On Monday, President Trump signed an executive order that will require hospitals and payers to disclose negotiated rates and out-of-pocket costs to patients.
While the "how" is yet to come, the intent seems relatively clear: the Trump administration wants patients to be able to shop for procedures the way we shop for things on Amazon or Google.
There will be arguments that challenge the legality of this order, but as I did in our previous blog about the effect of rate disclosure on provider technology, I'm going to look past these debates to a possible world where this order is enacted as intended.
What data do we need in order to provide an Amazon-like shopping interface for patients?
A Few Tables from Hospitals, a Few Tables from Insurance Companies, and the Formulas That Connect Them
Let's start with the impetus for this executive order in the first place: The patient. Per the intent of this mandate, the patient should not need to understand the complexity of the pricing logic, nor the HCPCS or PCS codes to attach to their medical issue, nor the nitty gritty details like whether the anesthesiologist is in-network at their hospital. They should simply do two things: have insurance coverage, and advocate for their health.
The patient's inquiry drives the need for data behind the engine. Just like Google, machine learning algorithms could parse the patient's request from layman's terms to claims pricing logic terms. The inquiry "how much is it to have a baby" could yield the following structured logic:
- DRG 805 Delivery Without Sterilization
- Average length of stay: 2 Days
- Subscriber's plan type: HMO Gold 123
- Subscriber's payment logic for inpatient delivery: $500 per diem up to 4 days
- Expected negotiated rate for drug costs, $1,000
- Extra 10% coinsurance due off negotiated rate for high cost drugs
- Professional fees are covered in per diem rate
- Likely cost: $1,100. Upper range: $2,200
It should then turn this structured logic back into layman's terms for the patient.
A responsible engine would also prompt the searcher for additional information: are there any complications with the pregnancy (that could lead to variations in payment)?
This type of output is imminently available given access to a patient's benefits logic, the negotiated rates between provider and payer, and enough claims data to account for clinical variation between inpatient stays.
In order to provide an accurate true cost, however, the engine wouldn't just stop at the cost of the inpatient stay.
For example, my friend is expecting a child and has insurance. He recently reached out with a common question about in and out of network coverage: What do I do if our doctor is in network, but the facility he delivers at is out of network? This (and the reverse scenario) are standard issues patients face that could be mitigated with a tool that connected a chain of commonly associated in-network services.
If you have a hip replacement performed, you will probably receive imaging services, anesthesia, an expensive implant and physical therapy during recovery. It shouldn't be up to the patient to figure out the logical sum of all these costs. The patient should just be able to type, "I need a hip replaced. How much is it?" in order to see the best total price.
Per the intent of the executive order, the duty to responsibly display these true out-of-pocket costs will fall to providers, payers and third-party "entrepreneurs," not the patient.
The Good News: This Data Already Exists. It's Just Scattered and Proprietary
Whenever an insurance company receives a claim from a healthcare provider (typically in a complex claims format called the ANSI 837), it parses the file into the data necessary to price and pay the claim. This involves a series of logical rules, such as:
- Is this patient even covered at the time of service? (eg: have they been paying their premiums?)
- Are the services billed covered by this patient's benefits? (eg: This is a bill for chiropractic services. Do we cover chiropractic?)
- Under the patient's benefits agreement, and per the benefits they've already used this year, what will the patient owe? (This is a complex part, as it may involve a litany of copayments, fee schedules, coinsurances and deductibles).
- Under our agreement with the provider, what do we owe the provider for services?
A responsible insurance company already has these benefits stored in a relational, queryable format. In fact, many insurances companies already expose some of their pricing logic through APIs for pricing and eligibility estimates. The only difference is that these APIs are currently not open to the general public.
But for the most part, proponents of the price transparency executive order should be encouraged: these complicated calculations are already occurring, and the data required to carry them out already exists somewhere in a structured format (sure, with exceptions).
For Many Hospitals, Estimates Will Always Be Estimates. But a Lot Can Be Done
Having reviewed hospital reimbursement for most of the past decade, I've seen a variety of reimbursement logic structures and a variety of patient encounters.
For both outpatient and inpatient encounters, many common procedure costs can be forecast before a patient's admission to the hospital. Hip replacements, knee replacements, pacemaker insertions - these are the types of planned visits where 90% of the claims look like carbon copies of the ones before them. However, there is still a lot of variation (and often surprise) for hospital staff when these claims are processed by insurance companies for payment.
Hospitals will also be tasked with communicating the uncertainty of any given patient encounter - after all, your health doesn't always cooperate. In a world where a procedure does not go as expected, or another health complication interferes, a hospital incurs additional costs, and the patient often does as well.
In a "Google-esque" price transparency tool, statistics should be used to communicate the likelihood of certain events and extra costs given the possibility of complications. Patients should know their upper bound cost and the likelihood they would pay it - whether it's the annual out-of-pocket max, or an extra per-diem coinsurance.
The good news here is that Trump's executive order also pushes for increased third-party access to claims data. In a world where companies are incentivized to offer the best price transparency tool (to win subscribers or patients), a good tool would be more than able to use statistics to display these considerations.
Would an Intuitive Price Estimate Tool Drive Patient and Subscriber Volume?
Many insurers, such as Anthem or UHC, already offer some form of cost estimate tool for already registered subscribers. The executive order, in contrast, would seemingly allow negotiated rates & out-of-pocket costs to be compared across plan, payer and provider lines, which does not yet exist.
For provider systems with clean chargemasters, simple procedure mixes, organized data and straightforward reimbursement contracts, there is at least a PR incentive to be a first mover towards a search-friendly price platform.
After all, providers might discover that patients find peace of mind in simply knowing their out-of-pocket costs, even if those costs are high. For example, a patient reviewing a published public chargemaster might currently be under the impression that he would be on the hook for the list price of a cardiac ablation. However, a price transparency tool could be a medium for payers and providers to show that - at least for certain procedures - the subscriber has already forked out most of the cost through his premium.
If more patients could Google their medical condition, trust the pricing logic to a degree, and see a true out-of-pocket estimate, maybe more of them would also reach out to schedule an appointment.
Are you curious about building a tool for patients or subscribers, but you don't know where to start? Reach out to us at email@example.com to start the conversation around patient-friendly web apps, price transparency tools, and how to get past technical hurdles at your organization.