Current Gaps in Clinical Data Management

By Suvarnala Mathangi | Date: April 30, 2018 | Blog | 0 Comment(s)

When it comes to data management, there are a lot of data gaps that requires filling. Below are the areas of data management where the industry is facing problems. If any of this sounds familiar, you might need help!

Data Capturing & Aggregation: The prevalence of ‘data heterogeneity’ in clinical trials is what makes data capturing and aggregation a complex process. Capturing data is yet to be perfected because it comes in different forms and formats. It is generated from multiple devices used by practitioners who follow distinct regulatory protocols. The cleanliness of clinical data is also subject to issues like non-adherence and data variability which arise due to a different set of on-site challenges. This primary challenge stifles the data lifecycle, affecting analytics and quality of insights.

Data Cleaning & Discrepancy Management: Clinical trials deal with unstructured data all the time. Though digital documents such as EDCs, EHRs and ePROs are embraced in clinical trial projects, use of PCRFs cannot be eliminated completely. This fractional use of PCRFs and multi-source aggregation adds to the complexity of data cleanliness. Clean data, devoid of discrepancies, ensures use of accurate and rationale datasets for further analytics.

Data Storage & Data Security: Data storage and security issues always give rise to the debate of ‘on-site or cloud model’ since it is coined with the function of cost. In either case, care must be taken to establish disaster recovery, cost efficiency, immunity against security breaches and healthcare specific compliance with HIPAA security rule.

Data Stewardship & Data Querying: Clinical data has longer shelf life, as it not just used for the current and specific research, it is archived for future research too. Sometimes, the trials also have traces of unutilized datasets that can solve disconnected healthcare issues. Owning and retrieving such data among the large volumes of data repository over time is an area that needs attention. Having historical and secondary data handy can resolve many issues. These also raise red flags through the procedure that pertain to data updation, interoperability and sharing.

About MaxisIT

At MaxisIT, we clearly understand strategic priorities within clinical R&D, and we can resonate that well with our similar experiences of implementing solutions for improving Clinical Development Portfolio via an integrated platform-based approach; which delivers timely access to study specific as well as standardized and aggregated clinical data, allows efficient data quality management, clinical reviews, and statistical computing.

Moreover, it provides capabilities for planned vs. actual trending, optimization, as well as for fraud detection and risk-based monitoring. MaxisIT’s Integrated Technology Platform is purpose-built solution, which helps Pharmaceutical & Life sciences industry by “Empowering Business Stakeholders with Integrated Computing, and Self-service Analytics in the strategically externalized enterprise environment with major focus on the core clinical operations data as well as clinical information assets; which allows improved control over externalized, CROs and partners driven, clinical ecosystem; and enable in-time decision support, continuous monitoring over regulatory compliance, and greater operational efficiency at a measurable rate”.

Clinical Data Ecosystem – It’s evolving faster than ever

By Suvarnala Mathangi | Date: April 30, 2018 | Blog | 0 Comment(s)

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Since the first few citations of registered and structured clinical data in the 1940s, the methods of capturing clinical data, use and application of clinical data, global inclusions, and role of payers, users and regulators have evolved.

At present, Clinical Data universe comprises of the siloed internal and external data sources within clinical development processes, which is a conceivable evolution; and currently, every other organization across this industry is experiencing similar scenario due to that.

This resulted data universe is primarily due to the strategic decisions made in the interest of business to support organizational consolidations, to manage cost & revenue pressures, to enable focus on core R&D and management, and to ensure continuous compliance to dynamically evolving regulatory guidance.

Staying focused on the core R&D business and continually improving operational & clinical performance have always been the topmost priorities among the business stakeholders; but, a collective and timely insight across the horizontally spread data and information haven’t always been possible. In such cases, the business decisions have often delayed, or relied on outdated information or lacked the cross-functional impact. The resulting outcome has become more like a spider-web with the knots at every corner.

Other reasons for this evolution could also be that for a long time, the conduct of clinical trials and healthcare initiatives have been carried out by separate functional silos within an organization using separate “legacy” applications & cookie-cutter solutions. Such approaches are typically focused on a specific process or function. This has resulted in multiple, disparate, and inefficient solutions that may not work well together. This has deprived organizations of the ability to make timely decisions, as well as increase efficiencies and overall productivity at the corporate portfolio level while controlling costs & mitigating risks involved at a specific study or functional level or at a portfolio level.

However, the adoption of digital means such as electronic patient reported outcome (ePRO) and electronic data capture (EDC) systems in place of paper-based case report form (PCRF) has changed the landscape of clinical data. It has reduced the time taken to collect data and relay into the next stage of the process from five days to fifteen minutes. Also, has aided in expediting the process to develop drugs.

Another milestone is the evolution of data structures. Big Data has allowed management of large volume of data and conversion of the same into comprehensible and insightful visualizations. To realize such benefits, clinical data management processes are compelled to use standardized fields, formats, and forms. This requires the application of the metadata-driven process.

The uprise of Clinical Trial Globalization has eliminated gaps between global study expectations and national standard protocols that lead to many operational complexities. Regulatory bodies like FDA and EMA have come together to synchronize requirements on a protocol-by-protocol basis.

To control financial risks for managing patient populations and to provide continuous access to electronic data, private networks have started building centralized data repositories. This can be further used for a range of analytics, including predictive modeling, quality benchmarking, and risk stratification.

These breakthroughs have eased out many challenges while implementing critical trials but have also increased the responsibility of CROs and Clinical data managers to upgrade and standardize their systems in response to these progressive developments.

Rising above the data silos

By Suvarnala Mathangi | Date: February 28, 2018 | Blog | 0 Comment(s)

How an Integrated Clinical Development Platform Helped a Client Tear Down Clinical Data Silos and Improved Decision-Making.

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How an Integrated Clinical Development Platform Helped a Client Tear Down Clinical Data Silos and Improved Decision-Making.

The Challenge – Riddled with mélange of data and challenges in standardization which hamstrung their decision-making capabilities, our client decided to implement an integrated platform-based strategy that would help them to

  • Ingest data from diversified sources
  • Establish a single-source of truth
  • Enable metadata repository-based standards compliance and,
  • Establish a controlled statistical computing environment on top – all seamlessly interoperable and metadata-driven.

The Objective – The mandate was simple. Enable teams to access data seamlessly and provide a unified view of the data to deliver regulatory standard deliverables on time and further perform exploratory analytics.

The Solution – An Integrated clinical data management platform which delivered three business critical solutions –

Metadata Repository (MDR)

  • Achieved metadata re-use and consistency of definition across the clinical data collection – analysis lifecycle
  • Minimized duplication of work and improved data integrity and quality.
  • Simplified the management (update, version control etc.) of metadata, standards and specs
  • Facilitated improved electronic metadata exchange
  • Enabled automated validation and conformance checking of data
  • Enabled automated data conversion (from one standard to another)
  • Enabled impact analysis and change management across metadata standard assets

Clinical Data Repository (CDR)

  • Efficiently integrated data across multiple studies and across pipelines thus providing a single source of truth
  • Maximized the value of data assets by making the data accessible in a consumable (neutral) and quality conformed format to stakeholders across R&D
  • Improved decision-making by ensuring reports are produced promptly and accurately, and enable prompt responses to regulators and other departments within an organization
  • Stored data in a structured and secure manner to support reproducibility and an improved ability to incorporate data into analyses.

Statistical Computing Environment (SCE)

  • Streamlined processes for workflow, version control, traceability, access management, audit trails that eliminated manual steps involved in compliance with operating procedures
  • Improved team collaboration through better organization and navigation of project artifacts across regulatory reporting process
  • Facilitated full traceability and improved regulatory compliance and inspection readiness

The Outcome – While the first feature laid the foundation for scalable analytics development and testing, the second and third feature provided the overarching structure for sharing knowledge and best practices. Most importantly, the platform provided a single unified view of data which allowed teams to finally speak the same language; something the company found immense value in.

Read the full case study here

About MaxisIT – MaxisIT® provides ONE Integrated Clinical Development Platform for biopharmaceutical industry – MDR, CDR and SCE combined in one scalable, integrated, cloud platform. The platform includes self-service data preparation and analytics products which are regulatory compliant, validated and delivered through alternate models viz. Enterprise SaaS, On-premise deployment, or as a Hybrid software-enabled service.

Wearables in Clinical Trials – Implications for you

By Suvarnala Mathangi | Date: January 31, 2018 | Blog | 0 Comment(s)

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The demand for wearables and sensors in clinical trials is on the rise. Pharma companies are increasingly challenged with both rising costs and the need to find novel ways to differentiate the drugs they are developing. One such way is by accessing the clinical data collected on remote medical devices.

By leveraging remote medical devices, there is an opportunity to collect novel endpoints and supplemental data that may improve the regulatory case, make the case for reimbursement more compelling, open up participation to a wider population and/or reduce site visits for patients who may not live close to an investigative site.

However, large volumes of continuous flowing data will increasingly require scalable cloud support along-with a proactive approach to data standardization. Data standardization assumes vital importance because there is large chunks of unstructured data that can overwhelm traditional functions and processes.

A good data standardization platform will enable better clinical data quality management, clinical data review and reduce cycle time by creating submission standard deliverables. An ideal platform should enable –

  • Self-learning mapping, metadata driven automation with built-in security and regulatory compliance
  • Ability to scale up and scale-out on demand for faster time value realization
  • An integrated self-service approach to automated data standardization.

MaxisIT has shown tremendous success in delivering value based on standardizing over 800 clinical studies. Be it the self-service platform or functional outsourcing, MaxisIT has delivered consistent quality, compliance and cost reduction. If you want to know more please get in touch.

Saving time and cost through metadata driven reporting process

By Suvarnala Mathangi | Date: January 31, 2018 | Blog | 0 Comment(s)

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Clinical teams are often stuck in a vicious cycle of static report requests, slowing clinical data review and database lockdown. The main struggle is to unify and analyze data across domains. If data is not uniform, it restricts access to complete clinical information which will affect the decision making on safety and efficacy. It is critical to make the right decisions early in the process failing which leads to loss of time and resource.

A metadata driven approach helps to speed up the process by automatically combining data. This allows clinical development team members to interactively explore information and discover new relationships. With the ability to quickly visualize and analyze data, team members can optimize the clinical trial process and focus their efforts on obtaining the insights and answers they need to bring drugs and devices to market faster.

To learn more on Metadata driven process read our whitepaper on Accelerated access to clinical data and reports through Metadata-driven process from EDC to reporting.

About MaxisIT – MaxisIT® is the only Integrated Software Platform for biopharmaceutical industry. We offer self-service data preparation and analytics products including clinical data repository, data management, data integration, statistical computing, advance analytics, reporting and visualization that are regulatory compliant, validated, cloud-based and delivered through alternate models viz. Enterprise SaaS, On-premise deployment, or as a Hybrid software-enabled service

Cloud based technologies and the future of patient care in an integrated health & life sciences ecosystem

By admin | Date: November 30, 2016 | Blog | 0 Comment(s)

 

MaxisIT-Integrated Clinical Development Platform

A closer look at most path-breaking enterprise cloud technologies, which have come about in the recent past, reveals that they are addressing either efficiency issues (time, cost or both), or a need which has been unaddressed or are solving for a latent need. Participants in the clinical trial industry have not been early adopters of technology – as compared to other industries such as BFSI, travel & transportation and so on. This is perhaps to do with the need for multiple levels of regulatory compliance in collecting, analyzing and reporting data related to clinical trials and the perceived risks outweigh immediate benefits.

The advantage of being a laggard has its own silver lining.

We can predict the evolution of technology in healthcare and life sciences industries much better and in fact increase the probability of success through learning obtained from other regulated industries such as BFSI.

Cloud based technologies are not new to the clinical trials industry. Studies estimate that cloud computing solutions can lead to a 30% increase in speed to trial for clinical work, and can result in considerable cost savings (some estimates suggest savings of up to $400 million until approval). If the market continues to adopt ePRO/EDC technologies, paper studies and subject diaries could cease to exist in 10 years; and focus would shift to new opportunities and/or risk-mitigation via multitude of meaningful information – all accessible conveniently. As the availability, and abundance of data continue to increase, the kaleidoscopic nature of data would prove to be more insightful, when analyzed in combination of other data.

Future of cloud-based information paradigm in an integrated health & life sciences industries have lot to offer for a holistic patient care.

Here are some of the changes which cloud based technologies are likely to drive in healthcare and life sciences industries, many of which are already manifesting in other industries.

  1. Cloud can enable BYOD in the context of clinical trials, thereby enhancing cost & time efficiency

BYOD is under active consideration by policy makers. The FDA requested for suggestions on use of technology in conducting clinical trials and specific questions around the usage of BYOD and compatibility between multiple devices. BYOD is a certainty in that the efficiency in collecting data, recruiting and retaining subjects it provides is lucrative. Cloud will enable the usage of BYOD – by making data capture convenient and efficient and also solving for compatibility related issues between multiple device types.

  1. Wearable as an integral part of clinical trials – collecting rich continuous data, rather than discrete data points

Most wearable technologies ranging from MCOT devices and other vital sign monitoring devices are leveraging a ‘smartphone-cloud’ technology to log a continuous stream of data and make it available for analysis. Clinical trial data so far has been discrete, i.e., various data points captured periodically during the course of the trial. The use of wearable enables clinicians to see a continuous stream of data and this has multiple implications:

  • Context Rich and continuous data is now available, which could explain other end-point phenomena observed during trials.
  • Clinical research organizations would need to be equipped to deal with unstructured data and make sense of this. They would need to get Big Data skills moving from the well tabulated data which we are all accustomed to.
  • A lot of longitudinal studies would be enabled which would have implications on the nature of clinical trials and personalized patient care we would see being conducted in the coming few years.
  1. The need to run analytics on Big Data will require us to rethink information architecture

It is a given that we would be dealing with a lot more unstructured data in the coming years than ever before. Another fact we need to reconcile to is that the breadth and depth of analysis expected to be run on trial data would multiply. The combination of complex analysis requests on data which is significantly larger than we see today implies that our cloud-based information architecture should be equipped to deal with this. Especially running multiple analysis on the cloud (since the data would be contained there), would require a new way to be architected. This is something one should be prepared for.

  1. Collaboration and multi-tenancy

Practicing clinicians will vouch for the fact that they expect many collaborative studies to happen in the future. We are witnessing collaborative studies bringing pharma, genomics, and information sciences together. The same data would be used to run multiple analysis towards various end-objectives. Cloud enables that. The corollary which goes with this is the issue of multi-tenancy and compliance. Those working on cloud technologies would need to build in enough flexibility and security to enable collaborative studies, while ensuring that access to data for different collaborators is kosher.

  1. Can we learn better and reduce risks (and therefore cost & time)?

The fact that the cost to bring a blockbuster drug to market is increasingly steep and that is well understood and several attempts are on, to rationalize this cost. However, an area which offers a goldmine to new studies is how those involved in clinical development can learn from existing data, within their own organizations, partners, collaborators, institution and from internet. One can anticipate emergence of collaborators who pool in data from multiple studies to draw from each other’s learning and avoid obvious pitfalls. This would be invaluable to further science, but also reduce risk and cost to bringing good science to market!

While #1 is an example of how cloud technology brings about efficiency, #2 through #3 are emerging needs which need to be addressed by technologists. #4 & #5 are those problem statements where the need is not acutely felt, but addressing these can lead to paradigm shifts in the way clinical trial analysis is done. For technologists and industry participants like myself, there could not be a more exciting time than this!

The 50th Annual Meeting of the DIA 2014

By admin | Date: July 31, 2016 | Blog | 0 Comment(s)

Celebrate the Past and Invent the Future with MaxisIT® at the DIA 50th Annual Meeting, June 15-19 2014 in San Diego, CA!

Hosted this year at the San Diego Convention Center, the DIA Annual Meeting celebrates its 50th anniversary, as a community of life science professionals across all disciplines gather to discuss, share and network with the common goal of fostering innovation in the field.

MaxisIT® (Booth 2121) will be joining a host of industry experts across all disciplines in the field of life science, with the common goal of coming together and sharing their innovations in pharmaceuticals, biotechnology, medical devices and related medical products.  Please join us at the event to network and share ideas which foster innovation with other like-minded professionals.

Team experts from MaxisIT® will be on-hand to showcase how we can help your company leverage information and make decisions in support of clinical research and development.  If you are not familiar with MaxisIT®, we are an Integrated Clinical Development Platform Solution, with the ability to improve how pharmaceutical and life science companies successfully plan, integrate, and accomplish their goals.

Our award-winning CTRenaissance Platform (put link to CTA button here) offers the unique ability to single-handedly manage processes such as study design, metadata repository, programming biometrics, analysis & reporting, drug safety and regulatory standards, modeling/simulation and all steps in between.  However, we do not just offer software as a service.  Our highly qualified outsourcing team experts provide collaborative support to CROs of all sizes, consulting companies, technology providers and clinical operations groups.

We all know clinical trials can be a daunting experience, but with our CTRenaissance Platform and a host of other affordable solutions available in our Integrated Clinical Development Platform, MaxisIT® can help you successfully leverage all of the necessary steps in one easily maneuverable cloud-based system, saving you both time and money.

Since our inception in 2003, we have earned a reputation as being an invaluable asset in the field, and to us that means we have succeeded in creating a quality product.  Thanks to the DIA 2014 Meeting, we can share it with you in person so you too will be able to see that reputation was justifiably earned.

Be sure to stop by and say hello June 15th through the 19th.  Our MaxisIT® team will be at Booth 2121, and we would be more than happy to speak with you one-on-one about your specific needs.  Feel free to come with questions, as our experts will be able to assist you on the spot.

Integrated Data Management – A Centralized Approach!

By admin | Date: June 30, 2016 | Blog, Uncategorized | 0 Comment(s)

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For quite some time CROs have delivered services to sponsors by hosting their clinical data, obviating the immediate need for a sponsor to build their own clinical database. This arrangement relieves the sponsor from cost and regulatory risk of establishing their own new clinical database, but can markedly compromise the sponsors’ ability to respond to evolving events (e.g. safety signals). CROs are increasingly making it easier for the sponsors to access and analyze their data while the CRO continues to host the database.

The challenge for the sponsor’s clinical data management is that the company rarely works with one CRO, and often relies on one EDC system as one size doesn’t fit all. Whereby in the quest of streamlined operations, efficiency and faster decision-making, the sponsors’ clinical review and data management users may end up with multiple hops to desired data with huge lag and have to rely on to multiple systems to access the required data.

Data Integration has become the way of life to connect numerous silos that are created in the process. Clinical research is a complex world; while the data management process remains hybrid, depending on the type of study, the size of the organization, and the distributed nature & type of the data capture, the complexity of data integration increases. The result is very long cycle from data Capture to Analysis and eSubmission.

  • How a life sciences organization with primary focus on research and goal of accelerating the drug development to market can maintain focus on core areas without getting distracted by the overhead of managing multiple CROs and multiple clinical software systems?
  • How does a sponsor organization maintain, control, perform faster review and gain real-time transparency of the process in an outsourced clinical data during trial and post-lock?
  • How does a sponsor organization gain operational efficiency and cost control by leveraging standardized metadata across the trials and further retain control of study design & setup by using reusable configurations?