The explosion of data in recent years has impacted the health tech industry enormously, opening new doors to collect data that can be used to more effectively monitor and treat patients. But this opportunity also comes with a series of challenges. The process of recording, updating, and analyzing the incredible volume of patient activity, hospital records, medical visits, insurance specifics, etc. is impossible without a central analytics platform. Even with the right tools in place, inaccessible information can lead to inaccurate diagnosis or improper treatment suggestions.

Collecting all of this information in one place and granting access to all relevant stakeholders is a big challenge. Utilizing a HIPAA-compliant unified data platform for health tech opens the door for faster access to information, a more comprehensive view into patient care, more accurate predictive modeling, and ultimately, healthier customers. There is a wide range of positive outcomes from an improved data platform for health tech; here are some of the most common:

Connect disparate data sources into a single source of truth

The recent boom in health data is due in large part to an increase in inputs and an expansion of an individual’s complete portfolio. For every patient, there are likely different input sources for doctor visits, lab results, drug prescriptions, physical activity, and more. The result of this is often silos that separate medical professionals from the information they need to make a decision about a patient. An advanced data platform collects information from all these disparate sources and creates one unified set of data that serves as a single source of truth for all parties involved. When every decision maker in the process has access to the same real-time information, a diagnosis or treatment recommendation can be made faster and with better accuracy. That data source also gives stakeholders across a company visibility on the business critical KPIs to enhance patient engagement and satisfaction.

Easily share personalized insights with end users

Once health data has been analyzed, it’s critical that the insights are shared quickly so they can be translated into patient care. With a modern data platform like Sisense for Cloud Data Teams, teams can easily embed professionally designed dashboards into a product. These live dashboards instantly update to include new information, giving end users the ability to explore the data on their own using filters, pivot tables, and drilldowns without requiring any additional work from a data team. Another common use case is programmatically sending personalized reports to users, at scale. With solutions like Sisense for Cloud Data Teams Render API, teams can call an API to generate reports specific to each party, eliminating the need to manually create individual reports. By empowering stakeholders and internal teams with access to data and insights, data teams can avoid repetitive tasks and focus their time and resources on exploring deeper issues.

Improved real-time insights

With health data, sometimes the window for identifying a trend is narrow, so a data platform that gives real-time information is extremely valuable. Consider how a drug store responds to a local epidemic; being able to identify the need for a shipment of medicine early could ensure that they don’t run out. In some cases, GPS-enabled inhalers can be used to identify areas with high pollen or pollution. These cases are just scratching the surface of the advantages of real-time health insights. New analysis capabilities will only increase the depth and frequency of these real-time insights, it’s up to health tech innovators to find ways to better serve their customers.

More complex analysis

As more advanced data processes are built, the focus of analysis is shifting from describing what is happening now to predicting what will happen in the future. The uses of this type of technology allow health tech companies to stay ahead of customer needs and focus on preventing issues before they become problematic. For example, Crisis Text Line uses Natural Language Processing to predict the outcome of a conversation and help cool down crisis moments. Oscar Health takes advantage of their base of customer information to build a comprehensive overview of their patients and prescribe care before an issue arises.

To learn more about how advanced analytics can be used to move health technology forward, read our white paper “Going Deep with Predictive Health Analytics.”