The healthcare industry is not lacking data. Unfortunately, most of the data systems are leveraged for billing. Further, the current healthcare system is driven by an outdated fee-for-service concept and service reimbursement activity. There remains little or no connection to value.
Compounding inefficiency, legacy systems block providers from delivering insights across multiple channels. The result is that systems cannot analyze patient-related data in real-time and deliver it quickly to caregivers and decision makers. The challenge is to bring together masses of data from disparate sources and
synthesize it into actionable information in real-time.
But speed for speed sake in healthcare analysis misses the point.
Great challenges may often bring great solutions. Cost pressures are pushing the advent of evidence-based medicine. At the same time, advances in genetics, biomedics, and computing technology are combining to enable more effective personalized medicine and treatments targeted (and tailored) to individual patient needs.
Perhaps not surprisingly, forward looking healthcare providers such as University Hospitals are efficiently leveraging big data analysis and IT platforms that link disparate pools of data within (and outside) healthcare organizations, and present the information with visualization tools that put actionable insights into the hands of caregivers and patients.
Utilizing in-memory computing to improve analysis and preoperative care, Universities and research organizations are pulling together data not only from different departments but also multiple organizations. Fortunately, this type of proactive collaborative analysis is particularly acute within collegial University communities that are breaking down silos for more rapid information exchange and analysis.
See a new white paper:
http://hbr.org/hbrg-main/resources/pdfs/comm/sap/how_big_data_impacts_healthcare.pdf
