As all kinds of information are being collected about every aspect of our lives, the data generated at this exorbitant rate can lead to advancements in research and health care. That is the idea behind big data” and it’s disruptive benefits for the health care industry. The term encompasses a searchable vast data collection for relative information in order to quickly identify trends. Like all other disruptive innovations, the focus is speed. However, medicine, unlike most industries, has never been quick to adapt to trends.
The history of medicine started out based on the knowledge of religious or spiritual theories. The process for medical decision-making was highly subjective, and a few thousand years later the advancements in clinical judgments were based on individual preference. Today, we would consider this an example of clinical-based medicine, practice based on individual or group observations. It wasn’t until the later in the 20th century that doctors and health care researchers began to use the limited data that had been collected and evaluate the effectiveness of individual patient treatments. Epidemiological methods were then devised to track explicit evidence of the effectiveness clinical practice guidelines and policies. This disruption in medicine would lead to policies and practice guidelines being anchored on experimental evidence gathered from data rather than expert opinions.
Big data is a huge collection of data that is unmanageable by traditional evidence-based means and is a seismic disruption in the field of medicine. One of the first published incidents of using big data to affect doctor decision-making was in 2011 at Stanford Lucile Packard Pediatric Hospital, where Dr. Frankovich searched through her medical records of pediatric lupus patients to determine whether or not to prescribe anticoagulant medication. Because there were not any published guidelines and scant literature on the subject, she resorted to analyzing the patterns revealed in her collection of medical charts.
Lloyd Marino, CEO of Avetta Inc., a global strategy company, says big data is not a quick fix for immediate answers, especially in health care. Unlocking the value of big data requires an ongoing process of the three A’s: automation, analytics, and action.
Automation sorts through and cleanses the data from numerous sources. By normalizing the collected data, it can be integrated with current health care models on a continuous basis in order to produce real-time outcomes. For example, medical records are filled with dozens, if not hundreds, of data points per patient and can be routinely updated inside an electronic medical record. Beyond just collecting information, medical records can be combed through by robust learning machines for patterns and filtered based on disease, risk factors, or outcomes.
However, machine-learning algorithms from auto-generated data needs to be built and mastered. Big data analytics explores deeper into the stream of healthcare information and finds solutions undiscoverable by traditional search means through moving beyond just managing data to mastering it. Analytics does not just offer insight but can help create efficient better hospital infrastructure and streamline drug testing.
Most importantly, the action taken must be deployed wisely and rapidly to achieve a high return on investment (ROI), and this would speed the pharmaceutical industry’s notoriously slow pace. Success also depends on how these solutions are aligned with key health care objectives, how easy for practitioners and invested health care workers to make use of solutions, and how well it integrates with existing protocols and procedures.
Evidence-based medicine is facing a disruptive force. However, it will never be fully uprooted; much like clinical-based medicine continues to exist today. Big data has the advantages of size and speed compared to evidence-based medicine. However, big data alone will not solve any issues for health care problems that exist for individual patients and communities. Proper implementation of automation, analytics, and action, can help properly leverage big data for new solutions to health care models.
Wenjay Sung is a podiatrist.
