MS Treatment Costs Increase Depending On Severity | PMSA


KMK Partners with Major Pharma Company in Recent RWE study analyzing MS treatment costs

(BPT) - People and their loved ones dealing with multiple sclerosis (MS) face many challenges. The disease can cause people’s immune systems to attack their myelin sheath, which covers nerve fibers and tissues between their brains and the rest of their bodies. Symptoms can vary from person to person and MS can disrupt nearly any nervous system function.

MS is still a prevalent disease in the 21st century. Between 400,000 and 500,000 people live with it in the U.S. today. And while some can keep their symptoms under control, others can experience relapses, which can often occur two years after diagnosis. These relapses can significantly reduce the quality of MS patients’ day-to-day lives and often require extensive medical treatment.

Because there is currently no cure, those living with MS must rely heavily on treatments to get by. Depending on the circumstances, treatment could include speech therapy, physical therapy and the use of several medications, including Glatopa, Avonex, Zeposia and Ocrevus.Patients and their families are often overwhelmed with the news of their diagnosis that they may not figure out what type of treatment they need. This is where health care providers can step in. However, they should back up their treatment suggestions using credible and transparent real-world data, collected by experts either through research or case studies.

MS patients face burdensome health care expenses

There are many options available for MS patients. However, prescriptions and medical care can be expensive, especially for those who have one or more relapses. According to the American Journal of Managed Care, as MS patients' disability levels increase, so do their medical bills. The average cost of mild MS disability care is around $30,000 annually.

In a recent study, Springer Link examined more than 8,000 MS patients. The participants were placed into three different groups based on the severity of their condition. Here's how they classified each group:

  • Severe relapse (SR): Patients who were hospitalized and were primarily diagnosed with MS.
  • Mild/moderate relapse (MMR): Patients primarily diagnosed with MS who received both emergency room (ER) and outpatient care. This group was also given corticosteroids within seven days after treatment.
  • No relapse (NR) cohort: Patients who didn't have a relapse within the 12 months the study was conducted.

For the study, researchers analyzed each patient's total medical expenses, including hospital visits, medical appointments and prescription drugs. Their results show that out of the more than 8,000 MS patients studied, more than 2,000 experienced a relapse and that more than 20% of those relapses were severe.

Calculating the total burden of expenses

From looking at the total costs incurred by patients in all categories, researchers found that:

  • The overall annual costs for all MS patients were between approximately $48,000 and $52,000.
  • The cost for MS patients with relapses was between approximately $56,000 and $66,000.
  • The cost for MS patients with severe relapses was between approximately $65,000 and $88,000.

For patients who didn't have a relapse, prescription medications were their biggest expense, accounting for more than half of their treatment costs. But for those who had severe relapses, medical costs were their most significant financial burden, accounting for more than half of their treatment expenses.

Higher cost burdens relate to severity of case

Springer Link found that patients who experienced relapses were the most likely to have expensive medical treatment. These costs can create substantial burdens for American families every year. Fortunately, they can address these issues before they occur by pursuing optimal preventative treatments.

For health care providers looking for MS treatments based on their patients’ and families’ needs, data or research studies from KMK Consulting can give them the data and research they need to make better decisions.



Using IDNs to Gain Insightful Data | LexisNexis


(BPT) - (BPT) - In our interlinked world, connections and networks invisible to the modern Life Science universe have created new challenges for brand teams at pharmaceutical and medical companies. While value-based care reimbursement models seek to improve outcomes and reduce costs, patients can encounter restrictions to branded therapies they may need. And since many hospitals and physicians’ practices are starting to merge, these restrictions are more common than they used to be.

However, Life Sciences companies can gain better access to this restricted care through Integrated Delivery Networks (IDNs).

Why are IDNs important?

IDNs offer comprehensive health care, no matter the life stage of the patient. These networks aim to provide a cohesive system that integrates and coordinates various healthcare providers and medical systems. Today, more than 1,000 distinct IDNs are operating across the United States. Some IDNs encompass thousands of facilities and tens of thousands of physicians in a single network. Others focus on providing care within a given region or multiple parts of the United States.

By using this model, patients can gain more seamless access to different types of care they may need over the course of their lifetime.

Navigating the complex IDN universe

Due to their complicated structures, current IDNs have forced Life Science companies to transition from a traditional sales rep provider model to an account-based business-to-business sales model. Such changes stem from health care entry-level intelligence, such as a single provider or an organization, to aggregate insights driven by robust IDN analyses.

If Life Science teams want to gain visibility in the IDN realm, they must understand the behaviors and diverse product needs of various IDNs. They should also gain insights into the business relationships among providers and organizations inside these IDNs.

However, it's crucial to know that gaining such insights is only possible when Life Science brand teams utilize medical claims to examine procedural volumes and treatment patterns.

IDNs can help brand teams examine data-based insights

When brand teams are part of an intertwined digital network, it can help them make better-informed decisions, especially when it comes to:

  • Highlighting people who make decisions: These can be business executives throughout the IDN hierarchy who have the power and influence to make major purchasing, supply chain and formulary determinations.
  • Locating high-volume practitioners: By identifying these individuals within IDNs through claims intelligence layered onto the IDN hierarchy, brand teams can better understand their treatment and referral patterns.
  • Finding key opinion leaders: With claims intelligence layered onto the IDN hierarchy, brand teams can better pinpoint who the best physicians in the country are and where they’re located.
  • Understanding overall care value: Brand teams can better access the best pharmaceutical or medical brands through different physician groups.
  • Prioritizing strategic efforts: This can help tailor, focus and time the messages you're trying to communicate, especially when sales and marketing resources are limited.
  • Quantifying and ranking IDNs: Whether they’re parent or child IDNs, claims intelligence can help health care organizations sort their most important data by claim and by volume.
  • Developing highly customized messaging: Tying medical claims to IDN hierarchies can help businesses spread their messages clearly and effectively, especially in a changing paradigm like this.

Leading the charge on health care system solutions

In today's crowded health care market, Life Science companies need easy access to data that gives them a competitive edge. LexisNexis can assist Life Science sales and marketing teams in getting that data, so they can make informed decisions to provide accessible care to patients.

How Coronavirus is Changing the Health Care Landscape | PMSA


(BPT) - COVID-19 has tested our economy’s limits in ways we never expected. Over the last few months, countless health care organizations have learned to either roll with the punches or get sidelined by constant and unpredictable change. It's taught them that if they want to avoid more infections and societal upheaval, they need to move quickly.

Almost every industry has felt the impact of the coronavirus. But the way it's affected the health care sector is unique because of how it’s changing the behaviors, finances, preferences and use of technology among patients and providers.

As health care providers clear through the clutter and try to predict what the industry will look like, data will become one of, if not the most crucial asset in adapting their practices. At Analytical Wizards, we’ve collected some recent findings on health care trends across the country to help you understand what’s happening now and how that could affect the future. Here’s what we found:

Demand for health care services is increasing, but hospitals face financial pressure

Health care professionals are working beyond the frontlines when it comes to the pandemic, so it’s no surprise the demand for these workers is on the upswing. It doesn’t matter what side of the income spectrum they’re on, employers are having a hard time finding employees from epidemiologists to call center representatives.

Despite increased service and labor demands, many hospitals are struggling to stay afloat. From our research, we found that countless hospitals are under severe financial strain, losing approximately $68,000 a year. The coronavirus has only made things worse. Many have had to reduce or cut elective surgeries, a substantial source of income, to focus on treating the virus. On top of that, some hospitals aren’t receiving money from coronavirus treatments due to insurance complications or patients’ inability to foot the bill.

As a result, some hospitals are undergoing mergers to avoid going further into debt or permanently shutting down.

More patients find themselves out of work and without health insurance

Patients are also feeling the financial strain of the pandemic. COVID-19 has put millions of Americans out of work. For some, job loss means they’re losing their health insurance as well. If these unemployed patients contract COVID-19 or have some other medical emergency, they may be left with a bill they can’t afford without insurance coverage. Because of this, we’re seeing more of a dramatic shift from private health insurance to more government programs like Medicaid. This trend shows that access to affordable health care is changing and that patients are increasingly demanding high-quality care at a more affordable cost.

Telehealth is changing how patients prefer health services

While demand for health services is high, clinic and hospital waiting rooms are becoming ghost towns. That’s because half of patients are calling in to their providers via telehealth services. However, most of them prefer this model of service. According to our research, 60% of patients reported a positive experience with telehealth appointments if they didn’t need to be in a clinic. Initially, many health insurance providers didn't cover telehealth visits but have since updated their policies due to the pandemic.

Data collection is key to keeping up with rapid change

If 2020 has taught us anything, it's that fully understanding the present can help us prepare and improve for the future. Analytical Wizards is at the forefront of collecting and analyzing what the health care industry needs to push forward. By using data to paint a picture of the present, we can help the industry grow and adapt to fit market needs.

Interested in learning more about our product offerings? Check out our website today.

Using Bayesian Reasoning to Predict When a Patient Will Discontinue Therapy

Can we predict with any accuracy whether an individual patient is likely to discontinue drug therapy? We're not talking about knowing if a patient has stopped using their meds, but if they're likely to do so. It might sound like something out of "Mission Impossible," but it's just data analytics at work in pharma. Yes, we're that cool.

This is regarding a presentation made at last year's PMSA conference by Jean-Patrick Tsang, founder and president of Bayser, a Chicago-based consulting firm dedicated to pharmaceuticals sales and marketing. JP is an expert in patient-level data and related analyses, and he's focusing on using a novel approach, Bayesian reasoning, to predict when a patient will discontinue therapy.

Look at the persistence curve of any chronic therapy and you'll invariably see a significant drop in the earlier stage of the therapy. We all know there is a slew of events that influence discontinuation. For starters, let's include factors like:
  • Admission to an ER
  • A visit to another doctor for a second opinion
  • A change in dosing
  • A side effect
  • An increased co-pay
  • The drug simply not working
  • Negligence (just forgetting to take it)
  • Psychology (If I'm taking medication, that means I'm sick.)
We know they have an impact but are not quite sure about the magnitude of the impact. We also sense that the impact of these events may not be the same if the patient is in the early stage or latter stage of the therapy.

Also, we cannot ascertain if the difference is limited to magnitude or also involves directionality. If we could somehow quantify the impact of an event on patient discontinuation while differentiating, say, between early stage (ramp-up) and latter stage (cruising), we would be able to establish which events are material and which ones are not.

We would then be able to focus on the important ones and identify relevant interventions both at the physician and patient levels that would significantly reduce the odds that the patient would discontinue therapy. We could gain additional insights by analyzing the impact on competitive drugs.

Enter Bayesian Reasoning

Bayesian reasoning is about how we update our belief in light of evidence. If we do not know much, we’ll assume that each patient has the same probability of discontinuing therapy. We can derive this from the adherence curve of the patient cohort we are tracking.

Using patient data, we know that patient 12345 who is under the care of Doctor John Smith has not filled the prescription for the last two months, has been admitted to the hospital, underwent a procedure, saw a second physician, has been diagnosed with a new diagnosis, was ordered a new lab test, or had another variable happen.

For each of the events, we have a likelihood that says how likely this is to appear if the patient were to discontinue compared with if the patient were to remain on the drug. We combine all the likelihoods and come up with a new probability of discontinuation that is specific to that one patient. This is called the posterior probability. We usually use a threshold to convert the probability into a yes/no answer.

Then What?

Many events that drive discontinuations have strong clinical rationale, including hospitalization and the seeing of additional specialists. But certain events may signal opportunities for intervention, including changes in dosing and change in the days' supply of the drug.

When we know patient 12345 is likely to discontinue with his therapy, we can have targeted interventions. It means contacting the patient's doctor. We can do this through visits from sales reps, emails and other forms of contact that will let the physician know their patient is likely to stop.

  • We can predict discontinuations with high accuracy.
  • More work is needed to effectively triage at-risk patients to a specific intervention.
  • The analytical path to identify root causes can now be more focused.
"This is a success story that shows that we can indeed predict with great accuracy when a patient will discontinue therapy, which some still have trouble believing we can accomplish," said JP Tsang.

It's about explainability. We need to understand why the algorithm is saying what it's saying. Explainability is very difficult to achieve but we can start with transparency. Bayesian reasoning, we found out, strikes a good balance between accuracy (neural net) and transparency.