Dashboards don't fix access -- decisions do

Most healthcare organizations aren't short on data. Many have dashboards. Yet access problems persist: long waits, high call volume, uneven utilization, and patients (and staff) who feel like every step takes too much effort.

When that happens, it usually isn't because "we don't have reporting." It's because the reporting isn't built to drive change.

The metrics don't map to something you can actually do.
Definitions vary across teams (what's a "no-show" versus a "late cancel" versus a "reschedule"?).
Numbers are too aggregated, hiding big differences by specialty, provider type, or location.
Data lands too late to prevent the problems.
There's no operational rhythm to review it, decide, and follow through.

Patient access analytics should function more like air-traffic control than a monthly scorecard. It should be "real-time" enough to spot bottlenecks early, segmented enough to pinpoint where they're happening, and structured enough to guide the next intervention.

This post lays out some of the metrics we've found matter most to our customers at MDfit. How we define those together, and how to operationalize them so they drive real improvement.

Start with what "access" means in your organization

Access often gets talked about like it's one problem to solve, but in reality it's a bundle of outcomes. Your analytics should map to those outcomes. And it's not all about Productivity and Efficiency (although many metrics related to those are always included too):

Timeliness

How quickly patients can be seen

Reliability

How often appointments happen as planned

Ease

How much effort it takes for a patient to get scheduled and prepared

Appropriateness

Whether the patient is matched to the right visit type, clinician, and resources

Equity

Whether access differs across populations and communities

If you don't call-out which of these (or which of your focus areas) you're improving, you risk ending up with analytic dashboards that look busy but don't lead to outcomes.

Which access metrics tend to matter most

Capacity and availability

These metrics tell you whether supply can meet demand and whether the supply you have is actually usable.

Time to Third Next Available (TNA / TNAA)

Third-next-available is a common access indicator because it reduces distortion from one-off openings. Like a single cancellation today makes tomorrow look "available" when your true backlog is weeks out. Track it by specialty, location, provider type, and appointment type. ^[1]^[2]^[3]

What you're typically watching out for is not "good" TNA. It's patterns like:

TNA rising for new patients but not follow-ups (a template mix problem).
One location consistently worse than others (a capacity or staffing distribution problem).
One provider type (e.g., APP vs MD/DO) with very different availability (a role design problem).

Appointment supply by type

Count and track available slots or total capacity by visit type and modality:

New patient capacity
Follow-up capacity
Telehealth capacity
Procedure capacity

This is where leaders often have an "aha moment". Many access problems aren't pure shortage. They're mismatches. You can be "busy" while still failing at the visit types patients actually need.

Utilization / fill rate

Consider tracking utilization in two ways:

Scheduled utilization: how full the calendar looks.
Completed-visit utilization: what actually happened after no-shows and cancellations.

For example, if TNA is rising while utilization is low, you likely have a mismatch problem, not a raw capacity shortage. Appointment scheduling research has long made the point that scheduling and access is constrained by both demand and how capacity is structured and allocated. ^[4]

Demand and intake

These metrics tell you how much potential "work" is arriving and whether it's converting into scheduled care.

Inbound request volume by channel

At minimum, consider tracking:

Phone calls
Portal messages
Web scheduling sessions
Chatbot / voice AI sessions (if applicable)

A key insight here is that demand can quickly shift between channels. Have great online scheduling? Expect much lower call volumes. But if your online scheduling has an issue, you will quickly see increased phone demand.

Reason-for-visit distribution

If you can categorize reasons for visit, you can manage capacity far better. Reason distributions are where you'll spot things like:

"New patient" demand spikes due to marketing, network change, or competitor disruption
Follow-up categories that are driving most reappointments
Seasonal shifts (respiratory/flu, dermatology, sports injuries, etc.)

You don't need perfect groupings to get value out and see important reason trends. As few as a dozen reason "buckets" can be enough to shape strategies.

Conversion rate

A patient's access "pain" isn't always wait time. It can also be the patient "tried but failed." Track conversion at a few key points:

Percent of people who start scheduling and complete booking
Percent of referrals that become scheduled appointments
Percent of waitlist opt-ins that accept an earlier slot

If conversion in a key area drops, your problem may not be demand. You might have a broken access pathway or technology issue.

Schedule integrity

These metrics tell you how stable the schedule is and how much variability your team is managing.

No-show rate

If you can, track no-shows by appointment type, lead time (days from booking to appointment), location, time of day, and booking channel. Interventions like reminders can improve attendance and reduce wasted capacity, and evidence supports the role of mobile messaging in improving appointment attendance. ^[5]

Late cancellation rate

Late cancels can be as damaging as no-shows because they erase the backfill window. If you only monitor no-shows, you're missing half the utilization story.

Reschedule rate

High reschedule rates often signal problems that occurred upstream:

The slot wasn't a fit (wrong duration, wrong clinician, wrong location)
Prep instructions weren't clear or feasible
Insurance barriers were discovered late
Provider templates are unstable

Same-day disruption rate

How often the day's schedule changes because of cancellations, provider changes, or operational disruptions.

Two clinics can have identical utilization but wildly different staff burden depending on how stable the schedule is. If the day is constantly being adjusted, your team is doing invisible work. Patients feel it as phone-tag, delays, and uncertainty.

Contact center / scheduling-team and operational access

These metrics are mostly common sense, and capture how hard patients have to work to reach you. They answer the question -- "do patients have access to our access systems?"

Call abandonment rate

If patients hang up before reaching someone, access is broken, even if appointment availability exists.

Speed to answer / time on hold

Track both the distribution and the average. "Long wait" experiences are what drive frustration and complaints.

Average handle time (AHT)

AHT is useful, but only when combined with outcome metrics. A fast call that doesn't resolve the need just creates repeat contact.

First contact resolution (FCR)

Did the patient's need get resolved without transfer, callback, or repeat contact?

Many "capacity problems" are actually "contact problems." Patients can't reach you to use the capacity you already have. Research in the Veterans Health Administration has linked telephone performance (like speed to answer) with patient perceptions of access. ^[6]

It's also worth mentioning that for some healthcare call centers, performance is explicitly monitored and defined through standard Government program performance measures (e.g., speed to answer and abandonment/disconnect rates). ^[7]

Digital channel metrics

If you offer self-service, you have to measure whether it works, and where it fails.

Self-scheduling completion rate

Track sessions started through sessions completed, and identify any drop-off points (provider selection, visit type, insurance, identity verification, etc).

"Call us" fallback rate

How often does a digital flow end with "Please call" and shift the load back to staff? These are very common with "helpful" chatbots.

Time-to-book

How long it takes a patient to go from intent to confirmed appointment.

This is where you might need to use "secret shopper" techniques. Digital access that fails silently increases call volume and erodes trust. If a patient tries online twice and fails, they think "this place is impossible to figure out."

Defining your metrics to make them usable

Most access analytics projects stall because metrics aren't defined consistently. Before you build a dashboard, align on a few definitions that may become landmines later:

What counts as "available"? Released slots only, or does it include held capacity?
How do you classify cancellations? Do you track late vs early, and what time threshold?
How you handle reschedules? Is it a cancel plus new booking, or a single reschedule event?
When does "time to schedule" start? At first contact, when referral is received or patient starts a session?
What's your definition of "completed visit"? Arrived and seen vs billed encounter?

If you're trying to reconcile metrics across multiple scheduling tools or workflows, fragmentation really can increase time to schedule and contribute to errors and overbooking when staff must view availability across systems. ^[9]

Turn dashboards into operational improvements

Analytics only help when there's an action/feedback loop. Here's a practical model:

Build a monthly access review rhythm

Bring together access/call center leaders, clinic operations, schedule template owners, digital/front door and marketing teams, and service line leaders. Keep it focused on:

What changed since last discussion
What's outside our expected thresholds
What decisions are we making this month

Define trigger thresholds tied to actions

For example:

If TNA rises above your threshold for new patients → release held capacity sooner, rebalance templates, or add sessions.
If abandonment spikes → add callbacks, shift staffing, or reduce transfers by fixing root causes.
If no-shows rise for a visit type → adjust reminder cadence and make rescheduling easier.
If digital drop-off rises → fix directory accuracy, visit-type mapping, or identity verification issues.

Track your work

When you change something, document:

what changed
where it applied
when it started
what metric you expect to move
when you'll reevaluate

Report these changes as a log at your regular meeting to avoid "random acts of optimization" and build organizational learning.

The Bottom Line

Better patient access isn't achieved by measuring everything. It's achieved by measuring what matters for your organization, defining it clearly, and using it to drive decisions. When analytics becomes an operational tool, you can reduce wait times, improve schedule stability, and make access easier for patients and staff.

References

Murray M, Berwick DM. Advanced access: reducing waiting and delays in primary care. JAMA. 2003;289(8):1035–1040. pubmed.ncbi.nlm.nih.gov
Brar S, Hopkins M, Margolius D. Time to Next Available Appointment as an Access to Care Metric. Jt Comm J Qual Patient Saf. 2019;45(11):779–780. pubmed.ncbi.nlm.nih.gov
Shah N, et al. Association Between Clinic-Reported Third Next Available Appointment and Patient-Reported Access Measures in Primary Care. JAMA Network Open. 2022. pmc.ncbi.nlm.nih.gov
Gupta D, Denton B. Appointment scheduling in health care: Challenges and opportunities. IIE Transactions. 2008. microsoft.com
Car J, Gurol-Urganci I, de Jongh T, Vodopivec-Jamsek V, Atun R. Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database Syst Rev. 2012;(7):CD007458. pubmed.ncbi.nlm.nih.gov
Griffith KN, et al. Call Center Performance Affects Patient Perceptions of Access and Satisfaction. Am J Manag Care. 2019. pmc.ncbi.nlm.nih.gov
Centers for Medicare & Medicaid Services (CMS). Key Part D Contacts in HPMS: Purpose and CMS Requirements (includes call center operating standards). 2006. cms.gov
Agency for Healthcare Research and Quality (AHRQ). Patient Experience Measures from the CAHPS Clinician & Group Surveys (includes the “Getting timely appointments, care, and information” composite). Updated 2011. integrationacademy.ahrq.gov
U.S. Government Accountability Office (GAO). Veterans Health: Improvements Needed to Achieve Successful Appointment Scheduling Modernization. GAO-25-106851. 2025. gao.gov

Patient Access Analytics: The Metrics That Actually Improve Access