Customer Value Optimization
Help service providers understand their customers to serve them better, thus Analytics is a step ahead of business intelligence (BI).
Comparative Effectiveness Research
Comparative Effectiveness Research is one that is based on how outcomes-based research determines what treatment works best for which specific type of patients. Comparative effectiveness research is done by analyzing comprehensive outcome data against the effectiveness of various medications and therapies/ treatments. It is here that, the advent of predictive analytics shows the maximum promise as one can critically analyze large data sets including patient characteristics, costs and outcomes to identify which are clinically the most effective medications & treatments. One of the most immediate results of applying analytics is to critically examine the costs of over and under treatment and can lead to huge savings in down-stream costs and overall risk reduction in the value chain.
Remote patient monitoring
A recent growing trend in the clinical operations space is the collection of data that is being used to monitor adherence of whether chronically ill patients are actually doing what is medically prescribed. Applying predictive algorithmic models to this data can lead to substantial gains and improvement to future drug and treatment options. The data collected from chronically ill patients( e.g. diabetes, hypertension, congestive heart failure) is near real time transmitted to medical databases and in the most simplistic ways can help reduce emergency hospitalization visits and the associated costs. By employing predictive analytics techniques on this data, can lead to significant reduction in emergency department visits, long term health problems, nursing home & out-patient physician appointments and overall hospitalization costs.
Patient profiling using advance analytics
Segmentation and predictive modeling can be applied patient profiles to identify individuals who could be potential beneficiaries of proactive care and/or lifestyle changes. The use of predictive modeling can help identification of patients that have a high risk of developing a specific medical condition like diabetes and could benefit from a preventive care program. Coupled with clinical data collected on pre-existing conditions, predictive models could enhance better selection of patients & thereby increase the efficiency of targeting the correct patients with the matching preventive care program.
Predictive modeling in R&D of drug discovery
Using the aggregation of research data, pharmaceutical companies can perform predictive modeling for new drugs to determine the efficient and cost-effective allocation of R & D resources. By the use of modeling & simulations based on pre-clinical or early clinical datasets, pharmaceutical & medical products companies can predict clinical outcomes earlier in the drug discovery cycle and thereby significantly reduce drug discovery cycle times & overall costs. Predictive modeling engines, that can be built iQG, can help companies reduce anywhere close to 3 years out of the usual 12 years it takes to introduce in the market.
Clinical trial data analysis
In the recent history of drug companies, an alarming number of drug withdrawals from the market place due to adverse reactions & indications have led many researchers to believe that an enhanced statistical and predictive analytics can help detect indications and adverse reaction effects early in the clinical trial cycle. By doing a critical analysis on clinical trial data and patient records, drug companies can identify additional indications and discover adverse drug effects. At iQG, we can help companies build predictive analytic models that assist in the re-adjustment of drug placement and positioning. By performing statistical analysis of large datasets with their outcomes, drug companies can detect signals that would help reposition their products for additional benefits. When applied appropriately, predictive analytics can also aid in pharmacovigilance and discovery of safety signals that might go undetected in a regular clinical trial or in cases where events were identified but without analytics capabilities had not been unearthed during the trials period. With drug withdrawals becoming a common occurrence, iQG believes that harnessing the power of predictive analytics can significantly reduce these incidents and thereby costs resulting out of claims and eroding shareholder value.
Predictive analytics for personalized medicine
In the drug R & D arena and gaining incremental success is the area of analysis of large datasets such as genome data. By using predictive modeling and analytics, researchers can examine interconnected relationships between specific drug responses, genetic variability within patients and predisposition to specific diseases. In the 3 areas of offering early detection, enhanced drug therapies and re-adjustment of medicine dosages, predictive analytics can play a profound influence on all these, as it can partially ensure that different specific treatment is being given to patients although they may have the same diagnosis. By leveraging predictive modeling techniques, we at iQG believe that there is a huge potential for cost savings by reducing the prescription of drugs to patients that do not respond to medication equally even though they have the same medical condition.