Implementing Automation to Improve Pre-Clinical & Clinical Trial Technology

Key Highlights

Since the start of the COVID-19 pandemic, life science and pharmaceutical manufacturers have rapidly embraced automation to drive digital transformation along every touch point in the pre-clinical and clinical trial processes. Digital transformation is fueling innovation at the world’s most successful healthcare, medical, life science, and pharmaceutical enterprises. Implementing data-driven decision-making is the key to overcoming uncertainty and harnessing your organization’s full potential.

Over 89% of sponsors embraced decentralized clinical trial technologies, and 88% of trial sites used some form of electronic consent (eConsent) service in 2022.

Electronic Investigator Site Files (eISF), eConsent, or electronic source data (eSource) are becoming industry standards for clinical trials.

The global automation and artificial intelligence in the healthcare market reached $14.6 billion in 2023 and is expected to grow to more than $102.7 billion by 2028—with a compound annual growth rate (CAGR) of 47.6% anticipated during the forecast period. 

Artificial intelligence (AI), machine learning (ML), and the ability to work with extensive and complex data sets using automation are transforming clinical research. Health data science is evolving rapidly as custom AI and ML software applications deliver the efficiency needed to improve preclinical and clinical trials dramatically. 

Machine learning is an area of artificial intelligence in healthcare concerned with recognizing patterns and learning based on repeated data analysis. ML allows medical researchers to work with larger and larger datasets to interpret details about patient populations to develop life-saving medical interventions. 

This article explains why bioscience and pharmaceutical enterprises implement automation to improve their preclinical and clinical trial technologies.

The Implementation of Automation to Improve the Accuracy and Efficiency of Clinical Trials 

A.I.-enabled automation is transforming how medical researchers analyze new drugs in many exciting ways. Machine learning and deep learning are branches of artificial intelligence that are being used to expand the efforts of research teams to improve the validity, reliability, and precision of analysis by extending human reach and the ability to process larger data sets of information in real-time. 

Implementing more innovative automation and healthcare business intelligence solutions is the key to enhancing clinical trials’ design, management, oversight, and documentation to discover new life-saving medical innovations. Intelligent bots improve technical processes and provide additional safety nets to ensure medical research teams have a deeper evidence base to inform their discoveries. 

Robotic process automation delivers measurable benefits for medical research teams. 

Deliver custom RPA solutions to harness these competitive advantages: 

• Increased efficiency at every step of the clinical trial process: It takes significant time, focus, and human capital to process all the different procedures connected to drug trials. Intelligent automation improves accuracy and efficiency. 
• Cost savings: Improving efficiency means less expensive human capital expenditures are necessary, freeing budgetary resources for innovation. 
• Enhanced data accuracy: As the entire end-to-end clinical trial process is streamlined, new treatments are more likely to be approved. 

The pros of automation for pharmaceutical manufacturers are immense. Digital transformation provides the foundation for success. Intelligent automation is the key to unlocking actual enterprise value by expediting complex processes, reducing the overall cost of discovery, and ensuring that clinical teams are focused on doing what matters instead of wasting time and money struggling to wrangle data. 

There are no significant cons to implementing automation to enhance clinical processes besides the need for organizational processes to change and evolve with increased technological resources. 

Change is never simple or easy, but with a focus on innovating, it’s clear that pharmaceutical enterprises benefit from completing capital investments to secure advanced robotic process automation solutions to expedite their time to market and fuel competitive advantage. 

Automation Use-Cases to Improve Preclinical & Clinical Trial Efficiency

Study Design

Automation and machine learning are used for protocol and language translation to cull data and information from diagnostic health libraries and existing protocol insights to design a new protocol for a future study. 

The ML algorithms aid the design process by ensuring the study is designed optimally according to all quality control and regulatory needs. These systems also allow for language translation services to be completed with much higher accuracy and reliability. 

Automation and machine learning offers many unique utilities for improving clinical trials. 

Study Setup

Machine learning is used to automate the development of case report forms and practice databases built from the knowledge accumulated during the preclinical and clinical phases. The automation allows the system to analyze CRFs quickly and recommend real-time edits and improvements. 

Validation reports deliver the clarification needed to be sure that any necessary improvements are applied before the system goes live. ML is also used to automate SDTM mapping and create SDTM annotated studies. 

Trial Management

Site Selection: Machine learning algorithms help to inform site selection criteria by bringing different enrollment, safety, compliance, and data quality insights together to provide accurate predictions about which locations would be the best choices for a new study based on a particular medical practice area. 

Patient Enrolment: Predictive data analysis drives patient enrollment processes to ensure that considerations such as therapeutic practice areas, study duration, study complexity, the prevalence of adverse events, randomization, and disease prevalence are considered according to which factors are most relevant to the validity and reliability of the research. 

Risk-Based Monitoring: Risk-based monitoring (RBM) is essential to reducing the levels of risk connected with the success of clinical trials. An example of RBM is using enrollment, safety, compliance, and data quality insights to inform site selection or patient enrollment. Still, this approach can be scaled across the entire organizational footprint. 

Chatbots: Chatbot solutions provide the perfect means to automate essential communications between clinical trial participants and research leads without unnecessarily burdening organizational resources. 

Data Management

Smart Queries: Smart query systems allow machine learning algorithms to analyze entered clinical trial data and explore which items can be highlighted for various field items. The system learns the expected value ranges and raises queries for issues outside the standard deviation. 

Medical Coding: Medical coding standards such as WHODD and MedDRA are much easier to work with using rules-based parameters that ensure each piece of data is coded according to industry standards every time. The system will match the verbatim text of the study with the dictionary terms for the given practice specialty to ensure all codes are accurate and reliable. 

Query Management: Responding to queries is often a time-consuming process that can dramatically extend the time and cost it takes to complete clinical trials. Intelligent automation delivers the quality control necessary to reduce redundancy and deliver more accurate and reliable results by clustering queries according to importance. 

Smart SDV: Source data verification helps to ensure that all data collection activities are optimized, and data sets can be proofed as quickly as possible using electronic data capture technology. 

Data Analysis

Automation and machine learning deliver robust utility in all data analysis, management, and utilization areas. Classification, clustering, and the use of predictive analytics are just a few areas where these systems help to cull actionable, medically useful insights from disparate datasets. 

Regulatory Submission

The final endpoint of clinical trial proceedings is being able to hand off findings to regulators successfully. This requires a substantial degree of documentation and the control necessary to ensure that all versions of data are valid and reliable. 

CSR Automation: Clinical study reports are generated using the Study Protocol and the Study Analysis Report (SAR). Under current ICH GCP guidelines, natural language processing (NLP) may be used to streamline these processes. 

Leverage Smart Automation to Improve Your Organization’s Clinical Trial Technologies 

Since the start of the COVID-19 pandemic, life science and pharmaceutical manufacturers have rapidly embraced automation to drive digital transformation along every touch point in the preclinical and clinical trial processes. 

Digital transformation fuels innovation at the world’s most successful healthcare, medical, life science, and pharmaceutical enterprises. Implementing data-driven decision-making is the key to overcoming uncertainty and harnessing your organization’s full potential. 

Asahi Technologies offers custom software development services that delivers business process automation to optimize every touch point in your preclinical and clinical trial processes. Get in touch today to learn how to implement smart automation at scale. 

Let’s Talk 

FAQ

• What are automation and artificial intelligence in healthcare? 

Artificial intelligence (AI), machine learning (ML), and the ability to work with extensive and complex data sets using automation are transforming clinical research. Health data science is evolving rapidly as custom AI and ML software applications deliver the efficiency needed to improve preclinical and clinical trials dramatically.

• How is machine learning used in clinical trials? 

Machine learning is an area of artificial intelligence in healthcare concerned with recognizing patterns and learning based on repeated data analysis. ML allows medical researchers to work with larger and larger datasets to interpret details about patient populations to develop life-saving medical interventions. 

• How much is automation in the healthcare market worth in 2023? 

The global automation and artificial intelligence in the healthcare market reached $14.6 billion in 2023 and is expected to grow to more than $102.7 billion by 2028—with a compound annual growth rate (CAGR) of 47.6% anticipated during the forecast period.

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