How Environmental, Socioeconomic, Factors Impact Missed Radiology Appointments

No-shows or non-attendance for radiology visits has a negative impact on patients and clinicians. It not only adds to the cost of overall healthcare through wasted resources and poor allocation. Generally, medical imaging departments schedule appointments for all modalities.

However, there is still a trend of no-show and it is important to understand the reason. What are the potential factors driving these missed tests, is a big question?

Rebecca J. Mieloszyk, Ph.D., the University of Washington in Seattle, and colleagues wrote, “the more patients delay in scheduling and attending their appointments, the longer they must wait for important diagnostic information to be communicated to their referring physician.” The team also added, “Improving appointment compliance is critical to providing timely and high-quality care.”

The Purpose of Research

The purpose of this research is to evaluate the impact of socioeconomic and environmental factors on outpatient cancellations as well as no-show visits in radiology.

A retrospective analysis was conducted by collecting environmental factors and related data to outpatient radiology visits that occurred between the years 2000 to 2015. Appointment attendance was tallied with daily weather data from the National Oceanic and Atmospheric Administration and the US Census American Community Survey estimated median income was taken into account.

In addition, a multivariate logistic regression model was developed for examining the connection between median income and NSV rate, maximum daily temperature, time to commute, distance, daily snowfall, and other relevant factors.

Overall, the research data consisted of 270,574 cancellations with 87,407 no-show visits (NSVs) among over three million scheduled outpatient radiology appointments.

Study Report and Revelations

It was found that there was a steady decline in the rate of cancellations from 14 percent to 8 percent, whereas NSV rates had fallen from 6 percent to 1 percent. At the same time, the median income increased from USD 20,000 to USD 120,000 per year.

After the multivariate analysis, the odds of no-show visits dropped by 10.7 percent per USD 10,000 rise in the median income as well as 2 percent per 10 degrees Fahrenheit rise in maximum daily temperature. Furthermore, the rate of no-show visits increased by 1.4 percent per 10-mile rise in the length of travel time as well as by 4.5 percent per 1-inch rise in the daily rate of snowfall.

Mieloszyk and the team of researchers write that the result of the study confirmed the findings of previous studies that found transportation to be a major contributing factor for people missing appointments. In addition, commute distance is also a determinant factor, especially for low-income groups of patients who are unable to afford to miss an appointment.

The authors of the research also wrote, “These populations rely more frequently on public transit than wealthier patients, who likely travel by a private vehicle. Transpiration difficulty could be addressed by scheduling patients at one of several neighborhood clinics or outpatient imaging centers currently part of the same health care system.”

It is also a proven fact that extreme weather conditions could be central factors in no-show visits. This data may be beneficial in formulating feasible strategies to limit no-shows and missed visits, as noted by the authors.

Mieloszky et al. wrote, “If extreme weather is anticipated, it may be wise for imaging staff to strategically shift examination scheduling and extend appointment opportunities to wait-listed patients or implement a dynamic scheduling strategy.”

Moving ahead, the authors suggested that combining both environmental data and socioeconomic factors with patient data gathered from the state’s electronic health record could also be useful in developing quantitative models to forecast appointment attendance.

The research team wrote, “Our analysis shows that appointment features from external sources, including socioeconomic status, weather, and geographical data, are useful in the prediction of appointment failure in outpatient radiology settings. A failed appointment risk estimation algorithm would benefit from incorporating such information.”


It may be noted that environmental factors are strongly related to the patient’s attendance at scheduled outpatient radiology examinations. Appointment failure modeling based on these environmental factors can help increase outpatient radiology appointments and attendance.

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