In the U.S., more than a third of patients are referred to a specialist each year, and specialist visits constitute more than half of outpatient visits. Referrals are the link that make this connection between primary and specialty care. From 1999 through 2009 alone, the absolute number of visits resulting in a physician referral increased 159 percent nationally, from 41 million to 105 million. This volume and the frequency of specialty referrals has steadily increased over the years and will only continue. Yet despite this rise in frequency, the referral process itself has been a great frustration for years.
Specialty referrals are a complicated business. There are many moving parts and players that all have a crucial role to play within the process. By breaking it down and looking at exactly what a referral is, who is involved, and the challenges they face, we can then look to fix what is broken. What needs to be improved? And could there be a digital solution?
Let’s start from the very beginning by looking at the stakeholders and their unique interests and concerns.
Patient – The patient experiences a health concern and needs care to get it resolved. The primary physician doesn’t provide the full solution and refers them to a specialist with more expertise about the patient’s condition. This is where the referral occurs. Currently, the extent of the referral is the physician handing a phone number to the patient to call and schedule the appointment. It’s up to the patient to contact the specialist and follow through with the next step, which explains why 20 percent of patients never even schedule the referral appointment.
Provider –There is more than one provider involved in the referral process. First is the referring (or sending) provider and then the target (or receiving) provider. The referring physician is the provider recommending (referring) them to a specialist. The target provider is the specialist that has been recommended. For a health system or physician group, there are obvious financial and quality of care benefits associated when a patient is sent to a trusted provider within network. When patients don’t go to their referral appointment, the health system or physician group loses in several ways. First of all, they have lost control over providing comprehensive care to the patient. If a patient gets readmitted to a hospital because of their negligence to follow through on a referral appointment, the health system gets penalized for the readmission. The penalty could result in CMS withholding up to 3 percent of the funding provided to the health system. The system also suffers in terms of the perception of their quality of care. If a patient is not secured with a provider within network, they may go to a competing system.
Plan – Health plans have several important considerations when a referral happens with a vested interest on three fronts to ensure the patient goes to the target provider:
1) The health plan benefits if the patient goes to a target provider within their network. Not only will patients be directed to providers that best meet their needs, but the plan also benefits when patients are referred to the providers in their Smart Network. These providers are trusted for superior care for the patient and reduced costs for the plan.
2) When a plan member doesn’t get the care they need to maintain good health, their likelihood of having major adverse events rises dramatically. This means they will end up in the ER or needing other expensive care, which represents big costs for the health plan.
3) The current approach to referrals often results in long lead times, which makes for a poor patient experience and can increase costs.
Guest post by Todd Greenwood, PhD, MPH, director of digital strategy, and Benjamin Dean, digital and business strategist, Medullan.
Once upon a time, all that pharmaceutical companies had to do to get their drugs on formulary was to package their clinical data and convince payers that their products performed better (or better enough) in clinical trials. Contracts were struck and the revenues flowed. For most new specialty drugs, those days are now history.
With the average retail price of a specialty drug used on a chronic basis exceeding $53,000 (according to an AARP study), nearly 200 times the average price of generics, payers are demanding that pharmaceutical companies make data-driven, value-based cases before access is granted. Even when payers are convinced , they build stipulations into value-based contracts that require manufacturers to prove that outcomes are being met with their covered lives, or else the pharmaceutical company will face additional penalties or further restrictions.
This all means that the data that manufacturers have used to drive regulatory approval are insufficient for garnering payer formulary access. Companies are being required to prove that their drugs work in the real world – not just within the carefully controlled environment of a clinical study. Across therapeutic areas from osteoporosis to oncology, payers have and are currently using real world evidence studies to define their formularies. Payers want to know how expensive specialty drugs will perform as patients adhere to (or in most cases don’t adhere to) their medications, and outside of the rarified air of a traditional clinical trial.
Equally importantly, payers want to know how drugs affect the most important (and most expensive) health outcomes. Clinical data showing that a drug performed some percentage better than either a category leader or a placebo is now insufficient for new specialty drugs. Instead, payers need to know how health outcomes improved and how effective the drugs were at keeping patients out of the hospital and away from the catastrophic costs.
While it may sound easy, providing this kind of data is far from simple. Clinical trial data is controlled, clean and contained. Surveillance data (AKA real-world data) is a different beast, because patients are complicated. We have multiple conditions, take multiple medications, and we are inconsistent, rarely complying with our doctors’ orders. Moreover, the outcomes that payers care about – hospitalization, disability and death – can be difficult to distill. The data needs to be compiled from a variety of sources: medical and prescription drug claims, electronic health records, the lab (genomic and pathology data) and directly from the patient. Compounded with this, different populations of patients have different risks, and comparing one to another is fraught with difficulty. Finally, real world data can take time to accumulate. In order to know if a drug is working “in the wild”, researchers need to follow enough patients, for long enough, to observe negative health events of interest.
Take, for example, the new class of hyperlipidemia drugs, PCSK9-inhibitors. These injection drugs have been shown to cut LDL cholesterol levels in half, compared with about a 20 percent reduction for statin-class drugs like Zetia. But given the high price of these drugs ($14,000 per year in the US) plus their potential to be prescribed to a significant percentage of the population, payers have largely refused an access foothold. Payer organizations in the US and around the world are asking the same question: how well do these drugs work in real patient populations and to what end? Given that these drugs will be sanctioned for high-risk patients (many of whom will continue to use statin class drugs as combination therapy), payers are concerned about adherence, and ultimately if there is lower cardiac risk and fewer related cardiac events in patient populations. Many economists are asking: can’t we achieve the same ends for far less money by getting patients to adhere more faithfully to their statins?
The need is clear: pharma companies who have invested significantly to develop and launch new specialty drugs have to prove their worth with real world data. But in markets like the US, where providers are typically siloed and disconnected, it’s challenging to capture patient-level condition and drug utilization data, and effectively append it with hospitalizations, other outcomes evidence and costs in order to develop a complete picture.
But there’s hope. As the specialty drug market begins to shift to a value-based model, new ways of tracking real-world usage and connecting it to outcomes are emerging. This is where digital health is poised to play a critical role.
The image provides a pretty concise view on some of the prevailing thoughts on the use of consumer’s mobile technology and how perceptions of the technology might potentially improve patient outcomes.
Not surprising, one third of smart phone users look up health information on their devices via the web. Most surprising to me, though, is that according to the graphic, 25 percent of low-income adults own a smartphone; I shouldn’t be surprised given people’s passion for the latest devices. Hopefully, though, this will help improve their care and outcomes, individuals who, of course, would likely fall into the class of people most likely needing care but not receiving it or receiving it through non-traditional means.
If nothing else, as Aetna suggests through the image is that technology and personal devices may allow greater access to care and to information to improve care.
Such technology, and its use, is clearly the future of individual care and actionable outcomes for individuals. I only wonder what it will take to harness and implement real programs that help real people received sustainable care and guidance at the individual level, and how long it will take to become wide spread