By Kyle Biesecker, Ph.D., Chris Condes, and Bill Woywod, Guidehouse
As one of the most prevalent health problems in the world, chronic lower back pain offers a huge market opportunity for medical technology companies that can crack it. Yet, this market proves to be among the most difficult to break into, even though an estimated 35 million patients in the U.S. alone seek treatment, and a vast treatment gap remains between the go-to standards of conservative care and surgery.
We have identified three market assumptions companies commonly make that stunt their product trajectory. This article shares insights on those common missteps and provides data-driven best practices for adroitly targeting this opportunity-rich market.
Inaccurate assumption #1: Patient pathology is clearly identifiable, and physicians will know when to use our product.
Best practice in action: Know your technology decision makers.
When someone says, “my back hurts,” it is clear to that person what they mean, and it is generally understandable to others that the person is experiencing discomfort. But from a clinical perspective, few reliable tests exist to narrow the diagnosis, and individual treatment options tend to be effective for specific types of pain, not widely applicable.
Chronic lower back pain (CLBP) causes and effects are extremely diverse and varied – numerous possibilities could be causing the pain. Accurate diagnosis is not guaranteed or easy to ascertain in most cases. In addition, it is difficult to determine the root cause, especially when other forms of back pain and potential comorbidities are present, as they usually are in most CLBP patients. Patients, especially elderly ones, often suffer from overlapping types of back pain, which complicates diagnosis and treatment significantly.
Measuring pain presents its own challenges. Pain metrics used in clinical practice only have moderate ability to distinguish characteristics of pain objectively. So, for example, because people tend to acclimate to pain over time, patients tend to rate rapid onset pain as more extreme than slower developing pain, even if the physical discomfort is technically similar.
Further compounding the challenge, back pain tends to come and go and often resolves organically over time. As a result, physicians tend to prioritize pain relief over accurate diagnosis, especially in cases where coverage and/or resources are limited.
For medtechs, this lack of clear-cut diagnostics and symptom volatility make it difficult to plan where your healthcare touchpoints will be and when patients will seek what type of care. But you can glean the best opportunities for therapy intervention and patient journeys by applying precisely designed and specified queries against Medicare and commercial claims data.
For example, a startup with a therapy for discogenic pain had significant difficulty helping physicians understand candidates for treatment. No clear diagnostic test or characteristic “giveaway” existed to indicate whether this specific disease was present and causing the patient’s pain. The company needed to figure out how to help physicians understand and spot the patients who would benefit most from this product.
To resolve this problem, a road map was designed to bridge early discoveries in imaging trials that indicated MRI targeting was possible to clinical practice guidelines using common imaging techniques. This diagnostic was also integrated into clinical trial strategy to better understand its connection to reported back pain.
To determine the pathology to pursue for your product, outline key factors that help identify the perfect patient, such as the physicians seen, diagnostic tests performed, and treatments tried and failed. Also, outline patient characteristics, like age bracket and comorbidities, keeping in mind that multiple pain generators in patients are extremely common. This is an issue when going to market, because clinical trial designs generally exclude these patients in favor of more predictable ones. So, it’s important to understand where overlaps might be and to investigate multimodal therapies to address different paint generators at once.
Keep in mind, providers that focus on back pain are not a monolith. They are self-selected from a variety of specialties and may have drastically different patient populations, treatment strategies, and priorities. Understanding the specific segment of providers who you want to target will allow you to fit your product into their existing preferences, maximizing the utilization of your product. It’s also important to think beyond physician specialties to consider their patterns of procedural behavior and what information and guidance might need to be provided to motivate them to adopt your technology.
Inaccurate assumption #2: Our product easily fits into the standard of care mix.
Best practice in action: Before launching a new product, it’s critical to understand who your ideal patients are and where your product fits.
The standard of care mix in low back pain is highly fragmented. The lack of effective therapies along with the relatively subjective nature of CLBP symptomology enable a treatment environment in which the individual judgment and preferences of physicians drive the treatment options provided.
CLBP patients initially present to any number of specialists, depending on who they know, what they think their problem is, and what network they are in, among other factors. There are approximately 15 types of providers – including primary care, chiropractors, neurologists, interventional pain managers, and orthopedic surgeons – involved in pain care, and about eight of these prominently treat back pain. The physician’s specialization generally dictates the primary treatment options presented to the patient.
Based on their specialization, providers often also have drastically different treatment strategies and priorities, and they tend to diagnose and prescribe treatment based on their area of knowledge and experience. So, for example, if a patient presents to a pain interventionalist, the patient is more likely to be treated with an injection, whereas a surgeon will be more likely to recommend surgical options.
Furthermore, because of the random nature of the care continuum, physicians tend to have a limited visibility into other treatment options. As a result, physicians often will retain patients and leverage readily accessible tools rather than referring patients to other specialists. Additional factors, such as insurance coverage and demographics, also play a role.
What all of this means for medtechs is the standard of care varies widely depending on the underlying pathology and the type of provider first seen. To launch a new product successfully requires in-depth analysis to determine where your product best fits and why and creating a strategic go-to-market plan to encourage use of the product in the patient cases where it maximizes impact on patient outcomes.
Inaccurate assumption #3: The back pain market has a straightforward patient journey and linear provider pathway.
Best practice in action: Before launching a new product, it’s critical to identify and profile the “perfect patient” and preferred provider.
Conventional wisdom dictates that patients with back pain will progress through four stages of increasingly more invasive standard of care treatment options, beginning with conservative care, then progressing to intermediate care, minimally invasive surgery (MIS), and surgery.
However, the reality is that patients move through highly varied and inconsistent pathways, both in terms of the physicians seen and the types of therapies received. As noted above, a wide variety of primary care physicians, specialists, and surgeons treat CLBP patients, and the treatment track largely depends on the specialty area of the clinician to whom a patient presents first.
CLBP patients tend to move through myriad possible referral pathways, depending on several factors, including perceived pain type, duration of pain, prior medical relationships, type of provider visited, coverage network, etc. And, while surgeons might refer a patient to a pain interventionalist if surgery is inappropriate, pain interventionalists tend to rely on recurring pain management treatments and rarely refer patients to surgeons. In fact, across the provider pathway, analysis shows referrals to another provider usually only occur within the same clinic or practice group.
Keep in mind, referral pathways are slow and expensive to disrupt, so capitalizing on existing pathways is key. Be sure you can answer: Who has the most to gain from this product? Who is most likely to use it, and why? And who are the potential allies and competitors of these physicians? Speaking with key opinion leaders or with your sales representatives is the most common way to get insight into referral pathways but often provides non-generalizable findings.
Rigorous claims analysis represents the best opportunity to gain an accurate understanding of the patient journey – who is referred where, when, and what treatments they are receiving – and the physicians most interested in and capable of administering your product.
Assess and define specific patient segments that stand to benefit the most from the therapy. Who is failing current standard-of-care treatment? What aspects of those patients’ pathologies or care pathways are most amenable to your product? Targeting your product to specific categories of patients (and providers) that stand to benefit the most will accelerate product adoption and will act as a “beachhead market” from which you can develop and target adjacent patient segments.
In addition, it is helpful to define where the perfect patient intersects with axes of pain. Meaning, determine how much the pain bothers the patient and for how long, as well as treatments they have tried, and the outcomes. Answering these questions will help you maximize your value proposition.
Unlock Insights To Secure Market Share
For medical technology companies interested in launching new therapies for CLBP, the market is fraught with obstacles and complexities. Figuring out where your product fits in the care continuum and how to maximize its potential requires a deeper-than-expected understanding of provider specialization, patient progression, standards of care, and referral networks.
The best way to do this is to get beyond gut instincts, assumptions, and generalized market research and instead rely on the analysis of comprehensive scientific data. Precision insights focused on defining the perfect patient, identifying decision makers, and uncovering the referral pathways are the key to unlocking this massive market. From there, leverage these insights to make order from chaos, take command of one corner of the market, and build from there.
About the Authors:
Kyle Biesecker, Ph.D., is a managing consultant in the Life Sciences practice at Guidehouse. With nearly 10 years of industry experience, he specializes in market opportunity assessment for medical devices through data-driven research. This includes primary and secondary research methodologies, as well as claims analyses. Recent projects include: market research for a novel biologic targeting low back pain caused by degenerative pathologies; product assessment for an orthopedic patient management software tool; and landscape analysis for a pharmaceutical across more than 15 prominent pain indications.
Chris Condes is a managing consultant in the Life Sciences practice at Guidehouse. He specializes in analyzing and designing forecasts of market opportunity for specific medical devices and pharmaceuticals through in-depth and data-driven research, synthesis, and modeling of key factors, including market size, patient segmentation, barriers to adoption, strategic investments, indication and geography attractiveness, and physician behavior. Recent projects include: deep brain stimulation for Parkinson’s disease, injections for lumbar radiculopathy, tricuspid valve annuloplasty for valvular regurgitation, and platelet mapping and platelet centric coagulopathy.
Bill Woywod, MPH, MBA, is a managing consultant in the Life Sciences practice at Guidehouse, where he leads Big Data analytics and machine learning projects. He has played a central role in expanding Navigant’s deep data analytics capabilities, including the development and execution of sophisticated claims data analyses and machine learning initiatives for clients in the medtech and biopharma industries. Previously, heworked as a data scientist at a large regional health plan, where he led the development and successful execution of the organization’s first machine learning program.