Opportunity for Data Compilation, Analysis, and Extraction
Adoption rates for electronic health record (EHR) systems continue to soar, and every day mountains of patient information are turned into digital health data. This amassment of digital data precludes a huge opportunity for data compilation, analysis and subsequent extraction of insights into best practices for patient treatment and care. Despite the fact that the technology exists to compile and parse this data, and that the industry as a whole stands on the precipice of meaningful adoption and use, there are a couple of key ingredients that are missing. While patient-specific unstructured data will have some uses, insight extraction is not one of them. The ability to collect data in a structured manner while also ensuring patient privacy by de-identifying it are essential steps to realizing the full potential of the EHR systems in healthcare.
Protecting Patient Privacy
Fortunately, innovative cloud-based EHR systems can help to break down the barriers to meaningful health data analysis so that physicians can make more informed decisions at the point of care. Cloud-based systems typically rely on one instance of the software where all medical data is stored, making them much more flexible and applicable across provider organizations both large and small. By providing de-identified patient data to physicians, cloud-based systems can advance medicine and improve healthcare outcomes, while also ensuring patient privacy and HIPAA compliance by utilizing high-grade SSL encryption. While clinical trials are the gold standard for medical research, only a handful of well-funded diseases are addressed in these clinical trials. Cloud-based systems can fill a niche that looks at real world clinical effectiveness to provide concrete feedback around a wide range of specific diseases and ideal treatments.
While ensuring access to data that has been properly de-identified for compliance and patient protection purposes is essential, it is only half the battle if health providers wish to leverage their patients’ data in a meaningful way to help improve care and outcomes. Unfortunately, most of the information currently collected through legacy EHR systems cannot help either doctors or patients, because many of today’s EHR systems are unstructured and function more like a word processor. Without structured forms of measurement, physicians and health outcomes researchers are faced with a lot of text, which they must read and interpret in order to draw their own conclusions. The most innovative EHR system vendors, however, are taking a strategic, long-term view by creating an intuitive user interface that visualizes meaningful medical trends and patterns using structured patient data.
Structuring Anonymous Health Data
In order to create consumable health data, some EHR systems employ standardized static global assessments, similar to those used by academic researchers and pharmaceutical companies for clinical trials and post-marketing surveillance studies. If all physicians use a platform that speaks the same standard language, then one doctor’s assessment of “better” is the same as another doctor’s assessment. This takes the term “better” from a qualitative point of opinion into the realm of quantitative, measurable fact. Working on a standardized form of structured data allows for analytics of nearly all data across physician patient populations.
Once the language is standardized and the data structured, the software can help physicians draw insights related to a disease at high levels across a whole population, or at more granular levels, considering characteristics such as race, gender and age. Instead of relying on the anecdotal experience of other physicians, providers can rely on real-world evidence with patients like their own. Additionally, structured data can save physicians time, by eliminating reading and analyzing blocks of text based purely on one physician’s experience and instincts.
Squeezing All of the Juice from Patient Data
Today, significant funding goes into clinical trials that only look at a handful of diseases and drugs within extremely focused groups. While tremendously powerful and useful in their own right, these clinical trials are limited by sample size and cost, and the insight they produce may only be applicable to hundreds of diseases, rather than the thousands of diseases found in the real world. Conversely, EHR systems utilizing structured, de-identified data can collect, centralize and parse hundreds of thousands of cases of common and rare diseases, so that providers can better understand the most effective treatments for all of their patients’ conditions.
Translating the potential of electronic health data into reality requires changes on many levels including technology, physician education, patient education and others. While there are virtually thousands of EHR systems currently on the market, most are built primarily for recording narrative and are unable to collect patient data in a structured manner. An EHR system that is highly intuitive and built to support de-identified structured data is essential to solving real world clinical challenges.
About the Author: Michael Sherling, MD, MBA is Chief Medical Officer and Co-Founder of South Florida-based Modernizing Medicine, Inc., a healthcare IT company that is revolutionizing the way in which medical information is created, consumed and utilized to increase efficiency, lower costs and improve outcomes through the use of its Electronic Medical Assistant® (EMA™). Graduating from Yale School of Medicine with honors and holding an MBA from Yale School of Management, Michael has been a practicing dermatologist since 2006 and is in private practice at Palm Beach Dermatology in Lake Worth, FL. He is board certified by the American Board of Dermatology and an expert in medical, surgical and cosmetic dermatology.