Pathologists and clinical laboratory scientists may find one hospital’s use of a machine-learning platform to help improve utilization of lab tests both an opportunity and a threat
Variation in how individual physicians order, interpret, and act upon clinical laboratory test results is regularly shown by studies in peer-reviewed medical journals to be one reason why some patients get great outcomes and other patients get less-than-desirable outcomes. That is why many healthcare providers are initiating efforts to improve how physicians utilize clinical laboratory tests and other diagnostic procedures.
At Flagler Hospital, a 335-bed not-for-profit healthcare facility in St. Augustine, Fla., a new tool is being used to address variability in clinical care. It is a machine learning platform called Symphony AyasdiAI for Clinical Variation Management (AyasdiAI) from Silicon Valley-based SymphonyAI Group. Flagler is using this system to develop its own clinical order set built from clinical data contained within the hospital’s electronic health record (EHR) and financial systems.
This effort came about after clinical and administrative leadership at Flagler Hospital realized that only about one-third of its physicians regularly followed certain medical decision-making guidelines or clinical order sets. Armed with these insights, staff members decided to find a solution that reduced or removed variability from their healthcare delivery.
Reducing Variability Improves Care, Lowers Cost
Variability in physician care has been linked to increased healthcare costs and lower quality outcomes, as studies published in JAMA and JAMA Internal Medicine indicate. Such results do not bode well for healthcare providers in today’s value-based reimbursement system, which rewards increased performance and lowered costs.
“Fundamentally, what these technologies do is help us recognize important patterns in the data,” Douglas Fridsma, PhD, an expert in health informatics, standards, interoperability, and health IT strategy, and CEO of the American Medical Informatics Association (AMIA), told Modern Healthcare.
Clinical order sets are designed to be used as part of clinical decision support systems (CDSS) installed by hospitals for physicians to standardize care and support sound clinical decision making and patient safety.
However, when doctors don’t adhere to those pre-defined standards, the results can be disadvantageous, ranging from unnecessary services and tests being performed to preventable complications for patients, which may increase treatment costs.
Flagler’s AI project involved uploading clinical, demographic, billing, and surgical information to the AyasdiAI platform, which then employed machine learning to analyze the data and identify trends. Flagler’s physicians are now provided with a fuller picture of their patients’ conditions, which helps identify patients at highest risk, ensuring timely interventions that produce positive outcomes and lower costs.
How Symphony AyasdiAI Works
The AyasdiAI application utilizes a category of mathematics called topological data analysis (TDA) to cluster similar patients together and locate parallels between those groups. “We then have the AI tools generate a carepath from this group, showing all events which should occur in the emergency department, at admission, and throughout the hospital stay,” Sanders told Healthcare IT News. “These events include all medications, diagnostic tests, vital signs, IVs, procedures and meals, and the ideal timing for the occurrence of each so as to replicate the results of this group.”
Caregivers then examine the data to determine the optimal plan of care for each patient. Cost savings are figured into the overall equation when choosing a treatment plan.
Flagler first used the AI program to examine trends among their pneumonia patients. They determined that nebulizer treatments should be started as soon as possible with pneumonia patients who also have chronic obstructive pulmonary disease (COPD).
“Once we have the data loaded, we use [an] unsupervised learning AI algorithm to generate treatment groups,” Sanders told Healthcare IT News. “In the case of our pneumonia patient data, Ayasdi produced nine treatments groups. Each group was treated similarly, and statistics were given to us to understand that group and how it differed from the other groups.”
Armed with this information, the hospital achieved an 80% greater physician adherence to order sets for pneumonia patients. This resulted in a savings of $1,350 per patient and reduced the readmission rates for pneumonia patients from 2.9% to 0.4%, reported Modern Healthcare.
The development of a machine-learning platform designed to reduce variation in care (by helping physicians become more consistent at following accepted clinical care guidelines) can be considered a warning shot across the bow of the pathology profession.
This is a system that has the potential to become interposed between the pathologist in the medical laboratory and the physicians who refer specimens to the lab. Were that to happen, the deep experience and knowledge that have long made pathologists the “doctor’s doctor” will be bypassed. Physicians will stop making that first call to their pathologists, clinical chemists, and laboratory scientists to discuss a patient’s condition and consult on which test to order, how to interpret the results, and get guidance on selecting therapies and monitoring the patient’s progress.
Instead, a “smart software solution” will be inserted into the clinical workflow of physicians. This solution will automatically guide the physician to follow the established care protocol. In turn, this will give the medical laboratory the simple role of accepting a lab test order, performing the analysis, and reporting the results.
If this were true, then it could be argued that a laboratory test is a commodity and hospitals, physicians, and payers would argue that they should buy these commodity lab tests at the cheapest price.