Mid-level Practitioner Role to Expand with Obamacare. Do You Have What it takes to Target Them?

Despite the current government shutdown which has caused grumbling across the nation, US citizens have been able to experience history in the making; the opening of the health insurance exchanges of the Affordable Care Act (“Obamacare”).  This groundbreaking portion of this legislation means that many who have been going without health care services will now be able to get treated.  The exact number is still being fiercely debated, however, it is estimated that by 2014, we will be adding at least 14M people to the insurance rolls and 24M over the next decade.

Regardless of the exact number a historic number of patients will be added to our current system.  Not to mention, a portion of them will likely have chronic conditions which have gone untreated for years.  This will vastly increase the need for primary care providers.  While the ACA has provisions for increasing the number of primary care physicians the ACA alone will not close the gap.  Therefore, funding is also provided for education and development of 600 nurse practitioners and nurse-midwives by 2015.  Even beyond this, much of the patient load will eventually fall on these mid-level practitioners.

Evidence of the value of mid-level practitioners by industry is already being seen.  Especially in certain disease states such as chronic diseases and even oncology (where nurse practitioners often lead the chemotherapy and supportive therapy), there has been considerable effort to create effective and appropriate outreach to these clinicians.  Furthermore, the movement of the industry toward “patient-centered care” puts nurse practitioners and midwives front and center.

This takes us to the issue of targeting and segmenting.  There are approximately 200,000 practicing nurse practitioners and physician assistants in the US.  That does not count the midwives or clinical nurse specialists who also have clear leadership for disease management in certain medical areas.  As with physicians, it is way more effective to determine who are the high-value mid-level practitioners and specifically reach them with targeted messaging and engagement opportunities.   Forward thinking life science companies are developing strategies to reach out to this important audience.

Looking at one of the customary methods for targeting clinicians, we spoke to a leading provider of prescription data for mid-level practitioners. For the entire group nurse practitioners and physician assistants, we were confidently told that only 1,000,000 prescriptions per year have been captured. Consider this fact: in a recent survey Snowfish, LLC conducted from 425 mid-level practitioners the average mid-level practitioner was writing 50 prescriptions per week.   Therefore, this sample alone (representing less than 0.2% of the NP/PA universe) is writing more than 1,000,000 prescriptions a year – more than the data providers are capturing for the entire universe. It is clear that a significant prescription volume of mid-level practitioners is not being captured by data providers.

The reason is quite clear; often prescriptions written by mid-level practitioners are not written under the provider’s name but the physician with whom they work.  It is almost comical to think that some of these physicians have prescription volumes attributed to them.  Literally they would have to spend their entire day and night only writing prescriptions.  This is also applicable to other forms of targeting data such as claims and longitudinal data.

There is no reason to give up hope.  There are proven methods for targeting, segmenting, and profiling mid-level practitioners which incorporate a multi-faceted data strategy.  Snowfish, LLC has developed and approach that takes into account the various considerations which make a certain clinician of high importance and is based on years of knowledge and experience working with this group.   Snowfish, LLC has developed a unique way to target the mid-level practitioner that is only growing in importance. 

As we embark on this momentous change in our health care system, we are aware that our industry is seeing the role of the mid-level practitioner increase in magnitude.  With the right approaches, companies can target them with the same pin-point accuracy that is achieved with physicians.   To learn more or request our white paper on the NP/PA world please click here.

Melissa Hammond, MSN is Managing Director at Snowfish, LLC and leads their mid-level practitioner strategy services. Please go to www.snowfish.net for more information.

Should Industry Be Looking “Beyond the Pill”?

Baby boomers are less healthy than their parents.  While this may be hard to grasp given the medical innovations over the past 20 years, this was exactly the conclusion in a recent study titled, “The Status of Baby Boomers' Health in the United States. The Healthiest Generation?”  Briefly, when investigators compared National Health and Nutrition Examination Survey (NHANES) results for adults aged 46 to 64 between the timeframes of 1988-1994 and 2007-2010 (i.e., baby boomers) the latter group was more likely to experience chronic diseases such as hypertension, diabetes and hypercholesterolemia.  Additionally, they tended to have higher levels of disability.  While baby boomers are predicted to live longer, they are not living healthier.

Delving into this further, we took a look at medical innovations that have taken place between those two time periods.  We saw a number of medical breakthroughs that came about including the intracoronary stent and the first “super aspirin”, Plavix.  With respect to dyslipidemia, one of the conditions highlighted in the study, between 1988 and 1994 the only treatments available included the statins Mevacor, Zocor and Pravachol.  Other classes consisted of resins, fibrates and niacin (slow-release).  Alternatively, the baby boomer generation was exposed to the benefits offered by Lipitor, which in fact was the top-selling drug in the history of pharmaceuticals which was not introduced until 1996.  They also had access to Crestor dubbed the “super statin” which followed almost a decade later in 2003 and Zetia introduced in 2002. Niaspan approved in 1999, afforded the effects of niacin without the facial flushing.  Furthermore, during the 2007-2010 timeframe, antihypertensive angiotensin receptor blockers (ARBs) were marketed while not available during the earlier period.   Overall, more combination products had been made available which would likely enhance compliance.

It is therefore quite perplexing that despite the innovations that have taken place not even an incremental improvement was observed in the health status of the baby boomers compared with the prior generation at the same age.  One likely reason for this phenomenon is that achieving outcomes with these interventions requires a more comprehensive approach than the treatment alone.  Optimizing outcomes in the management of chronic disease is fraught with considerable challenges including those as basic as medication adherence and lifestyle modification. 
 
This brings us to the concept of a “beyond the pill” strategy, i.e., offering services designed to address the needs of all stakeholders (patients, clinicians, payers, caregivers, etc.) along the patient pathway.  This should ultimately result in better health outcomes coupled with a higher value to stakeholders and thus, a competitive advantage for the company.  Pharmaceutical companies are in an excellent position to institute this form of strategy.  They are experts in the science and disease state and have an astute understanding as well as existing relationships with many of the key stakeholders.  They also market the products which form much of the backbone of management for many chronic conditions.  This approach is also relevant to devices particularly those which are designed to augment or even replace certain medications in chronic diseases.

The exact types of service-based programs implemented through this strategy would need to be customized to the company based on a number of factors including the product itself, geographic reach, internal capabilities, and cost structure.  Still, they could range from those which companies are already doing to some extent such as addressing medication adherence, supportive education and coordinating the “non-therapeutic” aspects of the disease to disease management and care delivery which has not customarily been within the realm of the therapeutics industry.  Regardless, a comprehensive analysis including multiple factors is required to discern the most appropriate strategy.

At a recent conference we attended at Wharton in 2013 we heard from several CEOs of major healthcare players.  One of the central themes of the conference was how progressive companies need to start looking beyond the pill and address ways to improve overall outcomes. 

It is not too late to shift the health status curve upward for the baby boomers and generations to follow so that they can reap the benefit from medical innovation. By taking a new focus on chronic disease and moving away from “delivering therapies” and towards “delivering outcomes” the industry will benefit through a loyal stakeholder base and a healthier aging population.  

Melissa Hammond is Managing Director at Snowfish, LLC.  Snowfish is a management consulting company provides services to executives in the life sciences industry designed to deliver unique insights that drive strategy.

Greetings from Data Land!!

Snowfish works with a significant amount of health care-related data supplied by multiple vendors including the industry’s leading companies.   Purchased data has multiple uses from identifying the appropriate physicians for an offering to understanding usage and adoption patterns.    Strategic decisions are often based on single sourced data purchases.
 
The bad news is that the data is often wrong, inaccurate, incomplete or some combination thereof.  The challenge for any company is how to inform business decisions with wrong, incomplete, or inaccurate information. 
 
The good news is that there are ways to get around this.  Before we propose a solution we will address some of our “interesting” data acquisition experiences.
 
What’s the link between hospital executives and golf courses?

Most would think this is quite the absurd question.  I would customarily agree except for an experience I had when acquiring data on executive-level hospital professionals. The data provider assured us that they could provide thousands of professionals fitting our criteria. When we received the results while the first 100 were appropriate, beyond that was completely irrelevant.  They included dentists, golf courses, funeral directors, and even comedians.  For that data provider they clearly felt that any data was good enough.

You really want all the data you requested? 

One of the more interesting challenges we commonly confront is that data providers fail to point out the limitations of their data.  For example, we recently requested data on oncologists for a certain type of procedure.   We thought by going to the largest and oldest data provider we would receive a very robust data set.  After receiving the file we noted that less than 50% of oncologists were in their data set when we compare the data to a separate file we use.  We asked the logical question, where is the rest of the data?  We were told initially this in the complete data set.  Doing some more digging and questioning the data provider finally acknowledged that they capture less than 50% of the market and a significant amount of volume goes through “specialty pharma.”  Failure to acknowledge the limitations of the data is a common and omnipotent problem we confront.

Every clinician in our database is phone verified…NOT!

Working with another well-known data vendor we were very happy to hear that we would be provided accurate demographic and profile information based on phone verification for each and every clinician in their database.  Not sure how this truly can be done, we still took them at their word that we would get the information that we needed to effectively inform our project.  What we found was a fairly large number of discrepancies in the data.  For example, the address for a particular physician said “Massachusetts” but their current institution was listed as “Cleveland Clinic”.  With a little detective work we found that the physician did indeed live in Massachusetts but went to medical school at the Cleveland Clinic.  It was clear on multiple counts that every physician is not being phone verified.  In actuality they are using the national provider identification (NPI) and bouncing that against another file and that qualifies as phone verification.   This clearly, is a unique definition of phone verification.

Oh you wanted those physicians?

When requesting certain physician specialties who coded for specific CPT codes we received a dataset with a large number of records that appeared inappropriate.   When questioning the data vendor they attempted to tell us that these were indeed appropriate and that their title was just another name for the specialists we were requesting.   We ended up spending several hours and finally identifying that they individual did not code the data request properly.  Quite simply, you need to check both the input and the output. 

Crediting certain physicians for multiple prescriptions by NPs/PAs they work with.

A lot of decisions in the industry are based on Rx volume by a clinician.  One of the greatest misperceptions out there is that only physicians write prescriptions.  A while back we decided to get to the heart of the issue.  We learned that some leading data providers default to the physicians due the ease from a data perspective.  For example, a nurse midwife practice could have five nurse midwives and one physician as part of the practice.  The data provider would credit the physician with all the Rxs even though at most one-sixth of the actual Rxs were written by the physician.  We then reached out to over 500 NPs/PAs and did our own independent research which is available in a white paper.  It clearly pointed out that NPs/PAs involved in the diagnosing and treating patients is being significantly under reported.  But that still is a dirty little secret. 

So now that we shared some of the common challenges we thought we would provide you some solutions to address the Data Land challenges:

• Make sure you understand the limitations of the data upfront.  The limitations are often never disclosed it will often take multiple telephone calls to really uncover the limitations.
• Make sure you understand how data is being captured.  For example, there are three “switches” that capture medical claims data.  No data source purchases data from all three they buy from one and sometimes two “switches.”  So realize the inherent limitations of the data.
• Make sure you never rely on one data source from decision-making.  The more robust and varied your data sources the greater your insights will be.
• Work with a company that knows the limitations of the data to help you develop a more sophisticated targeting strategy.

Data is a key element for developing strategic and tactical plans.  Working with a company that integrates and value adds the data into a business context can create a more robust and comprehensive picture for informing decisions.

Dave Fishman is President of Snowfish, a strategy consulting firm serving the pharmaceutical, medical device and biotechnology industries.

19th Annual Wharton Healthcare Business Conference: Key Insights from CEOs and Senior Healthcare Leadership

At the suggestion of a colleague, I recently attended the 19^th Annual Wharton Healthcare Business Conference titled “Reshaping Healthcare: Emerging Trends Changing the Face of Our Industry.”  It was a full day in which five CEOs from major healthcare companies along with senior leadership from dozens of others discussed issues such as US healthcare reform, emerging markets, aging, diabetes/obesity, and personalized medicine.   It was a unique opportunity to hear from and participate with individuals representing a broad spectrum of the industry.  Payers, medical device, pharmaceutical, and medical institutions were all in attendance. 

A great deal of the discussion dealt with the dynamic state of healthcare and the how effective use of collaboration, innovation and technology is critical to get and keep us on the right track.  While I currently focus on the business side I got my start in the clinical world and always have the individual patient in mind.  That is why it was particularly intriguing to hear CEOs of health plans and patient care delivery companies and top executives of pharma companies express the power of patient wellness to reinforce the bottom line.

It is clear that healthcare reform and the introduction of Accountable Care Organizations (ACOs) are doing a lot to drive this strategy. Unlike the fee for service structure, there is a major incentive among the various industry sectors to keep people healthy as the focus shifts from quantity of care to quality of care.  Additionally, the rules applying to the payers resulting in explosion of their membership and making it more likely that the member will remain with them for the long term is also driving some radical, but welcome change.  This being said, I have outlined the top 5 points regarding these and other related issues that were emphasized by the Conference faculty:

• Companies are re-defining their core competencies.  Payers have shifted the focus from sick care to the whole patient, i.e., wellness and prevention.  Companies such as Humana and United Healthcare are spending considerable resources to help patients improve adherence with lifestyle modification and medication adherence.

• Pharmaceutical companies are focusing beyond just pharmacotherapeutics to include diagnostics and disease management.  As stated by one pharmaceutical executive in the context of diabetes management, the industry’s role in management requires going “beyond the pill”.  This involves taking an active role in advocating lifestyle changes.  He referred to diabetes clinical trials in which all patients are prescribed lifestyle modifications regardless of treatment arm.  Improvement in glucose control is usually demonstrated in the placebo arm as well. The industry is focusing on answering the question of how to gain the benefits of lifestyle modification and further accentuate that benefit through the addition of drug therapy.

• These initiatives are not expected to be cheap and that is OK.  It was echoed by many of the experts that driving disease prevention, management and adherence is not an inexpensive endeavor but it is the right thing for the industry to do.  This was underscored during a session focused on the strategic impacts of an aging population.  The introduction of penicillin has resulted in a significant extension of the lifespan as the risk of certain death from infectious diseases has gone down exponentially.  However, individuals are living longer with chronic diseases and frailty.  While there was definitely concern as to how society will pay for care of this quickly growing population, the clear message was that the goal of effective disease management in the aging should not be designed to be cheaper, but more effective.

• Personalized medicine poses significant opportunity but many questions still remain.  There was much discussion of the role that personalized medicine will play in the evolution of the healthcare system.  Personalized medicine is designed to address the goal of a more efficient process to determining therapy.  Still, these approaches will at least initially, create new demands for the system.  There are concerns regarding what to do with the information gained from genotyping and phenotyping.  Will it drive meaningful treatment?

• Industry change will require appropriate leveraging of technology and data.  The majority of the discussion focused on the use of technology and patient data to facilitate wellness by driving adherence to medications and lifestyle modifications.  We learned of innovations ranging from web-based and mobile technologies to specially-designed pill bottles which send a signal thus stimulating a call or email to the individual reminding them to take their medication.

Essentially, the Conference reiterated that while these times may be quite trying for our industry, there is significant opportunity for all sectors if we think innovatively and collaboratively.  We can also explore areas that were not historically in our individual “sandboxes” in order to maintain an active position in this healthcare revolution.

Melissa Hammond, MSN, GNP is Managing Director at Snowfish, LLC a strategic consulting firm which works exclusively in the life sciences industry.  For more information, please check out www.snowfish.net.

Predicting Product Demand: Leveraging an Integrated Approach

The analytics team at Snowfish has worked with multiple life science companies helping identify patterns in large quantities of data.  As part of our offerings we have assisted companies in demand planning.  Predicting and planning for demand is a common and critical challenge pharmaceutical companies confront.  Multiple factors can affect demand including the product category and exogenous events.  Demand for products such as flu vaccine varies tremendously from year to year depending on the severity of the flu that year and the amount of media coverage.   Demand for allergy therapies is highly seasonal.   Furthermore, the seasonality may differ by geographic region and may in fact vary in the same region from year to year depending upon weather patterns.  Variation in demand may even be evident at the individual physician level for infrequently prescribed therapies such as oncology where treatment regimens can vary significantly.  Therefore, predicting demand for the overall market, individual products, and individual physicians can be particularly challenging.

The Right Approach is Critical

In all cases, it is important to first pick an approach that is most appropriate for the challenge.  It is all-too-tempting to throw all of your data into an algorithm without first analyzing the nuances of the situation.  If seasonality is a major concern, then it will be necessary to first discover seasonality trends in past years.   Keep in mind that those trends may shift in time and magnitude from one year to the next and may even vary depending on geographic region within a single year.   Once seasonal trends are identified, the next step is to see how they fit the current year in both timing and magnitude.   In some years the flu may peak early, and in other years it may peak late.   How this year fits past trends can be used to predict the next several weeks.

For infrequently prescribed medicines, it may be necessary to simply predict the probability that a particular physician is going to prescribe at all in the next three, six, or twelve months.  Such predictors include whether the physician has ever prescribed a particular medicine in the past, how recently, and their overall level of prescribing of all related drugs.   For new products, it is helpful to determine if certain physicians have been early adopters of other therapies when they were new.   A physician that lagged his peers in the past in prescribing new medicines is likely to lag on future new products even if he eventually ends up being a high-volume customer.   Likewise, if a physician has been an early adopter in the past, this increases his chances of being a leader in prescribing new products.

Data Is Often Incomplete: Do Not Rely on a Single Data Source

Incomplete and/or incorrect data are limitations that are going to negatively impact any technique.   Certain sales channels may not be reported to data vendors.   This tends to occur when a certain prescription is processed through an unreported channel.   Demographic data about the geographic area where a physician practices while very helpful when available may be incomplete for certain regions.   Therefore, it is important to include data from multiple sources such as internal sales data, prescriptions of competitors’ products, diagnostic codes, etc.   With multiple data sources, problems with any one source are less likely to have a major impact.

Use the Right Tools and Integrate Them

Approaches that integrate analytical skills, clinical knowledge, and business acumen yield the best results.   It is helpful to use the latest analytical tools such as Gradient Boosting, Random Forests, and Support Vector Machines as well as more traditional tools such as linear regression.   Additionally, clinical knowledge is crucial for understanding the disease state the medicine treats.   How long does typical treatment last?   Is the therapy used to treat multiple different diseases?   How is it administered?   Clinical knowledge affords the necessary background for data selection and creation of features for predictive statistical analysis.   Finally, business knowledge of outside factors that drive demand and purchasing decisions also should also be incorporated into data selection and feature creation.

Using an integrated approach to predicting product uptake and demand not only increases prediction accuracy, it also yields better insights into your data.   Trends that might have just been lost in the shuffle of statistical analysis might jump out at a team member with clinical knowledge of the disease state or business knowledge of the market.   When pharmaceutical companies face the challenges of predicting product uptake and demand, integrating analytical, clinical, and business skills into the team is the key to addressing those challenges.

Please feel free to reach out to Snowfish to learn more about our integrated approach to predicting demand.  Companies are already benefiting from our insights.

David Fishman is President of Snowfish, LLC a strategic consulting firm which works exclusively in the life sciences industry.  For more information, please check out www.snowfish.net.

Personalized Medicine, What’s the Future?

Personalized medicine is currently one of the hottest areas in life sciences, imparting significant levels of excitement among venture capitalists, life science companies, clinicians and patients.  It even has its own journal that “translates recent genomic, genetic and proteomic advances into the clinical context.” As Snowfish has had the privilege of working with genomics companies, we have heard firsthand the potential impact these new technologies will have on the way patients are treated and how the industry does business. 

In brief, personalized medicine refers to the customizing of medical treatment to the individual characteristics of each patient. Contrary to a common perception, it does not refer to the creation of drugs or medical devices that are unique to a patient but rather, the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or their response to a specific treatment. This information ensures that interventions are targeted only to those who will benefit, thus sparing expense and side effects for those who will not.

Most medical professionals will concur with the fact that patient response to both disease and therapeutic intervention are highly variable.  It is not uncommon for one patient to respond beautifully and tolerate a drug without incident and another to experience side effects while obtaining no benefit.  Depending on the source it has been documented that 30% to 70% of patients fail to respond to a drug treatment.  There is a variety of potential reasons including adherence, and even misdiagnosis.  Still, it seems highly probable that patient-specific factors such as variability in drug metabolism rates, the metabolic or genetic nature of the underlying disease, and other characteristics such as age are also contributing factors.  Enter personalized medicine.

Oncology is where personalized medicine appears to making the greatest amount of headway.  This makes sense as treatment response rates in cancer are amongst the lowest for any major disease.  The well-established genomic basis of cancer has put cancer research at the forefront of personalized medicine in the quest for more targeted and tolerable therapies.  Early examples of success are Herceptin in breast cancer and Gleevec in chronic myeloid leukemia (CML).

Various technologies are being employed in the effort to more effectively guide the treatment strategy based on the likelihood that the cancer will recur or metastasize.  For example, the clear molecular differences seen in breast cancer are highly applicable to genomic profiling, and through transcriptional profiling approaches, several prognostic and predictive assays have been developed. Prominent amongst these is Genetic Health’s Oncotype Dx, a 21 gene polymerase chain reaction (PCR) panel that predicts tumor recurrence at ten years in estrogen-receptor (ER)-positive, node-negative breast cancer patients receiving tamoxifen therapy. [1] Using a statistically defined algorithm, the gene expression profile is used to define a recurrence score that can be used to identify patients who are likely to benefit from additional adjuvant therapy. Patients with low recurrence scores and, therefore, good prognosis are spared the stress and risk of unnecessary therapy, and the healthcare system saves the costs of delivering additional treatment.  The assay has been endorsed by both the Association of Clinical Oncologists (ASCO) and the National Comprehensive Cancer Network.

Although it is one of the first, and certainly the one of the most successful to date, it is already clear that Oncotype Dx is merely the tip of the iceberg. In breast cancer alone, we have seen the emergence of multi-analyte tests based on techniques as broad as PCR, microarray, immunohistochemistry and fluorescent in situ hybridization, amongst others.   Genomic Health is also expanding the use of their assay in breast cancer as well as developing similar prognostic test for colon cancer, prostate cancer, non-small cell lung cancer, melanoma, and renal cancer. 

One of the main obstacles to the growth of personalized medicine has been cost.  Until very recently the devices used for genome sequencing cost $500,000 to $750,000.  Additionally, the individual tests run $5,000 to $10,000 and take days to produce results.  Things are changing though.  Just this past year, Life Technologies introduced an Ion Proton™ Sequencer that is designed to sequence the entire human genome in a day for $1,000 and the machine costs $149,000.  Clearly, the barriers to affordability are breaking down.   It is not hard to imagine that the cost of a complete genomic testing will be a few hundred dollars within a few years. [2]

In order to realize the full potential of personalized medicine, engagement of multiple stakeholders is critical.  Payers will need to be convinced of the clear benefits of specific genetic tests.  As companion diagnostics are critical to development and utilization of therapies, the Federal Drug Administration (FDA) will need to promulgate clear and straightforward paths for diagnostic approvals.  Clinicians will need to modify existing treatment regimens and include genetic testing as a core component and feel confident to withhold standard therapies when genetic testing indicates that these treatments are ineffective or no more effective than watchful waiting.  Treatment guidelines will require modification in order to account for the genetic makeup of patient populations.  Life science companies will have to develop a new mindset; where the goal is not the single multi-billion dollar blockbuster but rather a portfolio of more products which treat smaller populations.  That said there could even be the potential to review the “shelves” of failed products to determine if there could be success with a more appropriate genotype or phenotype.   

Personalized medicine is upon us and it will completely revolutionize how treatment is determined. Today, clinicians choose therapies based on research done on thousands of people that have a diverse genetic profile and have only a limited ability to adjust therapy based on individual differences.  In the case of cancer, treatment is currently based upon the tumor location.

In the future, the tumor itself will be tested and it will be based less on the location than on its genetic and molecular composition. Genomic testing will be able to identify which oncogenes are turned on and which oncogenes are turned off.   Most importantly, clinicians will be better able to identify the drugs and treatments that will yield the greatest benefit to the patient.  

We will eventually see this type of therapy for all human illness and will likely have access to tests that will portend the future and enable patients to avoid developing conditions such as diabetes, heart disease, and various types of cancer. 

1. Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004.351; 2817-2826. 

2. http://www.lifetechnologies.com/us/en/home/about-us/news-gallery/press-releases/2012/life-techologies-introduces-the-bechtop-io-proto.html

Dave Fishman is President of Snowfish, LLC a strategic consulting firm which specializes in helping life sciences companies by using data to address the most challenging issues.  More information on Snowfish may be found at www.snowfish.net or by emailing us at info@snowfish.net.