding tissues, which is why PET scans are useful for detecting and staging cancer. But inflammatory and infectious processes also increase metabolic activity and can light up in the same way. Radiologists use the location, shape, and intensity of the hot spot, combined with anatomic imaging like CT scans, to distinguish between benign and malignant causes. A single hot spot in a bone scan, for example, might be an old injury, while multiple irregularly shaped spots in a patient with a known cancer history would raise more concern about spread.