As outcomes research in anesthesiology progresses to novel heights, an increased emphasis on quality data reporting will be essential to optimize the impact of research, from practitioner to patient. Intrinsic to quality data reporting are the ability and consistency of EMR, or electronic medical records, in combination with actor leadership in the realm of research.
Electronic medical records (EMR) are emerging as an invaluable tool for harnessing the power of data.
While these three intents are consistent across the board, individual EMR platforms may vary by storage capability, metrics gathered, and/or access to data input. The database capabilities of an EMR platform are often a highly negotiated factor. As the transition from paper to EMR takes place among anesthesia facilities, practices, and hospitals, it is necessary for the storage capabilities of the platform to keep pace with the rate of data acquisition. This is particularly important in anesthesia services, which are often paired with other practices, e.g. surgery or rehabilitative care. Data must be easily stored and transferred among practices within a facility or hospital, with appropriate capacity to render large files and collect multiple quantities of data.
Moreover, what data should be collected? The metrics for anesthesia services are oft debated by practitioners, anesthesia management companies, and academics alike. High-quality metrics must include patient outcomes, patient experiences, and, more recently, the cost incurred to the patient, in order to form a comprehensive summary of the clinical case. The Anesthesia Quality Institute provides several frameworks for collecting outcome data from both the physician and patient perspectives. These metrics include such descriptors for adverse patient experiences (anaphylaxis, adverse drug reactions, cardiac arrest); operating room or facility events (OR fire, equipment malfunction); and administrative errors (wrong patient, wrong site surgery). The AQI is a preeminent source for practice-based quality management, and thus has a fundamental role in developing guidelines for metric-driven outcome research.
In collecting data, attention must be placed on confidentiality and right to access. The relationship between availability of data and patient confidentiality is a delicate balance that is increasingly addressed by EMR platforms, most recently by use of mandatory identity verification functionalities. The ability to access data varies depending on the selected platform. For example, large-scale EMR platforms may only be accessible by an anesthesiologist or CRNA, while more modern start-up EMR ventures may include patient inputs in the platform design.
The importance of harnessing data, as explored through the versatility of EMR platforms, is an essential component to maximizing outcomes research in anesthesia practices. However, EMR and robust data must work in concert with effective actor leadership in order to ensure that outcomes research plays a role in modifying anesthesia policies and practices. Historically, leadership for anesthesia outcomes research came primarily from physicians or academics. Yet, a modern approach to research-based practice includes a variety of actors. The American Association of Nurse Anesthetists notes in a scientific paper that, “80% of CRNAs conducting research participated in quantitative research”, concluding that “[it] is likely that the CRNAs conducting research are doing quality research” (Cowan et al., 2002). As active practitioners of anesthesia care, CRNAs demonstrate valuable potential to lead the field of outcomes research in anesthesiology