Category Archives: Life Sciences
It is not quite a year since I have been looking into the Idenfication of Medicinal Products (IDMP) ISO standard, and the challenging EMA IDMP implementation deadline of July 1st, 2016. Together with HighPoint Solutions, we have proposed that using a Master Data Management (MDM) system as a product data integration layer is the best way to ensure the product data required by IDMP is consolidated and delivered to the regulator. This message has been well received with almost all the pharmaceutical companies we talked to.
During the past few months, the support for using MDM as a key part of the IDMP solution stack is growing. Supporters of using MDM now include people that have been looking into the IDMP challenge and solutions for far longer than me: independent consultants, representatives of pharma companies with active projects and leading analysts have expressed their support. At the IDMP Compliance Challenge and Regulatory Information Management conference held in Berlin last month, using MDM within the solution stack was a common theme – with a large percentage of presentations referencing the technology.
However, at this conference an objection to the use of MDM was circulating during the coffee break. Namely:
Do we really have time to implement MDM, and achieve compliance before July 2016?
First, let’s revisit why MDM is a favoured approach for IDMP. The data required for compliance is typically dispersed across 10+ internal and external data silos, building a data integration layer to prepare data for submission is the current favoured approach. It has popular support from both industry insiders and independent consultants. The data integration layer is seen as a good technical approach to overcome a number of challenges which IDMP is posing in their initial draft guidance, namely:
- Organisational: It has to make it easy for data owners to contribute to the quality of data
- Technical: It needs to integrate data from multiple systems, cleaning and resolving attributes using as much automation as possible
- Co-ordination: The layer must ensure data submitted is consistent across regulations, and also within internal transactional systems
- Timing: Projects must begin now, and pose low technical risk in order to meet the deadline.
MDM technology is an excellent fit to address these challenges (for a high level summary, see here).
So back to the time objection, it seems a bit out of place if you follow the logic:
- In order to comply with IDMP, you need to collect, cleanse, resolve and relate a diverse set of product data
- A data integration layer is the best technical architecture
- MDM is a proven fit for the data integration layer
So why would you not have time to implement MDM, if this is the best (and available) technical solution for the data collection and consolidation necessary to achieve compliance?
My feeling is the objection comes down to the definition of MDM, which is (correctly) seen as something more than technology. Expert and analyst definitions variously include the words ‘discipline’, ‘method’, ‘process’ or ‘practice’. Current consensus is that the underlying technology merely enables the processes which allow an organisation to collect and curate a single, trusted source of master data. In this light, MDM implies the need for senior level sponsorship and organisational change.
The truth is IDMP compliance needs senior level sponsorship and organisational change. In all the discussions I have had, these points come out clearly. Many pharma insiders who understand the challenge are grappling with how to get the required attention from execs that IDMP needs in order to achieve compliance. July 1 2016 is not only a deadline, it is the start of a new discipline in managing a broad range of pharma product data. This new discipline will require organisational change in order to ensure the high quality data can be produced on a continuous basis.
So the definitions of MDM actually make the case for using MDM as part of the technology stack stronger. MDM will not only provide technology for the data integration layer, but also a support structure for the new product data processes that will be required for sustained compliance. Without new product data management processes as part of the IDMP submission process, there will be few guarantees around data quality or lineage.
I fear that many of those with the objection that they don’t have time for MDM are really saying they don’t have enough time to implement IDMP as a new discipline, process or practice. This will expose them to the risk of non-compliance fines of up to 5% of revenue, and recurring fines of 2.5% of revenue.
In my mind, the challenge is not ‘do we have time to implement MDM?’, but rather ‘can we be successful both by and beyond July 2016, without implementing MDM?’ By MDM I am referring to the technology and the organisational aspects of creating and delivering complete and accurate data.
CMS points out the overall improvement in quality which they position as the result of focusing, and incenting quality. I agree that putting funding behind a quality program was a valuable strategy to motivate the industry. This has not always been the case, in fact a former colleague who related a common dialog previous to this program:
- He would present a quality initiative to executive management
- They would nod politely and say, “Yes, of course we are interested in quality!”
- The conversation would continue until the cost of the program was disclosed.
The faces would change, and the response was, “Well, yes, quality is important, but funding is tight right now. We need to focus on programs with a clear ROI”.
Thankfully the Star program has given quality initiatives a clear ROI – for which we are all grateful!
The other dynamic which is positive is that Medicare Advantage has provided a testing ground for new programs, largely the result of ACA. Programs very similar to the Star program are part of the ACO program and the marketplace membership. Risk Adjustment is being fitted to meet these programs also. Private insurance will likely borrow similar structures to insure quality and fair compensation in their various risk sharing arrangements. MA is a significant subset of the population and is providing an excellent sandbox for these initiatives while improving the quality of care that our senior population receives.
My concerns are around the cultures and mission of those plans who are struggling to get to the magic four star level where they will receive the bonus dollars.
Having worked in a health plan for almost nine years, and continuing to interact with my current customers, has shown me the dedication of the staffs that work in these plans. One of my most rewarding experiences was leading the call center for the Medicare population. I was humbled each day by the caring and patience the reps on the phones showed to the senior population. I have also seen the dedication of clinical staffs to insuring the care for members is carefully coordinated and that their dignity and wishes were always respected. I sincerely hope that plans with a clear mission find the right tools and supports to improve their ratings to the point where they receive the additional funding to maintain their viability and continue to serve their members and the medical community. I am sure that there are poor quality plans out there, and I agree that they should be eliminated. But I am also rooting for the plans with a mission who are striving to be a bit better.
One Search Procurement – for purchasing of indirect goods and services
Informatica Procurement is the internal Amazon for purchasing of MRO, C-goods, indirect materials and services. Informatica Procurement supports enterprise companies in catalog procurement with an industry-independent catalog procurement solution that enables fast and cost-efficient procurement of products and services and supplier integration in an easy to use self-service concept.
Information Procurement at a glance
Informatica recently announced the availability of Informatica Procurement 7.3, the catalog procurement solution. I meet with Melanie Kunz our product manager to learn from here what’s new.
Melanie, for our readers and followers, who is using Informatica Procurement, for which purposes?
Melanie Kunz: Informatica Procurement is industry-independent. Our customers are based in different industries – from engineering and the automotive to companies in the public sector (e.g. Cities). The responsibilities of people who work with Informatica Procurement differ depending on the company. For some customers, only employees from the purchasing department order items in Informatica Procurement. For other customers, all employees are allowed to order their needs themselves. Examples are employees who need screws for the completion of their product or office staff who ordered the business cards for the manager.
What is the most important thing to know about Informatica Procurement 7.3?
Melanie Kunz: In companies where a lot of IT equipment is ordered, it is important to always see the current prices. With each price changes, the catalog would have to be imported into Informatica Procurement. With a punch out to the online shop of IT equipment manufacturer, this is much easier and more efficient. The data from these catalogs are all available in Informatica Procurement, but the price can always be called on a daily basis from the online shop.
Users no longer need to leave Informatica Procurement to order items from external online shops. Informatica Procurement now enables the user to locate internal and indexed external items in just one search. That means you do not have to use different eShops for when you order new office stationary, IT equipment or services.
Great, what is the value for enterprise users and purchasing departments?
Melanie Kunz: All items in Informatica Procurement have the negotiated prices. Informatica Procurement is simple and intuitive that each employee can use the system without training. The view concept allows the restriction on products. For each employee (each department), the administrator can define a view. This view contains only the products that can be seen and ordered.
When you open the detail view for an indexed external item, the current price is determined from the external online shop. This price is saved in item detail view for a defined period. In this way, the user always gets the current price for the item.
The newly designed detail view has an elegant and clear layout. Thus, a high level of user experience is safe. This also applies to the possibility of image enlargement in the search result list.
What if I order same products frequently, like my business cards?
Melanie Kunz: The overview of recent shopping carts help users to reorder the same items on an easy and fast way. A shopping cart from a previous order can use as basis for this new order.
Large organizations with 1000s of employees are even more might have totally different needs what they need for the daily business and maybe dedicated to their career level. How do you address this?
Melanie Kunz: The standard assortment feature has been enhanced in Informatica Procurement 7.3. Administrators can define the assortment per user. Furthermore, it is possible to specify whether users have to search the standard assortment first and only search in the entire assortment if they do not find the relevant item in the standard assortment.
All of these features and many more minor features not only enhance the user experience, but also reduce the processing time of an order drastically.
Informatica Procurement 7.3 “One Search” at a glance
Learn more on Informatica Procurement 7.3 with the latest webinar.
In my last blog, I talked about the dreadful experience of cleaning raw data by hand as a former analyst a few years back. Well, the truth is, I was not alone. At a recent data mining Meetup event in San Francisco bay area, I asked a few analysts: “How much time do you spend on cleaning your data at work?” “More than 80% of my time” and “most my days” said the analysts, and “they are not fun”.
But check this out: There are over a dozen Meetup groups focused on data science and data mining here in the bay area I live. Those groups put on events multiple times a month, with topics often around hot, emerging technologies such as machine learning, graph analysis, real-time analytics, new algorithm on analyzing social media data, and of course, anything Big Data. Cools BI tools, new programming models and algorithms for better analysis are a big draw to data practitioners these days.
That got me thinking… if what analysts said to me is true, i.e., they spent 80% of their time on data prepping and 1/4 of that time analyzing the data and visualizing the results, which BTW, “is actually fun”, quoting a data analyst, then why are they drawn to the events focused on discussing the tools that can only help them 20% of the time? Why wouldn’t they want to explore technologies that can help address the dreadful 80% of the data scrubbing task they complain about?
Having been there myself, I thought perhaps a little self-reflection would help answer the question.
As a student of math, I love data and am fascinated about good stories I can discover from them. My two-year math program in graduate school was primarily focused on learning how to build fabulous math models to simulate the real events, and use those formula to predict the future, or look for meaningful patterns.
I used BI and statistical analysis tools while at school, and continued to use them at work after I graduated. Those software were great in that they helped me get to the results and see what’s in my data, and I can develop conclusions and make recommendations based on those insights for my clients. Without BI and visualization tools, I would not have delivered any results.
That was fun and glamorous part of my job as an analyst, but when I was not creating nice charts and presentations to tell the stories in my data, I was spending time, great amount of time, sometimes up to the wee hours cleaning and verifying my data, I was convinced that was part of my job and I just had to suck it up.
It was only a few months ago that I stumbled upon data quality software – it happened when I joined Informatica. At first I thought they were talking to the wrong person when they started pitching me data quality solutions.
Turns out, the concept of data quality automation is a highly relevant and extremely intuitive subject to me, and for anyone who is dealing with data on the regular basis. Data quality software offers an automated process for data cleansing and is much faster and delivers more accurate results than manual process. To put that in math context, if a data quality tool can reduce the data cleansing effort from 80% to 40% (btw, this is hardly a random number, some of our customers have reported much better results), that means analysts can now free up 40% of their time from scrubbing data, and use that times to do the things they like – playing with data in BI tools, building new models or running more scenarios, producing different views of the data and discovering things they may not be able to before, and do all of that with clean, trusted data. No more bored to death experience, what they are left with are improved productivity, more accurate and consistent results, compelling stories about data, and most important, they can focus on doing the things they like! Not too shabby right?
I am excited about trying out the data quality tools we have here at Informtica, my fellow analysts, you should start looking into them also. And I will check back in soon with more stories to share..
Maybe the word “death” is a bit strong, so let’s say “demise” instead. Recently I read an article in the Harvard Business Review around how Big Data and Data Scientists will rule the world of the 21st century corporation and how they have to operate for maximum value. The thing I found rather disturbing was that it takes a PhD – probably a few of them – in a variety of math areas to give executives the necessary insight to make better decisions ranging from what product to develop next to who to sell it to and where.
Don’t get me wrong – this is mixed news for any enterprise software firm helping businesses locate, acquire, contextually link, understand and distribute high-quality data. The existence of such a high-value role validates product development but it also limits adoption. It is also great news that data has finally gathered the attention it deserves. But I am starting to ask myself why it always takes individuals with a “one-in-a-million” skill set to add value. What happened to the democratization of software? Why is the design starting point for enterprise software not always similar to B2C applications, like an iPhone app, i.e. simpler is better? Why is it always such a gradual “Cold War” evolution instead of a near-instant French Revolution?
Why do development environments for Big Data not accommodate limited or existing skills but always accommodate the most complex scenarios? Well, the answer could be that the first customers will be very large, very complex organizations with super complex problems, which they were unable to solve so far. If analytical apps have become a self-service proposition for business users, data integration should be as well. So why does access to a lot of fast moving and diverse data require scarce PIG or Cassandra developers to get the data into an analyzable shape and a PhD to query and interpret patterns?
I realize new technologies start with a foundation and as they spread supply will attempt to catch up to create an equilibrium. However, this is about a problem, which has existed for decades in many industries, such as the oil & gas, telecommunication, public and retail sector. Whenever I talk to architects and business leaders in these industries, they chuckle at “Big Data” and tell me “yes, we got that – and by the way, we have been dealing with this reality for a long time”. By now I would have expected that the skill (cost) side of turning data into a meaningful insight would have been driven down more significantly.
Informatica has made a tremendous push in this regard with its “Map Once, Deploy Anywhere” paradigm. I cannot wait to see what’s next – and I just saw something recently that got me very excited. Why you ask? Because at some point I would like to have at least a business-super user pummel terabytes of transaction and interaction data into an environment (Hadoop cluster, in memory DB…) and massage it so that his self-created dashboard gets him/her where (s)he needs to go. This should include concepts like; “where is the data I need for this insight?’, “what is missing and how do I get to that piece in the best way?”, “how do I want it to look to share it?” All that is required should be a semi-experienced knowledge of Excel and PowerPoint to get your hands on advanced Big Data analytics. Don’t you think? Do you believe that this role will disappear as quickly as it has surfaced?
I believe that most in the software business believe that it is tough enough to calculate and hence financially justify the purchase or build of an application – especially middleware – to a business leader or even a CIO. Most of business-centric IT initiatives involve improving processes (order, billing, service) and visualization (scorecarding, trending) for end users to be more efficient in engaging accounts. Some of these have actually migrated to targeting improvements towards customers rather than their logical placeholders like accounts. Similar strides have been made in the realm of other party-type (vendor, employee) as well as product data. They also tackle analyzing larger or smaller data sets and providing a visual set of clues on how to interpret historical or predictive trends on orders, bills, usage, clicks, conversions, etc.
If you think this is a tough enough proposition in itself, imagine the challenge of quantifying the financial benefit derived from understanding where your “hardware” is physically located, how it is configured, who maintained it, when and how. Depending on the business model you may even have to figure out who built it or owns it. All of this has bottom-line effects on how, who and when expenses are paid and revenues get realized and recognized. And then there is the added complication that these dimensions of hardware are often fairly dynamic as they can also change ownership and/or physical location and hence, tax treatment, insurance risk, etc.
Such hardware could be a pump, a valve, a compressor, a substation, a cell tower, a truck or components within these assets. Over time, with new technologies and acquisitions coming about, the systems that plan for, install and maintain these assets become very departmentalized in terms of scope and specialized in terms of function. The same application that designs an asset for department A or region B, is not the same as the one accounting for its value, which is not the same as the one reading its operational status, which is not the one scheduling maintenance, which is not the same as the one billing for any repairs or replacement. The same folks who said the Data Warehouse is the “Golden Copy” now say the “new ERP system” is the new central source for everything. Practitioners know that this is either naiveté or maliciousness. And then there are manual adjustments….
Moreover, to truly take squeeze value out of these assets being installed and upgraded, the massive amounts of data they generate in a myriad of formats and intervals need to be understood, moved, formatted, fixed, interpreted at the right time and stored for future use in a cost-sensitive, easy-to-access and contextual meaningful way.
I wish I could tell you one application does it all but the unsurprising reality is that it takes a concoction of multiple. None or very few asset life cycle-supporting legacy applications will be retired as they often house data in formats commensurate with the age of the assets they were built for. It makes little financial sense to shut down these systems in a big bang approach but rather migrate region after region and process after process to the new system. After all, some of the assets have been in service for 50 or more years and the institutional knowledge tied to them is becoming nearly as old. Also, it is probably easier to engage in often required manual data fixes (hopefully only outliers) bit-by-bit, especially to accommodate imminent audits.
So what do you do in the meantime until all the relevant data is in a single system to get an enterprise-level way to fix your asset tower of Babel and leverage the data volume rather than treat it like an unwanted step child? Most companies, which operate in asset, fixed-cost heavy business models do not want to create a disruption but a steady tuning effect (squeezing the data orange), something rather unsexy in this internet day and age. This is especially true in “older” industries where data is still considered a necessary evil, not an opportunity ready to exploit. Fact is though; that in order to improve the bottom line, we better get going, even if it is with baby steps.
If you are aware of business models and their difficulties to leverage data, write to me. If you even know about an annoying, peculiar or esoteric data “domain”, which does not lend itself to be easily leveraged, share your thoughts. Next time, I will share some examples on how certain industries try to work in this environment, what they envision and how they go about getting there.
The Physician Payments Sunshine Act shines a spotlight on the disorganized state of physician information, which is scattered across systems, often incomplete, inaccurate and inconsistent in most pharmaceutical and medical device manufacturing companies.
According to the recent Wall Street Journal article Doctors Face New Scrutiny over Gifts, “Drug companies collectively pay hundreds of millions of dollars in fees and gifts to doctors every year. In 2012, Pfizer Inc., the biggest drug maker by sales, paid $173.2 million to U.S. health-care professionals.”
The Risks of Creating Reports with Inaccurate Physician Information
There are serious risks of filing inaccurate reports. Just imagine dealing with:
- An angry call from a physician who received a $25 meal, which was inaccurately reported as $250 or who reportedly, received a gift that actually went to someone with a similar name.
- Hefty fines and increased scrutiny from the Centers for Medicare and Medicaid Services (CMS). Fines range from $1,000 to $10,000 for each transaction with a maximum penalty of maximum $1.15 million.
- Negative media attention. Reports will be available for anyone to access on a publicly accessible website.
How prepared are manufacturers to track and report physician payment information?
One of the major obstacles is getting a complete picture of the total payments made to one physician. Manufacturers need to know if Dr. Sriram Mennon and Dr. Sri Menon are one and the same.
On top of that, they need to understand the complicated connections between Dr. Sriram Menon, sales representatives’ expense report spreadsheets (T&E), marketing and R&D expenses, event data, and accounts payable data.
3 Steps to Ensure Physician Information is Accurate
In recent years, some pharmaceutical manufacturers and medical device manufacturers were required to respond to “Sunshine Act” type laws in states like California and Massachusetts. To simplify, automate and ensure physician payment reports are filed correctly and on time, they use an Aggregate Spend Repository or Physician Spend Management solution.
They also use these solutions to proactively track and review physician payments on a regular basis to ensure mandated thresholds are met before reports are due. Aggregate Spend Repository and Physician Spend Management solutions rely on a foundation of data integration, data quality, and master data management (MDM) software to better manage physician information.
For those manufacturers who want to avoid the risk of losing valuable physician relationships, paying hefty fines, and receiving scrutiny from CMS and negative media attention, here are three steps to ensure accurate physician information:
- Bring all your scattered physician information, including identifiers, addresses and specialties into a central place to fix incorrect, missing or inconsistent information and uniquely identify each physician.
- Identify connections between physicians and the hospitals and clinics where they work to help aggregate accurate payment information for each physician.
- Standardize transaction information so it’s easy to identify the purpose of payments and related products and link transaction information to physician information.
Physicians Will Review Reports for Accuracy in January 2014
In January 2014, after physicians review the federally mandated financial disclosures, they may question the accuracy of reported payments. Within two months manufacturers will need to fix any discrepancies and file their Sunshine Act reports, which will become part of a permanent archive. Time is precious for those companies who haven’t built an Aggregate Spend Repository or Physician Spend Management solution to drive their Sunshine Act compliance reports.
If you work for one of the pharmaceutical or medical device manufacturing companies already using an Aggregate Spend Repository or Physician Spend Management solution, please share your tips and tricks with others who are behind.
Tick tock, tick tock….
I’m astounded by the incredible turnout and response to MDM Day and other MDM-related events at Informatica World, and again, I see this as a sign of MDM’s importance in the business world. Attendees told their stories, swapped best-practices, and shared their visions of using MDM to improve up-sell, cross-sell, and other important business metrics. But now let’s keep the momentum going. Here I want to tell you about three free webinars that will help you to dive more deeply into MDM, and take your initiatives to the next level. The first is for any large organization, and the other two are for pharmaceutical companies. (more…)