Healthcare in the Modern Age


Over the last few years, big data analytics has been revolutionizing several of our most important industries. Notable examples include manufacturing, marketing, and finance, which have all seen drastic shifts in performance, efficiency, and business practice. However, no industry has evolved more than the healthcare industry.

With enormous sets of patient data, dependence on new technologies, and financial power, it would seem that healthcare and big data were made for each other. The managing of huge data sets was always an important element in medicine: hospitals collecting and keeping records on multitudes of patients; clinical trials involving extensive information on thousands of participants, and great epidemics requiring ultrafast data analysis to track the outbreaks on national scales. And like many other industries, data analytics has streamlined practices, increased profits, and cut overhead. But in addition to these benefits, data analytics has been transforming the modern medical industry in countless other ways.

Predictive Analytics

Predictive analytics (PA) uses statistical methods to delve into massive information sets, and analyze that information to accurately predict patient outcomes. The information can include data from medical research journals, massive databases, and past treatment results. One such example of predictive analytics is an ER using an algorithm that could query incoming patients, and compare the answers to those of thousands of other patients, to assist the doctor in diagnosing. Another way PA is benefitting the healthcare industry is in demographic analysis and public health. If a certain affliction, sickness, or unhealthy lifestyle choice is particularly prevalent in a certain neighborhood or region, public health entities can step in and educate those living in the identified areas before an emergency situation arises.

Prescriptive Analytics

Prescriptive analytics, like predictive analytics, uses large stores of information to find an improved outcome for a situation, but prescriptive analytics goes one step further: finding an integrated prediction combined with a plausible solution instantly. This can be done when a healthcare provider uses analytics to streamline and optimize its operation and care. For example, by predicting the next steps needed after treating a patient, the provider can coordinate both the schedule and location for follow up visits and procedures. In addition, in certain situations, the provider can give the preventative guidance and care patients need as a result of the complication prediction. One classic example is risk of infection – by asking family members to assist with sterile care, or by following up by phone to remind the patient to clean the area, providers can prevent re-admittance due to infection. This results in saving both time and money, and creating a more positive care experience for the patients.

Care Management

Hospitals are in a constant struggle to provide necessary services to the public; a major goal of healthcare as an industry is to keep people out of hospitals. The internet of things, including smartphone apps and other wearable devices, will allow physicians to drastically improve diagnoses and patients’ overall health. Health apps that measure information such as how many steps a person takes, their heartrate and blood pressure throughout the day, the calories they’ve consumed, and glucose monitors can illustrate a full picture of a patient’s daily health, routine and habits. The clearer the picture, the healthier the patient.

Management of Epidemics

In the past, outbreaks of great epidemics killed thousands of people in a relatively short time frame. The communications needed to monitor diseases couldn’t keep up with the speed at which they spread. But these days, mobile phone location data is able to track population movements, providing an invaluable tool in assisting doctors to place staff and resources in heavily affected areas, with enough time to effectively and efficiently treat infected patients. This technology was recently used during the Ebola epidemic in Africa, and also helped aid workers during the 2010 earthquake in Haiti.


In every industry, big data analytics continues to grow at an exponential rate. And for good reason - along with the financial benefits like increased profits, productivity and efficiency, data science is making our lives easier. But in the healthcare industry in particular, data analytics is helping us create a better world.