Patient-Centric Scheduling With the Implementation of Health Information Technology to Improve the Patient Experience and Access to Care: Retrospective Case-Control Analysis

Chung, Sukyung; Martinez, Meghan; Frosch, Dominick L.; Jones, Veena G.; Chan, Albert

doi:10.2196/16451

Cited by 18 publications

(10 citation statements)

References 20 publications

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“…Portal analyses have extended beyond MU-2 criteria in an effort to best meet provider and patient needs. Fast Pass—an “automated rescheduling system” requiring opt-in through a patient portal—not only indirectly measured patient use/adoption through logins to enter the program but showed that automated rescheduling prompts reduced no-show appointments by 38% [ 108 ]. Patient portal utilization extending beyond MU criteria has been critical during the COVID-19 pandemic, with Patel et al [ 109 ] describing how pediatric patient portals pivoted from traditional, in-person enrollment methods and Judson et al [ 110 ] detailing the creation of a COVID-19 self-triage and assessment tool for primary care patients.…”

Section: Discussionmentioning

confidence: 99%

Quantifying Patient Portal Use: Systematic Review of Utilization Metrics

Beal¹,

Kolman²,

Jones³

et al. 2021

J Med Internet Res

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Background Use of patient portals has been associated with positive outcomes in patient engagement and satisfaction. Portal studies have also connected portal use, as well as the nature of users’ interactions with portals, and the contents of their generated data to meaningful cost and quality outcomes. Incentive programs in the United States have encouraged uptake of health information technology, including patient portals, by setting standards for meaningful use of such technology. However, despite widespread interest in patient portal use and adoption, studies on patient portals differ in actual metrics used to operationalize and track utilization, leading to unsystematic and incommensurable characterizations of use. No known review has systematically assessed the measurements used to investigate patient portal utilization. Objective The objective of this study was to apply systematic review criteria to identify and compare methods for quantifying and reporting patient portal use. Methods Original studies with quantifiable metrics of portal use published in English between 2014 and the search date of October 17, 2018, were obtained from PubMed using the Medical Subject Heading term “Patient Portals” and related keyword searches. The first search round included full text review of all results to confirm a priori data charting elements of interest and suggest additional categories inductively; this round was supplemented by the retrieval of works cited in systematic reviews (based on title screening of all citations). An additional search round included broader keywords identified during the full-text review of the first round. Second round results were screened at abstract level for inclusion and confirmed by at least two raters. Included studies were analyzed for metrics related to basic use/adoption, frequency of use, duration metrics, intensity of use, and stratification of users into “super user” or high utilizers. Additional categories related to provider (including care team/administrative) use of the portal were identified inductively. Additional analyses included metrics aligned with meaningful use stage 2 (MU-2) categories employed by the US Centers for Medicare and Medicaid Services and the association between the number of portal metrics examined and the number of citations and the journal impact factor. Results Of 315 distinct search results, 87 met the inclusion criteria. Of the a priori metrics, plus provider use, most studies included either three (26 studies, 30%) or four (23 studies, 26%) metrics. Nine studies (10%) only reported the patient use/adoption metric and only one study (1%) reported all six metrics. Of the US-based studies (n=76), 18 (24%) were explicitly motivated by MU-2 compliance; 40 studies (53%) at least mentioned these incentives, but only 6 studies (8%) presented metrics from which compliance rates could be inferred. Finally, the number of metrics examined was not associated with either the number of citations or the publishing journal’s impact factor. Conclusions Portal utilization measures in the research literature can fall below established standards for “meaningful” or they can substantively exceed those standards in the type and number of utilization properties measured. Understanding how patient portal use has been defined and operationalized may encourage more consistent, well-defined, and perhaps more meaningful standards for utilization, informing future portal development.

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Section: Discussionmentioning

confidence: 99%

Quantifying Patient Portal Use: Systematic Review of Utilization Metrics

Beal¹,

Kolman²,

Jones³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…The median (IQR) number of requests for each appointment slot was 2 (1-2), which further highlights that this tool can free up staff time for direct patient care tasks or to assist patients who are less familiar with the EHR patient portal. This study builds on existing literature on the Fast Pass EHR tool by supporting its feasibility and implementation in a large tertiary health system and adding additional data on patient use of the tool, impacts across specialties, and potential revenue impacts [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

“…More advanced self-scheduling tools can allow for the maintenance of an electronic waitlist that can provide patients with notifications when an earlier appointment is available. Despite these reported benefits, data quantifying the efficacy of these tools, their impact on patient access, and their financial value for health systems are needed to help spur adoption and investment [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

An Electronic Health Record–Based Automated Self-Rescheduling Tool to Improve Patient Access: Retrospective Cohort Study

Ganeshan,

Liu,

Kroeger

et al. 2024

J Med Internet Res

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Background In many large health centers, patients face long appointment wait times and difficulties accessing care. Last-minute cancellations and patient no-shows leave unfilled slots in a clinician’s schedule, exacerbating delays in care from poor access. The mismatch between the supply of outpatient appointments and patient demand has led health systems to adopt many tools and strategies to minimize appointment no-show rates and fill open slots left by patient cancellations. Objective We evaluated an electronic health record (EHR)–based self-scheduling tool, Fast Pass, at a large academic medical center to understand the impacts of the tool on the ability to fill cancelled appointment slots, patient access to earlier appointments, and clinical revenue from visits that may otherwise have gone unscheduled. Methods In this retrospective cohort study, we extracted Fast Pass appointment offers and scheduling data, including patient demographics, from the EHR between June 18, 2022, and March 9, 2023. We analyzed the outcomes of Fast Pass offers (accepted, declined, expired, and unavailable) and the outcomes of scheduled appointments resulting from accepted Fast Pass offers (completed, canceled, and no-show). We stratified outcomes based on appointment specialty. For each specialty, the patient service revenue from appointments filled by Fast Pass was calculated using the visit slots filled, the payer mix of the appointments, and the contribution margin by payer. Results From June 18 to March 9, 2023, there were a total of 60,660 Fast Pass offers sent to patients for 21,978 available appointments. Of these offers, 6603 (11%) were accepted across all departments, and 5399 (8.9%) visits were completed. Patients were seen a median (IQR) of 14 (4-33) days sooner for their appointments. In a multivariate logistic regression model with primary outcome Fast Pass offer acceptance, patients who were aged 65 years or older (vs 20-40 years; P=.005 odds ratio [OR] 0.86, 95% CI 0.78-0.96), other ethnicity (vs White; P<.001, OR 0.84, 95% CI 0.77-0.91), primarily Chinese speakers (P<.001; OR 0.62, 95% CI 0.49-0.79), and other language speakers (vs English speakers; P=.001; OR 0.71, 95% CI 0.57-0.87) were less likely to accept an offer. Fast Pass added 2576 patient service hours to the clinical schedule, with a median (IQR) of 251 (216-322) hours per month. The estimated value of physician fees from these visits scheduled through 9 months of Fast Pass scheduling in professional fees at our institution was US $3 million. Conclusions Self-scheduling tools that provide patients with an opportunity to schedule into cancelled or unfilled appointment slots have the potential to improve patient access and efficiently capture additional revenue from filling unfilled slots. The demographics of the patients accepting these offers suggest that such digital tools may exacerbate inequities in access.

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“… 16 Another study outlined the potential benefits of an automated text messaging system, which could enhance technologies such as Fast Pass, which have been shown to decrease waiting times. 17 …”

Section: Discussionmentioning

confidence: 99%