How Analytics Centers of Excellence Improve Service & Save Costs

Centers of Excellence: Centralizing Expertise
The “Center of Excellence” as a business model has an assortment of definitions and uses. In general, such “centers” are established to reduce time to value, often by spreading multidisciplinary knowledge, expertise, best business practices and solution delivery methods more broadly across organizations.

They have been identified as “an organizing mechanism to align People, Process, Technology, and Culture” or - for business intelligence applications - as “execution models to enable the corporate or strategic vision to create an enterprise that uses data and analytics for business value.” Still others define these centers as “a premier organization providing an exceptional product or service in an assigned sphere of expertise and within a specified field of technology, business or government…

Using a CoE to Improve Business Intelligence
In approaching how the Center of Excellence (CoE) concept might improve business intelligence (BI), analytics, and the use of data in science-based organizations, PerkinElmer Informatics has developed an Analytics Center of Excellence to deliver service for our customers.

As a framework, the CoE offers ongoing service coverage by experts from a variety of domains, including IT & architecture, statistics and advanced analytics, data integration & ETL, visualization engineering and scientific workflows. In many cases an expert is located at your facility and then leverages a wider range of remote staff, to provide support, reduce costs, and eliminate red tape and paperwork.

There are four pillars to our Analytics CoE for your organization: 

Architecture Services
Mainly for IT, this covers architecture strategy, sizing and capacity planning, security and authentication, connectivity and integration planning, and library management

Governance Services
Centralizing planning, execution and monitoring of projects, Program Management approach to managing multiple work streams, Steering Committee participation, SOPs and best practices, and change management

Value Sustainment Services
Expertise for subject matter consulting, support, hypercare, roadmap and future planning, and analytics core competency

Training & Enablement Services
Training needs assessment, training plans, courseware development, training delivery and mentoring

Cost Savings with Standardized BI Solutions
PerkinElmer’s Analytics CoE leverages TIBCO® Spotfire to help our customers get the most out of this technology as quickly as possible - from the experts. Very often - especially at mid- to large-enterprises - the question is asked, “Why aren’t we standardized on a single BI solution?”

It’s a good question.

Rather than investing time, effort, and money in evaluating, implementing, and maintaining and updating several BI solutions, not to mention training staff to use them, considerable cost savings can be gained from deploying a standard business intelligence solution across the enterprise. And the savings can be further supplemented because the Analytics CoE covers both foreseen and unforeseen needs.

Under an Analytics CoE implementation, cost savings are derived from:

  • Economy of scale from a suite of informatics services
  • Reduced administration efforts for both customer and vendor
  • “Just-in-time” project delivery that engages the right resources at the right time

Reducing the Pharma Services Budget
After converting to the Analytics CoE model, a top 25 pharmaceutical company saved 50% on its services budget, relative to TIBCO® Spotfire. This was possible because the services were bid out once - not for every service engagement. Purchasing service engagements was significantly less fragmented, and the high costs of supporting multiple tools & platforms and responding to RFPs was greatly reduced.

Standardizing on an Ongoing Service Model
Centralizing around a formal service model focuses management of the vendor relationship on a single partner – who truly becomes a partner as they manage projects across multiple domains and departments. 

The Analytics CoE model, also called competency centers or capability centers, oversees deployments, consolidation of services, dashboard setup and platform upgrades - all without the additional burden of new RFPs, vetting of new vendors, and establishing new relationships.

The benefits of standardizing on an ongoing service model, centered on a standard BI platform, include:

  • Holistic approach to deploying analytics solutions across the organization
  • Cost savings from reducing the number of tools used 
  • IT organization isn’t spread too thin as it no longer has to support multiple systems
  • Greater departmental sharing
  • Improvements beyond the distributed model

In addition, there are numerous reasons for analytical organizations to adopt an Analytics CoE:
  • Program Management managing multiple project workstreams and chairing Steering Committee meetings to provide management insight into solution delivery.
  • High quality of subject matter expertise (SME) available for your projects; SMEs are pulled in as needed and are billed against CoE.
  • Significant savings over typical daily rates – up to 50%.
  • Flexible engagement period.
  • Hourly rate fees move from the FTE model to “pay for what you use” further reduce costs.
  • Multiple projects billed against Analytics CoE.

Are you ready for true service excellence in your data-driven organization? Find out if PerkinElmer’s Analytics Center of Excellence is a good fit.

Contact us at informatics.insights@PERKINELMER.COM

Accelerate Insights into Your Rave Data

Analytics and Visualization Enhance Clinical Data Management

Medidata Rave and other EDC platforms hold volumes of clinical research and clinical trials data. As electronic data capture (EDC) and clinical data management (CDM) systems, they help clinical trial teams to capture, manage, and report patient data. 

To fully realize the value of the investment in an enterprise-scale EDC solution, clinical leaders often seek to leverage EDC data for a wide range of analytics challenges. They aim to use EDC systems like Rave for Medical Monitoring, Risk-Based Monitoring, to optimize Clinical Trials Operations, and more.

Electronic Data Capture for Pharma & CROs

As the gold standard for EDC in global, complex, randomized clinical trials, Rave is a comprehensive solution to a major industry challenge for large and mid-sized pharmaceutical companies and contract research organizations. 

But are users getting all the insights they can from Rave’s tool set? Does it support full clinical data review, risk-based monitoring, and more? Could users benefit from purpose-built applications that enhance data-driven insights?

After implementing an EDC system and ensuring baseline requirements are fully met, it can be disappointing to learn there may be gaps that demand additional solutions. Yet, as the number of clinical trials grows, the importance of having the right informatics to heighten and speed information-to-insight in clinical operations becomes critical. Estimates are that the global e-clinical solution software market will reach $6.51 billion by 2020, due to an escalation of clinical trials data.

“When speaking with our pharma and CRO clients, I’ve found that the majority require more powerful solutions for RBM and CDR. It really comes down to the need for deeper levels of understanding with operational risks and how to quickly adapt and act on them,”
said Masha Hoffey, Director of Clinical Analytics at PerkinElmer..

Expanding the use of a deployed solution like Rave to cover additional medical review and RBM requirements can be risky. It is not always clear:

  • What the user requirements for the expanded use are.
  • What requirements Medidata supports out of the box.
  • Which requirements may need additional services.
  • Which requirements are supported if Rave is implemented using predefined standards.

While there is nothing wrong with using predefined standards, there is the risk that they compromise flexibility. What’s more, the decision to use the predefined standards often has to be decided at such an early phase in the Rave implementation that there is a good possibility such standards will not meet your immediate or future needs.

Five-Step Gap Analysis

A proven way to assess whether to use Rave’s standard analytical and visualization capabilities versus an alternative solution is to perform a five-step gap analysis:

  1. Identify the use cases for analytics and visualization in your medical review and/or monitoring workflow
  2. Spell out the user requirements
  3. Map Medidata Rave capabilities to those requirements
  4. Identify requirements that are not supported 
  5. Consider alternative solutions that offer connectors to Rave to mitigate any gaps

Complementary solutions, like PerkinElmer’s Clinical Data Review (CDR)  and Risk-Based Monitoring (RBM), with seamless connectivity to Rave, are purpose-built for clinical operations and safety teams. 

These solutions, with built-for-purpose analytics and visualization capabilities, offer necessary capabilities such as:


  • 360º view into patient profile, adverse events, and other safety domains
  • Optimized data surveillance across functional teams
  • Ensured high-quality data for faster database lock


This means you don’t have to manually export data from Rave, because the Connector for Medidata Rave can extract, transform, and load Rave data into TIBCO® Spotfire for clinical analytics and visualization. Using the standard CDISC ODM (Clinical Data Interchange Standards Consortium – Operational Data Model) format facilitates the regulatory-compliant acquisition, archiving, and interchange of metadata and data for clinical research studies

Working together, Medidata Rave and PerkinElmer provide a more complete, accelerated view of clinical data, increasing the overall quality and speed to insights while leading to better decisions from the Rave data. 

Want to supercharge your RAVE clinical data? Compare your gap analysis to our Clinical Solutions or contact us to better understand your requirements. Contact Us 

Real World Evidence: Making RWE Real

Can real-world evidence and advanced analytics accelerate the evaluation of drug safety and efficacy?

When the 21st Century Cares Act became law in December 2016, it ushered in a new era for real-world evidence (RWE) to help break bottlenecks in drug development and product approvals. The door has been opened, as drugs and medical devices are developed, to make use of vast amounts and divergent sources of health-related data:

  • • Claims data – medical and pharmaceutical
  • • Clinical trials data
  • • Clinical setting data – from electronic health records and lab results to genomic    or  pathology reports
  • • Pharmacy data – point-of-sale and Rx-fill rates
  • • Patient-powered data – self-reported outcomes, social media 

The Cures Act excludes randomized clinical trials (RTCs) from its definition of RWE – defining it as “other than” RTC data with regard to evaluating “the usage, or the potential benefits or risks, of a drug.” The New England Journal of Medicine says RWE is “health care information from atypical sources,” which includes billing databases and product and disease registries. 

The U.S. FDA needs to establish some guidelines for what it considers real-world data (RWD) and how it will allow RWE to be used. In July 2016 the agency introduced draft guidance relative to RWE and medical devices. The 21st Century Cures Act has given the FDA some timelines for drafting guidance relative to using RWE in these instances:

  1. to help support the approval of a new indication for a drug approved under section  505(c) 
  2. to help support or satisfy post-approval study requirements

An openness to RWE indicates recognition of the usefulness of data generated from actual use in a clinical setting. The goal is to ensure that relevant RWE data is applied methodically to the evaluation of drugs for safety and efficacy. 

The Promise of RWE

RWE provides a more comprehensive view of a drug product’s real-life therapeutic and economic value to patients, payers, providers, and sponsors.  It adds real-life clinical practice and actual health outcomes information to our understanding of drug therapies, and is being eyed for its potential in expanded labeling and repurposing of existing drugs. It can help us study physician utilization patterns, the patient treatment journey, and drug comparative effectiveness.

AstraZeneca, for example, used RWE studies to supplement RCT data in a 2013 study of COPD treatment. Consider this data pool:

  • • medical records of 21,361 patients over an 11-year period
  • • linked national, mandatory Swedish healthcare registries – including hospital, drug,  and cause-of-death data
  • • total anonymized data representing 19,000 patient years

The benefit to AZ and COPD patients? “By combining such large quantities of data with appropriate statistical techniques, the study gives healthcare providers a fuller picture of how COPD care has evolved and the impact of different COPD management strategies on outcomes for patients in actual clinical practice,” wrote AZ’s Georgios Stratelis, MD, PhD, in a blog post. 

From aiding regulatory approval decisions for new products, generating ideas for next products, providing longitudinal assessments, to proving post-market value, RWE offers considerable promise for pharmaceuticals.  It is said to provide as much as $450 billion in top-down opportunity for U.S. healthcare alone. 

Overcoming RWE Challenges: Advanced Analytics

Until the FDA establishes its guidelines and the global industry settles upon agreed standards for RWE use, various participants and stakeholders are working to clear hurdles and eliminate obstacles. Among those is the data challenge itself – data quality.

The Network for Excellence in Health Innovation (NEHI) hosted a roundtable in December 2014, “Real World Evidence: Ready for Prime Time?” that cited data quality as the top barrier to the use of RWE. “Most sources of RWD [real-world data] are not collected for research purposes. Many researchers become ‘data janitors,’ forced to ‘clean’ gaps and inconsistencies in data through methods that may not yet have wide acceptance for statistical validity,” the report states.

In addition to data sources like insurance claims, electronic health and medical records (Health Economics and Outcomes Research), and pharmacy bills, RWE can also include:

  • • radiographic images
  • • biobank data
  • • molecular genomic data
  • • vital statistics
  • • patient wearable-generated data

While exciting, this adds significant new volume (on top of already crushing data loads), data integration challenges, and a real need for long-running analytical and visualization capabilities. Unified access to all relevant data sources empowers scientists and others to make decisions based on the most comprehensive dataset, including RWE data. Technology platforms must be able to integrate and make sense of RWE data, in near-real time, for decision makers to make productive use of it. 

Accelerating time-to-insight is a top goal, and achievable with out-of-the-box RWE solutions offering pre-built analysis modules for:

  • • cohort-building with propensity score matching
  • • comparative effectiveness
  • • safety signal detection methods
  • • machine learning

Learn more about PerkinElmer’s solutions for integrating RWE and leveraging all data to accelerate drug development and get therapies to market faster.

Can real-world evidence and advanced analytics accelerate the evaluation of drug safety and efficacy?We’re convinced it can. Download our white paper, Real-World Evidence Through Advanced Analytics, for a more complete analysis of the challenges – and opportunities – at hand.