The Effect of Automated Mammogram Orders Paired With Electronic Invitations to Self-schedule on Mammogram Scheduling Outcomes: Observational Cohort Comparison

Association of Electronic Self-Scheduling and Screening Mammogram Completion

INTRODUCTION

The purpose of this study was to evaluate if an electronic health record (EHR) self-scheduling function was associated with changes in mammogram completion for primary care patients who were eligible for a screening mammogram using U.S. Preventive Service Task Force recommendations.

METHODS

This was a retrospective cohort study (September 1, 2014-August 31, 2019, analyses completed in 2022) using a difference-in-differences design to examine mammogram completion before versus after the implementation of self-scheduling. The difference-in-differences estimate was the interaction between time (pre-versus post-implementation) and group (active EHR patient portal versus inactive EHR patient portal). The primary outcome was mammogram completion among all eligible patients, with completion defined as receiving a mammogram within 6 months post-visit. The secondary outcome was mammogram completion among patients who received a clinician order during their visit.

RESULTS

The primary analysis included 35,257 patient visits. The overall mammogram completion rate in the pre-period was 22.2% and 49.7% in the post-period. EHR self-scheduling was significantly associated with increased mammogram completion among those with an active EHR portal, relative to patients with an inactive portal (adjusted difference 13.2 percentage points [95% CI 10.6-15.8]). For patients who received a clinician mammogram order at their eligible visit, self-scheduling was significantly associated with increased mammogram completion among patients with an active EHR portal account (adjusted difference 14.7 percentage points, [95% CI 10.9-18.5]).

CONCLUSIONS

EHR-based self-scheduling was associated with a significant increase in mammogram completion among primary care patients. Self-scheduling can be a low-cost, scalable function for increasing preventive cancer screenings.

Self-scheduling of Screening Mammograms Using an Online Patient Portal: Initial 8-year Experience at a Multisite Academic Institution

OBJECTIVE

The goal of this study was to determine patient factors associated with the use of self-scheduling of screening mammograms (SMs) using an online portal.

METHODS

All SMs scheduled at our multisite academic institution from January 1, 2015, to December 31, 2022, were included. The frequency of self-scheduling via an online portal was calculated per year. Univariate and multivariate logistic regression models with generalized estimating equation were used to estimate associations between patient characteristics and scheduling format after accounting for correlations between mammograms performed on the same woman.

RESULTS

During the study period, 250,369 SMs were performed in 74,860 unique patients (mean age 59 ± 12 years). Of these, 36,200 (14.5%) were self-scheduled via the online portal. Self-scheduling increased each year, from 3.7% in 2015 to 36.9% in 2022. Younger age, non-Black race, being an English speaker, and being from a nondisadvantaged zip code were significant predictors of self-scheduling on univariate and multivariate logistic regression. Age <50 years versus age ≥70 years was the patient characteristic that most strongly predicted the likelihood of self-scheduling (adjusted odds ratio 5.4, 95% confidence interval 5.2-5.6).

CONCLUSIONS

Over 8 years (2015-2022), utilization of self-scheduling for screening mammography using an online patient portal increased from 3.7% to 36.9%. Age < 50 years was the patient characteristic that most strongly predicted likelihood of self-scheduling.

Self-scheduling in a Large Multispecialty and Multisite Clinic: A Retrospective, Longitudinal Examination of Multiple Self-Scheduled Visit Types

Background

Self-scheduling of medical visits is becoming available at many medical institutions. We aimed to examine the self-scheduled visit counts and rate of growth of self-scheduled visits in a multispecialty practice.

Methods

For 85 weeks extending from January 1, 2022 through August 24, 2023, we examined self-scheduled visit counts for over 1500 self-scheduled visit types. We compared completed self-scheduled visit counts to all scheduled completed visit counts for the same visit types. We collected counts of the most frequently self-scheduled visit types for each week and examined the change over time. We also determined the proportion that each visit type was self-scheduled.

Results

There were 20,769 699 completed visits during the course of the study that met the criteria for inclusion. Self-scheduled visits accounted for 4.0% of all completed visits (838 592/20,769 699). Over the 85-week span, self-scheduled visits rose from 3.0% to 5.3% of the total. There were 1887 unique visit types that were associated with completed visits. There were just 6 appointment visit types of the total 1887 self-scheduled visit types that accounted for 50.7% of the total 838 592 self-scheduled visits. Those 6 visit types were a lab blood test visit (19.5%, 163 K visits), two Family Medicine office visit types (13.0%, 109 K visits), a screening mammogram visit type (6.6%, 55 K visits), a scheduled express care visit type (6%, 50 K visits) and a COVID immunization visit type (5.7%, 48 K visits). Twenty-one visit types that were self-scheduled accounted for 75% of the total self-scheduled visits. Four seasonal visits, accounting for 10.6% of the total self-scheduled visits, were responsible for almost all the non-linear change in self-scheduling.

Conclusion

Self-scheduling accounted for a small but growing percent of all outpatient scheduled visits in a multispecialty, multisite practice. A wide range of visit types can be successfully self-scheduled.

Role of Online Patient Portal Self-Scheduling and Self-Referral Pathways to Decrease Health Disparity for Screening Mammography

PURPOSE

The aim of this study was to assess the rate of self-scheduling and self-referral for screening mammography and to assess sociodemographic factors associated with their use in an academic health care system in southern California.

METHODS

Patients scheduled for screening mammography between February 1, 2021, and September 20, 2022, were included in this retrospective study. Multivariable logistic regression models were used to assess associations among sociodemographic factors, self-referral, and online self-scheduling pathways.

RESULTS

In total, 22,306 patients were scheduled for screening mammography (mean age, 59 years; 66.8% White, 20.4% Asian, and 20.6% Hispanic). Overall, 3,566 (16.0%) used online self-scheduling, and 1,232 (5.5%) self-referred for screening mammography. Patients 70 years or older (versus 50 years or younger) (odds ratio [OR], 0.41; 95% confidence interval [CI], 0.34-0.51), Spanish (versus English) speakers (OR, 0.22; 95% CI, 0.16-0.31), and those on Medicaid (versus commercially insured) (OR, 0.71; 95% CI, 0.50-0.99) were less likely to self-schedule. Hispanic patients (versus non-Hispanic) (OR, 1.39; 95% CI, 1.20-1.61), Asian patients (versus White) (OR, 1.64; 95% CI, 1.46-1.85), and patients residing in the most (versus least) disadvantaged neighborhoods (OR, 1.16; 95% CI, 1.02-1.33) were more likely to self-schedule. Furthermore, patients 70 years or older (versus 50 or younger) (OR, 0.70; 95% CI, 0.52-0.93) and Spanish speakers (OR, 0.05; 95% CI, 0.03-0.09) were less likely to self-refer, whereas Black patients (versus White) (OR, 1.89; 95% CI, 1.30-2.75), patients on Medicaid (versus commercially insured) (OR, 3.70; 95% CI, 2.65-5.13), and patients living in the most (versus least) disadvantaged neighborhoods (OR, 1.52; 95% CI, 1.27-1.82) were more likely to self-refer.

CONCLUSIONS

Sociodemographic differences in online patient portal use and self-referral for screening mammography suggest that the two pathways have been successful in addressing some of the existing scheduling barriers and are a step toward closing the disparity gap.

Self-scheduling Medical Visits in a Multispecialty, Multisite Medical Practice: Complexity, Challenges, and Successes

Background

Self-scheduling of medical visits is becoming more common but the complexity of applying multiple requirements for self-scheduling has hampered implementation. Mayo Clinic implemented self-scheduling in 2019 and has been increasing its portfolio of self-schedulable visits since then. Our aim was to show measures quantifying the complexity associated with medical visit scheduling and to describe how opportunities and challenges of scheduling complexity apply in self-scheduling.

Methods

We examined scheduled visits from January 1, 2022, through August 24, 2023. For seven visit categories, we counted all unique visit types that were scheduled, for both staff-scheduled and self-scheduled. We examined counts of self-scheduled visit types to identify those with highest uptake during the study period.

Results

There were 9555 unique visit types associated with 20.8 M (million) completed visits. Self-scheduled visit types accounted for 4.0% (838,592/20,769,699) of the completed total visits. Of seven visit categories, self-scheduled established patient visits, testing visits, and procedure visits accounted for 93.5% (784,375/838,592) of all self-scheduled visits. Established patient visits in primary care (10 visit types) accounted for 273,007 (32.6%) of all self-scheduled visits. Testing visits (blood and urine testing, 2 visit types) accounted for 183,870 (21.9%) of all self-scheduled visits. Procedure visits for screening mammograms, bone mineral density, and immunizations (8 visit types) accounted for 147,358 (17.6%) of all self-scheduled visits.

Conclusion

Large numbers of unique visit types comprise a major challenge for self-scheduling. Some visit types are more suitable for self-scheduling. Guideline-based procedure visits such as screening mammograms, bone mineral density exams, and immunizations are examples of visits that have high volumes and can be standardized for self-scheduling. Established patient visits and laboratory testing visits also can be standardized for self-scheduling. Despite the successes, there remain thousands of specific visit types that may need some staff-scheduler intervention to properly schedule.

Fast and Frictionless: A Novel Approach to Radiology Appointment Scheduling Using a Mobile App and Recommendation Engine

Many outpatient radiology orders are never scheduled, which can result in adverse outcomes. Digital appointment self-scheduling provides convenience, but utilization has been low. The purpose of this study was to develop a "frictionless" scheduling tool and evaluate the impact on utilization. The existing institutional radiology scheduling app was configured to accommodate a frictionless workflow. A recommendation engine used patient residence, past and future appointment data to generate three optimal appointment suggestions. For eligible frictionless orders, recommendations were sent in a text message. Other orders received either a text message for the non-frictionless app scheduling approach or a call-to-schedule text. Scheduling rates by type of text message and scheduling workflow were analyzed. Baseline data for a 3-month period prior to the launch of frictionless scheduling showed that 17% of orders that received an order notification text were scheduled using the app. In an 11-month period after the launch of frictionless scheduling, the rate of app scheduling was greater for orders that received a text message with recommendations (frictionless approach) versus app schedulable orders that received a text without recommendations (29% vs. 14%, p < 0.01). Thirty-nine percent of the orders that received a frictionless text and scheduled using the app used a recommendation. The most common recommendation rules chosen for scheduling included location preference of prior appointments (52%). Among appointments that were scheduled using a day or time preference, 64% were based on a rule using the time of the day. This study showed that frictionless scheduling was associated with an increased rate of app scheduling.

The Positive Effect of an Online Appointment Portal on a Breast Cancer Screening Program

BACKGROUND

The adoption of web-based appointment methods by health care systems is increasing.

OBJECTIVES

This study primarily aimed to evaluate in the setting of an organized breast cancer screening program the actual usage of an online appointment portal by the target population, i.e., how the online tool was used (type and timing of the actions performed) and by whom (users' characteristics); the effect of coronavirus disease 2019 (COVID-19) on its usage was also investigated. The effect of adopting this tool on the attendance to breast cancer screening was contextually investigated.

METHODS

Electronic data records of 75,903 women (45-74 years old, residing in the territory of Bologna Local Health Authority) were retrospectively reviewed.

RESULTS

In total, 12.4% of women logged into the online portal at least once. Most of them (79.9%) rescheduled, 15.7% viewed, and 4.4% cancelled their own appointment. In addition, 40.6% of all rescheduling actions were performed by the online portal; the remaining was performed by the toll-free number/dedicated email address. The highest peak (13.8%) of web accesses was registered at 10 a.m. Monday to Friday, when the toll-free number service is available. Percentages of portal usage were higher: (1) among the younger women, of Italian nationality, and for the first time invited to mammographic screening (p < 0.0001), and (2) in the pandemic period versus the prepandemic period (12.5 vs. 8.6%, respectively; p < 0.001). Finally, when compared to not using, the online portal usage led to an overall reduction in the no-show rate of almost 20% (p < 0.0001).

CONCLUSION

The action mainly performed by using the online appointment portal was the appointment rescheduling. The usage of this tool had a positive effect on the no-show rate and it can be speculated that has led to a reduction of the request load to be handled by the center's screening staff. Finally, this study confirmed that the COVID-19 pandemic boosted the use of digital technologies.

Online Self-Triage of Ear or Hearing Concerns in a Patient Portal: Comparison of Subsequent Diagnoses and Hospitalizations to National Emergency Department and National Ambulatory Ear or Hearing Visits

Background

Although online self-triage is easily accessible, little is known about the patients who use self-triage or their subsequent diagnoses. We compared ear/hearing self-triage subsequent diagnoses to ear/hearing visit diagnoses in emergency departments (ED) and ambulatory clinics across the United States.

Methods

We compared International Classification of Diseases version 10 (ICD10) coded diagnoses following online self-triage for ear/hearing concerns with those from national ED and ambulatory clinic samples. We used data from the Centers for Disease Control (CDC) National Hospital Ambulatory Medical Care Survey (NHAMCS) and National Ambulatory Medical Care Survey (NAMCS) for comparison. Using matched ear/hearing diagnostic categories for those aged 1 and over, we compared self-triage diagnosis frequencies with national ED and ambulatory diagnosis frequencies.

Results

Following ear/hearing self-triage, there were 1092 subsequent office visits with a primary diagnosis code. For five frequently diagnosed ear/hearing conditions (i.e., suppurative and nonsuppurative otitis media [OM], otalgia, otitis externa, and cerumen impaction), there was a strong correlation between diagnosis counts made following self-triage and estimated counts of national ED visit diagnoses (r = 0.94; CI 95% [0.37 to 0.99]; p = .016, adjusted r2 = 0.85). Seven diagnoses were available to compare with the national ambulatory sample; correlation was r = 0.79; CI 95% [0.08 to 0.97]; p = .037, adjusted r2 = 0.54. For ages 1 and over, estimated hospital admissions from the national ED visits for ear/hearing were 0.76%, CI 95% [0.28-2.1%]; estimated total national ear/hearing ED visits were 7.5 million (for 4 years, 2016 through 2019).

Conclusion

The strong correlation of ear-related self-triage diagnoses with national ED diagnoses and the low hospitalization risk for these diagnoses suggests that there is an opportunity for self-triage of ear/hearing concerns to decrease ED visits for these symptoms.

Self-Triage Use, Subsequent Healthcare Utilization, and Diagnoses: A Retrospective Study of Process and Clinical Outcomes Following Self-Triage and Self-Scheduling for Ear or Hearing Symptoms

Background

Self-triage is becoming more widespread, but little is known about the people who are using online self-triage tools and their outcomes. For self-triage researchers, there are significant barriers to capturing subsequent healthcare outcomes. Our integrated healthcare system was able to capture subsequent healthcare utilization of individuals who used self-triage integrated with self-scheduling of provider visits.

Methods

We retrospectively examined healthcare utilization and diagnoses after patients had used self-triage and self-scheduling for ear or hearing symptoms. Outcomes and counts of office visits, telemedicine interactions, emergency department visits, and hospitalizations were captured. Diagnosis codes associated with subsequent provider visits were dichotomously categorized as being associated with ear or hearing concerns or not. Nonvisit care encounters of patient-initiated messages, nurse triage calls, and clinical communications were also captured.

Results

For 2168 self-triage uses, we were able to capture subsequent healthcare encounters within 7 days of the self-triage for 80.5% (1745/2168). In subsequent 1092 office visits with diagnoses, 83.1% (891/1092) of the uses were associated with relevant ear, nose and throat diagnoses. Only 0.24% (4/1662) of patients with captured outcomes were associated with a hospitalization within 7 days. Self-triage resulted in a self-scheduled office visit in 7.2% (126/1745). Office visits resulting from a self-scheduled visit had significantly fewer combined non-visit care encounters per office visit (fewer combined nurse triage calls, patient messages, and clinical communication messages) than office visits that were not self-scheduled (-0.51; 95% CI, -0.72 to -0.29; P < .0001).

Conclusion

In an appropriate healthcare setting, self-triage outcomes can be captured in a high percentage of uses to examine for safety, patient adherence to recommendations, and efficiency of self-triage. With the ear or hearing self-triage, most uses had subsequent visit diagnoses relevant to ear or hearing, so most patients appeared to be selecting the appropriate self-triage pathway for their symptoms.

Automated patient self-scheduling: case study

This case study assesses the uptake, user characteristics, and outcomes of automated self-scheduling in a community-based physician group affiliated with an academic health system. We analyzed 1 995 909 appointments booked between January 1, 2019, and June 30, 2021 at more than 30 practice sites. Over the study period, uptake of self-scheduling increased from 4% to 15% of kept appointments. Younger, commercially insured patients were more likely to be users. Missed appointments were lower and cancelations were higher for self-scheduled patients. An examination of characteristics, benefits, and usage of automated self-scheduling provides insight to those organizations contemplating the implementation or expansion of similar consumer-facing digital self-scheduling platforms.

Self-Scheduling Process Efficiency and Utilization of Online Self-Scheduling of Lab Tests: A Retrospective Analysis of Self-Scheduled Appointments for COVID Testing

Introduction

The COVID 19 pandemic increased the need for rapid and accurate diagnostic testing for COVID. When testing became available, a systems response was needed to efficiently accommodate the high-volume flow of patients who needed testing. Self-scheduling of COVID testing was developed to help patients safely and efficiently schedule their COVID testing online or with a mobile app.

Methods

We captured the counts of COVID test appointments, time patients spent in scheduling COVID test appointments, appointment lead times, and no-shows for COVID test appointments. For 17 months of self-scheduling, we retrospectively compared self-scheduling with the concurrent staff scheduling of COVID tests.

Results

From November 2020 through March 2022 there were 619 104 scheduled appointments for COVID testing with 22% (136 252) being self-scheduled. For asymptomatic self-scheduled COVID tests, accounting for 10.3% (63 605/619 104) of total COVID tests scheduled, median time to self-schedule was 3.1 min, interquartile range (IQR) [2.4,4.7]. For symptomatic self-schedulers accounting for 11.7% (72 647/619 104) of total COVID tests scheduled, the median time to self-triage and self-schedule was 5.8 min, IQR[4.3,8.9]. Self-scheduled COVID appointments increased to 44% (42 387/97 086) of the total COVID appointments during the peak month of January 2022. Median appointment lead time for symptomatic self-scheduled COVID test appointments was 6.6 h compared to 2.9 h (P < .0001) for symptomatic staff scheduled appointments. However, adjusting for the 24% (32 194/135 252) that self-scheduled during hours when testing was unavailable, the median appointment lead time for symptomatic self-scheduled patients dropped to 3.6 h. No-shows were 2.5% for self-scheduled appointments compared to 3.0% no-shows that were staff scheduled (odds ratio 0.83, P < .0001).

Conclusion

COVID testing was self-scheduled for a large percent of scheduled COVID tests, taking patients only a few minutes to complete. Self-scheduling use increased over time, associated with a decreasing use of staff scheduled appointments and lower no-shows.