Thanks to the widespread adoption of wearables, fitness trackers and healthcare apps, collecting and compiling data for big data analytics has only become easier. With the power of big data, doctors would be able to obtain a 30,000 feet view of a patient’s health stats from a 3-inch distance. As a result, healthcare processes would become less reactive and more proactive as well as more aligned to meet the unmet needs of the patients.
Today, big data in healthcare is not any distant possibility. There are several successful use cases of big data in healthcare that healthcare organizations can seek inspiration from.
Out of the endless of such use cases, we present to you some noteworthy ones that you can look up to:
- Adoption of Electronic Health Records (EHRs)
A serious challenge that patients, as well as caregivers, face is keeping track of all medical reports and charts. Quite often, due to the mismanagement of this data, patients are forced to take duplicate tests which cost time, money and physical pain also. For healthcare providers, this translates into an inefficient way of administering health care. Not to miss mentioning that they might be led into taking miscalculated assumptions that can cause future health issues for the patient.
Electronic Health Records (EHRs) can prevent such mishaps. The PwC Health Research Institute provider executive survey 2017 only reasserts that fact.
Image Credit: PwC
According to the survey, more than one-half of clinicians and consumers view EHRs to help improve quality of care, patient experience and foster better communication with patients. EHR facilitates big data analytics, thus helping in improving the overall mortality rate.
- Proactive Detection of Diseases
As a result of digitized records like EHRs, proactive detection of diseases becomes possible. In fact, big data was deployed to track and fight the spread of epidemics like Ebola in Africa. Structured data like EHRs and unstructured data like mobile phone location data can be used to spot highly affected areas. Based on such insights more care and rehabilitation services can be allocated to the identified region.
- Remote Patient Monitoring
There is a unique challenge that third-world countries, especially those in the Asian continent are facing. That is the quadrupling of the ageing population. The United Nations Population Division reports that the population of the elderly (aged 65 and above) has increased four times the aging population of the 1900s.
Accompanied by a decline in birth rates, this means there are more elderly who need healthcare support than ever before. Time, distance and talent shortage makes it difficult to attend to these elderly on a regular basis. It is here that remote patient monitoring systems pitch in with a helping hand. Enabled with advanced technologies like the Internet of Things and telemedicine, healthcare professionals are now able to reach out to remotely located elderly easily. Singapore’s Elderly Management System (EMS) is a classic example of such data-driven health care initiatives.
- Strategic Planning of Care Services
Forecasting healthcare requirements on a regional level cannot be done with the siloed information. Doctors, hospitals and administration need a combined view of the population demographics, the health challenges that they face and the bottlenecks that need to be resolved to improve health care.
Big data helps in creating that single repository of information which helps in strategic planning of care services on a regional basis. The insights from data will also help in scaling healthcare services to more problematic areas.
- Control Substance Abuse
Substance abuse has killed more than 50,000 Americans in 2016. There are two causes of substance abuse that are broadly seen across the world – prescribed drugs and illicit drugs. While prescribed drugs are used for pain management, illicit drugs like heroin are used by vulnerable victims for self-gratification.
With the help of data mining, it is possible to match the reasons of substance abuse, the regions where it is largely seen and the countermeasures that can be taken to curtail it. Big data can help cut down the fraud of prescribing opioids to patients that lead to addiction and fatality. Health administration officials can be armed with big data to track down on fraudulent healthcare givers who are giving more than the prescribed dosage of opioids to patients. States like Missouri and Indiana are already banking on opioid usage data to spot abuse and to ensure patient safety.
Healthcare, like any other industry, is undergoing a sea change. Doctors who never had any exposure to data are now leaning on data analytics, especially heavyweight technologies like big data to mine out insights about their patients. Big data will enable healthcare providers to dish out proactive healthcare processes, thereby delivering better healthcare to patients.
Data-driven healthcare will help in pinpointing instances that could lead to serious epidemics. On an urban level, big data analytics in healthcare can also contribute to bigger purposes like controlling substance abuse, strategic planning of resources and much more.