We have conducted hundreds of interviews with payers, healthcare providers and patients. The number one factor that is constantly mentioned as necessary for adoption is always robust data. 

Payers demand robust clinical and economic evidence demonstrating that your digital health offering delivers cost effective, efficacious results for patient. Both payer and physician require compelling user engagement data recognizing that if the patient isn’t engaged the digital health application will not be effective. 

 

You will need data and data analytics for:

• Clinical evidence generation

• Health economic evidence

• Market sizing analysis

• Patient longitudinal cost analysis

• User engagement

• Health care provider targeting

• Determine cost of care

• Understand the patient journey

• Maximize customer targeting

• Determine HCP referral patterns

• Sales and Marketing resource deployment

 

Use cases:

Generate evidence to support reimbursement:

By connecting claims, EHR, lab data and PROs to support cost of care calculations to determine the economic impact of a digital therapeutic or digital health offering.

 

Commercial targeting for sales and marketing effectiveness:

By using pharmacy data / specialty pharmacy data, claims and clinical data to identify geographically high potential early adopter Health care prescribers and concentrations of target patient population.

 

Improve value-based care analytics:

Determine total cost of care estimates by linking EHR and clinical data.

 

In-depth patient profiling:

Cohort-level insights into key demographics, comorbidities, procedures, treatments, locations of care, specialties seen, and social determinants of health.

 

Real world health economic analysis:

• Tokenize to de-identify your data to determine overlap with various data sets.

• Compare total cost of care of patient using the digital application to a control group of patient