A powerful new tool is available to FDA through a first-of-its-kind cooperative research and development agreement (CRADA) between FDA and EduNeering, a developer of computer-based compliance training systems. That collaboration has resulted in a "virtual university"-dubbed ORA U after FDA's Office of Regulatory Affairs (ORA)-the agency's new online training program for inspectors and investigators. The collaboration is unique in that it makes the same educational content available to pharma companies.	

Like corporations, FDA needs to do more with less. For both the public and private sectors, constantly expanding responsibilities have run headlong into tightening budgets, forcing managers to search for better ways to handle all functions, including training.				

Several years ago, the Office of Regulatory Affairs began a process to standardize and enhance training for more than 4,000 of its own staff and for several thousand state and local regulatory agency employees charged with enforcing FDA regulations for food, drugs, and other commodities. That initiative dovetailed with provisions of the Federal Technology Transfer Act, which encourages agreements between government agencies and the private sector to promote collaborative research and development.	

ORA's agreement with EduNeering, signed in 1999 and renewed last summer for a minimum of another five years, laid the groundwork for what would become a collection of 110 online courses, with more yet to come. (See "ORA University-A Sampler," page 82.) FDA developed and reviewed the content of those courses and provides the internet access for its students. EduNeering provided the e-learning technology as well as the instructional design, programming, and technical support for the web-based system. The company also provides a learning management system to track, document, and manage the program so FDA administrators can have access to up-to-date information about each student's progress.	

Although ORA U's most important function is to support ORA's Inspector Certification Program, the pharma industry can benefit as well. The same system and courses used at ORA U are also available to all FDA-regulated companies-including pharma, biotech, and medical device manufacturers. (See "Sign Ups and Certification," page 80.)	

ORA U takes a "brick and click" approach: face-to-face courses and activities combined with distance e-learning including web-based training, satellite downlinks, and teleconferences. Each multimedia course offered through ORA U is interactive and uses cutting-edge instructional design plus narration  to create a stimulating learning environment. (See "Learning on Demand," page 83.)				

The courses are also extremely efficient. The average training module takes 30–40 minutes to complete and is the equivalent of two-and-a-half to three hours of classroom instruction. And students can take the training on their own time, bookmarking each course so they can return to it for study and review.	

That "blended learning" model has served FDA well since the first courses went online in December 2001. The numbers speak for themselves: FDA had 3,392 participants who completed 343 online courses in 2001. The virtual university increased the number of participants by 16 percent to 3,927 in the first quarter of 2002 alone. By the year's end, ORA U students had completed more than 80,000 online training activities including more than 30,000 web-based courses. Perhaps most telling, the 600 new inspectors FDA hired at the end of 2001 needed only three months to complete the basic training, compared with the previous standard of 6–12 months. Throughout their careers, inspectors will continue to have access to more advanced courses through ORA U.	

Currently, nearly 5,000 FDA employees from ORA and the agency's five regulatory centers are enrolled in the system. Within the next 18–24 months the courses will roll out to all state and local regulators as well, eventually boosting the total number of enrollees to 50,000. There are now more than 2,000 state and local regulators enrolled from 159 agencies and 49 states."	

Blended learning incorporates both traditional instructor-led training and web-based courses to provide the best possible training to the largest possible audience. Many organizations are hesitant to implement web-based programs because of negative experiences with the technology when it was first introduced. But during the past few years, online learning has changed dramatically. (See PE, "E-Learning Goes Interactive," May 2002.)	

In early experiments, providers simply moved established classroom training online without considering how to redesign content for the new medium. Such courses consisted of volumes of text, talking heads, and electronic paper-and-pencil tests. Fortunately, the teaching strategy evolved over time to engage students by organizing content into smaller, more meaningful segments and incorporating extensive student feedback. 	

Online training has proven successful, both in imparting skills and in helping students achieve desired levels of competency. ORA U uses satellite downlinks and teleconferences to create an interactive program that is stimulating to students and easy for managers to document. 	

Ever-changing industry technology calls for frequently updated training, which can be difficult to achieve with traditional methods. But ORA U's variety of techniques allows its web-based components to deliver current information almost immediately. Those up-to-date modules enable both FDA trainees and pharma companies to stay abreast of the changing regulatory environment. ORA U's ability to provide consistent instruction also ensures that US inspectors and investigators share a common base of knowledge and maintain identical inspection procedures and standards throughout the country.	

ORA U's format accommodates diverse training needs with modules that incorporate a series of unique programs, ranging from the basics of investigation to food and drug law, good manufacturing practices, quality system regulations, good clinical practices, and computer software validation, among others. ORA U also offers participants a complete curriculum consisting of complementary learning experiences including classroom, web-based training and exercises, discussion, and on-the-job training.				

In addition, ORA U allows companies to access the same content and curricula FDA uses to train its inspectors and investigators, keeping FDA and the industry on the same page. Most important, the virtual university is not a static program but a constantly evolving learning system with new modules coming online as needed. 	

Under the old frame of reference, managers measured a training program's effectiveness by the number of hours it ran. But today the benchmark is performance, not only for regulatory compliance, but also for internal cost/benefit analyses and for adherence to worldwide standards, such as those set by the International Standards Organization.				

Too often, the relationship between regulator and regulated has been, or seemed to be, adversarial. But through ORA U, the private sector can take a seat at the training table with FDA, with access to the same content. Agency inspectors and investigators gain consistent training, and pharma companies come away with a greater understanding of how FDA inspectors are trained. Everyone benefits.	

Articles

Features

A powerful new tool is available to FDA through a first-of-its-kind cooperative research and development agreement (CRADA) between FDA and EduNeering, a developer of computer-based compliance training systems.

E-Learning FDA Style

Contrary to popular belief, physicians still can't retrieve the full medical history of every patient who walks into their offices. Nor can they zap prescriptions through a clearinghouse that shows whether patients are eligible for coverage and reviews all medications they are taking to forestall drug–drug interactions. That era lies in the not-too-distant future. Nonetheless, pharma companies have access to vast amounts of patient data that they can use to their advantage today. 	

Patient data fall into two distinct groups, each serving a different market need. Patient-level data, derived from prescriptions filled and covering about half of the 3.2 billion scripts dispensed in the United States annually, can be projected to the entire US population. Patient-centric data include patients' other interactions with the healthcare establishment, such as hospitalizations and lab tests, and thus encompass more of each patient's medical history. Yet, those data represent only a sliver-in the low tens of millions-of the 200 million patients in the United States.	

This article provides examples of the different types of patient data currently available and shows how pharma companies can use them to better understand the marketplace and to tap into untold pockets of opportunity. (For a list of prominent data vendors and their flagship products, see "Who's Who Among Data Vendors," page 88.) 	

It also describes how to select the most appropriate patient data source based on the objectives of the analysis and the therapeutic area. Finally, it shows why superior patient-level data will make patient-centric data more useful in making projections about the drug-purchasing habits of US patients.	

Patient-level data are significant because they allow companies to track prescription activity in more detail than physician-level data. They are useful primarily in the areas of territory management and sales force compensation. 				

Physician-level data simply say, for instance, that Dr. John Smith wrote 20 prescriptions of Plavix (clopidogrel) last week. Patient-level data take the information further. First, they specify the patients' sex and age, which allows marketers to establish the demographic profile of Dr. Smith's patient base and to compare it with that of other leading Plavix prescribers. 	

Second, patient-level data specify the therapy the patients were on before they began taking Plavix. Marketers can then identify cases of new therapy starts (no prior therapy) and switches (patients taking a different drug before), as well as the direction of the switch (to or from), add-ons (patients adding Plavix to another therapy), and dosing changes (adjustment of medication strength).	

A few weeks into a product launch, marketers armed with patient-level data can go beyond the routine task of identifying early adopters. They can establish which patients doctors are most likely to start on the new therapy, which physicians have a large number of patients that match those profiles, which drugs doctors are shunning in favor of the new launch, and when they favor an add-on to the current therapy instead of a substitution. Privy to such underlying dynamics, marketers can craft the right message for each doctor. After that, it is a question of marshalling the sales force and other promotional resources to get those targeted messages across. 	

Patient data can also be a potent asset in winning market share when a competitor's product is recalled. By understanding the driving forces behind the substitution, product managers can quickly develop convincing arguments to take advantage of the boon.	

Bayer's recall of Baycol (cerivastatin) in 2001 offers a prime example. Competing statin manufacturers had only a small window of opportunity in which to claim the product's forfeited market share. The trophy went to the company capable of identifying Baycol users and switching them over before anyone else could flex a muscle. Merck did a great job initially, but Pfizer caught up to become the overall winner. 	

Patient-level data can be helpful in less volatile situations as well. Longitudinal analyses-tracking changes in a patient's medications over time-can help maintain, if not grow, market share by unveiling critical patient behavior such as switches, new therapy starts, multiple pharmacy use, and dosing, among others. Compliance and persistency are popular targets for patient-level analyses because a modest increase in either may spell huge revenue hikes for pharma companies and significant savings for insurance companies that must foot medical bills that result from non-compliance.	

Companies can use patient-centric data to analyze patients' medical histories as thoroughly as possible, including their prescriptions, hospitalizations, outpatient visits, diagnoses and procedures in ambulatory facilities, lab and hospital tests, and any other information their records reveal. It is not critical to capture every single individual in a country or ensure that the data are up-to-the-minute as long as they represent the patient population to which the findings will be projected. Patient-centric data are useful for everything from disease management to strategic marketing.								

By piecing together the pharmacy and medical components of patient histories, both manufacturers and payers may glean and wield big-picture insights. A pharma company might justify the high price of its drug by citing patient-centric data demonstrating that patients taking its medication are less likely to require costly hospitalization than patients taking alternative medicines, proving that the more expensive drug saves payers money. Payers could use predictive modeling-and technology similar to the neural nets credit card companies use to red-flag stolen cards-to monitor patient behavior, such as rising insulin levels, to pre-empt costly and devastating crises. 	

By using patient-centric data, insurance companies and pharmacy benefit managers (PBMs) can elicit best practices by establishing which therapies lead to the most favorable outcomes. Indeed, they have access to countless cases in which different therapies are administered to patients that share similar medical situations and, more important, to the results that ensue. Shifting the focus to healthcare providers, payers can grade the former based on their use or non-use of those best-practice therapies.	

Another promising area is the identification of untreated patients, an untapped market that companies can access immediately.	

Patient data come in different forms because they are captured at different points: at the pharmacy/switch-the computer system that connects pharmacies together and supports the adjudication process that ensues when patients tender their PBM cards-by PBMs, by payers, or by healthcare providers. A piece of information gathered at one point is likely to be missed altogether at another. That results in data source differences, such as the number of patients or scripts recorded, timeliness of the data, and geographic coverage. Those differences create competition among data vendors and confusion for data buyers new to the market. 				

Pharmacies can record cash payments for a product, but payers don't capture that information because someone else foots the bill. Yet payers know patient eligibility well, because it is the basis for accepting or declining payments, whereas the pharmacy/switch is unconcerned with where the money comes from.	

Data vendors ply their wares by stressing the type of information they can provide, particularly patient eligibility, cash payments, patient-centricity, and connectivity. Understanding those features and their ramifications is important, because they have a direct bearing on the quality of the analyses. Therefore, it behooves analysts to ensure that the most appropriate database is deployed to answer the sales and marketing questions posed. (See "Source Checklist.")	

Eligibility. According to AdvancePCS, eligibility data are essential for trustworthy persistency analyses. Without such data, it is nearly impossible to ascertain whether patients drop a therapy for medical reasons or simply because they no longer have insurance coverage. For that reason, failing to track the eligibility of all patients under study from start to finish may strongly skew the findings, because they may not represent the broader patient population, just as data about people who eat in five-star restaurants are unlikely to shed light on the eating habits of the average Joe. 	

Nevertheless, data vendors may infer patient eligibility by tracking families rather than individuals. If a family member has a prescription filled and reimbursed, chances are that other family members have insurance as well. Although such inferences have their limitations, they can provide insight into many cases.	

Cash payments. Cash payments are more important than they may seem, according to those who capture the data at the pharmacy/switch level. Leaving them out punches holes in the longitudinal data and sometimes contaminates analyses altogether. Patients pay cash for prescription medications for many reasons, including: 	

desire to dodge the tracking apparatus that a reimbursement sets off, particularly in the case of controlled substances such as OxyContin (oxycodone) or quality-of-life enhancers such as Viagra (sildenafil)

when the drug's price is comparable to, or lower than, the co-pay

Patient-centricity. This type of patient data centers on individual patients and emphasizes completeness by capturing not only drug prescriptions but also medical expenses, hospitalizations, and lab tests. For many therapeutic areas, such as cardiovascular, tracking prescriptions is only part of the equation, which can also include such factors as lab tests, hospitalizations, and clinic visits.	

Payer and employer group data are certainly a very good source of patient data because they track every penny, whether pharmaceutical or medical, expended on each patient. Some argue that healthcare provider data are even better. If a patient is admitted to the hospital, diagnosed with several medical conditions, treated for one and discharged, the payer's claim data will pick up only the code that corresponds to the patient's release, because that is the only information needed to process the payment. Yet the hospital's data will capture all the other diagnoses as well. For that reason, provider data may sometimes be a better source, as in the case of co-morbidity analyses.	

Connectivity. This feature refers to the fact that patients' actual medical encounters  with healthcare providers-physicians, pharmacists, lab technicians, nurses, hospital personnel-are captured in the database and actually recorded as pertaining to that patient. That is far from being obvious. Unless special procedures are put in place and dedicated efforts are expended to achieve that goal, the tracking system may not recognize that one transaction in one part of the system-say, the prescription part-refers to the same entity in another part of the system, such as the medical part. 	

That is not a sure thing even for patient-level data, even though it focuses entirely on drug prescriptions. It is not easy to be consistent in the tracking process, especially when the patient's spouse picks up the medication after the patient selects a pharmacy from a different chain-Walgreen's, say, instead of Albertson's-or when the dispensing pharmacy is located in another part of the country. In fact, connectivity is as important, if not more important, than metrics such as the number of covered patients or good representation in all geographic areas.	

Taking connectivity one step further, PharMetrics offers a broad view of the patients it tracks by covering not only pharmaceutical records but also medical histories, including hospitalizations, lab tests, and visits to ambulatory centers. 	

With records of 65 million patients, Medco-through its wholly owned subsidiary TIER-claims to have the largest database, followed by AdvancePCS, whose 75 million patient records come from two sources, Advance Paradigm and PCS. PharMetrics claims to track 27 million patients as a result of its agreements with 54 or so plans. They do not disclose the identity of those plans, prompting detractors to raise concerns regarding geographic bias and PharMetric's ability to seamlessly integrate data from disparate sources. 	

Ingenix, the wholly owned subsidiary of United Healthcare, on the other hand, does not face such an issue, because its 44 constituent plans all come from the same company. With only ten million patients on record, however, it is relatively small and may be geographically skewed.	

Recognizing the therapies a patient takes is a no-brainer for payers, but from the pharmacy/switch vantage point, transactions are more complex. If a patient who is currently taking asthma medication uses his card to pick up a refill for his diabetic wife, for instance, some major misrepresentation could contaminate the data unless special care is taken. Not only could the diabetes medication be wrongly assigned to the asthma patient, but his wife also could be proclaimed non-persistent because she failed to fill her own diabetes script.	

Currently, there is little difference between the types of analyses that data vendors say their data enable-they're all longitudinal. All the vendors promote the same list of offerings, including such mainstays as new therapy starts, compliance, persistency, and product switches. Yet patient-level data can do much more. 				

They can, for instance, identify influencers, their spheres of influence, and their referral patterns. The best physicians to target may be not only high prescribers but also those who influence them. The bulk of many high prescribers' scripts are for refills of prescriptions initiated by other doctors. Therefore, detailing them may not be the best use of reps' time. On the other hand, some physicians-opinion leaders, hospital heads, group practice leaders, and lead investigators of clinical trials-may write few scripts but have a great deal of influence on other physicians. Companies often assign a dedicated sales force to win over those important physicians with science-heavy dialogue.	

Primary research can, to a large extent, identify such medical thought leaders. In essence, it involves talking with a very large number of professionals and enlisting the probing capabilities of the field force. But patient-level data open up a host of avenues for tracking down those influencers. 	

A patient can be a link between two physicians. By tracking the flow of patients from one doctor to another, data analysts may elicit insights about relationships among physicians: who sends patients to whom, who belongs to the high-profile inner circles, who initiates therapies that patients tend to renew repeatedly even when they bounce from provider to provider. By bringing in additional information such as group practice membership, managed care affiliation, and composition of lead investigational teams, analysts can, with great precision, identify those influencers and how they shape trends in the physician community. In that way, mining patient-level data may uncover yet untold ways to better approach key influencers and make inroads into murky areas such as off-label usage.	

A 1999 Institute of Medicine study revealed that gross inefficiencies resulting from the use of pen and paper to record medical information lead to 1.3 million injuries and 7,000 deaths a year, and cost healthcare providers and insurers $77 billion. Last year alone, pharmacists placed 150 million follow-up phone calls to physicians to clarify prescriptions. That contributed to administrative inefficiencies adding up to 15–25 percent of the annual $1.2 trillion US healthcare expenditure. 				

In February 2001, those shortcom-ings prompted the three largest PBMs-AdvancePCS, Medco Health, and Express Scripts-to join forces. The result was RxHub, an online data clearinghouse that links physicians' offices to pharmacies. RxHub's three PBM partners represent a whopping 185 million consumers. So, even if physicians are slow to embrace electronic prescriptions-less than 10 percent of scripts are digital-big companies are demand-ing that patient data go high-tech, speeding its capture and rendering it more accurate in the process. 	

Yet, the path to that goal is not free of obstacles. Pharmacists are concerned that RxHub will divert scripts intended for brick-and-mortar pharmacies to its member PBMs' mail-order facilities. In response to that threat, the National Community Pharmacists Association (NCPA) and the National Association of Chain Drug Stores (NACDS) founded SureScript in August 2001 to focus on refills, which make up 80 percent of all transactions. NCPA alone represents nearly 25,000 independent community pharmacies, accounting for nearly $50 billion in prescription sales. 	

The claims processing industry is undergoing changes as well. Several major health plans-including Aetna, Anthem, CIGNA, Health Net, Oxford, PacifiCare, and WellPoint Health Networks-came together to form MedUnite, a conglomerate with a simple objective: to take on WebMD, the lone middleman standing between them and physicians. Nevertheless, MedUnite is not yet a good source of patient-centric data, because it currently processes only a small portion of its founders' claims.	

Other undertakings aim to connect multiple healthcare providers in a given geographic area, thereby producing high-quality patient-centric data. One such program is CareScience, which uses peer-to-peer technology-in which one computer links directly to another without going through an intermediary server-to connect medical groups, hospitals, clinics, and other healthcare organizations. As a result, 75 percent of the healthcare providers in Santa Barbara, California, have access to information about 300,000 patients.	

The dilemma of patient data is that it must be both timely and comprehensive, and that is a paradox. In pursuit of broader coverage, data providers will have to partner with a host of other data providers. And, because the smaller partners have less capital-intensive infrastructures, they are bound to operate at lower speeds, reducing the overall efficiency. Moreover, those partners may use different data formats and protocols, forcing companies to waste time cleaning and standardizing the data. 					

That will force vendors to shift their positioning to coverage and redefine timeliness in terms of days rather than minutes. Manufacturers will base their go/no-go purchase decisions on coverage, and the litmus test will be whether the data-after being chopped into 500 or so territories-still accurately capture patient activity. 	

Yet coverage will present a big challenge to data vendors for some time to come. Consider the type of prescription activity that IMS Health and NDC Health collect: "Dr. So-and-So wrote 16.73-not 16, not 17-scripts of Zocor (simvastatin) last week." Those data are obviously projected. And, considering that the number of US prescribers is 300 times smaller than the potential national patient population, it's no surprise that the patient-level data market is so complex. 	

Despite having incomplete raw data, all data vendors must offer the most comprehensive data possible to customers. That means that the vendor wielding the best projection methodology will have a formidable competitive advantage. 	

An approach that incorporates both patient-centric and patient-level data should succeed, because it will yield insightful projections that analysts can apply to the whole patient data universe. In turn, they can extrapolate desirable patient treatment patterns and outcomes observed in the patient-centric data to the larger but less comprehensive patient-level data. 	

In the same vein, physician-level data products such as IMS Xponent or NDC Source Prescriber can use the profiles of specific physicians-their specialties, managed care and hospital affiliations, association memberships, and interests-as a template to identify a full range of physician behavior. Because that kind of analysis requires patient-centric information, the industry will see patient-level vendors ink partnership deals with patient-centric players to gain access to their data. 	

Media Mix

Contrary to popular belief, physicians still can't retrieve the full medical history of every patient who walks into their offices. Nor can they zap prescriptions through a clearinghouse that shows whether patients are eligible for coverage and reviews all medications they are taking to forestall drug?drug interactions.

Patient Data Come of Age

ccording to many legislators and the media, pharma companies, abetted by Madison Avenue, lead the corporate villain list, just below auditors and errant CEOs. The critics' complaint: Compelled by clever-and expensive-advertising, gullible consumers are pressuring physicians to write prescriptions, then rushing out to buy unnecessary drugs, thereby pushing pharmaceutical costs up at double-digit rates. 	

The numbers seem to support the accusation. Medications accounted for more than 10 percent of healthcare spending in 2001, up from about 6 percent in 1990. And the leap of logic that many critics make, as Michigan Senator Debbie Stabenow told the New York Times recently, is that "excessive advertising only leads to higher prices at the pharmacy." 	

Ah, if only healthcare costs were as simple to understand as accounting methods. But the truth is more complicated than a single, villainous industry scenario. It also includes an institutional force that is hard to reform or control: namely, third-party payment systems that erode personal accountability in healthcare consumption. 	

This article describes how that payment system has created the fertile ground in which insured consumers do not pay-or even know-the full cost of Rx therapies. Therefore, they are susceptible to the influence of pharma marketing through direct-to-consumer (DTC) advertising and physician detailing.	

In the early 1960s, according to the Department of Health and Human Services, pharmaceuticals accounted for more than 10 percent of total healthcare spending. Although that number fell to half that by the 1980s, it returned to 9 percent by 2000. During the next four decades, marketing spending as a percentage of sales remained roughly constant, 25–30 percent of sales for most companies, according to financial reports. So, although marketing spending has increased in absolute dollars as pharma sales have grown, it has been constant as a percentage of sales. 				

So what really accounts for the increase in Rx costs? A critical factor is the growing divide between who selects and uses the medicines and who pays the bills.	

The evidence suggests that changes in third-party payments have had a significant impact on healthcare spending trends. Between 1960 and 1980, the proportion of total medical-care spending covered by third parties, including government programs, rose from 48 percent to almost 70 percent. During the same period, healthcare costs increased at almost twice the rate of the gross national product, rising from 5 to 9 percent of GNP, or from $27 billion to $246 billion (a $27 billion to $100 billion quadrupling calculated in constant 1960 dollars). 	

Although factors other than third-party reimbursement have contributed to the rise, they don't explain its magnitude. The US population grew by about 25 percent during the same period; the over- 65 age group grew by about 50 percent.	

Even as Medicare and corporate health insurance programs became institutionalized during those decades, most US consumers paid for drugs out of their own pockets. That kept pharma spending growth in check while growth in medical spending exploded. As a result, the proportion of healthcare spending on pharmaceuticals fell by half. 	

During the 1990s, the situation made an about-face. Although the proportion of medical care spending reimbursed by third parties rose marginally-from 74 to 79 percent-there was a dramatic shift to third-party payment for pharmaceuticals. (See "Out-of-Pocket Nose Dive," page 74 and "Payment Shift," page 76.) In 1992, only 38 percent of full-time employees had Rx coverage. By 2001, that number had reached 88 percent. That increase in third-party payment, driven in large part by employers' desire to attract and retain talent in a competitive economy, clearly contributed to the rise in medicines' share of the nation's healthcare bill. 	

With third-party payment came a jump in drug spending. Between 1990 and 2000 it tripled, from $40 billion to $122 billion, growing from 6 to 9 percent of healthcare spending. During the same period, all other healthcare spending put together didn't even double, rising from $655 billion to $1,177 billion.	

A Rand study recently published in the Journal of the American Medical Association (JAMA) gives further evidence of the impact of third-party payment on Rx spending. (See "Higher Co-Pays=Less Spend.") The study revealed that doubling patient co-payments from $5 to $10 per prescription cut spending by 22 percent-from $725 to $563 per year per person. The study included more than 400,000 employees in 25 large private companies, but regardless of whether it is representative, the impact of third-party payment on reimbursed Rx drug spending is clear: The more people must pay out of their own pockets per prescription, the less they spend overall. And that effect is independent of how much pharma companies spend on sales and marketing. 	

Some say the Rand study may have overestimated the decline in drug spending as third-party payment was reduced. That is because it failed to examine the extent to which patients switched to OTC alternatives when they were available. Pharmaceutical cost reimbursement-or the lack thereof-matters to patients, and they behave accordingly.	

Of course, the shift to third-party payment provides only a partial explanation of increases in Rx spending. Other factors play a role as well:				

a rising incidence of disease in therapeutic areas like asthma and diabetes

innovative therapies for conditions such as osteoporosis and anemia, which had no satisfactory treatments until the late 1980s and '90s

growing evidence that increased spending on appropriate drug therapy can, for some patients with diseases such as diabetes and asthma, reduce overall healthcare costs 

pharma industry practices such as DTC advertising, increases in the number of sales reps calling on doctors, price increases, and vigorous pursuit of patent life extensions. 

Pharma's increase in marketing and sales spending is well documented. On an absolute dollar basis, the spending has increased significantly, tripling between 1990 and 2000, from about $10–$12 billion to $30–$35 billion in 2000. The increase reflects growth in the size of the pharma sales force-from roughly 50,000 in 1997 to more than 80,000 today-as well as increases in DTC advertising, which was essentially zero in 1990 and amounts to more than $2 billion a year today. Although those increases are significant, they also mirror increases in pharma industry sales growth, so the percentage spent on marketing and sales activities is no higher today than it was in 1990. 	

All the drivers of drug spending increases mentioned previously are important. They are reflected in the rising number of patients on drug therapy, an increasing number of prescriptions, and more days of therapy per patient. They are also reflected in higher prices for innovative new therapies.	

Without those factors, drug spending would not have increased as quickly as it has. Nonetheless, third-party reimbursement is a critical underlying driver that has provided the financial "grease" for other engines of drug spending growth.	

The list of factors contributing to Rx price increases suggests several ways to reduce pharmaceutical spending, many of which have already been attempted. Increasing patient co-pays can result in reduced third-party reimbursement. Limiting sales reps' access to doctors can lower pharma marketing spend. DTC advertising can be controlled-or outlawed, as it is in many countries. The government, too, can control prices. 				

What's the right mix of interventions? They all "work," if success is measured by reduced drug spending, but they do so in very different ways. Two basic factors account for lower expenditures:	

who makes the decisions about how much to spend on medicines, including the trade-off between pharmaceutical spending and spending on other types of healthcare interventions

the relative impact of the number of prescriptions. 

The design of current third-party reimbursement plans distorts the choices patients and physicians make about pharmaceutical and other treatment options. In plans without co-pays, for instance, patients and doctors are insulated from the economic consequences of their choices. 				

Shifting costs back to patients through co-pays theoretically puts spending decisions in the hands of patients and physicians. But in practice the design of most co-pay programs preclude that. Employer-sponsored Rx benefit plans typically sort co-pay amounts into "tiers"-often three tiers with co-pay amounts of roughly $10, $15, and $20 per prescription for medications in each tier. A drug's tier and its co-pay depend on where it falls in the formulary: generic, preferred, or non-preferred brand-name medicines, respectively. To qualify products for "preferred" low co-pays, pharma companies provide discounts to benefit plan sponsors or their agents. More than 70 percent of Rx benefit plans-up from 38 percent in 1996-feature such arrangements.	

So, in today's typical tiered co-pay plan, the third party or its agent is still distorting choices about Rx selection by altering the economics of patient and physician choices. The term "third party" encompasses a combination of payers, including employers, state and federal governments, providers, physicians, and others. But all strive to serve their own best interests as well as the interests of individuals or patient groups. Those third parties, primarily insurance companies responding to employer demands for cost controls and managed care products, make most of the choices about how much healthcare is "enough," about the relative value of treatment options, and about the structure of co-pay programs.	

Moreover, co-pay programs are designed to put more pressure on unit volume than on prices, as patients have a fixed co-pay no matter what the Rx price, giving them no incentive to select a cheaper alternative. Although theoretically it is in payers' interest to push pharma companies for lower prices, the incentive to do so is attenuated, because payers do business through agents such as health plans or pharmacy benefit managers (PBMs), which make money by capturing part of the discounts pharma companies offer in exchange for preferred status. And because higher-priced drugs often have higher discounts, such a financial structure results in "perverse" incentives to find the highest absolute discount rather than the lowest absolute price. 	

So, although benefits with tiered co-pays are a step in the right direction, they are a halting step. Through price negotiations with manufacturers, third parties decide which products merit low co-pays and thus the greatest use. The national discourse rages on, to the detriment of the industry's reputation. 	

The central question remains: Is the price for a new treatment too high, or is it a bargain relative to the hospitalization consequences of not paying for it? More often than not, it is neither pharma companies nor those whose lives hang in the balance-patients-who wage that debate, but rather third-party payers and health plans working on behalf of those payers. 	

Many people advocate direct interventions, such as re-establishing the ban on DTC advertising, because the evidence shows that it increases pharmaceutical use. Critics argue two points: Advertising provides inadequate or misleading information, and even if it is truthful, it drives "unnecessary" or inappropriate use of medications. 	

To the first point, a ban on DTC advertising hardly seems necessary, because regulatory mechanisms ensure accuracy. As for the second point, the issue again is who decides how much pharmaceutical use is too high or inappropriate-third parties or financially accountable patients and their doctors?	

How do companies that want to provide prescription benefits to their employees give them a more direct stake in the decisions affecting their healthcare? Some are examining a "defined contribution" plan, also known as a consumer or self-directed health plan. Rather than pay for healthcare on a prescription-by-prescription or physician-visit basis, employers contribute a fixed amount of money that beneficiaries can spend as they see fit. That approach, which can assure coverage of catastrophic costs, puts decisions about medication selection and about the "value" of those medications back in the hands of those who benefit.  				

Some argue that individual decision makers lack the power of large health plans or PBMs to "push back" pharmaceutical price increases or marketing spend, but the evidence suggests otherwise. Booz Allen surveys of health plans show little or no correlation between health plan size and the level of discounts they receive from pharma companies. Instead, the plan's ability to affect patients' Rx choices and to move market share from one medication to another is what triggers discounts. Most pharma companies structure their discounts to plans as "pay for performance" deals, offering discounts only for share movement, often against a reference point such as last year's share or national average market share. 	

If defined contribution plans become more widespread and patients begin to vote with their wallets and select medicines based on price as well as performance, pharma companies will respond. As companies compete to make their case to patients and physicians based on performance and price, there will be market pressure on price as well as indirect pressure on marketing and ad spend. Debates about whether DTC advertising is appropriate will continue, but patients will retain the power to select products from companies that build a lower advertising margin into their prices. 	

Consumer-directed health plans put the decision about the "right" level of pharma spending where it belongs: in the hands of patients and physicians. They also put the onus on pharma companies to make their case to the right audience: patients and their doctors. Pharmaceutical spending may or may not drop as a result, but at least patients will have more choices.	

According to many legislators and the media, pharma companies, abetted by Madison Avenue, lead the corporate villain list, just below auditors and errant CEOs.

What's Really Driving Costs?

Just a few years ago, financiers were celebrating the "recession-proof" US economy, betting on the success of dot-coms and ridiculing the cautious few. Today, pharma industry experts tout mergers and acquisitions as a panacea to solve growth problems, reduce costs, increase sales, and renew R&D productivity. 	

But there are those who doubt the wisdom of that approach. Some believe that M&As are the bane of the industry, that they often destroy rather than create value. They believe that consolidation, because of its monolithic internal bureaucracies,  make it more difficult to post double-digit growth year after year. Some argue that the pharma industry is looking for a quick fix for slow growth-a painless solution that will rapidly restore historical profit margins. According to that line of reasoning, if the industry follows the path of consolidation, it will be tinkering with unsustainable growth, a practice much like rearranging deck chairs on the Titanic.	

This article proposes that avoiding the "bigger is better" syndrome might just save the sinking ship. It explains how companies can use existing assets to create new value for caregivers, patients, and stockholders by extending, rather than consolidating, the pharmaceutical enterprise. That can be achieved through alternative business structures, such as alliances, joint ventures, co-promotions, and start-ups. The article also offers a rationale for creating such extensions and provides practical strategies for doing so. 

Many industry insiders assume that mergers and acquisitions will quench pharma companies' thirst for more robust pipelines, decreased costs, increased sales, and greater breadth and depth of therapeutic classes. But executives should ask themselves:						

Why would merging two companies with weak pipelines result in a single company with a strong pipeline? 

Why would merging two inefficient administrations and infrastructures result in lower costs? 

Why would combining mammoth sales forces necessarily result in increased sales? 

Why would larger R&D groups-with increased bureaucratic layers and multiple therapeutic classes competing for finite resources-result in innovative therapies and decreased time to market? 

As an end goal, it's not. To explore why, it is useful to consider the basic business objective: to create the highest earnings possible by maximizing revenues and minimizing costs. Various strategies can help companies achieve their business goals, but they must reflect the company's overall objectives. Without that link, companies waste time and resources on initiatives that yield little benefit. (See "Back to Basics," page 64.)	

In the pharma industry, a company's business objectives could be earning profits to fund further research, improving patients' quality of life, and providing  a return on investment to stockholders. Potential alternatives through which to achieve those objectives could include making the pipeline more efficient to optimize time to market and peak sales, increasing customer focus by building and supporting the sales force, or outsourcing to minimize infrastructure and leverage third-party expertise. As with any business, the strategy selected depends on the overall state of the economy and the market's competitive response. 	

Once the company identifies a strategy, it must select an appropriate organizational structure. (See "Enterprise Options.") If the abilities needed to support the strategy are unavailable in-house, the company has four options for establishing an appropriate enterprise:				

Build capabilities internally. This approach requires heavy monetary investment, personnel recruiting, and systems implementation. It also takes significant time to design, build, and implement such capabilities. (See "Time and Money.")	

Stay the course. Although this option calls for no incremental investment and effort, it may not support the business strategy and, as a result, may set the company up for failure or acquisition.	

Merge/acquire. This popular choice demands significant investment to integrate disparate systems and processes. Cultural barriers may also create tangible and intangible costs. Consolidation, however, may enhance marketplace stature and image.	

Extend the enterprise. This option also requires investments, but they are shared across business partners. Enterprise extension may also enhance marketplace stature and image.	

Internal issues related to product pipelines, pricing, costs, and profits have caused, and will continue to cause, pharma executives to look for M&A– based solutions. M&A solutions seem easier to conceptualize and undertake than those that extend the enterprise externally through alternative business structures-but in practice, there is often no end to the difficulties that can arise from collapsing the capabilities of two companies into a single structure. That process necessitates a limited course of action that:	

focuses internally to make the "integration" work

puts technology first and process second-supported by enterprise systems

In contrast, establishing alternative business structures extends the enterprise through commercial arrangements with outsourcers (such as third-party distributors), joint venture partners, and co-promotion partners. An extended enterprise:	

focuses externally to create value across business partners

puts process first and technology second 

focuses on enhancing external effectiveness

Although M&A activity gives traditional enterprises a shot of "adrenalin," an extended enterprise results in sustainable productivity by magnifying output and value through collaborative business partners. 	

Unfortunately, companies assume that the decision to institute alternative business structures alone will result in success. Empirical evidence suggests that it takes more than that. Even if a specific decision to create an extended enterprise is correct, initiatives to design, build, and implement new structures can fail.		

To increase revenues, for example, a company could use underdeveloped assets in a start-up initiative. But if the new entity lacks the design and implementation needed to support those assets, an ineffective company launch, insufficient support infrastructure, and a poor competitive position could result.	

Most newly minted business-school graduates would say, that for a pharma company to launch an alternative business structure it must focus on global strategies and marketing campaigns. A few may mention the capabilities it must establish to support functions such as discovering, developing, supplying, and marketing products. But none are likely to mention that the company must also:	

establish a suitable infrastructure: office space, desks, phones, computers, and networking capabilities hire, manage, and pay employees

develop processes and document standard operating procedures

establish systems, point solutions, and interfaces

manage revenues, costs, capital, and cash flow

A business structure that extends the enterprise must perform the core functions of a traditional stand-alone company. As many failed dot-coms learned the hard way, a good idea or fancy office space isn't enough. Supply without distribution capabilities, processes that fail to satisfy safety and FDA regulations, and revenue without financial controls all result in the same outcome: failure. Ethics, traditional business rigor, and uncompromising attention to detail are required to manage any business, particularly the successful launch of an extended business enterprise.	

Building capabilities calls for pragmatism. Because departmental silos are strong, integrating their processes and sharing information almost always prove difficult and can highlight both technology and process weaknesses. Integration across multiple departments-or within a single department-involves hand-offs among individuals, units, or third parties. 	

Those process and systemic exchanges make integration more complex, which increases the potential for risk. Such interface/risk points can surface between multiple functional areas. The discovery process requires work between research and development, for instance, and product stocking cannot occur without good interactions between supply chain and trade relations. (See "Risky Business," page 64.) And all of those added complexities eventually draw on already tight budgets and timelines, reducing the probability of success. 	

Pharma companies can realistically deal with those issues by following four steps: 				

Just a few years ago, financiers were celebrating the "recession-proof" US economy, betting on the success of dot-coms and ridiculing the cautious few. Today, pharma industry experts tout mergers and acquisitions as a panacea to solve growth problems, reduce costs, increase sales, and renew R&D productivity.

A Better Way to Build

Check out the ranking of the world's largest pharma companies in the fourth annual Pharm Exec 50.

Fourth Annual Pharm Exec 50

In the excellent online magazine The Edge (www.edge.org), Gerd Gigerenzer, director of the Center for Adaptive Behavior and Cognition at the Max Planck Institute for Human Development in Berlin, poses a riddle about risk: Imagine that a 40-year-old woman has her first mammogram and it comes back positive. The incidence of the disease in her age group is 1 percent. The test is 90 percent accurate, and it has a false-positive rate of 9 percent. What's the probability that the woman has cancer?	

Gigerenzer has presented his puzzle to many physicians, including some with years of experience in mammography. Their answers? About a third think the probability is 90 percent. Another third think it's between 50 and 80. The rest say it's between 1 and 10 percent.	

And the real answer? Gigenrenzer explains it this way: "Think about 100 women. One of them has breast cancer. This was the 1 percent. She likely tests positive; that's the 90 percent. Out of 99 who don't have breast cancer another 9 or 10 will test positive. So we have one in 9 or 10 who tests positive. How many of them actually has cancer? One out of ten."	

The explanation is elegant, and as the pharma industry comes under increasing pressure to explain risk, we'll need to hear a lot from folks like Gigenrenzer, who devote their lives to bridging the gap between numbers and people. But for the industry, the puzzle-and Gigenrenzer's argument-raise some important issues.	

First, the goal that patients should be able to make informed decisions about treatments is a good one. But if Gigerenzer is right about doctors' ability to handle numbers, we're missing a link in the chain. Labeling and advertising can convey facts about benefits, side effects, and the like, but if doctors aren't committed to the idea of analyzing risk, it's unlikely that patients will make good decisions. Who will take on the task of educating innumerate doctors? I don't think pharma wants the job, but the industry may end up having to take it on anyway: If patients make bad decisions, we all know where the blame will ultimately fall.	

Second, if we want to go beyond mere compliance and truly communicate risk, we'll be getting into unexplored territory. "According to decision theory, rational decisions are made according to the so-called expected utility calculus," Gigenrenzer writes. "In economics, for instance, the idea is that if you make an important decision - whom to marry or what stock to buy- you look at all the consequences of each decision, attach a probability to these consequences, attach a value, and sum them up, choosing the optimal, highest expected value or expected utility." In theory it's how we all make decisions. But in practice, we don't, says Gigenrenzer. To improve our decision making we need what he calls "frugal heuristics"-mental tools for simplifying the calculation. Those heuristics are an area where cognitive scientists are breaking ground, and what may prove crucial for working with patients. 	

Finally, as Gigenrenzer points out, there's a big picture. Risk assessment isn't just a regulatory strategy. It's a way for human beings to cope with the world and its uncertainties. It's more accurate, more realistic, than the sorts of false certainties that patients-and doctors, teachers, and politicians-tend to rely on today. It's good independent of its value in getting products to market and fending off legal problems.	

As a risk approach becomes more and more a part of pharma's reality, here's a sustaining thought: Maybe this isn't just a regulatory fad. Maybe it's part of an evolutionary step forward in the intellectual life of the species. What are the odds? That, of course, is partly in our hands.		

Letter to the Editor

In the excellent online magazine The Edge (www.edge.org), Gerd Gigerenzer, director of the Center for Adaptive Behavior and Cognition at the Max Planck Institute for Human Development in Berlin, poses a riddle about risk: Imagine that a 40-year-old woman has her first mammogram and it comes back positive. The incidence of the disease in her age group is 1 percent. The test is 90 percent accurate, and it has a false-positive rate of 9 percent. What's the probability that the woman has cancer?

Pharmaceutical Executive-05-01-2003

E-Learning FDA Style

Patient Data Come of Age

What's Really Driving Costs?

A Better Way to Build

Fourth Annual Pharm Exec 50

Table of Contents

The Riddle of Risk