Clinical Automation Makes Workflows More Efficient
There are several examples of clinical automation in use across U.S. hospitals today. Digital health company Eko offers digital stethoscopes and stethoscope attachments that automatically check for heart murmurs in the background of a physician’s exam.
When AI algorithms are applied to radiology studies, healthcare organizations can reduce how often images need to be read by humans, which increases turnaround time. Further automation can be applied to escalate radiology images that can be analyzed by the AI or fill in documentation to take the burden off physicians.
AI and automation may also be applied to symptom checkers to automatically triage patients, making it easier for healthcare organizations to determine the right team and right modality to use for each patient.
Computer vision is another growing area of clinical automation that improves the documentation process. The technology strategy relies on cameras and touchless sensors to automate the collection of vital signs and the input of that data into the EHR, freeing up clinician schedules. Computer vision technology also can detect if a patient is turning when they shouldn’t or falling, and notify a nurse to assist.
The next iteration of these solutions will focus on generating a required set of actions from the collected data and the patient history to improve diagnosis and treatment. Multiple sensors will be able to combine their data from an instance in time to analyze a patient’s status. As the technology becomes more sophisticated, it might event predict a code blue situation and alert the clinicians about which intervention to deploy.
The goal is to free up physicians and nurses to spend more time with patients at the bedside. Clinicians don’t become caregivers to do paperwork, they want to care for patients. If clinicians aren’t burned out from the documentation burden, they’ll be less likely to leave medicine.
Overcoming Challenges to Clinical Automation Technology
Trust in technology is a major challenge to clinical automation adoption. Some of these technologies are still nascent, and both clinicians and patients may be hesitant to embrace the new processes. Initially, clinicians may not trust the technology to deploy interventions necessary for patient care, while patients may not trust technology, such as chatbots, to handle their health situation or protect their privacy.
Overcoming this challenge requires a culture shift. Healthcare organizations should emphasize to clinicians that clinical automation and computer vision AI technologies aren’t intended to penalize or replace them, but to support them in their workflow and improve patient care. It’s also important to create a culture of exploration led by clinical executives who are working with IT teams, clinicians and financial executives within the organization. That culture change needs to be owned at the top of the organization to drive acceptance.
Healthcare organizations shouldn’t just fit automation into their existing workflows but create new processes with a strategy supported by clinical automation. It’s recommended to start with a particular use case and measure the impact before scaling the technology. Technology shouldn’t be implemented for its own sake; it needs to enhance clinical practice without adding to the actual workload.
These new workflows should make sense within the organization’s long-term vision and consider the healthcare industry’s movement toward at-home health. Brick and mortar will continue to exist for intensive cases, but the future is shifting toward the home.
It’s an exciting opportunity for organizations that are willing to pioneer these solutions, but it will require more adoption and iteration before many clinical automation technologies receive widespread adoption.