SmartAnalytics

Express Pharma which is India's foremost Pharma & Biotech magazine recently published an article entitled ‘Leveraging data science to accelerate clinical trial results’ written by Sagar Anisingaraju, Chief Strategy Officer, Saama Technologies and Scott Clarke, Vice President, Life Sciences and Healthcare, Saama Technologies. The article focuses on different data science techniques and methods which can be used to improve the success for clinical trials. 

Managed Markets in Pharma - A Primer

The managed care model has seen huge growth in the pharmaceutical industry in the US. It is estimated that 85% of all prescription drugs are today reimbursed through a managed care plan, and the remaining 15% of cash paid prescriptions is still shrinking. This proportion is even higher for expensive new specialty pharmaceuticals. Managed markets are the dominant channel for pharmaceutical firms and ensuring unfettered access to their drugs is a critical competitive advantage that could spell the difference between commercial success or failure of the drug.

A panel of Pharma industry leaders like GlaxoSmithKline, Bristol-Myers-Squibb, AstraZeneca, Sanofi, Eli Lilly, Pfizer and Shire talked about the importance of Clinical Trials Optimization for 2 days at the recently concluded conference in Philadelphia clearly showing the importance of this topic in the present times of economic hardships and FDA’s pressure to lower healthcare costs. Pharma companies spend millions of dollars on research and probably even more on these clinical trials to ensure safety and efficacy of the drugs. Developing the right protocols, selecting the proper sites, setting the right expectations with all stakeholders, developing and tracking the right metrics and effective communication is the key to optimizing the resources and cost of clinical trials.

Companies like GSK are investing close to $95M on running programs like ‘Simplifying Clinical Development’ with its sole intent to optimize the entire clinical development process and make that cost efficient. Such programs need dedicated personnel, time, money and more importantly the right metrics. The GSK panelist admitted that even after knowing which metrics to capture and track and what to ‘do’, the actual ‘doing’ part of the data integration, data quality and analysis is where they fail. Dealing with humongous amount of data coming from multiple sources (Planning and budgeting systems, EDC, CTMS, etc.) leads to paralysis of analysis or as one of the panelists from Astellas said…DRIP (Data Rich and Information Poor).

Several presenters talked about which metrics to capture and track, how they are working with MCC (Metrics Champion Consortium) to determine the right ones, how to benchmark their performance, etc. But when asked basic questions like… what happens if you don’t have the relevant underlying data to support your metrics, how do you plan to integrate data across so many sources and still make perfect sense of the data, what impact does the data quality issues (especially with the manual entry of CRFs and EDC data) have on your metrics? None was able to provide satisfactory answers. The whole point is, while you build your strategy for optimization, do not under-estimate the efforts in data integration and analytics. It was clear from this conference that even biggies in Pharma are struggling with that. Site and study managers are working with their statisticians to better predict the outcomes, but none is tracking to see whether the actual decisions made and their outcomes were true to their predictions. The feedback loop to relook at the hypothesis or statistical model and make necessary corrections was completely missing.

Also, while most put a lot of efforts to know which metrics or KPIs to track, very few understand that these metrics make no sense if one is unable to act upon them to make decisions that will influence the business outcomes. There is a difference between ‘Business Intelligence’ and ‘Business Analytics’ and not many in the industry understand that. Monitoring and tracking metrics/KPIs in the form of reports/dashboards is ‘Business Intelligence’, but making meaningful sense of these metrics, co-relating them with other factors that influence them, understanding the trends and using statistical algorithms to predict outcomes is where the bang for the buck is…and that is ‘Business Analytics’. I have seen many Senior Executives flash numbers/charts in their board meetings, but can they accurately and confidently take very specific actions to make those numbers look better?  Isnt ‘experience’ or ‘gut feel’ play a majority part in the decision making? If so, these numbers/metrics/KPIs will have no meaning until the next time a report is generated….hoping that the numbers look better this time.

One article I read on Forbes.com said that the average cost of bringing a new drug to market is $1.3 billion (at times $4B to $11B for big pharma companies), a price that would buy 371 Super Bowl ads, 16 million official NFL footballs, two pro football stadiums, pay of almost all NFL football players, and every seat in every NFL stadium for six weeks in a row. This is ridiculous. A lot of that is spent on Clinical Development. Obviously the industry is obsessed with optimizing its efforts in that area and cutting costs. But, are they doing it the right way? Time, Quality and Cost are the factors companies look at and try to optimize each process. This is all fine as long as the processes are not interdependent. But are they? Do they understand enough of the external factors influencing each process, that some are beyond their control, that there can be ways to influence these factors? It is important to know all that before an ‘Optimization’ program is undertaken. ‘Business Analytics’ can help and ensure success of these programs.

It will be interesting to know how successful GSK was in their ‘Simplifying Clinical Development’ program. Having invested $95M, they better get it right 

Every Business Analytics consultant should watch the movie Moneyball (2011). This movie is indeed one of the best movies I’ve watched for a long time that showcases the application of analytics and statistics. Here’s something about the plot summary that IMDB has to say about Moneyball.

Just attended a whole day training about Pharmaceutical sales data by Kosta Tzavaras, the renowned author of books like ‘Pharmaceutical Sales Data 101’ and ‘Patient Data 101’. The information was quite insightful and definitely helped me better understand how different sets of data available from different sources are analyzed to serve the Pharmaceutical sales force.

With regulations creating these clear boundaries to prevent the "Medical Affairs" teams from being measured by the resulting sales, a common question that I've heard a few times is "Is there something we can measure beyond 'activities'".  Its quite common to find a bunch of metrics and charts that tell you how much time is being spent on various activities and whether they are "responsive" or "pro-active" etc.  But since these end up being "activities" its like reporting and analysis with your hands tied.

Pharma's traditional approach to getting in touch with Physicians and trying to promote their products has been seen as struggling to achieve its goals.  A recent industry report indicated that the number of Physicians who were not accessible has gone up from about 20% about 5 years ago to about 40% in 2010.  Many of these are because the organizations that the Physicians work at are creating policies that prevent sales reps from making calls on their physicians.

MDM has received quite a bit of attention in the recent past and we have seen many companies across the LS spectrum adopt and embrace this.  The Physicians Payment Sunshite Act has also been a key driver for enterprises to create a master data repository for their HCPs (Health Care Professionals), to ensure that the right spend amounts were being reported against these HCPs.  One of the easiest / simplest story lines to get funding for an MDM initiative that I've seen has been the classic "This is that way to ensure that the physician J Smith and Joe Smith as well as Joseph Smith are all either the same person or not".