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Managing Through the Pandemic — How Analytics can Provide Greater Insights

By IntrinsiQ Specialty Solutions

Data warehouses collect and combine data from a multitude of sources, both historical and current. That data is stored and analyzed on a set of business rules developed over time to provide insights that you cannot get from anecdotal information. Having those insights and actionable steps is imperative to understanding market dynamics for pharmaceutical products and treatments.

Yet, the past few months of the COVID-19 pandemic have increased the speed of needed information and insights dramatically. Some have leveraged artificial intelligence (AI), which can be implemented in the form of machine-learning. These help to close gaps in established business rules created over time that may not be totally applicable to the new data being captured.   

The pandemic has also changed how and when patients are seen and treated.  For those practices who could utilize it efficiently and effectively, telehealth became a primary contact point with their patients in the early onset.  However, the use of telehealth is dependent upon the type of condition a patient may have or be treated.  For instance, it is difficult to perform necessary physical exams for patients with retinal conditions 
Standard cancer screenings were delayed during the initial wave of the COVID-19 pandemic, resulting in a decrease of 65.2% incidence of new cancer diagnoses in April 2020.1  Some patients, instead of being referred to a specialty provider, have delayed seeing a provider for so long that they end up in the hospital or emergency room.

With the shift in behaviors on the part of the providers and patients, patient outcomes are also expected to be impacted. These changes will result in new learnings and business rules that can then compared to past experiences. Some products may benefit from benchmarking their performance against prior performance and measuring patient outcomes when treatment is delayed. The delay of diagnosis and treatment in cancer patients may reduce overall spending in the short term, but result in the use of more expensive, targeted treatment when their conditions become more advanced.

Analytics from the time of the public health emergency (PHE) will help guide several business decisions and can capture patterns not seen in a “typical” year. Demand for productions, the creation of virtual engagement programs for physicians and patients, as well as insight-driven education programs tailored for specific practices, are some of the examples where business decisions can be backed by data-driven insights.

Life Sciences companies need to pivot during this time.  While companies may see some data that only works during this PHE, some analytics will identify those patterns that will become predictive for future as patient behavior and care trend towards a new normal.