If Facebook can compile your personal data in order to choose the news and ads for your wall and Amazon can do the same to pick items you might like to purchase, why can’t healthcare organizations aggregate and analyze patient data as effectively? As a hospital administrator, you know the stakes are higher for health care. Administrators and physicians can’t afford not to organize health and medicine data to improve healthcare delivery.
And not just any data. Secure, quality data is most useful to clinicians making more informed decisions about patient care by not only reviewing the patient’s data but all the available data for similarly situated patients.
Two crucial outcomes of organizing and securing big data are:
- targeting treatments for patient illness and
- predicting outcomes to improve efficiency.
Both save time and money. When $600 billion of the total annual healthcare costs in the U.S. go to ineffective or harmful treatments, data organization that increases accuracy and efficiency is a big deal. Harnessing the power of big data to improve cost efficiency and patient outcomes also fuels the established fee-for-value healthcare model of reimbursement and delivery.
Aside from cost, however, big data is the key to better practice. Secrets to cures, treatments, and innovative procedures lie in the tons of documents, research, papers, and data from a formidable array of sources, like medical devices, wearables, scans, records, and studies, to name a few. In fact, the HIMSS reports, “approximately 1.2 billion clinical documents are produced in the United States each year [that]…comprise around 60% of all clinical data.” It’s a virtual gold mine of underutilized information.
Data Systems Promise a Gateway Into Untapped Resources
According to the doctors Cresswell, Bates, and Sheikh, at the Usher Institute of Population Health Sciences and Informatics at the University of Edinburgh and Harvard Medical School, successful healthcare data science strategies have five components:
1. Data repository
This is where data is amassed, described, matched, and tracked for use, reuse, and storage, usually handled offsite data science centers.
Entails integrating and indexing (Enterprise Master Patient Index or “data lakes”) data sets from disparate sources across organization information systems, including new patient data and duplication reduction. Integration also includes coordination of patient data across several departments—surgery, admissions, pathology, and pharmacy, for example, as well as from patient sources, like wearable technology
With the rising popularity of public clouds, patient privacy and information security are more important than ever. Data control protocols must be in place to train personnel on data handling, damage control processes for breaches, and adherence to technology standards (International Organization for Standardization). AI technologies, though expensive, may assist with preventing data breaches.
Data systematization requires professional data handlers, analyzers, interpreters, researchers, statisticians, AI experts, and computer scientists to make the data useful to medical staff.
AI and machine learning help analyze data for insights that assist real time decision making.
IBM’s supercomputer, Watson, can read and retain a million books per second and all 8,000 new medical research papers pumped out daily. This machine’s capability is just one data resource among the million pieces of data that, when captured, collated, integrated, analyzed, and interpreted, could save or extend lives.
Without an effective strategy, the myriad of data strands that potentially tie together—the cure for cancer, disease prevention, improved diagnostic accuracy, expanded pharmaceutical research, reduced errors throughout the healthcare system, predicted and curtailed epidemics (like West Nile Virus), and reduced hospital readmissions—remain dislocated and unexploited.
It’s not just the life and death of individuals but of the larger health care picture. All types of facilities, from medical offices to research medical hospitals, stand to gain from data science. Data systems enable organizations to run more efficiently, safeguarding the viability of healthcare service providers large and small. And physicians and patients get the most out of their EHR’s with the combined data from a cross-section of channels to streamline care and raise patient satisfaction.
While the cost of creating data science strategies may be steep at the outset, the long-term benefits in lives and dollars saved more than compensate for the outlay.