October 6, 2026 | Diagnostic labs have an underappreciated opportunity to team up with health plans on pilot programs focused on improving access to testing and reducing health disparities. The laboratory community should be thinking beyond medical policy coverage to design for completion, measure and address the gaps, and share accountability for action, advises Mark Hiatt, M.D., chief medical officer for the accreditation organization RadSite.
“Coverage opens the door, access gets patients through it, and follow-up converts a result into better care,” says Hiatt, speaking at the recent Next Generation Dx Summit in Washington D.C. He was joined at the podium by Eugean Jiwanmall, senior research analyst for technology evaluation and medical policy at Independence Blue Cross, reminding the audience that the shared operational model of this sort requires partnership and communication.
It also takes initiative on the part of labs. “Nobody’s closer to your test than you, and nobody knows the [intended use] population better than you,” says Jiwanmall.
The dossier of a lab test currently addresses its performance, for whom and where it performs, and what changes because the test is used, Hiatt says. He proposes that a fourth element be added about whether the technology is accessible in the real world to the patients who need it.
Eight steps comprise the pathway from coverage to care improvement, says Hiatt, each of which can be posed as a question. Is there an unmet need? Can the right patient be identified? Does the policy and benefit apply? Can the clinician order the test without excessive friction or too much prior authorization? Can the patient complete the specimen or study (e.g., lie on an uncomfortable CT gantry or submit to the narrow tube of an MRI scanner)? Is the result accurate, timely, and delivered to the right people? Does follow-up occur? Does care improve?
Addressing gaps on that pathway reduces avoidable services, which could be captured on a “payer-lab access scorecard” tracking progress with metrics around reach, access, completion, action, and equity and value, says Hiatt. The pilot model he describes “could be a different way for labs to get access into health plans ... payers have a heart and dedicated resources for this type of initiative.”
Neither coverage nor the collection of claims is the end-all, Hiatt says, since access rests between the two and distinct from what action might be spurred by testing results. The whole concept of health disparity can in fact be translated into a diagnostic journey, he adds.
“Coverage is indispensable [because] it opens doors, but it’s only the first gate,” says Hiatt. A test is covered provided that the benefit category, coding, payment pathway, medical policy, and utilization management requirements are sufficiently clear.
Whether a test is obtainable by eligible patients depends on whether they can “find a site, schedule the service ... undergo the testing, understand the instructions, complete the collection, and avoid some type of administrative dead end,” he continues. Obtained tests are actionable when results are delivered in a usable, interpretable form, and follow-up happens within the relevant clinical window.
If utilization is lower than expected, Hiatt advises labs and health plans to determine if the covered population can obtain the testing. “A favorable coverage decision is a milestone, not the point of access.”
Hiatt next offers definitions for the related terms of disparity and inequity. A disparity is an observable and measurable difference between population groups in terms of being identified for, offered, or scheduled for testing, or connected to follow-up. “An inequity is a disparity that is avoidable and unfair, and that could be some barrier related to the system.”
Its opposite, equity, is “a fair opportunity to benefit,” he adds. “It’s not that everyone gets the same thing; it’s just that everyone has an opportunity to get what is good for them.”
A useful way to operationalize disparity is to “map the full access cascade,” says Hiatt, since gaps can emerge at any transition point on the eight-step pathway. While patients experience one pathway, the journey comprises six different organizational silos.
One of these organizational silos is geography and capacity, says Hiatt, referring to the collection sites, imaging facilities, and qualified providers needed to provide a service. Patients needing to undergo endoscopic ultrasound need access to a qualified, sub-specialized gastroenterologist, he offers as an example. “These providers aren’t uniformly available in all places.”
Workflow and referral friction represent another silo, he continues. “This matters when eligibility is unclear, order entry is difficult, prior authorization is burdensome, or referrals loop between organizations.”
Language and health literacy are also key considerations, says Hiatt, affecting patient outreach, specimen collection, test-related instructions, and interpretation of results. Affordability likewise matters, including not just the cost of the test but transportation to get to a site where the specimen can be collected, time away from work, and childcare—as do feelings of uncertainty about getting tested at all.
Evidence and algorithm representation is of significance when the study population does not reflect the intended use population, Hiatt notes. “You may have the greatest test, but it’s targeted to a group of people who won’t truly benefit from its purpose. It could be the models in developing the test were trained on biased populations.”
Disability access is yet another organizational silo, says Hiatt, with physical access, alternate formats, and communications support all being elements that can potentially impact the ability of patients “to exploit the benefits of our amazing scientific technologies.” Disparities often appear at handoff points “where one organization believes its responsibility has ended.”
The partnering opportunity being discussed here assumes that labs have already met the evidentiary rules of a health plan for coverage and payment of a lab test, says Jiwanmall. The focus is on “bringing to our attention the subpopulations that are not getting the optimal benefit” from a test that has been determined to be medically necessary, safe, and effective.
“Date stratification is extremely important ... not only when we’re trying to figure out if something should be covered but subsequently as well,” Jiwanmall says. Diagnostic test owners have more information than health plans to help fill in access gaps, and he encourages them to identify unmet clinical needs from the perspective of the barriers—be they related to race, ethnicity, disability, language, or geographic location.
Benefit design matters, in terms of the availability of the site of service and network capabilities, he continues. If the current network laboratories in the plan are not fulfilling the needs, this could be the starting point for exploring a potential pilot around an additional “fair equity option” involving a clinically equivalent test. Quality and community partnerships are easier to forge in large metropolitan areas, but labs might consider enabling them in less advantaged regions if their tests are supposed to be available in all covered locations.
Health plans routinely track and report 22 HEDIS (Healthcare Effectiveness Data and Information Set) measures that can be stratified by race and ethnicity to identify subpopulations suffering from a disparity to assess the gravity of the situation and, more importantly, identify “where does the problem actually lie,” says Jiwanmall. Opportunities exist to measure each step within the broader trouble spot.
If the sample size permits, “intersection analysis can reveal barriers that a single category hides,” he says. Among populations eligible for a test, the problematic issue could variably be getting the test ordered, getting the specimen collected, or getting that sample to the right place, for example.
Jiwanmall encourages labs to look at variables beyond just race and ethnicity to “enhance your overall picture and present the problem in a more meaningful way.” Causes behind disparities notably include socioeconomic status for which ICD-10 codes are available.
In one ongoing initiative focused on blood pressure, diabetes, and maternal health, Independence Blue Cross held a couple of summits bringing members’ voice to the table, he says, which succeeded in identifying language and cultural support as the main hindrances. “You are not going to do any injustice if you bring that voice to us for your particular situation.”
Targeted analytics will reveal the segment of plan members who are not getting the services they need, and this can be the basis for a targeted engagement, says Jiwanmall. But since health plans are dealing with multiple stakeholders, the onus is on labs to bring those pilot ideas forward. “This is going to be where the communication and the data and the network operations will come together to paint a better picture [of the situation].”
“Labs control more of this access pathway than they sometimes recognize,” Hiatt emphasizes. Among the access choices are specimen choice (what performs well under controlled conditions may not work in the real world), collection strategy (e.g., CT-guided biopsy at an academic setting versus liquid biopsy in a mobile van), stability and transport (e.g., real-world temperature and shipping time), turnaround time (may or may not match the window needed for a clinical decision), result usability (e.g., one easy-to-understand page versus many indiscernible ones), and patient affordability (i.e., overall out-of-pocket costs).
The proposed payer-lab access scorecard is focused on reducing avoidable services and better managing the unavoidable variables, Jiwanmall says. Results of actions taken should also connect to clinical utility, which often means actual patient management and successful delivery of follow-up care on abnormal test results.
Hiatt suggests that labs get creative by embarking on a pilot with a receptive health plan leader using the scorecard metrics. In a 12-month payer-lab pilot he presented at the conference, first on the to-do list was to “define and prepare, [meaning] specify the eligible cohort and intended setting, map baseline funnel and disparities, agree on data, privacy and consent, train providers, and activate [the] access point.”
The next step was “launch and learn, [meaning] start targeted outreach and ordering support, monitor drop-off and specimen quality, resolve site, language, billing, and workflow friction, and review measures every two to four weeks,” he says. Finally, “evaluate and scale [to] assess completion and follow-up by subgroup, evaluate outcomes, resource use, and burden, test whether identified gaps narrowed, and decide whether and how to scale.”
Before asking for coverage, Hiatt concludes, labs should first ask themselves five questions: “Is there a need? Can the patient realistically get the technology? Does the evidence represent the population and setting in which the test will be used? What about the communication, and will the lab and payer partner together to improve access, outcomes, and value?