The term “big data” refers to the management of massive volumes of data, both structured and unstructured, that it is difficult to process using traditional means.  

In healthcare, we typically find large volumes of data when looking in-depth at the health of an entire population. These large healthcare data sets managed correctly, can help address some major problems that have been intractable in the past. Such problems include  prediction of high-risk situations and the spread of epidemics, improved care coordination and optimal management of chronic disease cohorts.  

Aggregate and analyse with AI and machine learning

 To achieve the potential of big data in healthcare, we first require the ability to aggregate large volumes of data and then analyse them.  “Analysis” includes both traditional analytic tools and emerging tools including artificial intelligence and machine learning.   

Big data is already showing promising results in healthcare by offering solutions that  improve individual patient care and the generation of value at the level of the whole population.   

An important aspect is the ability to work with data of all types from all manner of sources to bring a more complete picture of the patient.   

Relevant data types in population health include traditional clinical data, claims data, social determinants of health data, patient-generated data, device data, environmental data and genomic data.  

Let’s review a few applications of big data to population health and see how it’s shaping healthcare delivery. 

Applications of big data in healthcare  

Analysis of Big Data sets allows healthcare providers to identify high-risk populations (cohorts) prone to high cost, high importance diseases and clinical situations such as high-risk pregnancy.  

Early detection and management of patients at high risk within a population 

Identifying such populations allows healthcare providers to make data-driven decisions, targeting patients for interventions that will deliver the maximum benefit at the lowest cost. This is especially important in patients with complex histories and those suffering from multiple  chronic conditions.  

The Right Care at the Right Time and the Right Place 

Analysis of big data in healthcare populations provides new insights into diseases and an understanding of whether specific interventions deliver benefits at realistic levels of expenditure.  

Big data approaches enable clinician end-users and population-level managers to analyse and review data drawn from real-world experiences of real patients. This is a big improvement when previously information was gained from clinical trials, which were frequently very idealised.  

Real-world data improves the ability of care management teams to target patients most likely to benefit and prioritise those patients over others with less pressing needs or a lower likelihood of a successful intervention. 

 Avoiding unnecessary hospitalisation 

Today, with a growth in digital devices such as smartphones, wireless devices, and wearables, virtual care has become an integral part of many clinical situations. Clinicians use virtual care to provide personalised treatment plans and prevent hospitalisation or re-admission.  

 By keeping patients out of hospitals, virtual care can help reduce workload pressures on healthcare providers whilst gaining a huge amount of information about each patient.  

Most times, virtual care means patients can avoid long waiting times at the hospital or clinics. This reduction in consultation wait times means that patients with high priority health issues can see their clinicians face to face. Patients with low risk can be managed by their clinician remotely through virtual care. 

Population health exploration 

Once the infrastructure for big data has been established and real-time high volume data is available, healthcare providers can explore the data for meaningful insights, such as gaps in care.  

Big Data is inherent to population health in the 21st century as it enables a much better understanding of that population and the ability to target interventions where patients are most likely to benefit.   

Combined with emerging funding models such as value-based care, big data can make major contributions to the improvement of healthcare sustainably.   

Combined with emerging funding models such as value-based care, Big Data is poised to make a major contribution to the improvement of healthcare delivery and the sustainability of the health system. 

See our whitepaper on how big data coupled with machine learning can provide the right patient with the right care at the right place and time.

How Orion Health can help? 

Orion Health’s Intelligence is a platform that aggregates many types of health data from both traditional and non-traditional sources in one rich repository. This aggregated data coupled with intelligent machine learning can draw on information from entire populations to treat and manage a person’s health. Our virtual care solution allows clinicians to provide care when it is needed the most.  

Interested in learning about how Orion Health Intelligence or our virtual care solution can help with your big data?