As all of the discussion on Healthcare reform comes to a head, I think it warrants some comments from me regarding “The Greed of Pharma”. Let me start by saying that Pharma is a for-profit business like any other.
I think it is important to note that neither pharma (nor the insurance industry for that matter) has particularly high profitability (ROE %’s). The following industries have a higher ROE % (Return on Equity rating) than Pharma (21 of them, and in the order listed). Cigarettes has nearly triple Pharma’s profitibility (and the US Gov is a de facto share holder); Aerospace has double. So why would Pharma be expected to foot the healthcare bill (and squeal like a pig -to paraphrase Congressman Marion Berry of Arkansas) when there are so many other more deserving candidates?
Aerospace/Defense – Major Diversified
Information & Delivery Services
Diversified Computer Systems
Beverages – Wineries & Distillers
Processed & Packaged Goods
Auto Parts Stores
Education & Training Services
Industrial Metals & Minerals
And here’s the second abstract I submitted. This one is with colleagues from Data Management and Systems Validation (IT). It should be a really good session. Again, fingers crossed.
Building a Better Way: The Evolution of eDC/Clinical Database Validation Processes
The power and real-time feedback made possible by implementing “eDC”/eCRF systems makes them indispensable to Clinical Research. However, the Data Management groups that typically own these systems often struggle with the appropriate way to implement and validate them; and then struggle in their attempts to appropriately validate the study-specific databases that they build within them. There are many reasons for this including: cGMP-centric concepts and terminology associated with validation, inflexible/inscalable validation methodologies, Data Management groups often being “semi-technical” in expertise-level, the dual role Data Management has in terms of being both Developers and Users of the system, etc. By partnering with the right resources and using the right tools, a Data Management group can move to a more dynamic validation paradigm with processes that make their validation more meaningful, their testing more robust, and their timelines shorter.
I. From the QA Perspective: This session will identify the challenges encountered in the implementation of eDC, the tools and ad hoc methodologies that were used, the processes that were developed, and the evolving role of QA moving from “a police” role, to a “partner” role, and ultimately to a fully independent role in which QA audits a process in which they are not involved day to day.
II. From the IT Validation Perspective: This session covers the challenges of providing validation services to a GCP-regulated area, the importance of understanding the more dynamic nature of Clinical Research as contrasted to the cGMP/GLP environments with which most Validation Engineers are familiar, methods for evaluating, adapting, and supplementing a vendor’s “canned scripts”, the importance of managing external vendors who are conducting testing on behalf of the Validation Manager.
III. From the Data Management Perspective: This session explores the challenges experienced by a Data Management/Programming group while moving from a “The Vendor builds our trials” model to a “We build our own “eDC” trials. We will share our journey through the frustration of finding that none of the Validation tools/templates that were available met the needs of our DM (and Client) groups, through working with our IT, ClinOps, and QA partners to build the tools we need, through reluctant skepticism regarding timelines, and ultimately to a point where the process is both improving our output and shortening our timelines.
I’ve submitted this abstract to the SQA for their annual meeting in April and am really hoping it is accepted. A very similar one that I posted last year was not….but this year, it seems that this sort of stuff is on their “hot topic” list. Fingers crossed.
The Impact of Electronic Systems, eHRs, and eData on Clinical Research
Although the use of electronic data in the pharmaceutical industry is by no means a new phenomenon, its impact has evolved over time. Technology has revolutionized our lives in many ways; in the way we access information, in our entertainment, in the way we do business, and in the way we interact. This technological revolution has had very pronounced impacts on the GxP industries: eRecords, eData, eSignatures, Data Processing, Data Mining and the Regulatory Standards associated with them.
These impacts have been most apparent in the cGMP and GLP areas and there is a fair level of comfort with eData and Validation in those areas. Increasingly however, the impact of eData and the need for controls around its use can be seen in the GCP area. Due to its large reliance on external sources of data, Clinical Research has some unique challenges. This session will cover:
I. Technology in Pharma and Validation Overview: a discussion of these concepts (which are widely felt to be cGMP artifacts) and relating them to the GCP area through analogies.
II. Centralized GCP Technology: The use of centralized eData and eSystems, primarily on the “sponsor side” has some unique challenges due to very short timelines and the external nature of clinical data inputs. Also the challenges of study-specific validation activities will be addressed.
III. De-Centralized GCP Technology – “Technology at the Clinic”: The advent of electronic Health Records (eHRs) has had a profound impact on Clinical Research and the way in which source data is handled, however the adoption of eHRs has been uneven, piecemeal, and largely independent of any research concerns . Resultantly, source data at clinical sites varies widely in format, from purely paper systems through purely electronic integrated source systems. However, a large and growing number of sites use a hybrid paper/electronic model with various sources of data, scans of source destroyed by hospital systems, paper notes, dictated notes, email notifications, and central sponsor data collection systems. Some ideas on how to address various hybrid source data scenarios will be presented.
Level: Basic Validation, Intermediate Clinical
Key Words: GCP, Electronic Data, Hybrid Source, Validation
I’ve gotten some questions of late that have had me thinking about the core of what I do; fundamental questions as to what it is and why. This is a good thing and I feel that now is a good time to till this fertile soil and see what can be learned (or remembered).
Over the next few weeks, I plan to explore these ideas in a series of posts. I’m planning on submitting an abstract or two to the SQA conference and this will be a good stepping-off point to get that effort underway. I plan to present on a subfield in which I am working very heavily in my current role; Clinical System Validation. That is, Computer System Validation for the Clinical (GCP) arena within the greater pharma landscape (pharma, medical devices, and related industries).
In any case, its been too long since I’ve added some really good content here. Keep your fingers crossed that I’ll be able to put up something useful, interesting, or both.