If Private Equity Sized Up Your Business
Robert C. Pozen advises public companies to adopt many of the practices that top private equity firms use to generate superior returns for their investors (“If Private Equity Sized Up Your Business,” November 2007). Yet for the most part, the best public companies have already done so, and the top quartile consistently produces shareholder returns that are at least as good as (and when adjusted for leverage, maybe even better than) the limited partnership returns generated by top quartile private equity firms. When Pozen compares top quartile private equity returns with the S&P 500, he is effectively measuring premium private equity firms against only average public companies—an apples to oranges comparison—and ignoring the fact that an average public company has just as much to learn from top public companies as it does from leading private equity firms.
Nevertheless, Pozen is right to point out that too many public companies consistently fall short on his five key tests and thus produce mediocre returns for their investors. I have found that the leaders of many S&P 500 companies are bright, hardworking, experienced, highly motivated, and perfectly capable of passing Pozen’s tests and following his prescriptions. So why do so many public companies fail to meet the challenge? Pozen offers many reasons, but a fundamental cause is the fixation on earnings per share in the public markets. Leaders of public companies make countless decisions based on how those choices will affect EPS: avoiding even the smallest decline in debt ratings, holding “excess” cash, choosing to repurchase shares, hanging on to parts of the portfolio even when they are failing to earn their weighted average cost of capital, making acquisitions that will never justify their cost of capital—the list goes on and on.
Private equity firms, however, reward neither their general partners nor their portfolio company managers on the basis of EPS. That helps them avoid many traps that average public companies often fall into—hidden dangers that prevent “bold strategies and operating improvements” from ever seeing the light of day—and explains why the average S&P 500 returns are so paltry compared with those of top-quartile private equity firms. It also explains why they’re so paltry compared with those of top-quartile public companies, too. Bold strategies and successful implementation—along with efficient balance sheets, effective boards of directors, and well-designed compensation incentives—are their hallmark, as well.
The relevant comparison is not “private versus public”; it is shareholder value versus EPS. We should stop lionizing private equity as a superior form of capitalism. Instead, we should emphasize what all top-quartile companies—whether public or private—have in common: an unyielding focus on growing shareholder value and a greater understanding of what it takes to do so. I’ve seen up close and personal the huge difference this approach can make to the direction, decision making, execution, and performance of any company, public or private.
Realizing the Promise of Personalized Medicine
We read with great pleasure Mara G. Aspinall and Richard G. Hamermesh’s article concerning barriers to the adoption of targeted therapies (“Realizing the Promise of Personalized Medicine,” October 2007). Industry stakeholders are keenly aware of the need to overcome those barriers in order to reduce the costs of our bloated health care system and improve patient care, the ultimate goals of all involved.
The authors, however, reiterate two common misconceptions about personalized medicine that we would like to address. The first is that the pharmaceutical industry must abandon the blockbuster model in favor of targeted therapies—even though blockbuster drugs’ high ROI permits pharmaceutical companies to recover not only those drugs’ R&D costs but also the costs of other therapies that either fail to gain approval or have smaller market shares. Aspinall and Hamermesh suggest that stepping away from this model is as simple as exchanging, say, a single blockbuster drug generating $1 billion per year for five targeted therapies with yearly sales of $200 million each. This view ignores the tremendous investment needed to develop each compound. In addition to the costs associated with developing not only the drugs but also their companion tests (which the authors suggest the pharmaceutical industry should bear), each of the five targeted therapies would need appropriate investment in its launch—patient and doctor education, reimbursement negotiation, and cooperation with testing laboratories—to ensure market adoption. Thus, five targeted therapies, even if they were to generate $200 million apiece, simply could not equal a single billion-dollar blockbuster drug.
The second misconception is that the market segmentation implicit in targeted therapies necessarily translates into fewer patients and reduced returns. This view fails to take into account several key economic facets of both one-size-fits-all therapy returns and potential returns from personalized markets. “One size fits all,” for instance, is something of a misnomer. Though such therapies are presumed to capture between 50% and 60% of the overall patient pool, more often than not they capture less than 15%. Conversely, a personalized therapy that might work in only 25% of the overall patient pool behaves more like a first-to-market drug in a niche when it is enabled by a companion diagnostic and thus typically will capture a 40% to 50% market share of that subgroup of patients. This is because companion diagnostics are more efficient than traditional empirical diagnosis at identifying patients for whom the therapy is appropriate.
The numbers clearly demonstrate that targeted therapies do not inevitably result in smaller market shares and could have the same revenue potential if the optimal companion test program were in place. Consider, for example, a hypothetical one-size-fits-all therapy that has a potential patient population of 1 million patients. The drug is likely to capture fewer than 150,000 patients out of the pool, and that number will diminish through the attrition of those for whom the drug seems ineffective. A targeted therapy appropriate for a much smaller population, perhaps a third the size, is likely nevertheless to capture the same 150,000 patients. Yet because the attrition rate will be lower, this therapy may well translate into significant market share.
We agree with the authors, however, that the pharmaceutical industry is justifiably concerned about making any drug dependent on the performance and adoption of a companion diagnostic, given that diagnostic tests are frequently underutilized and often inappropriately applied in clinical practice. Historically, the diagnostic industry has been a high-volume, low-cost business that has never invested in the validation and launch of diagnostics to the same degree that the pharmaceutical industry has invested in drugs. And the diagnostic industry has not yet suggested an equitable solution for this disparity other than to place the burden on the pharmaceutical industry.
As the model for personalized medicine continues to develop, stakeholders will continue to work on building the best business platform to realize its potential. It is high time for the two main stakeholders—the pharmaceutical and diagnostic industries—to stop their finger-pointing and find a way to work together.
Aspinall and Hamermesh’s recommendation of a partnership between the pharmaceutical industry and the diagnostic-testing industry—combining testing and treatment under one roof—barely skirts the legal minefield of a monopolistic practice. And though all physicians decry the inadequacies and unjustness of the current reimbursement system, the authors’ push for greater reimbursement of diagnostic testing ignores both the added cost burden on health care (unless existing resources are redistributed) and the disproportionate rewards for procedure-based medicine—which includes current diagnostic testing—over cognitive clinical practice (patient-physician primary care services).
Physicians are also called to task for failing to understand the immense promise of genomics in personalizing care to individual patients. I do not believe, however, that clinician ignorance is at the heart of the problem. Rather, I think clinicians just aren’t convinced the juice is worth the squeeze. I submit that in an era of “evidence-based medicine,” studies in real-world settings comparing the cost-effectiveness of new tests and therapies against current practices should drive a change in physician practice.
Aspinall and Hamermesh make important points, but their conceptual misuse of the word “personalized” illustrates a fundamental problem. “Personalized” means tailoring treatment to the person. It does not mean biological specificity. Despite molecular advances in medicine, it is unlikely that there will ever be one “right” treatment for a person with a complex illness like coronary artery disease, diabetes, alcohol dependence, schizophrenia, or depression. Such conditions involve multiple genetic variants compounded by environmental, emotional, social, and behavioral factors. Moreover, individuals often suffer from more than one chronic illness concurrently. Diabetics, for example, have higher rates of heart disease, as do people with depression. This further complicates treatment choices.
Medical decision making for complex clinical problems, therefore, will continue to involve subtle differences in risk and benefit that require physicians and patients to consider both subjective factors like patient values and objective factors like genomic testing. Ignoring medicine’s inherent subjectivity risks introducing error. For instance, mislabeling “genomically informed medicine” as “personalized medicine” devalues the critical decision-making component: a trusting patient-physician relationship. A physician helps create such trust by using finely honed communication and listening skills to develop a shared understanding of the patient’s disease in the context of a whole life. Efficacious treatments are only one variable in individualized treatment planning.
Physicians need more substantial communication training and improved reimbursement for the time that effective communication and shared decision making require. (Currently, those practices rank below technical diagnostic and treatment procedures for reimbursement.) Only the proper recognition of what is personal will lead to personalized medicine.
Aspinall and Hamermesh respond: We agree with Peter Keeling and Mollie Roth’s second point—that targeted therapies often reach much bigger markets than originally anticipated. Indeed, we say as much in our article. In response to their first point, however, we would still assert that the pharmaceutical industry needs to abandon its overreliance on the blockbuster model. Clinical trials of targeted therapies cost less than those of broad-based medications; even more important, the pursuit of increasingly elusive new blockbuster drugs strains limited resources, which then hinders the development of more targeted therapies.
As Eric M. Wall suggests, more studies are needed. Still, even in cases where good data do exist, the adoption of personalized medicine has been too slow.
Finally, we concur with Joseph S. Weiner that physician judgment should be an integral part of personalized medicine and genetic information. Indeed, the probabilistic nature of much genetic information will make doctors’ and patients’ relationships—and ability to communicate with one another—essential.
Munchausen at Work
In his article “Munchausen at Work” (Forethought, November 2007), Nathan Bennett highlights the important and underdiscussed behavioral problem of MAW. I would like to add two observations based on my background as a psychiatrist working in both the health care and pharmaceutical industries.
First, individuals who have experience in the health care profession may be particularly susceptible to MAW because of the motivations and psychopathology that led them to that field in the first place. Managers in pertinent industries may find it useful to incorporate this additional consideration into an assessment of possible MAW.
Second, for high-energy people placed in low-energy roles, MAW serves as a means for maintaining energy homeostasis. Those individuals often exhibit MAW because the excitement of a crisis and the rewards of its resolution make up for the otherwise ordinary routines of their jobs. In addition to the remedies outlined by Bennett, managers should consider moving those employees into roles that better match their energy levels, thereby eliminating a key driver for MAW.
Bennett responds: Jane Tiller makes two important points that help improve our understanding of, and response to, MAW. Over the long run, a more comprehensive appreciation of the MAW behavior pattern’s etiology will indeed prove most useful. Some professions by their nature may attract individuals with a predisposition to “fire setting” and “firefighting” behavior. And the reward systems in others, such as management consulting, practically demand it: The more “problems” the consultant finds, the more the client is billed, and the more “valuable” the consultancy’s expertise appears to be. Clearly, industry norms, compensation practices, and selection criteria all play a role in setting the stage for MAW.
Tiller’s second observation raises a question for the short run: How can executives best manage employees who exhibit MAW behavior? An employee’s need to correct a mismatch between the level of excitement he or she craves and the level of excitement the job actually requires can be compelling. Redirecting employees’ energies in a way that does not put the company’s health at risk can only benefit all involved. Of course, as we learn more about the drivers of MAW, our ability to manage it will no doubt improve.
Clarification: In his article “Is It Real? Can We Win? Is It Worth Doing?” (December 2007), George S. Day noted that the R-W-W (“real, win, worth it”) screen is “sometimes known as the Schrello screen.” The reference was to Dominick (“Don”) M. Schrello, of Long Beach, California, who developed the original framework in the 1960s.