Category: Data Analytics

Data analytics plays a crucial role in clinical trial design and analysis by providing valuable insights into the effectiveness of new treatments and therapies.

The role of data analytics in clinical trial design and analysis

March 31, 2023

What is the role of data analysis in clinical trials? Can there be better clinical trial data analysis using R and other technologies? Is there a case for using big data analysis in clinical trials? Experts would certainly say Yes to all these questions. Clinical trials themselves have gone through sweeping changes over the last decade, with several new developments in immunotherapy, stem cell research, genomics, and cancer therapy among numerous segments. At the same time, there has been a transformation in the implementation of clinical trials and the process of identifying and developing necessary drugs.  To cite a few examples of the growing need for clinical trial data analysis, researchers gain quicker insights through the evaluation of databases of real-world patient information and the generation of synthetic control arms, while identifying drug targets alongside. They can also evaluate drug performance post-regulatory approvals in this case. This has lowered the cost and time linked to trials while lowering the overall burden on patients and enabling faster go-to-market timelines for drugs too.  What is driving data analysis in clinical trials?  Clinical trial data analysis is being majorly driven by AI (artificial intelligence) along with ML (machine learning), enabling the capabilities of collection, analysis, and production of insights from massive amounts of real-time data at scale, which is way faster than manual methods. The analysis and processing of medical imaging data for clinical trials, along with tapping data from other sources is enabling innovation of the entire process while being suitable for supporting the discovery procedure in terms of quickening the trials, go-to-market approaches, and launches.  The data volumes have greatly increased over the last few years, with more wearable usage, genomic and genetic understanding of individuals, proteomic and metabolomic profiles, and detailed clinical histories of patients derived from electronic health records. Reports indicate 30% of the data volumes of the world are generated by the global healthcare industry. The CAGR (compound annual growth rate) for healthcare data will touch 36% by the year 2025 as well. The volume of patient data in clinical systems has already grown by a whopping 500% to 2020 from 2016.  Data analysis in clinical trials- What else should you note?  Here are a few factors that are worth noting:  Synthetic control arm development  The role of data analysis in clinical trials is even more evident when one considers the development of synthetic control arms. Clinical drug discovery and trials may be fast-tracked while enhancing success rates and designs of clinical trials. Synthetic control arms may help in overcoming challenges linked to patient stratification and also lower the time required for medical treatment development. It may also enable better recruitment of patients through resolving concerns about getting placebos and enabling better management of diverse and large-sized trials.  Synthetic control arms tap into both historical clinical trials and real-world data for modelling patient control groups and doing away with the requirement for the administration of placebo treatments for patients which may hinder their health. It may negatively impact patient outcomes and enrolment in trials. The approach may work better for rare ailments where populations of patients are tinier and the lifespan is also shorter owing to the disease’s virulent nature. Using such technologies for clinical trials and bringing them closer to end-patients may significantly lower the overall inconveniences of travelling to research spots/sites and also the issue related to consistent tests.  ML and AI for better discovery of drugs ML and AI may enable a quicker analysis of data sets gathered earlier and at a swifter rate for clinicians, ensuring higher reliability and efficiency in turn. The integration of synthetic control arms in mainstream research will offer new possibilities in terms of transforming the development of drugs.  With an increase in the count of data sources including health apps, personal wearables and other devices, electronic medical records, and other patient data, these may well become the safest and quickest mechanisms for tapping real-world data for better research into ailments with sizeable patient populations. Researchers may achieve greater patient populations which are homogenous and get vital insights alongside. Here are some other points worth noting:  The outcomes of clinical trials are major metrics with regard to performance, at least as far as companies and investors are concerned. They are also the beginning of collaborations between patients, groups, and the healthcare sector at large. Hence, there is a clearly defined need for big data analysis in clinical trials as evident through the above-mentioned aspects.  FAQs How can data analytics be used in clinical trial design and analysis? Data analytics can be readily used for clinical trial design and analysis, expanding patient selection criteria, swiftly sifting through various parameters and helping researchers better target matching patients who match the criteria for exclusion and inclusion. Data analysis methods also enable better conclusions from data while also improving clinical trial design due to better visibility of the possible/predicted risk-reward outcomes.  What are the benefits of using data analytics in clinical trial design and analysis? The advantages of using data analytics in clinical trial design and analysis include the integration of data across diverse sources, inclusive of third parties. Researchers get more flexibility in terms of research, finding it easier to analyze clinical information. Predictive analytics and other tools are enabling swifter disease detection and superior monitoring.  What are the challenges of using data analytics in clinical trial design and analysis? There are several challenges in using data analytics for the analysis and design of clinical trials. These include the unavailability of skilled and experienced resources to implement big data analytics technologies, data integration issues, the uncertainty of the management process, storage and quick retrieval aspects, confidentiality and privacy aspects and the absence of suitable data governance processes.  What are the best practices for implementing data analytics in clinical trial design and analysis? There are numerous best practices for the implementation of data analytics for the analysis and design of clinical trials. These include good clinical data management practices, clinical practices, data governance

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Overcoming Data Silos In Healthcare For Better Outcomes

March 28, 2023

Data silos in healthcare are a pressing problem for healthcare providers, hospitals, and industry stakeholders due to diverse reasons. Healthcare players now have to contend with big data silos while working out a fine balance between tapping opportunities that arise with more actionable intelligence and insights, while managing increasing technical complexities. The healthcare sector is known for the sheer depth of these data silos, presenting multifarious challenges or obstacles for providers. It begins with medicine R&D patient records and more. Eliminating data silos will contribute towards a win-win proposition for all stakeholders including patients, healthcare service providers, policymakers, and so on. There are however concerns relating to data security with a more complicated data landscape and rapidly evolving threats. The need of the hour is proper healthcare analytics with an emphasis on accompanying privacy-by-design framework, security analytics, encryption, multi-factor authentication, and other techniques. Interoperability is another aspect worth considering. This is the scope of data exchange and interpretation across various IT software applications, systems, and devices. Without proper guiding frameworks for interoperability, data exchange may turn confusing, time-consuming, and complex, hindering information flow and patient care alike. Data complexity has to be reduced by eliminating silos for service providers, doctors, and patients. Challenges for Interoperability There are several hurdles towards interoperability though. While eliminating data silos is possible with big data in healthcare analytics, there are several issues for providers even today. The present interoperability framework is a makeshift system for most healthcare industry players. 93% of hospitals and other healthcare systems make records available online for patients and this has increased from 27% in 2012, as per the Sharing Data, Saving Lives: The Hospital Agenda for Interoperability report in 2019. 88% of hospitals also share their data with ambulatory care as per these reports. However, the critical challenges include the fact that while 90% of hospitals are deploying certified IT solutions, several out-of-the-box options are muddling data exchange owing to silos. Other issues include concerns relating to privacy and security along with restrictions pertaining to the present HIEs (health information exchanges) and also the absence of any compatible linguistic or technical standards for making sure that shared data stays intact and relevant. The Need to Develop Superior Infrastructure A key hurdle towards extensive interoperability is the absence of suitable technology-driven infrastructure. While most providers use EHR (electronic health records) platforms, many of these were not developed keeping data exchange at the forefront. At the same time, health information exchanges were implemented for electronic leveraging of healthcare data and also in a secure manner. Yet, many of them cannot finish total data exchange in a reliable manner through varying source technologies or healthcare systems. A few HIEs also do not facilitate access to patient data which is counterproductive to the actual reasons for their implementation.  This report also mentioned how 97% of hospitals were already using certified EHRs, thereby making the case stronger for doing away with data silos. There is a need for proper systemic infrastructure for recording and transferring vital information securely throughout the ecosystem. Other aspects like APIs (application programming interfaces) are also vital for health data sharing. Accessible, open and FHIR (Fast Healthcare Interoperability Resources) standards-based APIs are seen as some of the best ways to quickly scale up interoperability. More than half of developers of technological solutions will have to ensure access to electronic health data via public and standard APIs in the near future. This should rise further in the current decade.  At the same time, big data in healthcare analytics is steadily attaining higher sophistication and refinement en route towards fusing with better governance and regulatory systems to tap better intelligence and operational efficiencies, along with keeping data silos at bay. Ensuring Greater Security Across The Ecosystem There have to be mechanisms in place for healthcare stakeholders with regard to relying on the accuracy and relevance of the shared healthcare data along with ensuring compliance and security at multiple levels. Privacy issues are still a concern in this space. IT developers and vendors should be able to integrate privacy and security protocols and needs for each infrastructural layer including APIs and third-party applications.  This technological infrastructure should have verification methods for information requests and their authorisation, while each entity which has access to patient information will have responsibility for securing and using data respectively. With more connected health IT systems, there will be growing cyber-security threats and one system’s vulnerabilities may lead to all connected systems getting exposed as a result. This will be an ongoing resolution for healthcare players, with regard to building data privacy and security standards, while complying with regulatory aspects seamlessly. Third-party security layers may also be possible through testing, identifying threats, and evaluation of technological upgrades.  In the end, eliminating silos is a vital task for the global healthcare industry today. Developing big data analytics techniques for penetrating deeper into available data is a key priority for several healthcare players. They are using these technologies for understanding the connections between applications, SSL certificate installation, server functions, and more. Machine and wire data is being analysed and gathered for insights while helping organisations zero in on blockage points which lead to these data silos. Integration of disparate systems across the sector is also vital for accomplishing interoperability at a bigger scale.  FAQs What are data silos in healthcare? Data silos naturally form across several data categories and departments have stored information. These make information inaccurate and inaccessible while hindering effective sharing due to blockages. What are the challenges of data silos in healthcare? The challenges include barriers to sharing critical patient and healthcare data across systems, providers, and the entire network. This impedes quicker decisions and end-consumer fulfilment at multiple levels. At the same time, silos prevent a holistic view of the entire framework for providers. What are the benefits of overcoming data silos in healthcare? The benefits include more accessible and usable data throughout multiple systems and stakeholders along with better collaboration across departments and improved decision-making.

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Digital Behavior Analytics in Insurance

Why Digital Behaviour Analytics Should Be A Top Priority For The Insurers

March 14, 2023

Behaviour analytics in insurance is steadily gaining ground, with the steady evolution of consumer demands and an increasing focus on more flexibility and personalisation of offerings. User behaviour analytics is crucial for helping insurance companies meet varying and evolving requirements better while gaining invaluable insights in the process. Predominant user behaviour analytics software tools enable data analytics in a more specific form for the prediction and understanding of the habits of consumers.  How Behaviour Analytics In Insurance Are Beneficial And Other Vital Aspects   Predictive analytics of consumer behaviour enables diverse use cases for insurers, ranging from customised services to taking measures to combat fraud. Insurers usually use predictive analytics customer behaviour for gaining newer insights into consumer habits and offering more personalised services including things like recommendations, cross-selling new offerings, and lower premiums for safer drivers or healthy customers, or even healthy living tips for reducing claims in the future.  These are only a few examples of the usage of behaviour analytics in insurance.  Not for nothing has the user behaviour analytics market witnessed growth by leaps and bounds. This technology can be spread throughout the entire value chain by insurance companies and it is fast becoming a priority.  Along with smoother implementation and the right software tools, the importance of proper behavioural analytics security is also a focus point for insurance companies.  This is important since there is a huge volume of confidential data that is being gathered and analysed across segments. Hence, ensuring proper security is necessary at multiple levels.  Customers are now looking for more customised experiences with their insurers. 1/5th of insurance buyers reportedly state how their insurers do not provide any personalisation although 80% of them want the same.  This has been outlined in a DataArt report that takes information from Youbiquity Finance. At the same time, 77% of people surveyed in the report stated that they were eager to exchange behavioural information for getting customised services.  Some More Reasons And Use Cases For Behavioural Analytics In Insurance  The biggest reason for leveraging behavioural analytics in insurance is that customers are now looking for more flexibility, control, transparency, and customisation according to industry experts.  They want a scenario where their insurance costs are reflective of their specific behaviours and wish to tailor their insurance plans to their lifestyles.  For instance, if a consumer is medically in prime condition, then he/she will want this aspect to be reflected in premiums for policies.  Automotive insurance has been a great hunting ground for testing behavioural analytics for many insurance companies. Telematics devices in vehicles have helped generate data which is enabling price reductions and other benefits.  Life insurance is another category where customers are looking at evolving coverage amounts and controllable tenures.  Behavioural analytics is already helping people re-evaluate their requirements on a regular basis. Insurance companies will be able to tap these analytics to identify higher-risk consumers while meeting market requirements.  Global trends indicate how 5% of patients account for almost half of spending on healthcare. Hence, predictive analytics will play a crucial role in helping insurance companies identify risk factors for patients before these cases turn problematic.  These analytics can also enable firms to evaluate the regular activities of policyholders and responses in order to judge the various risks faced by them.  This will help in the removal of activities that might otherwise lead to premium increases for policies. Insurance companies can also move towards a more advisory role that is tailored toward the interests of the consumer. These analytics may also help prevent the occurrence of claims in many cases.  Behavioural analytics has been successful with regard to reducing losses, understanding customer interactions and networks within the ecosystem, and propensity modeling. It has also helped cross-sell various offerings along with up-selling whenever the time is ripe. It has also enabled insurance companies to swiftly offer assistance to customers at the time of claims and in other scenarios as well.  Hence, these benefits make a compelling case for the usage of user behaviour analytics by insurance firms.  FAQs What is digital behaviour analytics? Digital behaviour analytics is a specific form of data analytics that measures the user habits of consumers. It tracks consumer activity and interactions, along with their behavioural patterns in order to identify their needs, risks, and offer them more personalised solutions.  Why is digital behaviour analytics important for insurers? Insurers benefit from using digital behaviour analytics, since they can identify high-risk customers and instances while combating fraud and lowering claims and losses. They can also personalise their products and recommendations for consumers, giving them tailored solutions for various needs. At the same time, insurers can use these analytics to cross-sell/up-sell along with adopting an advisory role for customers.  What types of data can be analysed using digital behaviour analytics? Various types of data can be analysed through digital behaviour analytics. This includes customer interactions and activities throughout social media platforms and on the internet, along with their activity across various sites and applications. In-store, web-browsing, survey, advertising, and customer service data can also be analysed, to name a few sources. 

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omnichannel customer experience analytics

What Is Omnichannel Customer Experience Analytics, And How Should You Use It?

February 27, 2023

Omnichannel customer experience analytics are steadily gaining ground across the business spectrum, particularly in an environment where customer journey mapping is even more crucial for understanding how to fulfill prevalent requirements and personalize experiences.  Omnichannel is the way forward for businesses these days, combining online shopping and engagement channels with offline stores and other experience centers.  With the continued evolution of communication methodologies across industry segments, customers are now demonstrating interest in multi-channel or omnichannel business interactions.  Omnichannel customer experience analytics has grown to be a specific solution that helps companies leverage indispensable customer insights. This enables the collection and analysis of data from several channels including call center engagement, social media platforms, e-mails, and more.  Along with suitable customer data management and user feedback management, companies can analyze all this valuable data from multiple sources simultaneously, gaining insights and context which is not possible upon the analysis of only one data source.  Such dynamic user data analytics helps companies get decision-making inputs and actionable insights with a view toward ensuring better services for customers, developing newer products, expanding markets, and a lot more.  Why Omnichannel Customer Experience Analytics Are Important Tracking customer interactions and engagement throughout several channels is a key priority for most companies in recent times.  At the same time, they can maximize their value through this system of simultaneous analysis across sources.  From analyzing satisfaction and predicting customer behavior to understanding preferences, gaps in satisfying customers, the scope of new products and services, and geographical expansion or consolidation possibilities, the sky is the limit once data is analyzed across all touch points in the journey of the consumer.  This naturally makes engagement simpler while ensuring that companies get better visibility into the effectiveness and results of their marketing campaigns and outreach strategies.  It also helps enhance business revenues along with ensuring higher customer retention, loyalty, and conversions alike. Here are some key points worth noting in this regard:  Information is consolidated and made shareable throughout several channels, enabling better operational systems for reps and other personnel, saving their time and effort greatly.  It also encourages higher customer conversions by lowering the effort required to complete transactions.  The team can lower operating expenditure while tracking marketing spends closely.  AI may be leveraged to get more intuitive and valuable insights from organizational data.  With proper customer journey mapping¸ marketing processes can be better optimized along with tracking the impact of each channel on the end-consumer. Marketing strategies can be aligned better with the interests of consumers while personalizing marketing resources to suit their individual requirements.  Omnichannel analytics can help companies forecast inventory accurately along with combating diverse supply chain problems and logistics hurdles.  Analytics tools can also help find compliance and regulatory problems, along with possible organizational threats.  With the rapid evolution of buying habits of consumers globally, organizations will have to analyze newer mechanisms for doing business, with a focus on relevance and effective marketing and outreach.  It is here that omnichannel analytics becomes an invaluable tool for companies at every level.  FAQs What is omnichannel customer experience analytics?  Omnichannel customer experience analytics is a specialized form of analytics that gathers and analyzes customer data throughout multiple sources simultaneously, helping organizations derive crucial and actionable insights.  What is the purpose of using omnichannel customer experience analytics?  Omnichannel customer experience analytics helps companies boost customer experiences, understand pain points and gaps in service, plan expansion or the introduction of newer products/services in response to market demand, and also demystify customer personas and engagement, while also tracking how effective marketing campaigns are.  How can you use omnichannel customer experience analytics to improve customer experience? These tools can help companies gather data on the aspects that consumers are satisfied by and areas that require improvement as far as their experience is concerned. These insights help companies simplify operational processes and experiences for customers based on segmented and specific feedback.

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