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Healthcare Analytics

Harness the power of advanced analytics and artificial intelligence

Drug development

According to recent reports, about 90% of all investigational new drugs fail to reach the market. Furthermore, just one-tenth of that meets sales targets.

The roadblocks to success

The average cost of bringing an IND to market is $2.5 billion. Various impediments to drug development success exist, such as very low (5%) patient population involvement in clinical trials, rising trial complexity, and so on. Our analytics and AI service mastery can give much-needed relief

i3 Infotek’s sophisticated analytics and AI expertise enable high clinical trial success, allowing medication producers to achieve desired success while reducing time-to-market and improving realizations across the following:

  • Disease Prevention / Discovery (Drug Development / Genetics and genomes)
  • Administration (Optimizing healthcare delivery) (Optimizing healthcare delivery)
  • Patient-technology interface (wearables, diagnostics, medical image analysis, predictive medicine, virtual care)

How we can help?

What to know

Randomized Clinical Trials (RCT) have long been the gold standard for life sciences organizations seeking to bring innovative medicines to market. Given the expense (average $2.5B) and time (5-10 years) required to complete an RCT, there has been a current push to investigate data-driven alternatives to drug discovery. The Pragmatic Clinical Trial (PCT) has gained popularity as a technique that combines traditional patient/site interactions with analytical-centered features to speed testing outcomes without sacrificing effectiveness.

How we can help

Our AI expertise can enable computer simulations even before the clinical trial phase to understand how bench compounds interact with body proteins, allowing for real-time formulation iterations.

Genetics and Genomics:

What to know

The advancement of DNA research continues to improve illness detection and therapy. When applied to individuals, this study, known as personalized medicine, has the potential to transform the standard of care across a wide range of disease conditions.

How we can help

By merging genomic and traditional laboratory data with data science approaches, i3 infotek’s data scientists and AI professionals help expedite more profound knowledge of the influence of DNA on health, uncovering the scientific relationship between genetics, diseases, and therapeutic response.

What to know

Analytics can significantly cut the cost of providing good healthcare. In-depth analysis of billing and clinical data can reveal trends in room occupancy and resource availability, leading to more efficient, timely, and cost-effective patient care.

How we can help

Our life sciences analytics professionals perform the following tasks.

  • Time series analytics of performance measures stacked atop benchmark indicators to quickly identify operational gaps and consequent revenue loss.
  • Examine previous discharge data for correlations to re-admissions and to identify cause and effect events that might be avoided in the future.
  • To arrive at optimal staff assignments, forecast projected patient loads based on historical data, current conditions, and other known criteria.
  • Seasonal (e.g., flu) or event-based (e.g., holidays) patterns can be anticipated, ensuring that the appropriate number of people, equipment, and medicine are accessible when needed.

What to know

Analytics and artificial intelligence (AI) can improve the effectiveness of patient-technology interfaces.How we can assist

How we can help

Wearables:

  • Capture real-time data from wearables and use AI, Machine Learning, and big data approaches to derive valuable insights.
  • Advanced analytical models can assess a patient’s health variations, diagnose abnormalities or troubling symptoms, and predict potential health implications, allowing for prompt and accurate medical treatment.

Diagnostics:

  • Help healthcare providers conduct more efficient assessments and get more accurate diagnoses for their patients by analyzing clinical and laboratory data with data science techniques such as Deep Learning.

Image Analysis in Medicine:

  • Apply Deep Learning algorithms to medical pictures for enhanced interpretation and faster disease identification.
  • Allow medical professionals to use extensive data toolkits to compare candidate/patient photos to hundreds – often millions – of reference scans in minutes, significantly enhancing the likelihood of identification.

Connect with our team

We will be happy to assist you.

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