Predict Patient No-Shows For Radiology Appointments

Value Proposition

Efficient and appropriate outpatient scheduling is critical to the operational and economic success of a radiology department. When patients fail to appear for their scheduled appointments, revenue opportunities are lost, and employee productivity declines. In some cases, time-sensitive medications or tools are specially ordered for a specific patient’s appointment, resulting in economic losses when a patient fails to appear. Many of these factors may be out of a patient’s control, such as comorbidities or lack of transportation. Identifying patients that are at risk of “no-showing” may allow interventions that increase the likelihood of appointment adherence, or enable the department to quickly fill time slots in the event of a missed appointment. 

Narrative(s)

A 65-year-old male patient is being evaluated for an oncologic clinical trial. Trial enrollment requires that he receive a staging Dotatate PET/CT before beginning therapy. Eager to start their patient on treatment, the oncologist requests a PET/CT ASAP and the patient is given an appointment for Monday morning at 7 AM. A 4 mCi dose of Dotatate is ordered for the patient the Friday before for Monday morning delivery. A PET technologist and nurse are assigned an early shift with a 6 AM start time to receive the dose, prep the department, and prepare for the patient’s arrival. Monday at 7:30 AM arrives and the patient has not shown up for his appointment. The patient is unreachable by telephone. The next patient to receive Dotatate is not scheduled until tomorrow, all others are receiving 18-FDG. Realizing that the Dotatate dose will soon expire, the technologist speaks to the schedulers upon their arrival in the department at 8 AM. A patient cannot be found to fill the slot before the dose expires. The department loses 1 hour of the technologist and nurse’s time, 1 dose of Ga-68 Dotatate, and 1 hour of PET/CT scanner time.

A 72-year-old woman has an adenocarcinoma in the left upper lobe of the lung. Deemed a poor surgical candidate, she is scheduled to undergo a CT-guided ablation of the nodule. Given how peripheral the nodule is, her interventional radiologist selects cryoablation. Her local hospital owns a microwave probe but rents a cryoprobe for special procedures. Her IR physician requests a cryoprobe for Tuesday at 11 AM and the CT scanner is held for this procedure. The IR physician blocks their schedule for 2 hours. The patient fails to report to pre-op as scheduled for 10 AM that day. The department loses rental fee for the cryoprobe, 1 hour CT scanner time, and 2 hours of procedure time for the IR.

Workflow Description

A patient order is received for an outpatient imaging exam and/or image-guided procedure. A time slot is then identified in the schedule. This triggers three pathways – a reminder system for the scheduled patient, modification to other exams scheduled the same day, and a backup patient list. Exams requiring special imaging contrast, radionuclides, or equipment are preferentially clustered together. 

1. Scheduling one exam requiring a special dose or tool triggers subsequent exams of the same type to be scheduled on that same day. In the event that a patient does not show, the dose or equipment can be used by backup patients scheduled later in the day. 

2. A reminder system is triggered based on multiple patient factors. An algorithm can be trained using data about previously missed appointments to identify risk factors such as patient age, location, access to transportation, co-morbidities, previously missed appointments, days between scheduling and appointments, modality type, insurance type, etc. This algorithm can then assign a risk score for missed appointments to each patient and a cutoff threshold can be used to trigger interventions such as patient phone calls and text messages, transportation arrangements, overbooking, or backup patient lists. 

3. Patients with an appointment later than they would like are placed on a standby list. A missed appointment automatically triggers a phone call to patients scheduled for similar exams as the missed appointment until a replacement can be found.

EXAM TYPES:

1. Imaging Only 

   1. PET/CT

   2. MRI

   3. Nuclear medicine (aside from PET)

   4. CT

   5. US

   6. Mammography

2. Image-Guided Procedures

   1. Ablation – CT and fluoroscopically guided

   2. CT and US-guided biopsy

   3. Breast biopsy

   4. Vascular interventional

MODIFIERS TO SCHEDULE TIMESLOT SELECTED:

1. Is time slot available on the same day as another exams/procedure requiring the same equipment already scheduled for that week?

2. Days between scheduling and appointments

3. Was the schedule made for the patient or did the patient make the schedule (latter has been shown to improve no-show rates)?

4. Weather at the time of appointment (temperature, snow fall, etc.)

PATIENT DATA

1. Imaging/procedure required

2. Special medication or equipment required?

3. Appointment attendance history including history of calling to cancel or reschedule (as these would be used as exclusion criteria). Also, this should include information on punctuality. 

4. Primary language

5. Means of transportation

6. Insurance type

7. Deceased?

8. ICD 10, Axis I mental health diagnoses (major depression, bipolar disorder, posttraumatic stress disorder [PTSD], etc), alcohol dependence, substance dependence, lack of housing, homelessness, or unspecified housing status

9. Socioeconomic factors?

10. Distance

11. Admission? [can the system automatically see if they were admitted]

PATIENT STANDBY LIST

1. Imaging/procedure required

2. Special medication or equipment needed?

3. Proximity/travel time to the imaging center

4. Best means of contact

5. No-show history

Inputs

Access to Facility Schedule

Access to Patient Data

Access to Exam Data

Access to Patients on Standby

Primary Outputs

Recommended Schedule Modifications

 No-Show Probability

Secondary Outputs

 Identify patient(s) who could replace the no-show

 Scheduled reminders to patients on schedule and on standby

H.B. Harvey, C. Liu, J. Ai, et al.
Predicting no-shows in radiology using regression modeling of data available in the electronic medical record
J Am Coll Radiol, 14 (2017), pp. 1303-1309