Program overview – scdm 2018 annual conference gas stoichiometry calculator

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With aspects of clinical research seeing rapid expansion into more automated and advanced approaches to day-to-day work tasks, clinical data management is nearing a required adoption of more sophisticated methods of mining study metadata, identifying and cleaning anomalous data, and working with a wider variety of available data sources beyond the traditional electronic data capture (EDC) system model, including EMR/EHR systems. As clinical trials continue to move toward the use of ePRO, wearable technology, and biosensors for data collection, data volume and data velocity will likely render non-algorithm based approaches to data cleaning and error identification obsolete. Deep learning, predictive analytic and machine learning algorithms will become commonplace tools available to clinical data management to expedite and, in some cases, eliminate tasks that have traditionally been handled manually. In addition to the standard arena of clinical data, there are a number of non-traditional areas in which clinical data management is likely to expand. That being said, there is a salient concern that Data Management as it exists today will not have a place in the clinical research enterprise in a future potentially dominated by AI applications. ideal gas definition chemistry In order to keep up with current trends and remain relevant, those in clinical data management positions must be ready to adapt to the rapidly-changing landscape. This session will be description of the gap between the current state of our industry and where we are likely to find ourselves in the near future based on the evolution of data systems and technology. In addition, we will attempt to paint a clear and actionable path to improving our skill set for those in Data Management today.

For decades, integration between EHR and EDC systems has been seen as the “holy grail” to optimize clinical research. Finally, key technologies and standards are now making such integration a reality. The presenter will explain how SMART and FHIR on the EHR side and CDISC CDASH and ODM on the EDC side allow for integration — without protocol-specific custom coding. The speaker will describe the solution developed by Clinical Pipe and implemented successfully in oncology clinical trials across multiple academic clinical trial centers. Clinical Pipe can integrate with any SMART on FHIR EHR system and connect to any EDC system with a real time API. As of July 2018, the application integrates the 4 leading EHR systems and export data in real time to Medidata Rave and Protocol First EDC. The speaker will also address downstream operational impacts of acquiring data “automatically” from the EHR.

Abstract: Adopting an eConsent solution comes with many benefits by itself, less burden on patients and sites, improved learning and retention of trial information and better overall patient retention. ogasco abu dhabi But more benefits are unlocked when eConsent is used in conjunction with a platform that allows patient data to seamlessly flow into RTSM and EDC capabilities.

With aspects of clinical research seeing rapid expansion into more automated and advanced approaches to day-to-day work tasks, clinical data management is nearing a required adoption of more sophisticated methods of mining study metadata, identifying and cleaning anomalous data, and working with a wider variety of available data sources beyond the traditional electronic data capture (EDC) system model, including EMR/EHR systems. As clinical trials continue to move toward the use of ePRO, wearable technology, and biosensors for data collection, data volume and data velocity will likely render non-algorithm based approaches to data cleaning and error identification obsolete. Deep learning, predictive analytic and machine learning algorithms will become commonplace tools available to clinical data management to expedite and, in some cases, eliminate tasks that have traditionally been handled manually. In addition to the standard arena of clinical data, there are a number of non-traditional areas in which clinical data management is likely to expand. That being said, there is a salient concern that Data Management as it exists today will not have a place in the clinical research enterprise in a future potentially dominated by AI applications. In order to keep up with current trends and remain relevant, those in clinical data management positions must be ready to adapt to the rapidly-changing landscape. This session will be description of the gap between the current state of our industry and where we are likely to find ourselves in the near future based on the evolution of data systems and technology. In addition, we will attempt to paint a clear and actionable path to improving our skill set for those in Data Management today.

Despite the best intent to leverage the newest technologies and comply with the recent regulation changes, most Data Management organizations have been processing data the same way for a long time. Fortunately for some and unfortunately for others, the accelerating pace of change is calling for action. The volume of data collected outside EDC is fast growing as our industry is crying for patient centricity which is leading to rapid adoption of eCOA, wearables, sensors and eSource solutions. electricity and circuits ppt The increasing cost of Drug Development and the need for better predictability of outcome requires use of more complex study designs such as adaptive and hybrid. Not to mention the need to embrace risk based approaches and advanced analytics. Unfortunately change will not stop there! Solutions based on Natural Language Processing, Artificial Intelligence and Machine Learning are maturing rapidly. So, is Data Management ready for all this?

The objective of this session is to provide a pragmatic and concrete view on how regulations and technology innovations will change the role and profile of Clinical Data Management within the next 5 years. We will also consider changes to related functions such as Clinical Programing and Medical Coding. We will share insights and roadmap from the SCDM Innovation committee.

With an exponential growth of data in recent times & rapid advancements happening on technology front, the need of hour is to generate insights from this massive amount of data. These emerging technologies help to improve the quality of data that we collect and enable us to take informed decisions. Artificial Intelligence (AI) is the ability of a computer or machine to simulate human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages, while Machine learning (ML) may be simply defined as a compilation of algorithmic techniques that can be used to identify patterns to enable or automate decision-making activity. electricity equations physics As an example, Natural Language Processing (NLP) can be used to ‘read’ free text in CRFs & assist in scientific review of data. Large data sets from past clinical programs can ‘train’ the ML algorithm and can then be used for subsequent data review, cleaning and analysis. NLP can also help with describing relationships between medically coded terms. Some of the following themes that could be put into practice:

Regulatory authorities have been clear that Informed Consent is a multifaceted process that goes far beyond obtaining a signature. Genuine consent involves providing potential participants with adequate information about the research to allow for an informed decision to participate, facilitating and verifying comprehension of the information, and allowing adequate opportunity for questions and consideration. The process often continues after enrollment. Investigators are frequently obligated to provide additional information to participants as the research progresses, and even obtained informed re-consent.

Electronic informed consent (e-Consent) must accommodate all these requirements. Done well, e-Consent can maximize patient understanding, engage non-English speakers with multilingual tools, improve documentation and reporting, and standardize the consent process across sites, all while reducing cost and administrative burden. Attendees of this session will learn how to determine the suitability of e-Consent in light of a study’s setting, participant profile, and indication (among other attributes), as well as the best way to adapt the principles of fully informed consent in its usual, paper-based context to one where the process is electronic.

While Electronic Data Capture (EDC) and Clinical Trial Management Systems (CMTS) streamlined the execution of clinical trials, the ultimate goal of entirely paperless trials is still to be achieved. With a variety of new software applications emerging almost every day, data managers must be educated on these systems, EDC in particular, to fully compare and contrast their options and determine which products will be optimal for their studies. It is critical that researchers ask the important question: how and where is this software application hosted? High cost, among other factors, has incited the movement away from on-premises hosting towards “the cloud.” Even when a researcher opts for a cloud-based clinical software, several hosting variations within “the cloud” exist. These important cloud-based hosting variations can affect both privacy and performance during the life of the study, thus affecting sponsors, as well as CROs and sites. This presentation will discuss the pros and cons that surround on-premises, public cloud, and private cloud options. Depending on a study’s needs, where and how an EDC software is hosted can have long-term and short-term implications on cost, efficiency, and compliance. It is important to be aware of the differences between these hosting options and ask the right questions when speaking to vendors. electricity experiments elementary school This presentation will serve as a primer to guide Data Managers through the various options and better equip them to ask the right questions.

The landscape for data managers have changed drastically from the past. From earlier days when a data manager would be doing data entry, make a database and send queries to site, the data manager now has many stakeholders like statisticians, medical writers, preclinical scientists, study responsible physician, medical monitors to name some. There is an expectation from data managers to borne skills which can bridge gap between technical and scientific information and deliver a complete package.

It is increasingly becoming important for data manager understand the impact of data he / she is handling on the overall analysis. This expectation requires a person to understand not only data management as well as SDTM; but also, an understanding of how the drug functions and why a particular parameter can become very important for overall efficacy of the drug. Very often there are expectations that a data manager should have experience in a particular therapeutic area.

While technical skills and soft skills of data managers are being discussed, very little is being talked about the therapeutic area knowledge requirements of a data manager in terms of; what exactly is required, an understanding of assessments in a particular TA or also theoretical knowledge? Is this a necessity and what are the value adds? And most importantly, how to develop TA knowledge of data managers? This session aims to look at the concept of virtual trials and patient centric research through the eyes of a data manager. We’ll look at how using eSource and patient dashboards will challenge the traditional role and tools of a data manager.

Data Management in the world of ‘Direct to Patient’ or ‘Virtual Trials’ Clinical Research has come a long way since using paper in the 1990s. Most studies have adopted electronic solutions with rapid improvements in connectivity and wide availability of clinical technology solutions. Continued enhancements in technology and expansion of clinical research industry in emerging countries and in established geographies are triggering newer models for clinical trials.

Direct to patient or virtual studies allow inclusion of patients without proximity to a specific site or location. This patient centric approach puts patients rather than the trial site at the center of the process. chapter 7 electricity test The design of the study as well as the conduct of these studies ensure their patient centricity. Instead of a patient going to a site for the clinical trial, the clinical trial comes to the patient’s home. Patient recruitment, enrolment, engagement and retention, data collection including its management and follow ups are all usually managed from a central office and are supported by staff at regional or local hubs. This new model creates opportunities for new way of collecting and managing trial and operational data. Very limited or zero onsite monitoring creates new challenges in cleaning the clinical trial data.

• Attendees will be able to describe advances in clinical trial technology solutions that enable virtual clinical trials and utilize such solutions and approach to clinical trials in their organizations. We will also focus on Data Management in such trials and attendees will understand any changes in approach needed to plan and execute DM services for such trials