Top 6 use cases of data science in healthcare

Published January 6, 2019   |   

Healthcare industry is generating a copious amount of data every day. Electronic medical records, billing, clinical systems, data from wearables, and various pieces of research continue to churn out huge volumes of information. This presents a valuable opportunity for healthcare providers to ensure better patient care powered by actionable insights from previous patient data. Of course, data science is making it happen.
With the help of advanced machine learning and analytics, data scientists across the world are gradually revolutionizing the healthcare industry. From improving care delivery to achieving operational experience, they’re working to optimize every aspect of healthcare operation by unlocking the potential of data.
Below are some of the major use cases where data science is making a big difference in the healthcare industry.

Drug discovery

Drug development is not a simple process. It takes a significant amount of research, testing, time and monetary investment before a drug is launched in the market. It is estimated that the cost of bringing a new drug to market can be as much as USD 2.6 billion.
Data science can leverage various sets of structured and unstructured biomedical data obtained from numerous tests, treatment results, case studies, social media etc. across diverse disciplines. It can then use advanced mathematical algorithms to create a simulation of how the drug would interact with body proteins and predict the rate of success.
The simulation can speed up the process making initial screening sufficient to determine the possibility of the drug efficacy. Not only does it mean a huge reduction in the cost and time of drug development, but it also mitigates the risks of failure.
Data science methods are also able to integrate with genomic data in research to provide accurate insights into genetic issues arising out of specific drugs and diseases.


Wearables are quickly becoming ubiquitous. Apart from making a cool accessory, they encourage self-health management in people. They record important health readings like blood pressure, heart rate, sleep pattern, pulse etc.
For the elderly, in particular, wearables are a great help. They help the family members stay updated on patient’s health and apprise the medical staff immediately in the case of emergencies. Wearables are connected with a mobile device and continue to generate and store volumes of patient data in the cloud which can be accessed when needed.
But the question is–what do we do with that data?
Using AI, machine learning, and big data, data scientists can analyze this raw information from wearables to deduce meaningful insights. Through advanced analytical models, they can observe variations in patient’s health, and detect a disorder or a concerning symptom. This helps doctors to predict a possible health issue and provide preventive care.


Diagnosis is a critical part of the patient care cycle as it determines the nature of the treatment to be provided. But even in this 21st century, diagnosis is far from perfect. A 2018 BBC story revealed that in the US alone, diagnostic errors result in 40,000-80,000 deaths. Although there is a massive amount of data which can be used to run effective tests, most of the existing models aren’t capable to do so.
Through data science, analysts can apply deep learning techniques to process extensive clinical and laboratory reports to conduct a quicker and more precise diagnosis. Data analysis can allow them to detect early signs of an issue and enable the doctors to provide preventive care and better treatment to the patients.
Additionally, this data can also be used by medical researchers to diagnose chronic diseases at early stages and identify treatment options that have proven success records. It can be key to curing ailments such as cancer and diabetes.

Public health

Many healthcare organizations have already started to leverage big data in an effort to improve overall public health. There is a massive amount of scattered healthcare data from various sources like websites, wearables, social media and Google maps. This data holds the key to understanding the overall public health in a specific geography. Data scientists can analyze it to prepare heatmaps pertaining to parameters like population, health ailments, medical results of people in the geography etc.
This analysis helps them understand the signs of an imminent health crisis in that region allowing them to familiarize them with the availability of medical facilities available in that region. It also helps them identify the reasons that prevent people from opting for treatment.
Using the findings, healthcare professionals can take preventive measures for the prevailing or possible health crisis in the region.

Reduced healthcare costs

Healthcare costs only appear to be rising with time and this proves to be an impacting factor in delivering a superior patient experience. However, with analytics and BI tools, this can be addressed as well. Data scientists can look into billing data and information from clinical systems pertaining to categories of charges and variables. This allows them to drill down to the trends in room usage and required resources available to cater to patient needs; thus, helping identify potential areas of operational gaps and revenue losses.
Providers can also leverage data science to optimize their supply chain and review equipment maintenance schedules to prevent unexpected breakdowns. This can help them understand how to keep the costs low. Using analytics, it’s also possible to monitor patient recovery and planning discharge protocols accordingly so as to minimize readmissions.
Put together, these data-driven provisions can make way for reduced operational costs which translate to lower healthcare costs for patients with improved satisfaction. This will be instrumental in optimizing care delivery and patient experience.

Optimal staffing

Healthcare needs are only going to increase, and providers may often find it challenging to have adequate medical staff for patient care at any given point in time. Any change in patient flow will always affect the units working in an inflexible schedule, for example, ICUs and emergency care units. More than required staff will increase labor costs. On the other hand, an insufficient number of workers can end up working overtime and reach a burnout stage.
So, how can you keep the optimum staff available? Data analytics has the answer. It can predict fluctuations in patient visits based on historical data over the years and come up with a pattern in staff allocation based on admission rates in the past. This would give providers an idea as to when the center is going to need what staff level. Thus, beds, staff, and other required resources can be allocated to patients accordingly and timely.
Clearly, in addition to transforming patient care, data is helping healthcare providers in tackling issues related to resource management, inadequate medical staff, and high treatment costs. Moreover, it’s also empowering the patients by spreading awareness among them and involving them in the treatment process.
Needless to say, this amount of data would be impossible to interpret if not for data science and AI.  “By one estimate, AI could help reduce health care costs by $150 billion by 2026 in the U.S. alone,” according to Stanford Medicine’s 2018 Health Trends Report. As the developments in healthcare data science continue, we can look forward to a revamped healthcare model in the days to come.