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Data Governance In Pharmaceutical Industry

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Data Integrity: A Closer Look From NSF | Pharma Biotech

Presentations of two projects implemented recently by Striped Giraffe for the pharmaceutical industry aroused great interest among the participants of our meeting in Zurich.

The first project being discussed was aimed at improving the quality of regulatory data stored in the Regulatory Information Management System due to the planned migration to the new platform. The solution implemented makes it possible to constantly monitor and maintain the high quality of data by utilizing reusable data quality rules and automated workflows. The goal of the second project was to develop a solution for data quality checks between the European Medicines Agencys IDMP-SPOR data management services and the key companys systems that store and process data required for compliance with the ISO IDMP standards.

Figure 3 Examples of various data quality problems.

Figure 4 Data quality project phases.

New Ways To Utilize Salesforce Crm

A Customer Relationship Management platform can fast track not only drug development, but also the drug commercialization processes. Salesforce provides a variety of CRM categories and systems for pharmaceutical needs. In particular, the Salesforce Marketing Cloud can help contract research organizations to enroll patients faster. Furthermore, the integration of the legacy systems within the Salesforce Marketing Cloud allows for all-inclusive marketing intelligence and a refined customer engagement.

Salesforce CRM is a very flexible system with workflow and parameters that can be fully tailored to the clients needs. For instance, one of Avengas clients has implemented a highly configurable Salesforce-based suit providing real-time access for key clinical trial needs. The system is capable of providing insights into every phase of clinical trial management, empowering clients and key stakeholders to connect and use data within their studies in an entirely new way.

Once CRM is configured and tailored to the life sciences workflows, it can aid with displaying project risks, presenting actual study progress, alerting research teams of key milestones, and being a platform for collaboration and exploration.

The Foundation Of Your Strategy

Data governance defines how an organization manages its data assets, and, in a digital world, how improved decision-making should be operationalized. This calls for an appropriate authority model to manage data functions. Many healthcare leaders understand the importance of data governance, but struggle to:

  • Understand where their data lives and how to access it
  • Put in place effective processes to protect data from threats of inappropriate release and access and
  • Acquire and develop the right resources and skillsets to manage healthcare data.

To access the very latest thinking on the subject, we have gathered the experience of KPMGs leading global D& A professionals and interviewed healthcare CEOs and CIOs to better understand their concerns and ambitions. Our framework for designing and implementing data governance aims to demystify the topic and helps to overcome common challenges and pitfalls, by outlining practical steps to effectively manage enterprise data assets.

First, we define data governance and its key elements. Appreciating the importance of data stewardship, ownership, policies, and standards lays the groundwork for sustainable governance. We highlight some of the typical data governance traps that healthcare organizations fall into when beginning their D& A journey.

Finally, we explore other important considerations, such as protecting information privacy , data sharing , and enabling technologies for data management.

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General Di Principles And Enablers

This subhead is the title of Section 7 of the PIC/S guidance, and it emphasizes good documentation practices and discusses the nine ALCOA+ criteria . Section 7.5 is essentially a table covering the criteria, and the requirements for each one.

Appendix 1 of the WHO guidance provides more comprehensive coverage of ALCOA principles with:

  • a definition of each of the five terms
  • a table showing the expectations of paper and electronic records side by side for each term
  • a presentation of special risk factors for each term.

However, later sections of the PIC/S document provide further information on ALCOA+, but it is not highlighted. My suggestion is to use the definitions of ALCOA+ in section 7.5 of PIC/S PI-041 as an overview , but to use Appendix 1 of the WHO guidance for more detailed reference and in your data integrity program.

Section 7 finishes with guidance on how to create a true copy of a record, and the limitation of remote review of data in summary reports.

What You Will Learn

Information Management In Pharmaceutical Industry

Only 1 in 10 medications that enter the clinical trial phase actually reach the market. Can drug development processes be improved? What are the possible solutions?

  • Predict the behavior of chemical compounds in potential drugs and their pharmaceutical ADME properties using neural networks for drug discovery.
  • Identify molecules that are synthesizable, stable and refined for multiple criteria with the help of genetic programming and evolutionary algorithms.
  • Collect, consolidate, structure and extract valuable entities from unstructured data. Find medications that can treat COVID-19 using knowledge graphs.
  • Determine prospective target molecules from millions of candidate compounds. Improve the outcomes and quality of the compound analysis using high-throughput screening.
  • Store data from virtual compound screening on cloud services, while scaling up and down to address the computational demands.
  • Identify influential principal investigators who can empower clinical trials by recruiting eligible patients using natural language processing for patient enrollment.
  • Predict patient outcomes from electronic health records using NLP, machine learning and recurrent neural networks.
  • Obtain data-driven definitions of diseases using computational disease phenotyping.
  • Revolutionize real-time monitoring of diseases by collecting essential data, like heart rate, glucose levels, movement disorders, concussions and other medical events using wearables and IoT for remote patient monitoring.
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    Suspect Data Integrity Breaches

    The data governance system should include documented procedures requiring employees to notify management if they become aware of data falsification, unauthorized changes, destruction, or conduct that raises data integrity concerns, and a reporting mechanism for suspected data integrity breaches should be established .

    Procedures should be established for investigating any alleged intentional action, poor practice, or inadequate system/procedure that raises data integrity concerns. The investigation should determine the root cause and impact on product/data quality, from which appropriate CAPAs are derived and implemented.

    Disciplinary actions. There should be documented procedures regarding disciplinary action due to wrongful acts, including: data falsification, unauthorized modification, or destruction violation of the written policies and/or procedures and any conduct that would raise data integrity concerns.

    Regulatory agency notification. Companies should commit to prompt regulatory notification if the company becomes aware that a product in commerce is impacted by a data integrity breach or if a pending or approved submission contains untrue statements or has omitted statements of material fact. A company must investigate all data integrity breaches and take the appropriate corrective actions to report the correct and complete data/information to the regulatory authorities.

    Data Quality In The Digital Reality

    During the event, the main presentations on various aspects related to data quality in the pharma industry were given by our supreme expert Krzysztof Winiewski, Senior System Architect DWH & BI, who has recently been involved in several data quality projects for some of the renowned European market leaders. Mr. Winiewski discussed the most important issues concerning data quality assurance in todays digital reality as well as the importance of developing data quality solutions and procedures as the main factors determining the success of the digital transformation in an enterprise.

    Figure 1 Top factors impacting data quality.

    Participants were provided with the expert knowledge on the data quality-related topics such as:

    Figure 2 Data quality dimensions.

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    Assess And Assign Privileges And Permissions

    Privileges and permissions define who can access what data, and what they may do with it. As a best practice, data access should be governed according to the principle of least privilege. This means limiting access to information as much as possible without getting in the way of someones ability to do their job.

    The healthcare industry has a growing number of interoperability standards, which dictate how information is stored and shared between devices. Before you assign privileges its important to:

    • Define types of data that different areas need to access
    • Define who within a functional area needs to access the data
    • Outline how they can access the data, including details about devices, geographic locations, and time of day

    For example, a phlebotomist needs to know the patients name and date of birth. However, they may not need access to the patients entire medical history. Too much access increases the risk that data can be changed or stolen.

    Daily Challenges Of Data Integrity

    The Future of the Pharmaceutical Industry: Adapting to a Changing Society

    The laboratory analyst and the production operator in their daily activities follow standard operating procedures , analysis methods or batch records. They do this by documenting the entire process and recording the results. To facilitate these processes many companies have adopted digital systems such as LIMS and ELN in their laboratories and EBR in production departments. These systems are undoubtedly of enormous help in automating operations. However, this conversion is often partial, for two reasons:

    • These solutions are designed to mainly cover the display part of instruction sheets or analysis methods and to aggregate the results of analytical tests and department operations, entered into the system manually by the operator.

    • These solutions are not prepared natively for interfacing with electronic devices for the purpose of collecting process and analysis data.

    Moreover, the data managed in production and in the laboratory cannot be treated only as a simple set of parameters, as they have a complex and not at all standardized structure:

    • results collected by analytical instruments
    • production line sensor readings

    • methods applied and metadata

    The problems caused by manual/paper work flows are:

  • it is easy to underestimate the priority of requirements

  • risks are not always adequately managed

  • Briefly: failure to comply with GLP, GMP and GAMP regulatory requirements

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    What Makes For Good Data Governance

    There is no common framework for data governance and practices vary considerably between organizations. There is an ISO standard but there is no requirement for businesses to adopt this. Some organizations employ a chief data officer who sits at the strategic board level, whereas other companies do not have a chief data officer at all. The downside with the latter is that there exists a disconnect between data governance strategy and implementation. The gaps that result from this can cause ethical or legal issues.

    As to what makes for a good data governance policy, it should entail:

    • Establishing data governance principles.

    Pharmaceutical Companies Rely Heavily On Huge Amounts Of Data That Is Used In All Processes From Interpreting Clinical Findings To Measuring The Effectiveness Of Drugs Based On Real

    Topics related to data quality assurance in the pharmaceutical and life science industries were the main theme of this years Digital Future Day which took place on 25 October 2019 at the Swiss headquarters of Striped Giraffe in Zurich. The meeting was attended by several representatives of the largest Swiss pharmaceutical and life science companies.

    This years event in Zurich demonstrated huge interest for emerging digital technologies in the area of data quality analysis and assurance. In his welcome note, Igor Kleiman said that data quality has to be considered being crucial standard building an important foundation for the companys market value. This standard needs to be actively managed and supported he emphasized.

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    Regulatory Consulting For Life Sciences Organizations

    Uniting together software development and our regulatory consulting expertise, we can assist our customers to address pharmaceutical regulations requirements, providers expectations and anticipate patients needs. Our strong consulting team can help you clearly define and smoothly sail through regulatory and software development hurdles in order to reach your patients quicker. This allows you to easily transition your breakthroughs from the laboratories to the patients that need the medication the most.

    We provide regulatory consulting services to help pharmaceutical organizations all over the world to incorporate scientific discoveries into newly released drugs. From drug development to drug commercialization, our knowledge and expertise is reinforced with a deep belief in what we do.

    Affordability and depth of expertise have made them a critical development partner. Their team easily scales to accommodate project size and is equally flexible with scheduling across time zones.

    Suhail MughalCTO, QPharma

    With technology and regulatory consulting for pharma organizations, we empower our clients to accelerate their timeliness, improve conventional workflows and decrease the risk of postponement.

    Computational Disease Phenotyping For Precision Medicine Development

    Information Management in Pharmaceutical Industry

    As the digitized EHRs resulted in huge amounts of medical data, new opportunities emerged to refine and review diagnosis definitions and boundaries. Considering that diseases are traditionally characterized by a set of manual clinical descriptions, computational disease phenotyping aims to obtain data-driven definitions of illnesses. Machine learning and data mining techniques are able to detect more fine-grained illness descriptions. Computational disease phenotyping is a huge step forward towards precision medicine and personalized healthcare.

    Computational phenotyping enables the data to speak for itself by detecting relationships and concepts from unstructured medical data without any supervision or bias.

    Explore how software development for clinical trials can equip and complement biotech and pharma companies that are seeking out facilities to run their clinical trials with the utmost efficiency, and to move new treatments to the market faster than the competition.

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    Mhra Defined Some Principles Of Data Integrity As Given Below

    ALCOA: Attributable, Legible, Contemporaneously Recorded, Original & Accurate.

    Attributable: This should include who performed an action and when.

    Legible: All data recorded must be legible and permanent.

    Contemporaneously: Contemporaneous means to record the result, measurement or data at the time the work is performed.

    Original: Original data sometimes referred to as source data or primary data is the medium in which the data point is recorded for the first time.

    Accurate: For data and records to be accurate, they should be free from errors, complete, truthful and reflective of the observation.

    Raw Data: Original record and documentation, retained in format in which they were originally generated or a true copy.

    Data Life Cycle: All phases in the life of the data from initial generation and recording through processing use, data retention, archive/retrieval and destruction.

    Original Record: Data as the file or format in which it was originally generated, preserving the integrity of the record, e.g. original paper record of manual observation, or electronic raw data file from a computerized system.

    Audit Trail: The audit trail is an integral requirement of an electronic record, ensuring the validity and integrity of the record and the link between any electronic signature and the record associated with it.

    Metadata: A set of data that describes and gives information about other data. It provides information about a certain items content.

    Wearables For Remote Patient Monitoring

    Wearables opened up a whole new world of opportunities for pharma and life sciences. They enable 80% faster decision-making, thanks to the workforce enablement, which revolutionized the real-time monitoring of diseases by collecting essential data, such as heart rate, glucose levels, movement disorders, concussions and other medical events.

    Discover 20 examples of wearables and IoT disrupting healthcare

    Wearables help to decrease healthcare costs by reducing the number of in-person visits to the clinic. The health data that is collected by a simple medical wearable device can be life-saving. IoT devices can be used to potentially intervene in certain circumstances. In addition, combining the mounds of microscopic edible sensors ingested in our bodies and the ones that we wear on our body is transforming diagnostic and preventive care as we know it now.

    There are numerous success cases of how wearables are reshaping healthcare. For example, a combination of cloud software and wearable devices can monitor patients vital signals and send alerts to medical personnel about potential accidents or falls. This system proved to be so effective in a facility that serves eldery patients, that now even the patients relatives can remotely monitor the well-being of their family members.

    To utilize the full potential of wearable technology, life sciences organizations may utilize the help of experienced product development companies, like Avenga.

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    New Group To Tackle Data Governance And Guide Digital Transformation In Pharma

    Posted: 7 April 2021 | Hannah Balfour |

    The Pistoia Alliances Data Governance Community of Interest will develop best practices and advance digital transformation in the pharma/life sciences industry.

    The Pistoia Alliance, a global, not-for-profit alliance advocating for greater collaboration in life sciences R& D, has launched its Data Governance Community of Interest . The CoI was set up after a roundtable discussion assessing industry priorities, attended by professionals from pharma companies such as AbbVie, Bayer, Bristol Myers Squibb, Novo Nordisk, Pfizer, Roche and Sanofi, and OSTHUS, highlighted data governance as a key area to focus on.

    The alliance stated that data governance is essential to enable digital transformation in pharmaceuticals and life sciences and to advance innovation, since, without a data governance strategy, secondary uses of data to further innovation are inhibited. Some examples of secondary uses include using synthetic comparator arms in trials, the deployment of artificial intelligence , machine learning and natural language processing and the use of real-world evidence to inform drug discovery and improve clinical trial design. Therefore, the new group will develop and publish best practices and standards for data governance and provide a platform for companies to come together to discuss common problems.

    Organizational Influences On Data Integrity

    Data & Digital adaptation in Pharmaceutical Quality Operations

    The scope of Section 6, shown in Figure 3, covers some critical areas for data integrity . These include the following:

    • Expectations are set for staff ethics and behavior with respect to recording, interpreting, calculating, and reporting data that are clearly communicated to all.
    • Management needs to ensure that staff are aware that breaching ethics and behaviors could result in disciplinary procedures.
    • Unacceptable behaviors must be identified, documented in policies, and communicated clearly to staff, along with the range of company actions if they commit unacceptable behavior.
    • Quality culture is the responsibility of management to establish and foster. In my view, this is the most difficult part of any data integrity program, and requires management to lead by example
    • Section 6.4 deals with modernizing the PQS so that it is able to detect and correct weaknesses that could lead to data integrity lapses. Particular areas for a laboratory are second-person review, quality oversight, and the purchase of instrumentation and systems to ensure data integrity. This latter topic will be covered in Part 2 of this review of PI-041.

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