Many have discussed the shortcomings of big data within the health care industry, with some raising possibilities for tangible progress that might be made within this venue (e.g., improving workflow automation and improving the general reliability of the technology hospitals have before investing in new innovations that may or may not pan out as desired), and some calling attention to the challenges of the fragmentation of the American system as being a prime culprit in our limited ability to use data to the fullest: "Despite the technological integration seen in banking and other industries, health care data has remained scattered and inaccessible. EHRs remain fragmented among 861 distinct ambulatory vendors and 277 inpatient vendors as of 2013. Similarly, insurance claims are stored in the databases of insurers, and information about public health—including information about the social determinants of health, such as housing, food security, safety, and education—is often kept in databases belonging to various governmental agencies. These silos wouldn’t necessarily be a problem, except for the lack of interoperability that has long plagued the health care industry" (Kaushal and Darling, Brookings Institution).
One obvious concern with respect to the use of big data in healthcare is HIPAA compliance, requiring the privacy and security of patient data by those in the medical field. Another is the reality that adopting any new system, even one built to improve efficiency and patient outcomes, has often significant start-up costs, which can be difficult to invest in when trying desperately to stay afloat with patients and billing. It also requires that people know how to use the data efficiently in order to justify those costs and deliver results.
But consider the potential value in such investments. We already have technologies through smart phones and the like that track our steps, through which we can track our caloric intake, and our other fitness goals. Given the right tools, we can track vitals and measurements, test results, and symptoms, and leverage predictive modeling to gauge patient predispositions toward conditions, the propensity of developing a problem or his/her likely responsiveness to a treatment, given that patient's history as well as the wealth of data from patients with similar genetic predispositions, medical histories, and/or lifestyles. If it is true that an ounce of prevention is worth a pound of cure, then surely such investments would improve patient care and outcomes, potentially averting the need for more expensive treatments made necessary because of delayed diagnoses.
The Pittsburgh Health Data Alliance, a joint venture among the University of Pittsburg, UPMC, and Carnegie Mellon University, is a novel and important organization working toward this very goal of bridging health care and technology, beginning with developing technologies aimed at reducing patient falls, preventing and monitoring ulcers, and improving the accuracy of cancer diagnoses and personalizing treatment plans. Among these projects is the Clinical Genomics Modeling Platform, which aims to build precision-based models for different diseases and populations. While even with the continued expansion of health care under the Affordable Care Act and beyond, we will continue to have a fragmented health insurance system (Medicare, Medicaid, Obamacare, private insurance from a number of different providers, private pay) that hampers our ability to have integrated health care data, such collaborations -- which hopefully will become more common in the years to come -- provide important new advancements for this industry that affects us daily and that has lagged behind for far too long.