Harnessing data power to overcome pharma industry challenges
Tremendously rising population of our planet (to be over 80 million by 2050, according to UN report) requires improved and viable resolutions for our well-being and nourishment. Incidentally, today’s digital technology has grown enormously and possesses every capability and flexibility to proactively predict and tackle such challenges.
How is this possible?
Today, digital data science, with analytics and AI capabilities wields the power to significantly renovate the health and well-being of patients. Pharma and healthcare industry can leverage on the dramatically growing data analytics technology to deliver superlative patient outcomes.
For early detection of diseases through patient collected, processed by high end analytics ML and AI techniques provide radical insights into the patient’s medical condition. Treatments to patient’s medical conditions can be tailored specifically for patient requirements.
Data analytics-driven solutions can easily spot such needy patients and provide medical treatments at radical speeds.
Early disease detection through data analytics
Spotting diseases early, even before they start showing, hold significant impacts on patient outcomes. Digital analytics capabilities aid medical practitioners, paramedics, and other healthcare professionals diagnose medical conditions drastically early.
- Using CTPA (computed tomography pulmonary angiogram) for remarkably early detection of CTEPH (chronic thromboembolic pulmonary hypertension) is a typical example. CTEPH is generally an uncommon type of pulmonary hypertension
- However, recent intense knowledge digital techniques such as CTPA scans facilitate radiologists in early detection of CTEPH symptoms
- The CTPA processes digital image data pertaining to vital conditions such as cardiovascular activity, pulmonary vessel analysis and lung perfusion, coupled with the processing of patient’s pulmonary embolism history
- Algorithms of AI techniques are a boon to medical practitioners to detect very early, threatful cardiovascular conditions in patients. High-risk conditions such as Heart attack strokes can be detected through precision AI algorithms
- Typographic classifications Patients: Latest data analytics techniques possess classification capabilities processed based on certain unique patient character factor combinations. The factors analyzed include patient genealogy history of chronic clinical risk conditions such as diabetes, as well as the demographics of the patient. Outcoming data from digitally driven, fast-paced clinical, genealogy studies, and image data on patients are applied and processed for Classification
Data-driven, customized treatments
Revolutionary data analytics and AI technologies possess remarkable customized patient therapy and treatment capabilities. The core intent of data-driven analytics, ML and AI health care techniques is to deliver tailored health care specific to individual patient’s medical condition, chronic or otherwise.
- For instance, minute, intricate conditions such as NTRK gene fusion in tumorous cells in patients can be easily detected early through data-driven AI algorithms. NTRK gene fusion condition, if not detected early and diagnosed, will result in TRK fusion protein, which will surely cause risky cancerous growth in patients
- Though considered a rare condition, numerous individuals including children have been affected across the globe, making them mandatory to be tested. Based on pathology result data collected pertaining to the tumor, specific AI algorithms help doctors easily identify them early and verified later through regular methods
- AI algorithm helps fast-paced testing of TRK fusion cancers which otherwise would be a laborious, monotonous, process if undertaken manually
- Numerous patients have been benefitted through data-driven precision oncology treatments for such tumors
- Mobile Gaming: This is the most innovative, patient beneficial offering, combining data science, analytics, ML, and AI. Going ahead of the usage of drugs and pills, such digital technologies have started building complementary healthcare techniques, which otherwise would be branded as time-consuming treatments by doctors. Techniques such as mobile gaming help individuals easily tackle even chronic conditions such as hypertension, anxiety, depression, etc. to be nipped off in the bud
Data analytics and new drugs R&D
Developments in data analytics have over the last decade accelerated drug R&D, clinical trials, and drug development to classic levels. Prior to digitization, it would take around 12 to 15 years for a new drug to reach the approval stage for market introduction from the R&D stage. Historically and empirically speaking, most drug R&D projects used to be abandoned mid-way simply and sadly due to reasons such as project complexity, tedious processes, etc.
However, the introduction of data analytics in drug R&D have greatly contributed to a huge jump in drug discovery, to their market introduction stage speeds. Data analytics-driven processes have hugely improvised on top quality, optimally costed drug development life cycles.
- Use of Big Data & analytics technology: Big data and enhanced analytics pave way for accurate, rapid, and effective drug discovery life cycle up to their market introduction stage
- Techniques such as disease stage modeling have been enhanced through AI technology application. Data analytics-driven computational biology is a stream of medical science that has contributed to a huge productivity increase and high-speed drug development process
- Rapid Clinical Trials: Digitally driven Clinical Trial techniques are displaying enormous increase in their swiftness and efficacy rates
- Earlier, Clinical Trials used to be undertaken in the midst of a Research Site walls, which brought about extremely slow results
- Today, a single Clinical Trial can be conducted on an anytime-anywhere basis through data-driven, decentralized Clinical Study research points. These research points may include wearables, e-consent, sensors, telehealth devices, surveys, personal visits from labs, and medical services
- Such decentralized digital clinical trial sites facilitate increased participation from subjects because of their accessibility and sheer convenience
- As a result, the number of real-life data collected become more reliable, rendering efficiency and augmented speed to clinical trials
- AI interfaced patient stratification and risk prediction techniques have further rendered lesser clinical trial life with outstanding patient outcomes
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