Healthcare delivery is transforming, and genomics is playing a big part. When clinical care providers combine demographic and population health data with genomics, individual patient data from electronic health records, and real-world evidence on patient behavior culled from wearables, social media and elsewhere, they can use the power of precision medicine to determine the most effective approaches for specific patients.

This combination of data brings tremendous potential to reviewing, managing and treating a complex condition such as depression, which until recently has involved a haphazard improvisation and trial-and-error approach to finding the right treatment plan. There is no standardized path, which reflects the very nature of depression: One person’s illness may arise for different biological reasons than another’s, and therefore one treatment does not fit all.

Treating depression — a complete data perspective

A wealth of data is now available to determine the likelihood of depression, based on the patient’s age, gender, comorbidities, genomics, life style, environment, etc. — as well as broader patient cohort data. For example, population-level data shows a correlation between depression and chronic medical conditions such as heart disease. This information, when combined with new diagnoses of heart disease, could trigger proactive counseling that may help patients cope with how their illness will affect their daily lives. By gathering, analyzing and presenting this data through a dashboard, care managers can determine how to care for those patients to reduce their risk of developing depression and to help them change their lifestyles to strengthen their overall mental and physical health and well-being.

Adding genomics

A large number of gene variants are linked to a risk of developing depression, highlighting potential concerns for antidepressant reactions or absorption issues. Where patients have undergone genome sequencing, care managers can use that data to better determine both the likelihood of developing depression and the optimal medication profile. This profile would include which antidepressants to avoid and what dose to use to reduce the risk of adverse reactions, as well as ways to manage the variations in how individual patients metabolize and absorb certain medications.

With this kind of data, care managers have access to information about potential reactions before they occur, can minimize futile treatments and help ensure that their patients receive more effective treatment sooner. The data can also help decrease costs for healthcare organizations by reducing unplanned admissions from adverse reactions or the wrong treatment protocol.

As public interest in genomic health and screening increases, knowledge related to disease risk factors and suitability for certain types of medications will improve, ensuring that patients are treated appropriately from the outset and also are properly informed and engaged in their care.

With consensual access to an individual patient’s electronic health records as well as health and lifestyle data obtained through smart devices that record information about activity levels, diet, blood pressure and so on, caregivers can further extend their knowledge about patients and enhance treatment plans.

Digging deeper to change the health paradigm

The future of precision medicine lies in the data and investing in the means to achieve insights: data gathering; advanced analytics; technologies such as artificial intelligence and clinical decision-support analytics; and research into health trends through patient-specific and patient cohort data — all facilitated by cloud computing.

Ultimately, data analytics could prove to be the catalyst in advancing precision medicine to treat today’s most critical health conditions. Not only will genomics and precision medicine play key roles in clinical care, they will also have huge implications for the broader healthcare industry, including pharmaceutical companies and pharmacists.

In the future, pharmaceutical companies could leverage data and dashboards to highlight information about medication best practices, and even provide advice to patient cohorts on medication use and adherence. The pharmacist could also, in the future, leverage this knowledge to provide personalized guidance on medication, supporting patients to use their medicines safely and effectively.