Intake Quality Index
Composite score weighted across access reliability, data integrity, throughput, financial protection, safety-screening readiness, and equity safeguards.
Latency and Readiness Curve
Estimated intake minutes per encounter across the day after pre-arrival completion and automation assumptions.
Annual Value
Includes recoverable denials, labor capacity released, and no-show capacity protection.
Ready on Arrival
Share of patients expected to arrive with intake, insurance, safety screen, and prep items complete.
Clean-Claim Reliability
Planning proxy for preventable front-end revenue-cycle defects.
Monthly Slots Protected
Estimated exam slots protected by lower no-show exposure and targeted reminders.
Assumption Control Panel
Use the sliders to tune the model to a local imaging center, hospital outpatient department, or scaled radiology service line. Presets are intentionally conservative starting points, not claims of guaranteed performance.
ROI Waterfall
The chart separates value drivers so leaders can see whether the model is being justified by revenue-cycle control, labor capacity, or protected access.
Sensitivity Summary
These indicators move in real time as assumptions change.
| Measure | Current estimate |
|---|---|
| Annual exams | 30,000 |
| Recoverable denial base | $878k |
| Labor hours released | 3,000 |
| No-show slots recovered | 462 |
| Value per scheduled exam | $14 |
Value Driver Bars
A second visualization for board or lender presentations. It shows the relative magnitude of each modeled value source.
Operational Flow Illustration
The redesigned model emphasizes pre-arrival completion, exception routing, and staff-assisted recovery lanes rather than a brittle “digital-only” patient experience.
Readiness Funnel
Estimated monthly patient flow through the front-end system.
Capacity Protection
Monthly scheduling capacity under baseline no-show conditions compared with the expected recovered slots after intervention.
Interactive Patient Scenario Simulator
Select a scenario and advance the phone simulation. The model deliberately keeps a human review pathway for higher-risk imaging encounters.
Prepare
Patient receives plain-language preparation instructions, arrival time, and channel options.
Verify
Insurance, demographics, order completeness, and safety screen are checked before service.
Triage
Exceptions route to trained staff before the patient arrives.
Risk and Equity Radar
A redesigned intake system should not simply digitize work. It should reduce operational defects while protecting patients who need language, phone, literacy, disability, caregiver, or staff-assisted pathways.
Guardrail Scorecard
The scorecard identifies whether the model is balanced or overdependent on technology adoption.
Implementation Guardrails
Do not allow automation to bypass MRI safety screening, implant documentation, renal/contrast considerations, sedation planning, or protocol exceptions.
Separate eligibility, authorization, medical-necessity documentation, order completeness, estimate generation, and denial tracking.
Maintain phone, caregiver, translated, low-literacy, and staff-assisted pathways so self-service does not become an access barrier.
Evidence-Informed Design Notes
This model converts published findings into an operational design framework. It does not claim that any single technology automatically improves outcomes. The design implication is more practical: digital intake, reminders, self-scheduling, portal access, and targeted outreach can support performance when paired with usability, workflow redesign, staff escalation, and equity safeguards.
Systematic reviews of patient access to electronic health records generally report positive associations with engagement, communication, satisfaction, self-management, and resource use, while also identifying usability barriers.
Diagnostic imaging self-scheduling can improve convenience, but published radiology research has identified differential uptake across language, race/ethnicity, and insurance groups.
Radiology reminder studies support automated, text, and phone-based strategies, especially when targeted to patient risk and modality-specific workflow needs.
Selected peer-reviewed sources embedded in the model
- Alomar, D., et al. (2024). The impact of patient access to electronic health records on health care engagement. Journal of Medical Internet Research.
- Lyles, C. R., et al. (2020). Using electronic health record portals to improve patient engagement. Annals of Internal Medicine.
- Ganeshan, S., et al. (2022). Impact of patient portal-based self-scheduling of diagnostic imaging studies on health disparities. Journal of the American Medical Informatics Association, 29(12), 2096-2100.
- Liu, C., et al. (2017). Text message reminders reduce outpatient radiology no-shows but do not improve arrival punctuality. Journal of the American College of Radiology, 14(8), 1049-1054.
- Kenniff, J., et al. (2023). Evaluation of an automated reminder system for reducing outpatient MRI no-shows. Journal of Patient Experience.
- Oikonomidi, T., et al. (2023). Predictive model-based interventions to reduce outpatient no-shows: A systematic review. Journal of Medical Internet Research.
Use local baseline data for deployment: denial categories, authorization cycle times, abandoned appointment rates, modality-specific no-shows, staffing minutes by task, safety-screen exceptions, patient language needs, and portal adoption.