High Volume Portal Usage Impacts Practice Resources

Chavez, Augustine; Bracamonte, Jesse D.; Kresin, Molly; Yardley, Michael H; Grover, Michael

doi:10.3122/jabfm.2020.03.190401

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…A recent study by Huerta et al [105] provided guidance on patient portal log file analysis and developed a taxonomy of computed analytic metrics. Patients who utilize their portal for longer periods are more likely to prefer communication through said portal, highlighting the importance of analyzing both patient and provider utilization [114].…”

Section: Principal Findingsmentioning

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: Principal Findingsmentioning

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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show abstract

“…Portals that do not allow incoming messages or any secure messaging would reduce or eliminate this time requirement but might also limit patient engagement and other potential benefits of the portal. Since the clerical burden associated with electronic environments in health care has been associated with professional burnout, [ 22 , 26 ] it is important to consider the provider time requirement associated with patient portals. The time and cost associated with incorporating a patient portal are currently not specifically addressed in either fee-for-service or capitated Canadian primary care funding models.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, a qualitative study examining online patient access to their own health records found that providers felt that their workload had increased as a result [ 23 ], while another found that some providers anticipated fewer administrative requests for information when patients had access to their own health records [ 24 ]. One study found that online patient access to encounter notes did not significantly affect physician workload [ 25 ], although others have described high volumes of portal messages sent by patients [ 26 ]. However, no studies have actually tracked the provider time spent specifically on portal-related work.…”

Section: Introductionmentioning

confidence: 99%

The Impact of an Electronic Portal on Patient Encounters in Primary Care: Interrupted Time-Series Analysis

Ferguson¹,

Fraser²,

Tuna³

et al. 2023

JMIR Med Inform

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Background Electronic patient portals are online applications that allow patients access to their own health information, a form of asynchronous virtual care. The long-term impact of portals on the use of traditional primary care services is unclear, but it is an important question at this juncture, when portals are being incorporated into many primary care practices. Objective We sought to investigate how an electronic patient portal affected the use of traditional, synchronous primary care services over a much longer time period than any existing studies and to assess the impact of portal messaging on clinicians’ workload. Methods We conducted a propensity-score–matched, open-cohort, interrupted time-series evaluation of a primary care portal from its implementation in 2010. We extracted information from the electronic medical record regarding age, sex, education, income, family health team enrollment, diagnoses at index date, and number of medications prescribed in the previous year. We also extracted the annual number of encounters for up to 8 years before and after the index date and provider time spent on secure messaging through the portal. Results A total of 7247 eligible portal patients and 7647 eligible potential controls were identified, with 3696 patients matched one to one. We found that portal registration was associated with an increase in the number of certain traditional encounters over the time period surrounding portal registration. Following the index year, there was a significant jump in annual number of visits to physicians in the portal arm (0.42 more visits/year vs control, P<.001) but not for visits to nurse practitioners and physician assistants. The annual number of calls to the practice triage nurses also showed a greater increase in the portal arm compared to the control arm after the index year (an additional 0.10 calls, P=.006). The average provider time spent on portal-related work was 5.7 minutes per patient per year. Conclusions We found that portal registration was associated with a subsequent increase in the number of some traditional encounters and an increase in clerical workload for providers. Portals have enormous potential to truly engage patients as partners in their own health care, but their impact on use of traditional health care services and clerical burden must also be considered when they are incorporated into primary care.

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“…The COVID-19 pandemic has increased reliance on telemedicine and electronic health record (EHR) communications, as patient-initiated EHR message rates increased over 200% . Electronic messaging has the potential to improve clinician-patient communication, but high message volumes can impair efficiency and impose burdens on clinicians already experiencing burnout, ultimately resulting in patient morbidity from delayed responses …”

Section: Introductionmentioning

confidence: 99%

“… 1 Electronic messaging has the potential to improve clinician-patient communication, but high message volumes can impair efficiency and impose burdens on clinicians already experiencing burnout, ultimately resulting in patient morbidity from delayed responses. 2 …”

Section: Introductionmentioning

confidence: 99%

Use of Natural Language Processing of Patient-Initiated Electronic Health Record Messages to Identify Patients With COVID-19 Infection

Mermin-Bunnell,

Zhu,

Hornback

et al. 2023

JAMA Netw Open

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ImportanceNatural language processing (NLP) has the potential to enable faster treatment access by reducing clinician response time and improving electronic health record (EHR) efficiency.ObjectiveTo develop an NLP model that can accurately classify patient-initiated EHR messages and triage COVID-19 cases to reduce clinician response time and improve access to antiviral treatment.Design, Setting, and ParticipantsThis retrospective cohort study assessed development of a novel NLP framework to classify patient-initiated EHR messages and subsequently evaluate the model’s accuracy. Included patients sent messages via the EHR patient portal from 5 Atlanta, Georgia, hospitals between March 30 and September 1, 2022. Assessment of the model’s accuracy consisted of manual review of message contents to confirm the classification label by a team of physicians, nurses, and medical students, followed by retrospective propensity score–matched clinical outcomes analysis.ExposurePrescription of antiviral treatment for COVID-19.Main Outcomes and MeasuresThe 2 primary outcomes were (1) physician-validated evaluation of the NLP model’s message classification accuracy and (2) analysis of the model’s potential clinical effect via increased patient access to treatment. The model classified messages into COVID-19–other (pertaining to COVID-19 but not reporting a positive test), COVID-19-positive (reporting a positive at-home COVID-19 test result), and non–COVID-19 (not pertaining to COVID-19).ResultsAmong 10 172 patients whose messages were included in analyses, the mean (SD) age was 58 (17) years; 6509 patients (64.0%) were women and 3663 (36.0%) were men. In terms of race and ethnicity, 2544 patients (25.0%) were African American or Black, 20 (0.2%) were American Indian or Alaska Native, 1508 (14.8%) were Asian, 28 (0.3%) were Native Hawaiian or other Pacific Islander, 5980 (58.8%) were White, 91 (0.9%) were more than 1 race or ethnicity, and 1 (0.01%) chose not to answer. The NLP model had high accuracy and sensitivity, with a macro F1 score of 94% and sensitivity of 85% for COVID-19–other, 96% for COVID-19–positive, and 100% for non–COVID-19 messages. Among the 3048 patient-generated messages reporting positive SARS-CoV-2 test results, 2982 (97.8%) were not documented in structured EHR data. Mean (SD) message response time for COVID-19–positive patients who received treatment (364.10 [784.47] minutes) was faster than for those who did not (490.38 [1132.14] minutes; P = .03). Likelihood of antiviral prescription was inversely correlated with message response time (odds ratio, 0.99 [95% CI, 0.98-1.00]; P = .003).Conclusions and RelevanceIn this cohort study of 2982 COVID-19–positive patients, a novel NLP model classified patient-initiated EHR messages reporting positive COVID-19 test results with high sensitivity. Furthermore, when responses to patient messages occurred faster, patients were more likely to receive antiviral medical prescription within the 5-day treatment window. Although additional analysis on the effect on clinical outcomes is needed, these findings represent a possible use case for integration of NLP algorithms into clinical care.

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High Volume Portal Usage Impacts Practice Resources

Cited by 7 publications

References 16 publications

Quantifying Patient Portal Use: Systematic Review of Utilization Metrics

Quantifying Patient Portal Use: Systematic Review of Utilization Metrics

The Impact of an Electronic Portal on Patient Encounters in Primary Care: Interrupted Time-Series Analysis

Use of Natural Language Processing of Patient-Initiated Electronic Health Record Messages to Identify Patients With COVID-19 Infection

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