Quality Improvement processes are an essential part of the clinical care continuum. I was a math major in college (way back when) and I “buy-in” to the science around statistical process control charts and statistical methods employed in QI. At the end of the day, in my opinion, science is the basis for improving the process of care whether we utilize rigorous mathematical deviations or we simply employ big data and advanced analytics.
How does that relate to Population Health? I attended a session at HIMSS 2015 that discussed just that. The folks at Texas Children’s Hospital talked about how they are using technology (science) and performance improvement techniques to:
- Identify common gaps in the care delivery process that interfere with high-quality care that represents the biggest opportunities for improvement and cost savings through the application of best practices
- Define a strategy for managing populations by integrating evidence-based science and data management with a team-based approach to iterative care improvement
- Discuss the development of evidence-based shared baselines that can be used to standardize care processes and reduce variation in care delivery, as well as for benchmarking to assist with the assessment and refinement of quality improvement interventions
- Explain how near real-time data from an enterprise data warehouse and analytics applications is used to improve the outcomes of healthcare teams through the prediction, testing, deployment and continuous refinement of evidence-based care processes
I found this very interesting and pondered what will happen when we can get to the point that both process improvement and the science of precision medicine can be interjected into this equation. Most medical treatments have been designed for the “average patient.” As a result of this “one-size-fits-all-approach,” treatments can be very successful for some patients but not for others. This is changing with the emergence of precision medicine, an innovative approach to disease prevention and treatment that takes into account individual differences in people’s genes, environments, and lifestyles.
Precision medicine gives clinicians tools to better understand the complex mechanisms underlying a patient’s health, disease, or condition, and to better predict which treatments will be most effective. Healthcare organizations and research centers are under a lot of pressure to translate scientific advances in molecular biology and target therapies, especially in the treatment of cancer. Today, we all recognize that cancer is an incredibly diverse group of disease and the ability to group individuals with matching genomic or proteomic profiles is a perfect example of a novel clinical trial design that can speed up saving lives.
Oracle’s Enterprise Healthcare Analytics is an example of a predefined data model for correlating clinical and research data. Perficient has the expertise in all these areas and implementing solutions – including strategy and / or the physical implementation or guiding an organization through the entire process.
Attending HIMSS? Stop by to meet our dynamic team of life sciences and healthcare experts at booth No. 4460.
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