Tag: data analytics

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Applied Innovation

AI-Enhanced Connected Vehicle Technologies Transforming Fleet Management

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Applied Innovation

AI-Enhanced Connected Vehicle Technologies Transforming Fleet Management

Adopting cutting-edge technologies is essential to remain ahead of the competition in the ever changing automotive sector. At the vanguard of this change is vehicle connectivity, specifically through vehicle-to-everything (V2X) communication. V2X makes it possible for cars to communicate with a variety of ICT equipment, such as other cars, buildings, people, and external networks. In order to prepare their fleets for the future, companies are finding that connected car technologies are crucial.

Enhancing Driver Safety

For fleet managers, ensuring driver safety has always been a top priority. Conventional dashcams work well for detecting incidents in real time, but more preventative safety measures were required.

Driver safety has been increased as a result of AI’s integration with camera video monitoring systems. Through the analysis of facial cues and behaviors, AI-powered driver-facing cameras are able to identify high-risk behaviors like weariness and distraction. By warning the driver and the fleet management about possible hazards, these cameras assist to avert collisions before they happen.

By taking a proactive approach to safety, fewer accidents occur on the road, improving fleet safety as a whole. With the use of this data, fleet managers may implement corrective measures that will guarantee safer driving conditions and enhance driver performance.

Optimizing Driver Training

It has long been difficult to recognize and deal with unsafe driving practices. Driver behaviors were not usually changed by conventional training techniques.

The way fleet managers keep an eye on driving habits has been completely transformed by telematics devices. These gadgets gather information about driving behaviors such excessive speeding, hard braking, abrupt turns, and engine idling. This information may be used by fleet management to fully comprehend how each driver behaves while driving.

Fleet managers can provide specialized training programs designed to address certain driving patterns by identifying areas for development. Individual driver performance is improved by this tailored strategy, which also improves fleet efficiency and safety as a whole.

Ensuring Regulatory Compliance

Ensuring adherence to safety requirements is essential for fleets operating in regulated areas, including construction. Conventional incident management techniques were frequently insufficient.

Dashcams that are incorporated into fleet management systems offer a practical way to handle incidents and adhere to regulations. These dashcams, which are equipped with both audio and video capabilities, allow fleet managers to thoroughly examine occurrences involving injuries, crashes, or aggressive conduct.

Dashcam data in real time guarantees timely incident reaction and offers useful documentation for regulatory examinations. This improves adherence to safety regulations for lone workers and safeguards drivers of specialist vehicles.

Streamlining Route Optimization

For fleet management, effective route planning is crucial, but it is frequently hampered by erratic factors like traffic, weather, and road conditions.

AI is becoming a vital component for fleet management’s route optimization. Large volumes of data may be analyzed by AI-driven fleet management software, which can then spot trends and design the best routes possible depending on variables like cost, time, and distance.

AI keeps an eye on factors like traffic, weather, and road conditions to make real-time route adjustments for optimal efficiency. Better cost management, lower carbon emissions, and quicker task completion times are the outcomes of this, which is particularly important for last-mile delivery.

Enhancing Communication

For operations to run well, drivers and fleet management must communicate effectively, which can be difficult with conventional approaches.

The use of Natural Language Processing (NLP) technology has improved fleet management system communication. Effective communication between drivers and fleet management is made possible by NLP, which gives AI-based systems the ability to comprehend, interpret, and react to human language.

Text-to-speech technology allows fleet management to provide drivers immediate feedback, especially when dangerous driving patterns are identified. This AI-powered communication makes sure that drivers get informed about critical developments while maintaining their attention on the road.

Streamlining Vehicle Maintenance

For operational effectiveness, fleet vehicle health maintenance is essential, but anticipating repair requirements can be difficult.

By generating historical data sets through predictive analytics, artificial intelligence (AI) and cloud computing play important roles in fleet data management. These data sets assist in preventing malfunctions and informing maintenance choices.

AI predicts possible vehicle faults ahead of time by analyzing both historical and current data. Fleet managers may plan maintenance in advance and monitor service intervals with this predictive maintenance capabilities. Fleets may save expensive repairs and preserve operating effectiveness by averting unplanned malfunctions.

The Role of OEMs in Fleet Management

Original Equipment Manufacturers (OEMs) must provide strong support in order to fully realize the promise of connected car technology.

The technology infrastructure and data analytics skills required to enable connected car systems are supplied by OEMs. AI gives fleet managers useful insights by analyzing massive volumes of data from embedded and networked OEM hardware devices.

These realizations increase production, lower expenses, and optimize operational efficiency. Modern technology is radically altering fleet management, from improved communication and real-time route optimization to predictive maintenance and driver safety. AI’s predictive ability increases with further development and data collection, resulting in more user-friendly and effective fleet management.

Takeaway

Fleet management is being revolutionized by the integration of cutting-edge technology like telematics, AI, and V2X communication. Fleets are becoming more effective, safer, and sustainable via increasing driver safety, simplifying route optimization, boosting communication, guaranteeing regulatory compliance, optimizing driver training, and improving vehicle maintenance.

In this transition, OEMs play a critical role in supplying the required technology infrastructure and data analytics skills. AI’s capacity to forecast and optimize fleet operations will only become better as it develops and gathers more data. Businesses will be in a better position to dominate the sector in the future if they adopt these technology now.

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Applied Innovation

Transforming Clinical Trials Through Data Analytics and Risk Management

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Applied Innovation

Transforming Clinical Trials Through Data Analytics and Risk Management


Clinical trials are similar to scientific tests carried out to evaluate novel medications or therapies prior to their release to the general population. To assure the security and efficacy of novel medicines, these studies include intricate procedures, vast volumes of data, and meticulous monitoring. Enter a ground-breaking platform that simplifies clinical trials’ data analytics and risk management processes.

A Shift in the Clinical Trials Landscape

Consider yourself a scientist in charge of a medical investigation. You are in charge of keeping an eye on a lot of data points, ensuring sure the trial is going according to plan, and looking for any potential risks that can jeopardize its success. This is a difficult undertaking, but data analytics platforms may assist you in making it simpler.

Visualization and Centralization of Data

The platform acts as a central digital repository for information gathered from multiple parties engaged in the clinical study. This contains medical records, test findings, and reviews from patients. The software turns this data into user-friendly graphics like graphs, charts, and diagrams rather than displaying it in a raw, overwhelming format. Researchers may more easily identify trends, patterns, and outliers that could otherwise be hidden in statistics thanks to these graphics.

Finding and Evaluating Risks

Finding possible hazards that might affect a clinical trial’s development and validity is a crucial component of managing it. By methodically comparing the trial’s ongoing actions with its established guidelines, it shines in this area. The platform indicates any inconsistencies as possible threats if they appear. The severity and possible effect of these hazards are then classified and evaluated.

Making decisions in real time

Real-time insights are one of the platform’s most noteworthy characteristics. Access to current data and risk assessments allows researchers and trial administrators to quickly make defensible judgments. This proactive strategy minimizes delays, lowers the possibility of negative outcomes, and boosts the trial’s overall success.

Collaboration and Enhanced Workflow

Teams from both on-site workers and central monitoring teams participate in clinical studies. By serving as a bridge, the platform enables effective cooperation and communication across various teams. It provides a workflow that is easily adaptable to direct daily interactions and duties, ensuring that everyone is on the same page and adhering to the stated plan. For accountability and quality assurance reasons, the platform also keeps track of activities completed, establishing a verifiable history.

Conclusion

In essence, the platform completely transforms how clinical trials are organized, tracked, and assessed. It enables researchers and stakeholders to take informed decisions that result in safer and more effective clinical trials by combining data from many sources and turning it into usable insights. The trial process is made more efficient and productive by its real-time risk assessment and collaboration capabilities. Tools like this are crucial in ensuring that new medicines are thoroughly vetted and introduced to patients with the greatest levels of safety and efficacy as the healthcare sector develops.

Please write to us at open-innovator@quotients.com to know more about such innovative solutions and partnership opportunities,

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Applied Innovation Healthtech

Improving Healthcare with Clinical Data Intelligence

UK government funding innovative research on artificial intelligence (AI) to advance healthcare
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Applied Innovation Healthtech

Improving Healthcare with Clinical Data Intelligence

Clinical Data Intelligence for Life Sciences solutions is making data gathering and categorization effective and intelligent, lowering mistakes and speeding up submissions.

In the age of machine learning, artificial intelligence, and semantic data pools, no nugget of information is wasted. The healthcare sector has advanced significantly in clinical decision support and predictive analytics in just the last few years.

As Data are becoming more accessible in the healthcare sector as opposed to a siloed strategy. The use of technology and data and data-driven value creation is now being witnessed throughout the network. Healthcare organizations now have the chance to better leverage data, improve patient care, and increase revenue while handling increasing risks in patient privacy and data security as new data technologies with advanced intelligence capabilities become available.

With businesses investing more in population health management and accountable care, the use cases for big data are multiplying quickly, and consumers are keeping up with the demand for affordable services that take advantage of the ease of their preferred applications and devices. This results in better treatment outcomes, individualized care, and preventive interventions. We would be discussing some of the emerging use cases going forward.

Preventive Healthcare: Preventive Healthcare is one of the use cases for clinical data intelligence. It enables experts to identify dangers early and take preventative measures. Through the use of data science techniques like AI and machine learning, wearables and other tracking devices that gather and track data are producing forecast models that can correctly identify a person’s health risks and enable carers to take preemptive action. It is feasible to anticipate and avoid chronic cardiac conditions, autism meltdowns, etc. by utilizing genetic and historical data.

Data-Driven Care: Data science technologies can make uses like medical image analysis and pathology reports that read with high precision possible because a large number of patients perish each year as a result of diagnostic mistakes. To analyze and understand X-rays, MRIs, mammograms, and other imaging studies, as well as to spot trends and identify illnesses, data models and algorithms can be created. This will increase output and aid doctors in making correct diagnoses.

Individualized Care: A one-size-fits-all strategy for medical care and medication is also now considered ineffective. The ability to monitor individual data and improvements in gene technology are enabling customized medicine. Based on a patient’s prior medical history, gene markup, and current data, machine learning, and deep learning algorithms can now help a doctor determine whether a specific drug will be effective for the patient.

Lowering Costs: Insurance firms are putting weight on healthcare organizations to improve therapy outcomes in order to lower readmissions. Longer personal care is a consequence of bed shortages in some nations. By enabling doctors to remotely monitor their patient’s vital signs, receive alerts when conditions deteriorate, and take appropriate action when required, data science and intelligence can significantly assist in resolving these problems.

Drug discovery: Drug development and clinical studies are lengthy, expensive procedures. Data intelligence tools can aid scholars in the analysis of huge data sets and in the creation of computer models for various tests. Additionally, text mining can assist medical academics by automatically reviewing thousands of web resources and performing analytical processing quickly to give the required information. Clinical trials will use data science apps to accelerate findings and reduce costs.

Thus providing companies with cutting-edge capabilities can improve care, accomplish improved treatment outcomes, increase patient experience, and make new discoveries in drug discovery, data science, and intelligence will have a major influence on the future of the healthcare sector.

However, healthcare companies are still having trouble mastering descriptive analytics, particularly when valuable insights call for a variety of data sources. Despite the data-driven promises, the majority of healthcare companies still have a lot of work to do before they can turn their growing big data analytics skills into actually usable clinical information.

Are you interested in implementing data science and intelligence in your company? Quotients through its partner networks offers a quicker, more affordable alternative. Using advanced data science technologies like AI, machine learning, deep learning, etc. without the constraints of time, money, and resources is made possible by our solutions. Please write to us at open-innovator@quotients.com

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Fintech Applied Innovation

AI-Based Models aiding Financial Institutions with Credit Score Assessments

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Fintech Applied Innovation

AI-Based Models aiding Financial Institutions with Credit Score Assessments

Artificial intelligence has been around for a while now, and technology is only getting more capable. From self-driving cars to personal digital assistants, AI is integrating into our daily lives in ever-more sophisticated ways. In this article, we’ll explore how artificial intelligence can help financial institutions to generate credit reports and make lending decisions.

Importance of Credit Score for FI

A credit score is the first thing lenders check when you apply for a loan or a credit card. This three-digit numeric is a summary of the entire credit history that determines a consumer’s creditworthiness, the higher the score, the better the borrower’s chances of getting a loan. It is prepared based on lenders’ data and consolidated in the Credit Information Report or CIR. Criteria such as payment history, credit utilization ratio, Credit history length, Credit mix, etc are factored in for this. 

Limitations of Traditional Credit Scores

Although credit scoring systems are being implemented and used by most banks nowadays, they have limitations and can’t be used to make accurate predictions. The score depends upon the quality of data that is used, if the data is erroneous with missing values or outliers the resultant scores may not be accurate. Along with the availability of high-quality data, the data should also be predictive so conclusions can be drawn from it and defaults can be predicted. Traditional credit scores due to the limitations discussed are getting outdated and even irrelevant in some cases. 

AI-based Credit Scoring

Artificial intelligence (AI) can play a role here and make precise predictions based on smart models. AI through its cutting-edge analytical technologies can hugely impact the financial sector and can offer excellent returns on their investment. AI-based credit scoring unlike focusing on the past performance of the borrower can be more sensitive to real-time indicators of a potential borrower like the existing level of earnings, employment prospects, and their potential ability to earn. These models can also give individualized credit score assessments based on real-time factors, giving access to finance to more people with income potential.

With these scoring models, banks also can get unique insights into their customers’ financial behavior and leading to better customer segmentation in terms of associated credit risk. Also after the disbursement of the loan these customers can be monitored and red flags can be raised as soon as a behavior is deviant from standard practices. With the help of AI, it is also possible to speed up lending decisions and processing of loans leading to better customer service and productivity of employees.

Credit scores are determined by algorithms neural network-based scoring. A neural network is a machine-like system that can learn from data and make decisions based on those data sets. These types of scoring can detect small variations in data sets and make more accurate predictions and can help solve the problem of credit scoring. As these models can learn from data without requiring any rules-based algorithms they can better assess the credit risk. With these advantages, financial institutions are relying on machine learning to process big data and produce better insights.

Alternatives to Credit Score

Data points produced by a large number of digital transactions can offer valuable insight into how people manage their financial commitments. There are endeavors going on for developing alternatives to create credit score models based on AI methods to evaluate a person’s creditworthiness, particularly for those who do not have a formal credit repayment history.

In the various financial institutions, these solutions are being implemented at different levels and improving how they operate. Please contact us at open-innovator@quotients.Com if you would like additional information or explore this and other rapidly evolving solutions in a variety of fields.

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Innovator's Vista

AI-driven health tech platform optimising care management

Image Courtesy: HEAPS
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Innovator's Vista

AI-driven health tech platform optimising care management

With the increased digitization in healthcare, immense amount of data is being generated, as a result data analytics and artificial intelligence are increasingly being applied within this field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. These advanced technologies are transforming many aspects like providing high quality patient care, responding to real-time situations and saving more lives on the clinical front; and optimising the use of resources, improving the processes and services and reducing the costs on the operational and financial front.

Patient care management, a program that involves comprehensive health services to assist patients in managing their health, is also one of the fields where these advanced technologies can play a significant role. A B2B healthtech startup, HEAPS, is already working on this. The Startup provides SaaS and Platform that leverages data analytics, artificial intelligence, and machine learning and creates care management systems.

Care management systems for insurers, hospitals, corporates and patients:

Heaps, working with the aim to revolutionise healthcare delivery and payment models at a global level, has created care management systems for insurers, hospitals, corporates and patients.

For Corporates, the company helps in improving the quality of care and support health care support to employees while reducing group policy premiums. It ensures comprehensive health care for employees and their dependents with 24/7 support and data on call for employees as well as corporations to pre-empt future health and resource risks. Heaps also provides personalised health care plans for the mental and physical well-being of employees, thus  can improved employee loyalty and satisfaction. The startup has also designed a “Risk Severity Index” that helps patients understand their health better and prevent complications and thus reduces hospitalisations.

For Hospitals, it can help in reducing 30 day readmissions and preponement of discharge by analysis of areas of concern. This leads to increase in patient satisfaction and stickiness. It can also result in better performance by analysis of complaints and satisfaction scores at a Specialty & Doctor level. It helps in generating revenue for Hospitals through Cross-speciality referrals, Home care Nursing, Physiotherapy, Diagnostic and other ancillary medical services. There is also a “Health Compliance & Risk Management (HCRM)” program that provides proactive outreach, personalised care management and real-time risk assessment to improve patient outcomes. 

For Insurers, the startup helps in reducing their cost of hospitalisation and rehospitalisation by actively monitoring patients’ needs and reduce unwarranted hospitalisation and health care costs. By leveraging past analysed data any avoidable deterioration of health of the patient can also be predicted and can be followed by adequate action.

Funding:

Founded in the year 2020, the company is expanding its presence in India, with offices in Mumbai, Hyderabad, and Bengaluru. Heaps has also been successful in making inroads into the North American market and plans to enter Europe and the MENA region this year. The company has also raised a Series A fund of $7.4 million in January 2022 and is backed by well known investors.

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