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Pharmaceutical Detailing: in the US the Details are Tied the Prescriber’s Name

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By: Allison Dennis B.S.

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Source: pixabay

While U.S. privacy laws protect patients from direct pharmaceutical marketing and shield their personal information from data mining, physicians are routinely identified based on their prescribing habits and targeted by pharmaceutical companies through personalized marketing campaigns. By their very nature, these campaigns aim to influence the behavior of prescribers. In other countries, including those protected by the European Union’s Data Protection Act, the personal identification of prescribers through medical data is strictly forbidden. However, in the U.S. these personalized campaigns are made possible by a robust pipeline of data sharing.

The pipeline begins with pharmacies, who routinely sell data derived from the vast volume of prescriptions they handle. While the prescribers’ names are usually redacted, IMS Health, a key health information organization in the pipeline, can easily use the American Medical Association (AMA)-licensed Physician Masterfile to reassociate physician ID numbers with the redacted names. The physician ID numbers are issued by the U.S. Drug Enforcement Administration (DEA) and are sold to AMA through a subscription service. IMS Health uses the prescription data to develop analytic tools for sale to pharmaceutical companies desperate to gain a marketing edge with individual prescribers. The tools consolidate the activity of nurse practitioners, dentists, chiropractors, and any professionals who can legally file a prescription. Marketers can use these tools to determine how much each named physician is prescribing, how that compares to other named physicians, what their specialty is, etc.

The data contained in the AMA’s Physician Masterfile is applicable for informing research and conducting surveys of practicing physicians, yet the need to identify physicians by name is usually not needed for public health research and enables prescriber manipulation.  The prescriber reports compiled by IMS Health enable pharmaceutical companies to take a data-driven approach to direct-to-physician advertising, a practice known as detailing. During a 17-month period between 2013 and 2015, pharmaceutical companies reported spending $3.5 billion in payments to physicians covering promotional speaking, consulting, meals, travel, and royalties. While many of the expenditures may be tied to legitimate collaborations between pharmaceutical companies and medical professionals, the U.S. Department of Health and Human Services warns that free samples, sham consulting agreements, subsidized trips, and industry-sponsored continuing education opportunities are all tools used by vendors to buy medically irrelevant loyalty. Indeed, physicians themselves seem conflicted over the significance of these relationships. When residents were asked if contact with pharmaceutical representatives influenced their prescribing practices, 61% believed they were unaffected. However, the same residents felt that only 16% of their peers were similarly immune to contact with pharmaceutical representatives.

Studies examining the role of detailing  have found it associated with higher prescribing frequency, higher costs, and lower prescribing quality, all with no contrasting favorable associations. Recent concerns over conflicts  of  interest arising from increased exposure of physicians to detailers led several academic medical centers to restrict sales visits and gift giving and implement enforcement mechanisms. Compared to hospitals with no detailing limitations, hospitals with limitations underwent an 8.7% relative decrease in the market share of detailed drugs and a 5.6% relative increase in the market share of non-detailed drugs. Overuse of brand-name drugs, which are most commonly associated with detailing, cost the US approximately $73 billion between 2010 and 2012, one-third of which was shouldered by patients. Advocates of the practice lament the lack of formal academic opportunities for physicians to learn about new drugs, believing the educational materials provided by pharmaceutical representatives fulfills a need.

The most tragic example of the potential harms of detailing targeting individual prescribers comes from the early days of the prescription opioid crisis. Purdue Pharma, the maker of OxyContin, used prescriber databases to identify the most frequent and least discriminate prescribers of opioids. Sales representatives, enticed by a bonus system that tracked their success according to upswings captured in the prescriber database, showered their target prescribers with gifts while systematically underrepresenting the risk of addiction and abuse from OxyContin. Recruitment into Purdue’s national speaker bureau and subsequent paid opportunities were further used to entice lukewarm and influential prescribers.

The last decade has seen several attempts to address the influence of detailing at the institutional, professional, and executive levels. Individual hospitals have begun limiting the access of physicians to vendors. The American Medical Student Association began issuing a conflict-of-interest scorecard, allowing all U.S. medical schools to track and assess their own detail-related policies, including those related to the limiting of gifts from the industry, industry-sponsored promotional speaking relationships, permitted accesses of pharmaceutical sales representatives, and overall enforcement and sanction of these policies. In 2016, 174 institutions participated. The AMA, which licenses the list of physician names used by health information organizations companies, has offered physicians the chance to block pharmaceutical representatives and their immediate supervisors from accessing their prescribing data. However, the Physician Data Restriction Program does not limit the ability of other employees at a pharmaceutical company to access prescribing data of doctors who have opted out. Physicians must renew their request to opt out every three years and are automatically added to the Masterfile upon entering medical school. Five years after the program’s introduction in 2006, just 4% of practicing physicians listed on the file had opted out.

In 2007, the state of Vermont outlawed the practice of selling prescription data for pharmaceutical marketing without prescriber consent. The law was quickly challenged by IMS Health, the Pharmaceutical Research and Manufacturers of America, and other data aggregators and eventually struck down by the U.S. Supreme Court. Vermont legislators held that detailing compromises clinical decision making and professionalism and increases health care costs and argued that the law was needed to protect vulnerable and unaware physicians. However, the Court held that speech in the aid of pharmaceutical marketing is protected under the First Amendment and could not be discriminately limited by Vermont law.

Congress made the first federal attempt to address the issue by enacting the Physician Payment Sunshine Act in 2010, which required companies participating in Medicare, Medicaid, and the State Children’s Health Insurance Program markets to track and collect their financial relationships with physicians and teaching hospitals. The transparency gained from the disclosures have allowed many researchers to systematically evaluate connections between conflicts of interests and prescribing behavior.

As policy makers and private watchdogs scramble to address the issues of detailing, the availability of physician names and prescription habits continues to facilitate the implementation of novel tactics. Limits on face time have pushed detailers to tap into the time physicians are spending online. When the names of prescribers are known, following and connecting with prescribers through social media accounts is straightforward. Companies like Peerin have emerged, which analyze prescriber Twitter conversations to learn whose conversations are most likely to be influential and which prescribers are connected. LinkedIn, Facebook, and Twitter all offer the ability to target a list of people by name or e-mail address for advertising. While all online drug ads are limited by the U.S. Food and Drug Administration, pharmaceutical companies are experimenting with the use of unbranded awareness campaigns to circumvent direct-to-consumer regulations.

While personalized prescriber marketing campaigns may be turning a new corner in the internet age, a simple opportunity exists at the federal level to de-personalize the practice of physician detailing. It is unclear the extent that the DEA stands to gain from selling physician ID subscriptions. However, in context of the downstream costs of the overuse of name-brand drugs this may be an appropriate loss. The U.S. Government’s central role in the reassociation of prescribers’ prescriptions could be directly addressed through systematic implementation of revised policy in order to preempt downstream prescriber manipulation.

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Written by sciencepolicyforall

November 9, 2017 at 10:41 pm

Science Policy Around the Web – August 5, 2016

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By: Fabrício Kury, MD

Genetic engineering

‘Gene drive’ organisms should be tested in field trials, not widely released, experts say

While the Zika virus shows spread into the US, with mosquito-borne transmission having been reported in Miami, the scientific community is eager to kick-start the use of the new biotechnology called Gene Drive. This technique allows for the creation of genes that cheat the trial of chance and get passed on to nearly 100% of the offspring. This way, it is possible to alter the genome of entire populations of species, for example, by making populations of Aedes mosquitoes unable to transmit the Zika or Malaria viruses — if not plainly kill all the Aedes.

The danger of Gene Drive is our lack of knowledge about the impact of drastic alterations in the behavior or biology of one species, and also the consequences from the quick removal of a pervasive species from an ecosystem. The slow progress of Zika into the U.S. through warmer and wetter edges such as Florida and Puerto Rico seems like a window of opportunity for attacking the spread of the disease while it is still relatively isolated. However, the National Academies of Sciences, Engineering and Medicine call for tightly controlled experiments before wide use of the gene drive. As MIT Media Lab professor Kevin Esvelt put it, “there is a nontrivial chance that [the genes] will spread from a single organism released into a wild population into most or all members of the local population — and very possibly into every population of the target species around the globe.” (Ike Swetlitz, STAT news)

Technology and Healthcare

Why lawmakers are trying to make ransomware a crime in California

Ransomware is a type of malware (a “virus”) that can make money for a hacker very quickly. The ransomware program encrypts files in the target computer, then demands a ransom, usually to be paid in cryptocurrency (the most popular is Bitcoin) which can be hard to track, to release the key that decrypts the files. Hospitals are perfect targets for ransomware attacks because they are often big institutions, are mostly unprepared to defend themselves against cybercrime, and hold precious data in its computers. Most often, ransomware makes the system of computers functionally “locked inside a black box” or completely unable to be used, creating mounting losses and outright risks that outweigh the price of the ransom.

This includes the medical data that is kept private inside those computers and becomes locked behind the ransomware’s military-grade encryption. Other times, the cyberattack consists of “kidnapping the privacy” of the patients. Here the hacker makes a copy of the data and requests a ransom not to release it to the public. In 2015 alone, 113 million patients had some or all of their health records stolen, and the hospital hacks showed increase of 600%. It has been called “The Year of the Hospital Hack.” Moreover, according to the FBI, ransomware as a broader industry is on the rise. In the first three months of 2016, victims of ransomware lost more than $209 million, compared to $25 million in the entire 2015. (Jazmine Ulloa, Los Angeles Times)

Affordable Care Act Effects

How I Was Wrong About ObamaCare

The strategy implemented by the Patient Protection and Affordable Care Act (PPACA, “ObamaCare”) for the purpose of controlling health care costs is one that strives for paying for healthcare by value provided instead of service provided. The promoted understanding, as summarized by former health policy advisor to the Obama administration Dr. Ezekiel Emanuel, 2011, is that such force will pressure the health care industry to undergo vertical consolidation into Integrated Delivery Systems. These systems, whose likes could be named as Kaiser Permanente, Geisinger Health Care System, and Intermountain Healthcare, are consolidations of all types of providers (physician, imaging, therapy, nursing, surgery, home care, specialty care etc.) and strives to be at least internally coordinated to provide the best value per cost, since its payment is not completely tied to the number of procedures or services performed.

Two PPACA-derived value-based reimbursed programs were launched in 2012 — the smaller and more cautious Pioneer Accountable Care Organizations, reserved for groups of providers with more experience in integrated health care delivery, and the larger and more ambitious Shared Savings Program Accountable Care Organizations. Their data has been released along the past year. The data shows that, along the first performance year of the Medicare Shared Savings Program, 58 ACOs generated $705 million in savings, feat which earned them $315 in bonuses as per the program’s workings, leaving net $260 million in savings to CMS. In April this year, the first study of the official CMS claims data indicated that the better savings were among the ACOs classified as small groups of providers. This is understood as evidence against the “Kaiserification” of healthcare as envisioned by Dr. Emmanuel, since the savings come not from having all providers as employees of a big conglomerate, but instead in giving more autonomy and power to the health care provider at the forefront of the contact with the patient. (Bob Kocher, Wall Street Journal)

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August 5, 2016 at 11:00 am

Science Policy Around the Web – July 15, 2016

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By: Leopold Kong, Ph.D.

Healthcare Policy

United States Health Care Reform – Progress to Date and Next Steps

On Monday, President Obama published a special communication in The Journal of the American Medical Association summarizing the impact of the Affordable Care Act (ACA) during his tenure in office.  The report outlined the president’s initial motivations for health care reform, including his frustration over the relatively low insurance coverage across the US population when he first entered office, even though the U.S. was devoting over 16% of its economy to health care.  The report noted that since the implementation of Medicare and Medicaid in 1965, the uninsured population in the United States had stabilized to around 15% since the early 1990s.  With the creation of the ACA, the uninsured population has dropped 43% from 16% in 2010 to 9.1% in 2015.  Importantly, the health care reform has not decreased employment rates, while it has decreased insurance payment prices in the private sector by improving detection of health care fraud and by increasing insurance provider competition.  President Obama is optimistic that coverage will further expand, considering that many of the reforms that are part of the ACA have not yet reached their maximum effect. Policymakers must be on guard, however, against backtracking in the years ahead, considering there are continued attempts to repeal parts of the ACA. The report notes: “We need to continue to tackle special interest dollars in politics. But we also need to reinforce the sense of mission in health care that brought us an affordable polio vaccine and widely available penicillin.” (Barack Obama, JAMA)

HIV Health Policy

South Africa ushers in a new era for HIV

Next week, the International AIDS Conference returns to Durban, South Africa to discuss research and health care policy challenges in the country with the largest HIV epidemic in the world. Nearly 7 million people in South Africa have HIV, about 15% of the global HIV infected population. Remarkable progress has been made over the last two decades with the advent of more effective antiretroviral therapeutics and their wide dissemination.  South Africa’s average life expectancy has increased from 54.4 years in 2004 to 62.5 in 2015, and mother-to-child transmission has fallen from 30% to 1.5%.  Furthermore, AIDS-related deaths have been cut in half since 2006, from 400 to 200 thousands per year.  It is hopeful that continued gains in therapeutics accessibility would greatly improve the situation in South Africa, though substantial challenges remain. These include maintaining patient compliance in the face of a disease that no longer appears to be immediately life threatening, and dealing with the inevitable development of drug resistance that would require constant and costly patient monitoring.  Surprisingly, in South Africa, but not in Europe, people on therapy appeared to have better quality of life than their HIV-negative peers, highlighting the general benefit of increased interaction with health practitioners. Health policymakers in a country with over 3 million on antiretroviral therapy must also consider the side effects of the drugs, which include increased risk of hypertension, diabetes and obesity for older populations. With continued advances in small molecule and antibody therapeutics, as well as novel vaccine platforms, there is increased hope for millions of people living with HIV. (Linda Nordling, Nature)

NASA

First virus-hunter in space will test DNA-decoding device

Earlier this week, virus-hunter turned astronaut Kate Rubins arrived at the International Space Station with a pocket-sized DNA sequencer, the MinION (9.5 x 3.2 x 1.6 centimeters, ~ 120 grams) developed by Oxford Nanopore Technologies.  Unlike conventional sequencers, the MinION “reads” DNA strands by passing them through nanopores on the device that detect changes in electrostatic charge.  The small size of MinION is important to curb expenses, as it costs about $10,000 per pound of equipment flown to the space station. “Altogether, it’s an extremely exciting research package and a great capability on board station,” Rubins said. NASA hopes this project will improve scientific microbial research and disease diagnostics in space.  The MinION technology may also be used to detect extraterrestrial life, though further development may be needed, especially if non-DNA based life forms are expected.  Importantly, the experiments in space could encourage the expansion of genomics-based medicine utilizing MinION technology to more remote and poorer areas on Earth where the use of large, conventional DNA sequencers would not be practical. (Marcia Dunn, Associated Press)

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July 15, 2016 at 1:45 pm

The Debut of Health Care Data Science

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By: Fabrício Kury, M.D.

Image source: MedCityNews.com

It is easy for a millennial – a person born between mid-1980’s to late-90’s – to be unaware of just how young the current methods used in health care research really are. Controlled randomized clinical trials (RCT), dating only from the late 40’s, are probably younger than most millennial’s grandparents. Case-control methodology and Kaplan-Meier curves only originated in the 1950’s, while meta-analyses were only accepted by medical researchers in the late 70’s. Step into the 80’s and early millennials are as old, if not older, than propensity scores and the concept that is today called cost-effectiveness research. The term “Evidence-Based Medicine” is as young as a millennial born in the early 90’s, while the late 90’s and 2000’s saw the explosion of genomics, proteomics, metabolomics, and other -omics research. Finally, the 2010s so far might be credited for when the term Data Science (“the fourth paradigm of science“) gained widespread notoriety, and established its modern meaning as – long story made short – the practice of producing knowledge out of data that had been created for other purposes.

While the second half of the 20th century transformed health care research into an ever more rigorous and technology-driven science, it also saw the cost of the health care sector of the U.S. unrelentingly grow from a comfortable 5% of the Gross Domestic Product in 1960 to a crushing 18% in 2015. Medical bills have become the leading cause of personal bankruptcies in the nation, while life expectancy, as well as other basic health indicators, depicted a country nowhere close to getting a similar bang for each buck as other developed nations. In 2009, the Obama administration prescribed to the health care sector a remedy that had previously brought efficiency and cost savings to every industry it had previously touched: information technology. The Health Information Technology for Economic and Clinical Health (HITECH) Act (part of the American Recovery and Reinvestment Act of 2009) literally gave away as much as $36.5 billion of taxpayers’ money to hospitals and physician practices for them to buy and “meaningfully use” electronic health records (EHRs). This outpouring of money was overseen by the Office of the National Coordinator of Health Information Technology (ONC), which had existed since 2004 as a presidential Executive Order, but became solidified as a legislative mandate via HITECH. This act fiercely transitioned the country from mostly paper-based health care in 2008 to near-universal EHRs adoption by 2015, giving electronic life, and potential reuse for research, to streams of health data previously dormant in paper troves.

Moreover, in March, 2010, the Patient Protection and Affordable Care Act (PPACA, a.k.a. “Obamacare”) was signed into law and, among so many other interventions, secured a few hundred million dollars for the creation of the Patient-Centered Outcomes Research Institute (PCORI). The mission of the PCORI is to do research that responds directly to real-life concerns of patients. For that purpose, among the first initiatives by the PCORI was the creation of PCORnet, a network of institutions capable of providing electronic health data for research. Most recently, in January 2015, President Obama announced the Precision Medicine Initiative (PMI). The PMI seeks to craft a nationwide and representative cohort of 1 million individuals, from whom a wealth of health data will be collected with no definitive goal besides to serve as a multi-purpose prime-quality dataset for observational electronic research. Meanwhile, private sector-led initiatives such as Informatics for Integrating Biology and the Bedside (i2b2) and Observational Health Data Sciences and Informatics (OHDSI) were also launched with the mission to access and do research on health care’s big data, and their publications can be easily found in PubMed.

These initiatives depict a political and societal hope – or hype? – that information technology, among its other roles in health care as whole, can make health care research faster, broader, more transparent, more reproducible, and perhaps also closer to the everyday lives of people. One premise is that by using existing EHRs for research, instead of data collected on-demand for a particular study, the researcher gets closer to the “real world” individuals that ultimately receive the treatments and conclusions produced by the study. In traditional clinical trials and other studies, the patients who participate are highly selected and oftentimes remarkably unrepresentative of the general population. Moreover, in EHR-based research there is also the potential to investigate more individuals than any previous method could possibly attempt. This broader reach makes rare conditions (or combinations of conditions) not so rare that they cannot be readily studied, and allows subtler variations in diseases to become detectable. On top of that, these studies can be done at the speed of thought. De facto, electronic health records-based clinical research has been recently published in the Proceedings of the National Academy of Sciences (PNAS) and evinced to be feasible at international, multi-hundred million patients scale at a breathtakingly swift time span. Altogether, one can sense in this picture that the millions of dollars spent on HITECH, PCORnet, PMI, and the NIH’s Data Science research grants might not have been just unfounded hype.

The relationship of IT and health care must, however, recognize its rather long history of frustrated expectations. In 1968, for example, Dr. Laurence Weed – the father of today’s prevailing paradigm of patient notes – predicted that in the future all text narratives present in electronic health records would be entered in structured form that enables scientific analysis. Today, to say the minimum, we have become less confident about whether such change is feasible or even desirable to begin with. In 1987, Barnett and colleagues believed that “relatively simple computational models” could be used to construct “an effective [diagnostic] assistant to the physician in daily practice” and distributed nationwide, but such assistant is yet to arrive at your physician’s office downtown (although, truth be recognized, it might be around the corner). While presently teaming with excitement and blessed with incentives, the journey of IT into health care and health care research is invariably one of uncertainties and risks. Health information technology has been accused of provoking life-threatening medical errors, as well as – like previous technological breakthroughs along the history of Medicine, including the stethoscope – harming the patient-physician relationship and the quality of care. The editors of the New England Journal of Medicine early this year went as far as to state that data scientists are regarded by some clinical researchers as “research parasites.”

Moreover, the Federal Bureau of Intelligence has investigated that medical information can be sold on the black market for 10 times more than a credit card number, while at the same time cybersecurity experts are stunned by the extreme vulnerability of current U.S. health care facilities. This provides sensible ground for concern about patient privacy violation and identity theft once the health records have moved from papers into computers. Unlike a credit card, your medical and identity information cannot be cancelled over the phone and replaced by a new one. Patient matching, i.e. techniques for recognizing that data produced at separate sites refer to the same person, oftentimes confronts blunt opposition by civil opinion, while the ultimate ideal of a National Patient Identifier in the U.S. is explicitly prohibited by present legislation (HIPAA). Such seamless flow of interoperable health data between providers, however, is the very first recommendation expressed in 2012 by the Institute of Medicine for realizing the Learning Health Care System – one that revolves around the patient and where scientific discovery is a natural outgrowth of patient care.

With or without attaining the ideal of a Learning Health Care System, the U.S. health care system will undergo transformation sooner or later, by intervention or by itself, because the percentage of the GDP that is spent on health care can only continuously increase for so long. Information technology is at minimum a sensible “bet” for improving efficiency – however, the power of IT for improving efficiency lies not in greasing the wheels of existing paradigms, but in outclassing them with novel ones. This might be part of the explanation for the resistance against IT, although there does exist some evidence showing that IT can sometimes do more harm than good in health care, and here the word “harm” sometimes can mean patient harm. The cold truth is that, in spite of decades of scientific interest in using computers for health care, only very recently the health care industry became computerized, so we remain not far from the infancy of health care informatics. Nevertheless, Clinical Informatics has been unanimously approved in 2011 as a board-certified physician subspecialty by the American Board of Medical Specialties, signaling that the medical community sees in IT a permanent and complex duty for health care. Similarly, the NIH has in late 2013 appointed its first Associate Director for Data Science, also signaling that this novel field holds importance for health care research. Finally, there might be little that can be done with the entire -omics enterprise, with its thousands over thousands of measurements multiplied by millions of patients, that does not require data-scientific techniques.

The first cars were slower than horses, and today’s high-speed, road-only automobiles only became feasible after the country was dependably covered with a network of roads and freeways. Such a network was built not by the automobile producers, but by the government upon recognition that it would constitute a public good. The same principle could very well be the case of health care IT’s important issues with privacy, security and interoperability, with the added complication that it is easy for an EHR producer to design a solution but then block its users from having their system interact with software from competing companies. Now that health care records are electronic, we need the government to step in once again and build or coordinate the dependable freeways of health care data and IT standards, which will also constitute a public good and unlock fundamental potentials of the technology. Health care, on top of its humanitarian dimension, is fundamentally intensive in data and information, so it is reasonable to conjecture that information technology can be important, even revolutionizing, for health care. It took one hundred years for Einstein’s gravitational waves to evolve from a conjecture based on theoretical fundaments to a fact demonstrated by experiments. Perhaps in the future – let us hope not a century from today! – some of the data-scientific methods such as Artificial Neural Networks, Support Vector Machines, Naïve Bayes classifiers, Decision Trees, among others, in the hands of the millennials will withstand the trial of time and earn an entry at the standard jargon of medical research. Just like how, in their generations, meta-analyses, case-control studies, Kaplan-Meier curves, propensity scores, and the big grandpa of controlled randomized trial were similarly accepted.

Written by sciencepolicyforall

July 13, 2016 at 11:15 am