An Informatics-Based, Payer-Led, Low-Intensity Multichannel Educational Campaign Designed to Decrease Postdischarge Utilization for Medicare Advantage Members: Retrospective Evaluation
Bibliographic record
Abstract
BACKGROUND: Readmission avoidance initiatives have been a priority for the Centers for Medicare & Medicaid Services for over a decade; however, interventions are often high-intensity, costly, and resource-intensive, and therefore, rarely scalable or sustainable. Large national payors are in a unique position to leverage data to identify members in real-time who are at high-risk of readmission to prioritize the scaled delivery of tailored behavior change techniques to provide an educational intervention to modify health behaviors. OBJECTIVE: To examine the impact of an informatics-driven, multichannel educational messaging campaign implemented to decrease 30- and 90-day acute inpatient readmissions and emergency department (ED) visits among Medicare Advantage members of a large national payor. METHODS: A quality improvement initiative was designed and implemented to provide an evidence-based outreach campaign using human-centered design and behavior change principles to deliver multiple intervention functions, including timely, contextual, and relevant delivery of education, enablement, and persuasion, to reinforce health promoting behaviors related to planned or unplanned inpatient admissions. Outcomes including 30- and 90-day acute inpatient readmissions and ED visits were retrospectively evaluated from Medicare Advantage members enrolled in a large national health plan residing across the United States between May 2020 and July 2022. Leveraging utilization management data, rules-based logic identified members (N=368,393) with a planned acute inpatient procedure (i.e., pre-admission) or discharged from an acute hospital stay (i.e., post-discharge) within 15 days. Members were sequentially assigned to a standard (N=141,223) or an enhanced (N=227,470) messaging group, whereby the standard group received usual outreach and the enhanced group received an educational intervention via a messaging campaign deployed through multiple low-intensity communication channels (e.g., text message, email, direct mail) in addition to standard outreach. RESULTS: Members who received enhanced outreach had fewer relative 30-day acute inpatient readmissions (-4.1%, 95% CI: -5.5 to -2.7 %; P<.001) and ED visits (-3.4%, 95% CI: -5.0 to -1.7%; P<.001) compared to members receiving standard outreach. Similarly, these findings persisted for relative 90-day outcomes such that members receiving enhanced outreach experienced fewer acute inpatient readmissions (-5.4%, 95% CI: -6.5 to -4.3%; P<.001) and ED visits (-3.8%, 95% CI: -5.0 to -2.5%; P<.001) compared to members receiving standard outreach messaging. CONCLUSIONS: Behavior change techniques deployed via educational interventions as low-intensity multi-channel outreach is an effective strategy to reduce avoidable 30- and 90-day inpatient readmissions and ED visits in recently discharged Medicare Advantage members (primarily >65 years).
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".