Alert-based remote monitoring: A model for increased reimbursement to meet device clinic workload while achieving overall health system cost savings
Bibliographic record
Abstract
Remote monitoring (RM) is recommended to follow patients with implantable cardioverter defibrillators (ICDs).1Ferrick AM, Raj SR, Deneke T, et al. 2023 HRS/EHRA/APHRS/LAHRS expert consensus statement on practical management of the remote device clinic. Heart Rhythm Sep 2023;20:e92-e144.Google Scholar RM enables early detection of critical conditions and reduces in-clinic evaluations. However, adoption lags. An acknowledged barrier is insufficient personnel, linked to insufficient reimbursement, to conduct the “invisible” clinic work associated with virtual care (e.g., triaging alerts, documentation, patient communication and scheduling).2Diamond J. Varma N. Kramer D.B. Making the Most of Cardiac Device Remote Management: Towards an Actionable Care Model.Circulation Arrhythmia and electrophysiology Mar. 2021; 14e009497Google Scholar This encourages a re-examination of the RM schedule and cost/reimbursement structure. Current RM implementation comprises scheduled appointments (remotely plus in-person (IPE)) with continuous monitoring, and management of unscheduled evaluations. The framework of routine assessment every three months is a vestige from earliest ICD technologies requiring frequent manual capacitor reforms and threshold and battery tests but rendered unnecessary with current ICD platforms. Now, only a minority (6.6%) of routine evaluations trigger clinical action, usually reprogramming or medication changes. Device integrity issues are better detected by RM. Therefore, routine follow-up (whether IPE or remote) during continuous monitoring imposes a large nonactionable service burden.3Varma N. Love C.J. Michalski J. Epstein A.E. Investigators T. Alert-Based ICD Follow-Up: A Model of Digitally Driven Remote Patient Monitoring.JACC Clin Electrophysiol Aug. 2021; 7: 976-987Google Scholar Alert-driven RM (i.e., device clinic visits prompted by alerts, rather than routinely scheduled at regular intervals) redresses this by avoiding the bulk of non-actionable, routine patient encounters. However, its financial implications are unknown. To better understand the economic impact of alert-driven RM from the US hospital perspective assuming current Medicare reimbursement structures, we conducted a budget impact analysis quantifying costs associated with (i) IPE only, (ii) RM-Conventional (IPE+RM), and (iii) RM-Alert, from a published economic model derived from the TRUST (The Lumos-T Safely Reduces Routine Office Device Follow-Up) trial.3Varma N. Love C.J. Michalski J. Epstein A.E. Investigators T. Alert-Based ICD Follow-Up: A Model of Digitally Driven Remote Patient Monitoring.JACC Clin Electrophysiol Aug. 2021; 7: 976-987Google Scholar,4Chew D.S. Piccini J.P. Au F. Frazier-Mills C.G. Michalski J. Varma N. Investigators T. Alert-driven vs scheduled remote monitoring of implantable cardiac defibrillators: A cost-consequence analysis from the TRUST trial.Heart Rhythm Mar. 2023; 20: 440-447Google Scholar Ethics approval was obtained from the Conjoint Health Research Ethics Board at the University of Calgary. TRUST randomized 1,339 ICD recipients 2:1 to RM or IPE alone and measured scheduled and unscheduled evaluations.3Varma N. Love C.J. Michalski J. Epstein A.E. Investigators T. Alert-Based ICD Follow-Up: A Model of Digitally Driven Remote Patient Monitoring.JACC Clin Electrophysiol Aug. 2021; 7: 976-987Google Scholar,4Chew D.S. Piccini J.P. Au F. Frazier-Mills C.G. Michalski J. Varma N. Investigators T. Alert-driven vs scheduled remote monitoring of implantable cardiac defibrillators: A cost-consequence analysis from the TRUST trial.Heart Rhythm Mar. 2023; 20: 440-447Google Scholar The total cost of health care resources utilized was contrasted among follow-up strategies over a 1-year time horizon (Figure). Costs were valued in 2021 USD, and included outpatient encounters (such as in-clinic visits or remote evaluation), inpatient hospitalization, physician fees, and nursing care.4Chew D.S. Piccini J.P. Au F. Frazier-Mills C.G. Michalski J. Varma N. Investigators T. Alert-driven vs scheduled remote monitoring of implantable cardiac defibrillators: A cost-consequence analysis from the TRUST trial.Heart Rhythm Mar. 2023; 20: 440-447Google Scholar In a prototypic device clinic following 1,000 patients, the estimated annual hospital budget would be $6.6 million (IPE), $6.3 million (RM-Conventional) and $5.7 million (RM Alert). The overall annual estimated cost-savings would be $292,000 using RM-conventional vs. conventional IPE, but $821,000 if transitioned to RM-Alert. In RM-Conventional vs. IPE, overall cost savings (-$292 per patient) were driven by reduced hospitalization costs (-$311 per patient) although clinic costs were marginally higher (+$70 per patient). In RM-Alert, cost savings (-$821 per patient) were driven by both reductions in clinic-associated (-$401) and inpatient (-$311) costs compared to IPE only. Regarding clinic costs alone, RM-Alert leads to a 66% reduction in annual costs (Figure). This reflects the enormous cost of non-actionable work. In summary, a strategy of alert-driven RM for patients with ICDs is associated with a significant projected annual cost-savings for hospital budgets. In the US, physicians and device clinics are largely reimbursed by private health insurers and Medicare/Medicaid through a fee-for-service system billing every 90 days. This is considered insufficient for staffing the current RM-Conventional follow up strategy. However, cost-savings with RM-Alert may be redirected to reimburse a full complement of device clinic staff, assuming total reimbursement remains unchanged. Their tasks may be switched from a predominantly non-actionable service burden to actionable work (i.e., unscheduled IPEs and high priority alerts) where patient need is greatest. Alert-based RM, while promising to decompress an overwhelmed system, is disruptive to traditional clinical care pathways and reimbursement models. Since the associated workload cannot be measured by patient-facing encounters, alternative payment models require consideration. For instance, device clinics may be reimbursed on an annual per-patient basis for maintaining remote connectivity regardless of volume of transmission and IPEs conducted (i.e., bundled payment model). This could incentivize a reduction in non-actionable routine follow ups and transition to continuous remote monitoring. Alert-based RM may be enhanced through use of artificial intelligence algorithms to reduce the load of unnecessary transmissions, and to link individual patient needs to treatment pathways that improve outcomes. From the perspective of the health system / hospital payer, our economic analysis suggests alert-driven RM programs would still provide overall cost-savings at the hospital system level, realized through fewer hospitalizations, even when reimbursement is increased to adequately support RPM device clinic staff.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".