Electronic health record note review in an outpatient specialty clinic: who is looking?

Abstract

Note entry and review in electronic health records (EHRs) are time-consuming. While some clinics have adopted team-based models of note entry, how these models have impacted note review is unknown in outpatient specialty clinics such as ophthalmology. We hypothesized that ophthalmologists and ancillary staff review very few notes. Using audit log data from 9775 follow-up office visits in an academic ophthalmology clinic, we found ophthalmologists reviewed a median of 1 note per visit (2.6 ± 5.3% of available notes), while ancillary staff reviewed a median of 2 notes per visit (4.1 ± 6.2% of available notes). While prior ophthalmic office visit notes were the most frequently reviewed note type, ophthalmologists and staff reviewed no such notes in 51% and 31% of visits, respectively. These results highlight the collaborative nature of note review and raise concerns about how cumbersome EHR designs affect efficient note review and the utility of prior notes in ophthalmic clinical care.

Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures

“…Reviewed articles mentioned several limitations of EHR log research ( Supplementary eTable 4 ). Three of the most frequently mentioned limitations echo those observed in the prior review of audit log research 11 : EHR logs do not provide a full view of clinical activity which can involve physical and digital interactions outside the EHR (22 articles), 3 , 26 , 34 , 43 , 55 , 57 , 58 , 61 , 65 , 68 , 73 , 74 , 84–86 , 89 , 91 , 92 , 95 , 103 , 109 , 112 qualitative methods are needed to better understand the context and motivation for observed work (15 articles), 32 , 46 , 53 , 62 , 64 , 68 , 72 , 80 , 85 , 91 , 96 , 97 , 101 , 115 , 118 and logs may not contain enough detail to observe complex workflows (13 articles). 4 , 26 , 32 , 43 , 51 , 56 , 59 , 69 , 75 , 81 , 89 , 90 ,…”

“… 75 , 93 , 94 , 113 Only one of the experimental studies was a randomized controlled trial. 75 Across both observational and experimental studies, 41 articles compared EHR use across different groups of users including comparisons by specialty (14 studies), 4 , 7 , 25 , 29 , 31 , 36 , 42 , 48 , 51 , 58 , 59 , 85 , 105 , 115 clinical role (12), 24 , 26 , 33 , 37 , 46 , 65 , 76 , 95 , 99 , 106 , 108 , 115 gender (8), 19 , 34 , 47 , 49 , 66 , 68 , 70 , 106 year in residency (8), 31 , 32 , 35 , 39 , 52 , 56 , 57 , 95 organization (3), 24 , 44 , 75 and country (1). 27 Vendor-measure studies were more likely than investigator-measure studies to make such comparisons of EHR use by user group (65% vs 25...…”

Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts

“…Specific documentation standards and guidelines and physician buy-in will be needed to promote standardized data entry conducive to codified cohort phenotyping without increasing the documentation burden of the EHR. 83 , 84 Inclusion of other data such as visual acuity may have important implications in outcomes studies for these diabetes patients and would enable improved public health reporting to potentially address disparities in diabetes and DR. 25 Standardized vocabularies such as the Observational Medical Outcomes Partnership model may be a promising means to codify clinical findings such as visual acuity. Other solutions may include high-throughput phenotyping using EHR data, which has been employed extensively in genomic analyses, and may also have a role in reproducibly selecting patient cohorts.…”

Usability and Clinician Acceptance of a Deep Learning-Based Clinical Decision Support Tool for Predicting Glaucomatous Visual Field Progression

“…There remains a significant gap between algorithmic development and AI integration into clinical workflows, a crucial step in realizing the benefits of AI for patients. Implementation of AI models into the EHR is a logical next step, as clinicians use the EHR routinely in their workflows 35–38. A recent systematic review by Lee et al39 revealed several challenges prohibiting widespread clinical implementation of predictive models in the EHR.…”

“…Implementation of AI models into the EHR is a logical next step, as clinicians use the EHR routinely in their workflows. [35][36][37][38] A recent systematic review by Lee et al 39 revealed several challenges prohibiting widespread clinical implementation of predictive models in the EHR. For example, CDS tools may increase time spent in the EHR, cognitive burden, and alert fatigue for clinicians who already experience high alert burden.…”