Leveraging Project Echo Telementoring to Improve Sickle Cell Disease Care in the Midwest: Expanding Provider Education during a Pandemic
Bibliographic record
Abstract
Introduction Approximately 15,000 individuals in the Midwest live with sickle cell disease (SCD), a lifelong, painful disorder with complications and comorbidities. Providers self-report limited knowledge and confidence in treating patients with SCD, leading to limited access to providers knowledgeable about evidence-based management and treatment guidelines and therefore poor health outcomes for patients. Sickle Treatment and Outcomes Research in the Midwest (STORM) is a regional sickle cell network, established to improve outcomes for individuals with SCD living in Indiana, Illinois, Michigan, Minnesota, North Dakota, Ohio, South Dakota and Wisconsin. One goal of STORM is to increase provider knowledge about evidence-based management of SCD. STORM TeleECHO, a replication of the Project ECHO (Extension for Community Healthcare Outcomes) telementoring model, was launched in March 2016 as an innovative, lifespan approach to provider education in the Midwest. In March 2020, in response to the COVID-19 public health pandemic, the STORM coordinating team quickly pivoted to host additional COVID-19 and SCD focused ECHO sessions. Methods STORM TeleECHO virtual clinics include didactic presentations with a curriculum based on the National Heart Lung and Blood Institute Evidence-Based Management of Sickle Cell Disease guidelines, as well as a de-identified case discussion presented by providers seeking feedback on the management of challenging clinical scenarios. Other additional medical and psychosocial issues have also been presented. Participants join the monthly hour-long sessions using Zoom© or telephone. COVID-19 and SCD sessions followed the same format and topics focused on emerging medical and psychosocial pandemic issues, such as: serology, multi-system inflammatory syndrome in children, the COVID and SCD surveillance registry, blood safety and transfusion practices, telemedicine, back to school issues, mental health, and Sickle Cell Disease Association of America patient and provider advisories. These sessions also provided an open forum for this hematology/primary care provider community of practice to share rapidly changing clinical practices and patient resources. Results Since March 2016, 58 sessions have been held with over 175 unique attendees. Evaluation data shows 100% of providers will continue participating and would recommend STORM TeleECHO to a colleague. Over 80% reported learning best practice guidelines for SCD; developing clinical expertise; and applying knowledge into practice. Data analysis has shown a statistically significant increase in provider confidence to identify eligible candidates and prescribing hydroxyurea. STORM has awarded over 750 CME credits (since 2016), over 300 MOC Part II from the American Board of Pediatrics and the American Board of Internal Medicine (since 2017) and 70 CNE credits (since 2019). Since March 2020, the 11 special COVID-19 sessions have averaged 42 attendees per session- nearly a 150% increase compared to the standing ECHO sessions in previous years (Table 1). Over 115 unique providers have attended at least one COVID-19 session, with 61 participants being new to STORM TeleECHO. Participants have cumulatively attended an average of 4 COVID-19 sessions and represented 17 states and Canada. On average, 93% of respondents rated the COVID-19 STORM TeleECHO sessions as "very good" or "excellent". Discussion STORM TeleECHO has been instrumental in increasing knowledge and comfort of providers caring for patients with SCD. Data shows that STORM TeleECHO has the potential to improve outcomes and decrease health disparities in this underserved and medically vulnerable population. Moreover, STORM COVID-19 and SCD TeleECHO session participants have been highly satisfied with this educational forum for addressing emerging issues during the public health emergency. Our program quickly and successfully leveraged the existing STORM TeleECHO framework to expand educational forums for this community of practice. The continued increase in participants sustained throughout the COVID-19 ECHO's has demonstrated the need and interest for this educational forum to share practice changes, new clinical protocols, patient education and other resources. While the regular STORM TeleECHO sessions will continue, there are also plans to continue the additional COVID-ECHO sessions for the foreseeable future. Table 1 Disclosures No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".