Patterns and clinical impacts of hematology electronic consults: A retrospective, single-center analysis

Abstract

Abstract Background: With the several months wait for new patient appointments in hematology, many institutions have been exploring electronic consults (e-consults) to provide rapid answers for non-urgent hematology questions. An e-consult is an asynchronous communication between a referring provider (RP), such as a primary care provider (PCP), and the hematologist based on review of the chart and laboratory data. At University of Massachusetts Medical Center (Umass), hematology e-consults were offered from March 2023 to December 2024. We analyzed all hematology e-consults to identify patterns of referral and impacts on wait times for all new patients referred to hematology. Methods: We reviewed 151 e-consults between March 1,2023 to December 31,2024 to collect the following data-referral diagnoses, RP specialties, time spent per e-consult, number of days taken to complete the e-consult, percentage of e-consults that needed a face-to-face (F2F) visit and time to F2F visit. We also assessed 1353 patients referred to hematology in the years before (Y1) and after (Y2) e-consults were introduced. We compared Y1 and Y2 data including time from referral to visit (wait time), reasons for referrals and the demographics of the patients. Results: The most common reasons for e-consults were MGUS (30.5%), anemia (19.9%), and anticoagulation management (18.5%). Referrals originated from 8 specialties, most commonly from PCPs (68.9%) and rheumatology (20.5%). Most e-consults were completed within 24 hours of referral (82.1%); 91.4% were completed within 72 hours(h). Clinician time spent completing e consults was under 15 minutes in 72.9% and over 15 minutes in 27%. Overall, 33.1% of e-consults led to a F2F visit. A total of 15.9% of e-consults were declined. The most common reasons for e-consult denial included concern for malignancy (29.2%), need for hypercoagulability or myeloproliferative work-up (20.8%), and complex anticoagulation questions (16.7%). Among in-person visits, 16% occurred within one month, 46% within one to three months, 30% within three to six months, and 8% after greater than six months. We then analyzed the wait times for all patients referred to hematology in Y1 and Y2. In Y1, 671 hematology patients were seen with the mean wait time of 55 d. In Y2, 682 hematology consults were addressed, of which 55 were addressed via e consults. The median time to complete an e consult was 1 day. The mean wait time in Y2 was 58 d, which was not statistically different from Y1 (p value 0.06). The demographics of patients in Y1 and Y2 were similar, ~ 60% female and ~40% male. The age distributions were also similar- 46% were 35-64 years (y) old, 38% were >65y, 16% were 18-34y.The distribution for referral diagnoses for the patients who had in-person visits were: anemia 35.5%, DVT/PE 19.6%, leukocytosis 9%, ITP 8.8%, leukopenia 8.7%, MPN 6.8%, MGUS 5.9%, others 6%. Conclusion and next steps: E-consults can provide rapid answers for non-urgent hematology questions. In our study, the mean wait times for in-person appointments were ~ 55 days. In contrast, most e-consults were completed within 72h of placement of e-consult order. The majority of these patients (~ 77%) did not need a F2F visit, and this has the potential to save costs and time for the patients and the healthcare system. It can help with patient anxiety and improve patient and provider satisfaction scores, as shown in other studies. Identification of common referral patterns allows development of diagnostic algorithms and educational seminars. We have subsequently developed a webinar on anemia and IV iron administration for RPs. In addition, we have initiated a Quality improvement (QI) project with a standardized MGUS order set and built-in interpretation. The data collected from e-consults will guide medical education and QI projects and disseminate information on approach to common hematologic diagnoses, potentially reducing the backlog of patients waiting to see hematology and expediting their care with their RPs. Many e-consults had standardized text and order sets for the common referral diagnoses, and this might pave the way for artificial intelligence tools to triage incoming referrals and establish baseline labs prior to hematology visits.