Inefficiencies plague the U.S. healthcare system, and the many pressing areas for improvement are proving to be big data’s biggest opportunities and challenges. Indeed, the need to address these inefficiencies is the catalyst to big data’s penetration of the healthcare sector.
Data analytics offer a quantitative, optimization-oriented solution to making significant changes in the healthcare system. Healthcare has an abundance of data ready for processing courtesy of electronic health records (EHRs)—themselves used to streamline the diagnoses of patients who travel between medical specialists—and the databases compiled during pharmacological research.
Translating this data into insight is a multifaceted affair. Data can be interpreted differently by players in the healthcare profession, but are usually appropriated by the reduction of operational costs and the development and provision of appropriate treatments and care. One particularly important segment is preventive medicine, which achieves both objectives.
Findings gleaned from big data can be translated into best practice through the reduction of preventable hospitalizations. Data can reveal in advance patients who are at heightened risk for common medical emergencies, allowing them to receive the appropriate treatment early on.
Recent developments include machine-learning software that could use patient data to predict these health risks. Predictions backed by big data are remarkably accurate; a program designed to measure risks to cardiovascular health, for instance, has an accuracy of 83 percent.
Patients stand to benefit from these analyses even in the event of a false positive, allowing doctors to prescribe better health practices and lifestyle changes to eliminate additional health risks. And with checkups costing less than hospitalizations, the system offers to eliminate a considerable portion of healthcare costs for both the institution and the patient.