Patient Intake Improvement Model
An interactive maturity and performance simulator for the five linked stages of the front door: scheduling, check-in, registration, encounter, and payment. Set a baseline, exercise five operational levers, and surface the trade-offs beneath digital transformation.
- Ten linked views
- Live calculation engine
- Three-pass method
- Equity and rebound detection
- Financial impact model
Overview and Front Door Reliability Score
The FDRS is a composite of three weighted dimensions: Experience at 30 percent, Accuracy at 40 percent, and Throughput at 30 percent, with a friction penalty applied when digital adoption outpaces assisted-support coverage. The score maps to a five-stage maturity ladder, providing a shared language for project plans and weekly huddles.
out of 10
Execution is consistent and defects are detected reliably. They are not yet being prevented at the source.
No friction penalty. Assisted support is keeping pace with digital adoption.
Headline KPIs
Live recalculation against the active scenario. The badge on each card shows movement against your saved baseline actuals. Adjust inputs in Baseline Config and Five Levers to see the numbers move.
Door-to-imaging table-time interval
Encounters with a downstream-correctable error
First-pass payer rejection
Scheduled exams not completed
Staff time correcting upstream defects
Composite of access, communication, dignity
Each dimension is scored out of 10 and then weighted. Accuracy carries the largest weight because registration defects propagate into every downstream measure.
The front door as a linked pipeline
Figure 1. The front door is a single linked pipeline, not five independent desks. Registration carries the heaviest defect load in the model because it is where insurance, demographic, and authorization data enter the record. The dashed path is the attribution problem: the defect is created at stage three and discovered at stage five, which is why the stage with the loudest symptom is rarely the right place to intervene.
How to use this model
Run three passes. The value is in comparing them, not in any single number.
Map the current state
Use Baseline Config to enter today’s actuals from your weekly ops scorecard, then save the result as Baseline. Do not adjust the levers yet.
Model a realistic plan
Modest portal uptake, a readability uplift, and staffed assistance that grows with it. Save as Near-Term Plan.
Stress-test the aggressive strategy
High kiosk adoption with reduced staffing. Save as Aggressive Plan and watch for equity friction and error rebound.
Baseline Configuration
Enter your current-state actuals. These six inputs anchor every downstream calculation. Pull the values directly from your weekly intake huddle scorecard or from revenue cycle reporting, not from memory.
Door-to-imaging-table interval, captured at the modality.
Encounters with at least one downstream-correctable defect in insurance, demographics, or authorization.
First-pass payer rejection. HFMA MAP Keys set a target below 5 percent.
Scheduled exams not completed, counting cancellations inside 24 hours and non-arrivals.
Minutes of registrar or technologist time spent correcting upstream defects, per encounter.
Composite from access, communication, and dignity domains, scored 0 to 10.
Three-pass methodology. Save your current state as Baseline first, then move to Five Levers to model the Near-Term and Aggressive plans. Each saved scenario persists in your browser and appears in Scenario Compare. Saving stores both the six baseline inputs and the current lever positions together, so a scenario is always a complete, reproducible configuration.
The Five Levers
These are the operational design choices that actually move the front door. Move a slider and the model recalculates the headline KPIs, the stage heatmap, the equity flags, and the FDRS in real time. Watch what happens when portal adoption climbs faster than assisted-support coverage.
Share of patients completing pre-arrival registration through the patient portal.
Front-desk and scheduling FTE relative to the demand benchmark. A value of 1.00 means matched.
Share of on-site check-ins routed through a self-service kiosk rather than a registrar.
Score for plain-language clarity, multilingual coverage, and screen-reader support. Below 5 the model amplifies self-service defects.
Share of patients who can reach live assistance by call, in person, or video when a digital channel fails them.
Model assumption rather than a lever. It sets the ceiling on how much of the denial rate the front door can possibly move.
Marginal effect of a one-step increase from the current position: ten points for portal, kiosk, and assisted support, one point for readability, and 0.10 for the staffing index. Bars below zero are improvements for the defect, wait, and rework measures.
Read the shape, not just the height. Because the levers interact, the marginal effect of any one lever depends on where the other four are sitting. Readability has almost no effect when portal and kiosk are both near zero, and becomes the largest single lever once most of the volume has moved to self-service. This is why the chart is recomputed from your current position rather than published as a fixed table.
Stage Analysis
Defects do not always surface where they are caused. The attribution model traces downstream denials and delays back to their upstream root cause. Published revenue cycle work consistently places the origin of most preventable denials in scheduling, registration, and eligibility, even when the rejection lands at the back end.
Each stage carries a different exposure to the digital channel and to staffing, so the levers do not move them together.
Each stage’s weighted share of total defect load. This is the model’s proxy for root-cause attribution of downstream denials.
Each stage’s defect rate converted to a quality score out of 10 using the same threshold curve that drives the Accuracy dimension of the FDRS. A perfectly round shape means the burden is evenly distributed, which almost never happens.
Figure 2. The detection lag is the operational reason front-door defects persist. The team that could prevent the defect receives no feedback for weeks, and the team that receives the feedback cannot prevent it. Closing that loop, by routing denial reason codes back to the registrar who created the record, is usually cheaper than any technology purchase on the roadmap.
Diagnostic prompt. The highest-defect stage is rarely the right intervention point. Read the attribution chart first. If registration sits upstream of most of the defect load, fixing the payment stage is treating the symptom.
Scenario Compare
Side-by-side view of the three saved scenarios with delta indicators against Baseline. The radar shows the shape of the difference and the table shows the magnitude. If a column reads “not saved”, go back to Baseline Config and save that scenario first.
Six normalized sub-scores. Further from the centre is better on every axis, including the ones where a lower raw number is the goal.
Composite score by scenario. The gap between the Near-Term and the Aggressive bar is the risk premium you are being asked to accept.
KPI delta table
| Metric | Baseline | Near-Term | Change | Aggressive | Change |
|---|---|---|---|---|---|
| Save a scenario to populate this table | — | — | — | — | — |
What a good comparison looks like. A credible near-term plan improves five of the seven rows and leaves the sixth flat. A plan that improves wait time and throughput while degrading experience and denial rate is not a plan, it is a cost reduction with a patient-facing side effect. Present both to your operating committee, not just the one that scores well.
Equity and Risk
Two patterns degrade the front door silently. Equity friction appears when digital adoption outpaces assisted-support coverage, so the system gets faster for the digitally fluent and slower for everyone else. Error rebound appears when staffing cuts and self-service automation arrive together, so rework climbs exactly as the safety net thins.
Equity Friction Index
0.00Within tolerancePortal adoption is supported by adequate assisted-support coverage. Keep monitoring as portal grows.
Error Rebound Risk
LowStableStaffing index and kiosk mix are in balance. No automation rebound signal.
Vulnerable-Population Coverage
41%InadequateEstimated share of patients with limited English proficiency, low digital literacy, or age-related barriers who have a viable assisted-support pathway. Scales with both assisted coverage and readability, because a live-assist line that only operates in English is not coverage.
Figure 3. Digital adoption is funded as a project and grows on a project curve. Assisted support is funded as labour and grows, at best, on a budget curve. The gap between the two lines is not a transition artefact that closes on its own. It widens, and the patients standing inside it are the ones least able to advocate for themselves. The model applies a friction penalty to the FDRS once that gap exceeds 20 points.
What the model is watching
Portal minus assisted support exceeds 20 points
A friction penalty is applied to the FDRS. Patients without portal access experience longer waits and higher defect rates, because the registrar workflow is no longer the primary path and is no longer staffed as though it were.
Staffing below 0.85 while kiosk mix exceeds 60 percent
Error rebound is flagged. Self-service captures the volume but produces input errors that reappear as denials and rework downstream, with too few staff left to absorb the correction work.
Readability below 5
Defect amplification is applied to the kiosk and portal channels. Plain-language rewriting and multilingual coverage are the prerequisite for any self-service strategy, not an accessibility upgrade to schedule afterwards.
Friction index at or above 0.25
A gap this size is structural, not seasonal. It requires financial counselling, language-line investment, scheduled assist appointments, or preservation of a paper track. Slider movement alone will not close it.
The uncomfortable finding. Run the aggressive preset on the Five Levers tab and return here. Every throughput measure improves and the composite score still falls, because the friction penalty and the rebound multiplier both engage. That is the model working as designed. A front door can be made faster and worse at the same time.
Financial Impact
Quantifying the shift from clerical cost to protected revenue. Every assumption below is exposed and adjustable, because a number a finance partner cannot interrogate is a number they will not fund.
Completed outpatient encounters per month across the service line.
Net collected per completed exam, blended across payer mix and modality.
First-pass rejection rate before any front-door intervention.
Share of denials whose root cause sits inside the front door. Linked to the same assumption on the Five Levers tab.
Share of the addressable intake-attributable denials the programme actually prevents. This is the assumption most business cases overstate.
Fully loaded staff cost to rework and resubmit one denied claim. Adjust to your own time study.
Sum of the three components below
Reimbursement retained on prevented denials
Staff time not spent on resubmission
Registration minutes returned to the service line
Protected reimbursement usually dominates, which is why capture rate and reimbursement per exam are the two assumptions worth arguing about.
Total annual value across the full capture range, holding every other assumption at its current position.
Model logic, stated in full
Prevented front-end denials equal annual exams multiplied by the denial rate, the intake-attributable share, and the capture rate. Annual exams are monthly volume times twelve, currently 30000, producing 0 prevented claims. Value is then the sum of three terms: protected reimbursement at the net rate per exam, avoided rework at the cost per corrected claim, and released front-desk labour, calculated as the captured share of registrations converting at 55 percent to eight released minutes each, valued at $12 per registrar hour-equivalent.
Two cautions before this number goes in a capital request. First, released labour is capacity, not cash. It becomes savings only if the position is actually removed or redeployed, and if it is removed you should return to the Equity and Risk tab and check whether the assisted-support coverage assumption still holds. Second, the intake-attributable share is the single most contested assumption in this model. Published estimates range from roughly a quarter of denials when attribution follows the denial reason code to around 70 percent when it follows root-cause review. The default here is the conservative end. Move it and watch the total: if your business case only works at 70 percent, it is not a business case, it is a hope.
The Zero-Touch Journey
This walkthrough follows a patient from a pre-arrival link on a phone to an arrival pass at the modality. Automating identity and insurance capture at the front door shrinks the waiting room and stops registration errors before they can become denials. Step through it and watch which stage of the pipeline each screen is actually protecting.
University Radiology
Good morning, Sarah.
Your CT brain scan is scheduled for 2:30 PM today at the Main Campus imaging suite.
Fast-track check-in
Skip the desk. Confirm your coverage now and walk straight through to imaging when you arrive.
Insurance verification
Position your card inside the frame. We read the plan details from the image so nothing is typed by hand.
Checking coverage
Running a real-time eligibility request against the payer. This normally takes a few seconds.
You are verified
Coverage is confirmed and your estimated responsibility today is zero. Show the pass below at the arrival kiosk.
What each step is actually protecting
- 1Image-based capture of identity and insuranceRemoves hand-keyed plan and member identifiers, which are the single largest source of eligibility-related registration defects. Protects stage 3, where the model places the heaviest defect load.
- 2Real-time eligibility and coverage verificationMoves the eligibility check from post-service to pre-service, so a coverage problem becomes a phone call before the appointment rather than a denial after it. Protects stage 5 by acting at stage 1.
- 3Transparent arrival pass and wait statusSets an accurate expectation before arrival. Wait time is experienced as the gap between what was promised and what happened, which is why communication moves the experience score even when the clock does not change.
The step this walkthrough does not show. Every screen here assumes a patient with a smartphone, a data plan, adequate vision, and enough English to parse the prompts. The equivalent journey for the patient who has none of those things is the assisted-support lever on tab three, and it needs to be designed with the same care as this one. A zero-touch path that serves 70 percent of patients is a two-track system, and the second track deserves a walkthrough of its own.
Toolkit
Operational artefacts that translate model output into a project plan. Every field below persists in your browser and prints with the page.
PDSA worksheet: front door improvement cycle
Plan
Do
Study
Act
Weekly intake huddle, fifteen minutes
A3 project canvas
Intervention library
Each card maps to one or more levers. Treat it as a starting menu, not an exhaustive list.
Sent five days out, 24 hours out, and on the morning of the exam with one-tap access. Captures insurance, demographics, and consent before the patient leaves home.
Roving assistance for kiosk users, patients with limited English proficiency, and complex authorizations. This is the role that backstops automation rather than being replaced by it.
One-tap escalation to a registrar and automatic flagging of incomplete fields before submission. The call button is what separates a kiosk from an abandonment machine.
Rewritten consent, preparation instructions, and authorization questions, with translation available at the point of use rather than on request.
A fifteen-minute pre-appointment call for patients flagged as high friction. Replaces day-of confusion with a planned conversation and reliably reduces both no-shows and registration defects.
The patient sees expected cost before arrival, which reduces surprise-billing complaints and the time-of-service rework that follows them.
Reliability Specification
The specification defines what each measure means, how it is calculated, who owns it, and the threshold for each maturity stage. Without this, the model produces numbers that cannot be defended in a budget conversation.
KPI definitions
| KPI | Definition | Numerator and denominator | Source | Cadence | Owner |
|---|---|---|---|---|---|
| Wait time | Door-to-modality-table interval. | Sum of table time minus arrival time, divided by encounters | RIS arrival and start timestamps | Daily | Imaging Operations Manager |
| Registration defect rate | Encounters carrying at least one downstream-correctable error. | Defective encounters divided by total encounters | Revenue cycle workqueue plus audit sample | Weekly | Patient Access Director |
| Initial denial rate | First-pass payer rejections. | Denied claims divided by submitted claims, first 60 days | Remittance advice | Monthly | Revenue Cycle Director |
| No-show rate | Scheduled exams not completed. | No-shows plus late cancellations, divided by scheduled | Scheduling system | Weekly | Scheduling Manager |
| Rework per encounter | Staff time correcting upstream defects. | Corrective minutes divided by total encounters | Time study plus workqueue resolution log | Quarterly | Patient Access Director |
| Patient experience | Composite of access, communication, and dignity domains. | Weighted mean of domain top-box percentages | Survey vendor or internal short form | Monthly | Patient Experience Officer |
| FDRS | Composite reliability score. | 0.30 Experience plus 0.40 Accuracy plus 0.30 Throughput, less the friction penalty | Computed from the rows above | Monthly | Imaging VP or Service Line Lead |
| Equity Friction Index | Gap between digital adoption and assisted-support coverage. | Portal minus assist minus 20, divided by 100, floored at zero | Computed | Monthly | Health Equity Officer |
Maturity stage thresholds
| Stage | FDRS | Defect rate | Initial denial rate | Patient experience | Hallmark |
|---|---|---|---|---|---|
| Reactive | Below 5.5 | Above 12% | Above 10% | Below 6.5 | Heroics. Variation is absorbed by staff overtime. |
| Defined | 5.5 to 7.0 | 8 to 12% | 7 to 10% | 6.5 to 7.5 | Documented basics. The process exists but adherence is inconsistent. |
| Standardized | 7.0 to 8.0 | 5 to 8% | 5 to 7% | 7.5 to 8.2 | Consistent execution. Defects are detected, not yet prevented. |
| Optimized | 8.0 to 9.0 | 3 to 5% | 3 to 5% | 8.2 to 9.0 | Predictable variation. Continuous improvement is embedded. |
| Adaptive | 9.0 and above | Below 3% | Below 3% | 9.0 and above | Self-correcting. The system learns from each defect and equity gaps are actively closed. |
These thresholds are the engine, not a legend. The scoring curves that convert each raw KPI into a sub-score are anchored to the boundary values in this table, so a facility sitting exactly at the Optimized threshold on every measure scores exactly at the Optimized boundary. The dashboard runs a self-test on load confirming that identity holds.
How the composite is assembled
Figure 4. Six measured KPIs feed three weighted dimensions, which combine into the composite. Wait time appears in both Experience and Throughput deliberately, because a long wait is simultaneously a capacity fact and an experience fact and the score should reflect both. The friction penalty is subtracted last, after the weighting, so it can pull a technically high-performing configuration down a full maturity stage.
Governance and cadence
Wait time and no-show
Modality-level huddle at the point of care. Two numbers, five minutes, no slides.
Defect rate, rework, equity friction
Service-line huddle using the template on the Toolkit tab. This is where lever changes are proposed and where the previous week’s change is judged.
FDRS, denial rate, patient experience
Operating committee with a formal maturity-stage review. The stage, not the decimal, is the thing to report upward.
Scope and limits of this model
This is a decision-support simulator, not a prediction engine. The coefficients express directional relationships that are well supported in operations and revenue cycle practice: that registration defects propagate downstream, that self-service quality depends on readability, that thin staffing amplifies rather than absorbs error, and that unassisted digital adoption transfers burden onto the patients least able to carry it. The magnitudes are calibrated so that the published maturity thresholds behave correctly, not fitted to any single facility’s data. Treat the outputs as a structured argument about trade-offs, and replace the assumptions with your own measured values before any figure leaves the room.