The guaranteed benefits of data analytics and the federal HIPAA mandates have been driving healthcare organizations to adopt EHR systems. The transition from paper-based record keeping systems to EHRs not only improves the efficiency of record keeping but also allows rapid retrieval and analysis of patient medical history. It is clear that medical decisions supported by a large amount of easily accessible, real-time data are advantageous over ones based on anything less. More data equals more accurate and encompassing diagnoses.
Currently, many of the electronic medical systems are integrated with their own respective hospital and, at best, their partners’ systems. However, large scale integration on a national level would be obviously optimal. Integrated healthcare systems provide a platform for performing advanced causation analyses and predictive modeling. Vast amounts of accessible medical data holds potential opportunities for research and improvements in everyday healthcare practices, such as
- Advanced diagnoses
- New pharma research
- Improved patient treatment plans
Healthcare cost has been skyrocketing yet medical errors and wrongful deaths are continuing to be a serious problem. Misdiagnosis are alarmingly frequent in primary care, accounting for an estimated 40,000 to 80,000 hospital deaths per year. There are many reasons that could lead to a misdiagnosis but some truly are preventable, such as those relating to the misinterpretation of patient conditions, oversight of patient medical history, unawareness of similar mistakes made in other clinics, and even misreading of lab results due to user interfaces. This is where data analytics come in. Analytical tools not only integrate data from multiple sources but can interpret the seemingly unrelated data and find previously, unseen relationships. The possibility of some misdiagnoses can essentially be eliminated. The output of analytical tools are typically presented in visual formats for easy interpretation of results. An easy to use medical BDA tool would significantly reduce a doctor’s cognitive stress when treating many patients on a daily basis.
New Pharma Research
The discovery and development of new medicines is a long, complex process. It consists of basic research, drug discovery, preclinical studies, clinical trials, FDA review, and post-approval research and monitoring. It takes on average, at least ten years from drug discovery to its finals release to the marketplace. The process requires a deeper understanding of the inner workings of human disease at the molecular level. It requires increased collaboration and convergence across a range of sectors and fields. Massive amounts of data and computational capabilities are required. Big data analytical tools can help identify new potential candidate molecules by performing complex analyses on massive molecular and clinical data; help to build better clinical trial patient profile with information from the patient’s genetic factors; provide real-time monitoring of clinical trials to capture safety related signals to avoid delays or costly issues.
Improved Patient Treatment Plans
Big data analytics is used to develop customized care pathways. For example, Columbia University Medical Center developed complex correlations from streaming physiological data of patients with brain injuries to provide professionals with information to treat complications 48 hours earlier than the traditional method. Similarly, Hospital for Sick Children in Toronto, Canada, uses big data analytics to process vital-sign data from the bedside monitoring devices up to 1,000 times per second. Based on the massive amount of live streamed data, the doctors can detect potential sign of life threatening nosocomial infections in infants as much as 24 hours sooner than with previous methods. The potential for improved patient care is great but the room for this growth is unfathomable.
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