With umpteen amounts of scattered data in cloud and varied premises, healthcare systems have a tough time in good clinical data management. Millions of dollars are spent on an EMR (Electronic Medical Record) system to solve cluttered data crisis and improve patient care. EMR holds important data about every patient but the challenge is in deriving information for valued decision making for every healthcare organization to improve quality and reduce cost. EMR is a huge investment for every healthcare organization because the potential insights from the data is huge.
Unfortunately, data storage and access is not just a challenge in today’s world of healthcare but in making the available data more useful everyday. Investment in data analytics is expected to bring down costs and improve care. Every healthcare organization has to cope with a heavy federal fine if a patient seeks re-admission for the same issue within a month after release. That means the quality of care provided to a patient is of ultimate consequence. One of the biggest challenges in healthcare is personal data entry by patients that has a huge margin of error. Considering this is just a fraction of the entire set of challenges, the next most daunting challenge is data fragmentation. Incomplete data entry about a given patient is common although significant policies speak differently, encouraging multiple physicians of the same patient to have an interactive and updated EHR (Electronic Health Record). This gap associated in painting the right medical profile of a patient can potentially be dangerous in terms of medical treatment.
One of the reasons, the policy makers have not succeeded in ensuring a comprehensive medical profile for every patient is because security concerns make it imperative for constant efforts in de-identification of data to preserve privacy of every patient information.
Many medical organizations have been struggling to eliminate data silos that obstruct substantial analytical efforts. Good Big data analytics are driven by integrating disparate sets of information related to operational, clinical and financial data. Adequate facilitation of information flow from all these data reserves will provide insights for quality care coupled with economic costs in proper resource utilization. EHRs are more constricted in their data analytics perspective than healthcare organizations are ready to concur. One of the biggest reasons being, every interoperable organization has a complicated system of working and is thus resistant to change.
For a successful use of Big Data in healthcare, there needs to be a right combination of right data entry method (e.g – The nurse after a careful consultation with the patient feeds in the data), a timely feedback mechanism and measurable outcomes, good predictive analytics, chronic care insight, interdisciplinary integration of data (e.g – the patient’s clinical information, financial information, mental health information, hobbies etc). There is an immediate need to change the existing leadership or work culture in health organizations. Rather than a fancy IT team, a tendency to share information and step up communication between every clinical compartment would help on capitalization of Big Data.