Tag: Clinical Trials

virtual clinical trials

Virtual Clinical Trials: Analyzing its Role in Reshaping the Future of Life Sciences Industry

What are Virtual Clinical Trials?

Conventional clinical trials that adopt sequential processes are widely accepted and are proven to adhere to the industry standards to help ensure the efficacy and safety of new medicines. However, certain factors are now extending the length of clinical trials and also contributing to high failure rates. That’s where virtual clinical trials come in. Virtual clinical trials can improve clinical cycle times while reducing the cost of clinical drug development.

Virtual Clinical Trials or VCTs, also called remote or decentralized clinical trials, are a relatively new, underutilized method of conducting clinical trials taking full advantage of technologies such as apps, electronic monitoring devices, and online social engagement platforms.

We have several years of experience in helping life sciences companies to optimize clinical trial processes by combining data, analytics, and technology. Speak to our experts to know more.

Healthcare has now become a costly matter as every country now spends a higher percentage of its gross domestic product for the healthcare sector. Also, the growing expectations of the taxpayers and patients are forcing the life sciences industry to increase the quality and quantity of information and evidence generated during normal clinical trials. This is also leading to complex clinical trial design. The increasing adoption of virtual clinical trials amid the crisis can be attributed to several challenges associated with the traditional clinical trial methodology, including:

  1. Increasing time duration of clinical trial
  2. The success rate of a drug depends on the disease, which might not work properly on another individual from a different demography
  3. Data security and storage

Our clinical trial data analysis solutions can help companies to access the critical trial data in near-real-time on interactive visual dashboards. Request a free proposal to gain comprehensive insights into our solutions portfolio.

Apart from these three main challenges the clinical trials have a huge cost factor associated with it, including costs driven by:

  1. Patient recruitment
  2. Outsourcing cost
  3. Patient retention
  4. Site recruitment
  5. Site retention
  6. Clinical trial data management cost
  7. Tech cost

The patients who sign up for a clinical trial on them also face numerous challenges we have however noted the most significant ones which are-

  1. Patient safety
  2. Unclear regulatory acceptance

Amid the growing complexities in today’s healthcare systems, leading life sciences companies are turning to virtual clinical trials owing to its capacity to change the drug development scenario. Technologies like artificial intelligence and machine learning have been proven to transform the traditional clinical trial data analysis methodology, making it an essential aspect of all clinical trials. Virtual clinical trials can change the current protocol layout design to a study execution which will result in improving the success rate of the trials and also lessen the burden of research and development.

virtual clinical trials

Virtual Clinical Trials and Modern Life Sciences Industry

The life science industry is ready for some significant changes. From chronic diseases and cancer to radiology, there are numerous opportunities to leverage clinical trial data analysis and advanced analytics to deploy more efficiency and impactful innovations in patient care.

As healthcare becomes costlier patients are expecting and demanding more from the providers. The volume of available healthcare data is thus increasing at a high pace bringing in more complexities to the highly regulated healthcare environment. As a result, virtual clinical trials are now the new ray of hope for driving improvements across the healthcare sector.

Virtual clinical trials offer a number of advantages over conventional clinical trials. Learning algorithms can become more accurate as it will interact with clinical trial data analysis, which will allow the life science industry to gain unprecedented insights into care processes and patient outcomes.

Virtual clinical trials offer the ability to reduce risk in drug development. Data from such trials can be accessed by scientists in real-time and will create more chances of success. It gives a remote monitoring capability that allows improvements in trial designs based on accumulating data thus drug termination decisions can also be made faster than usual. Virtual clinical trial data analysis can also give insights about patient conditions and it can reduce expenditure on failed trials.

Using technology to communicate is no more a new idea but creating interfaces between computers and the human mind and body is a new innovation in the field of research and it has numerous benefits. Neurological diseases can take away a human’s ability to speak and explain at times it seizes the ability to move, virtual clinical trials and artificial intelligence in such cases can restore these basic experiences and abilities. Thus virtual clinical trials enhance the success of clinical trials in a much shorter time.

To know more about how our virtual clinical trial data analysis solutions can help you identify unexpected patterns and predict treatment responses, request a free solution demo.

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Clinical Trial Data Analysis Engagement Helped a Leading Global Pharma Company to Determine Chance of Disease Relapse – A Quantzig’s Success Story

The client is a leading global pharma company who had conducted clinical trials in partnership with more than 10 different hospitals for patients suffering from melanoma. They approached Quantzig to leverage its clinical trials data analysis expertise to compare the two treatment groups for disease-free interval and survival using advanced statistical analysis techniques.

For companies in the pharmaceutical industry, it is difficult to analyze the vast amounts of raw, unstructured patient data. Clinical trial data analysis requires tested statistical methods that have to be specified with the regulatory authorities before the trial even begins. With nearly 80% of clinical trials failing to meet enrollment timelines, pharmaceutical companies need a more efficient approach to improve their data analysis capability. This is where data analytics tools can help. Clinical trial data analysis done using data analytics tools can help pharmaceutical companies to understand the epidemiology landscape and develop an effective enrollment forecast. This can further help in understanding and analyzing the accuracy of patient data and provide a complete view of the patient journey.

Quantzig’s analytics experts help pharmaceutical companies to optimize strategic clinical processes by combining data, analytics and technology. Get in touch with them to know more.

Business Challenge

The client, a global pharma company, conducted clinical trials for patients who were operated and were on medication therapy A or B for more than 2 years. Throughout the trial, the patient’s status, laboratory parameters, vital signs, skin examinations, and other parameters were constantly monitored and tracked to achieve the following goals:

  • Identify the recurrence rate of the disease
  • Analyze factors impacting survival rate

Despite employing quality control measures to analyze the completeness and accuracy of patient data,  the pharma client was not satisfied with the outcomes.. They wanted to compare the two treatment groups for disease-free interval and survival using clinical trial data analysis techniques, for which they approached Quantzig.

Our clinical trial data analysis solutions help companies to analyze performance with access to critical trial data in real-time. Request a FREE proposal to gain in-depth insights into our portfolio of analytics solutions.

Solution Offered and Value Delivered

Quantzig’s analytics experts adopted a comprehensive approach comprising three phases to help the client tackle their core business challenges. The first phase revolved around understanding the business scope and objective of the analysis through multiple discussions with the client. During the second phase of the clinical trial data analysis engagement, our experts collected and integrated disparate sets of patient data and created basic data quality reports based on an in-depth clinical trial data analysis. The third and the final phase of this engagement revolved around designing parametric hazard models, and life tables to analyze survival rate and the recurrence rate of the disease in different groups. Also, they conducted a survival analysis for each treatment group and compared the survival curve with the log test rank.

Quantzig’s analytics solutions help pharma companies to access all their trial data in real-time via an intuitive, role-based dashboard that helped them to visualize performance metrics. Request a Free demo to know more.

The solution offered helped the client to gain valuable insights into adverse events, patient characteristics, and segments level. Also, the client was able to improve their patient data quality check mechanism by highlighting current inefficiencies in the data entry process.

CLINICAL TRIALS DATA ANALYSIS ENGAGEMENTQuantzig’s clinical trial data analysis engagement helped the client to:

  • Reduce the risk of multiple surgeries
  • Predict the optimal therapy line as well as medication-based on model results
  • Evaluate the effectiveness of the prescribed medication

To know how our analytics solutions can help you identify unexpected or novel patterns and predict treatment responses and patient behavior, request more information below.

Top Challenges in Clinical Data Management

Today, all modern industries and companies are embracing the power of digital technologies. The healthcare industry is also following the trend by using patient data management systems, which keeps track of patient information and medical history thereby replacing handwritten medical files. Since patient data is sensitive, medical companies and professionals should adopt new strategies to manage such healthcare data securely. Clinical data management simplifies a lot of operational tasks thereby increases the efficiency of healthcare providers. However, it Free democan be challenging to manage such large and complex sets of healthcare data. Clinical data management is further complicated by various government regulations and compliance issues.

HIPAA Compliance

The Health Insurance Portability and Accountability Act (HIPAA) requires security measures for EHRs to be shared among medical practitioners and be made accessible to patients. Majority of the healthcare providers have to balance between operating a closed-network system and implementing shared data access and security protocols. Also, after providing a platform to share data between practitioners, a data breach could cost healthcare companies significantly higher than in other industries.

Sharing Patient Data

Sharing patient data is still a grey area for players in the medical industry. Some regulations restrict sharing of data with a medical representative and other medical professionals. To tackle such issues, the medical industry is working on strategies like EHRs and cloud computing to facilitate sharing of clinical data. Additionally, integrating and standardizing the sharing platform for every medical professional to grant access to healthcare data is also an ongoing challenge.

Mobile Computing

The demanding nature of today’s digital technology has placed great importance on mobile computing. Professionals want access to data at the comfort within their fingertips. However, shifting the platform to mobile devices seems quite a challenge in the field of clinical data management. It’s a lot easier to upload medical entries to a tablet directly then scribbling on medical charts for transcription later. The problem arises when providing a secure wireless access throughout the care facility to medical professionals. It requires developing a completely new security and compliance protocols for mobile devices.

Operational Analytics

The clinical data management systems and EHRs have helped improve the quality of care that is provided to the patients. Alongside patients, healthcare professionals and workforce has also benefitted with a substantial increase in operational efficiency. For instance, healthcare workforce management is largely measured using patient care and healthcare data, which isn’t an ideal metric for performance measurement. As a result, it has urged HIS managers to look for new strategies to mine healthcare data in order to perform productivity and profitability analytics to identify true measure of performance and identify improvement areas.

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Top Five Uses of Data Analytics in Hospitals

Hospitals today are under pressure to improve patient care by adopting the latest technologies, which subsequently increases the cost of care. Hospitals have to fine-tune this trade-off to provide the best possible care by incurring Free demoreasonable costs. Advancements in the field of data analytics have enabled hospitals to achieve operational efficiency and improve their quality of care. Here are some of the reasons how hospitals are using data analytics to boost their performance:

#1 – Administrative Cost Optimization

All day-to-day tasks within a hospital generate a lot of data relating to patient admissions, patient service time, wait-times, and drug inventory. Such data can be processed by data analytics tools to optimize workforce, patient management, and drug inventory to save on administrative costs.

#2 – Improve Patient Wellness and Outcomes

Hospitals keep track of their patient records and with the help of data fed from wearable devices, they can monitor and remind patients to maintain a healthy lifestyle. Data analytics can be used to suggest patient’s necessary modifications that are required in their lifestyle to improve their health condition.

#3 – Identify Fraud and Abuse

Hospitals can identify insurance frauds and abuse cases by analyzing large unstructured data sets of claims and use machine learning algorithms to identify suspicious patterns and anomalies. Fraud cases can be easily identified through the identification of specific patterns such as patient visiting multiple hospitals for the same case, over utilizing services in a short time, or filing for the same prescription in multiple places.

#4 – Clinical Decision Support

The doctors are equipped with electronic health records of a patient’s case, be it x-ray, medical history, or any reports. Use of data analytics tools provides doctors with a dashboard to access all information relating to a patient with ease. It helps the doctors in providing the best mode of treatment to the patient to improve outcome.

#5 – Obtain Actionable Insights

Data analytics tools can provide the hospitals with key metrics such as patient admission rate, treatment errors, mortality rates, and readmission cases. A quick comparison of such metrics with industry standards can identify problem areas so that they can take corrective actions to ensure patient safety.

For more information on big data analytics and its implication on hospitals:

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Pharma company gains visibility into clinical trials by standardization, aggregation, and analysis of trial repository

Business Challenge: Setting up data repository for analysis of clinical trials

A leading client in the pharmaceutical companies space with a growing pipeline wanted to setup a data repository to analyze data across clinical trials.

Situation: Limited visibility into clinical trials

The client in the pharmaceutical companies space had legacy and on-going clinical trials data in different formats and systems. They had limited ability to visualize and analyze data across trials, and inability to aggregate and standardize data for analysis and submission.

Solution/Approach: Standardization of all clinical data using CDISC SDTM and CDASH standards

We worked with the client to determine their goals and requirements, and setup clinical data repository. We aggregated and standardized all of their clinical data using CDISC SDTM and CDASH standards and mapped their legacy data leveraging next generation ETL capabilities. We developed programs and derivations to convert, integrate, and standardize their data into the platform.

Impact: Better access to multi-dimensional data for decision making

The client in the pharmaceutical companies space was able to access and perform analysis and on multi-dimensional clinical data views by role (i.e., safety analysts, data managers, medical monitors, biostatisticians, and clinical trial managers). Our solution also helped the client implement data standards based on CDISC’s SDTM for all legacy, on-going, and future trial data to expedite data analysis and submission.

Healthcare firm improves clinical trial enrollment through clinical trial reporting and analytics

Business Challenge: Improving clinical trials management and performance through enrollment optimization

A leading European healthcare firm wanted an efficient program to reduce the costs and improve the functionality of its clinical trial studies for generic drugs.

Situation: Optimization of clinical trials performance

The client wanted to improve the clinical trial management process through better study planning. In order to do this, client was looking for insights on identifying the best performing sites and investigators, creating robust enrollment plans to improve enrollments, identifying the causes of trial delays, and preventing them.

Solution/Approach: Consolidation of various hospital records and performance analytics

We used predictive analytics, simulation, visualization, and data aggregation for monitoring of actual enrollments and forecasting enrollment requirements, gaining insights into potential problems related to site and trial management and preventing them before they occur.

Impact: Reduced cycle time, improved enrollment rates and site productivity

The client was able to improve the enrollment rate or its clinical trial studies by up to 35%, reduce the clinical development cycle time, and improve the site productivity by 20%.

Global healthcare conglomerate improves quality adherence in clinical trials using pharmacovigilance analytics

Business Challenge: Improving process safety during clinical trials for new cardiology drug

A global conglomerate in the healthcare industry, with ongoing clinical trials in 15+ countries, wanted to ensure process safety and real time monitoring of the trials.

Situation: Real time monitoring of new drug clinical trial to ensure process safety

The healthcare industry client wanted real time monitoring of clinical trials for its new cardiology drug, in order to maintain clinical safety, closely monitor the use of the product in clinical practice, and predict and prevent potential serious safety risks

Solution/Approach: Pharmacovigilance analytics and CRF generation for hybrid reporting

We used process analytics, monitoring of summary of product characteristics (SmPC) compliance, benefit risk analysis, and adverse events reporting for understanding of protocols adherence and regulatory compliance in all data collection and reporting; along with case report forms (CRFs) generation for use in both EDC and paper based, (i.e. hybrid) reporting.

Impact: Improved trial accountability, prevention of serious safety risks

Through the insights from pharmacovigilance analytics, the client improved the accountability for drug development clinical trials, ensured regulatory compliance through documentation, and was able to conclude the study meeting all the safety objectives.

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