The following is a guest article by Debi Taylor, MSN, RN, SCRN, Director of Clinical Learning and Development at RapidAI.
Whether a patient is suffering from a stroke or another acute life-threatening condition, outcomes are often closely connected with time to treatment. Although there are many inefficiencies that can arise before a patient even enters the hospital’s care — whether it is the time it takes to call the operator, their mode of transportation, or how they present when they get there — the use of emerging technologies within the emergency department can go a long way toward overcoming and compensating once they arrive.
At the highest level, the goal is to reduce communication breakdowns, support informed decision making, accelerate communication between doctors, and help align team members in order to deliver optimal care. While in reality this is far from easy, introducing machine-learning driven platforms into the process has shown major promise in achieving this end, by streamlining care and improving patient outcomes as a result.
Below are three of the biggest and most universal workflow challenges hospitals face and how technology is helping to overcome them.
Diversity Of Participants
One of the many institutional challenges within the hospital workflow is the large number of patients with various conditions, being treated by an equally diverse group of people between administrative and clinical staff — a problem that has only worsened as the healthcare complexity has continued to grow. In order for technology to result in meaningful change for patients, it must meet both the needs and skill of everyone involved. Solutions designed for one person or one problem within the process will have little to no impact on overall outcomes.
Digital platforms in the form of mobile apps help automatically increase a tool’s potential value, by reducing barriers to entry and making it easier to adopt. Whether it is a new CT technologist on call or a non-tech savvy clinician, the familiarity and efficiency of mobile applications can automatically make strong clinical technology even more valuable operationally.
“The Attention Problem”
Another challenge all hospitals looking to improve workflow are contending with is the fact that there are a finite number of resources and care available at any given time — resulting in a zero-sum game. Drawing attention to one patient at one time and not having quality dip for everyone else is very difficult. Intelligent, machine-coordinated platforms help to overcome this challenge by looking at the entire ecosystem of activity, and drawing attention to the same problem at the same time. This is the crux of hospital efficiency.
Volume Versus Value
The final problem that many, if not all, clinicians are facing in this digital age, is the sheer volume of messaging platforms being used within the hospitals today — many of which provide little value to the speed of clinical care in the emergency setting. The very task and friction of switching between platforms can come at a clinical and even monetary cost. Despite many hospitals’ best intentions, overburdening clinicians or processes with messaging tools can actually increase the risk of missing information and result in often meaningless information being flagged as high priority within the hierarchy of technology and triage. The most valuable machine learning tools aim to improve a process, rather than a problem.
Where AI Can Go Further
Despite the potential value of this technology, by automating the wrong parts of the process we can risk actually decreasing our efficiency. Centralizing communication is only valuable so long as the process is worth centralizing. By using machine-learning in other areas of the hospital for other conditions, you run the risk of competing urgency that robs conditions like stroke of the attention it deserves.
As resource centralization continues to be studied and we identify new ways of improving the workflow for operations, financial, and ultimately clinical gains, the most time-efficient and resource-conserving version will be the one that makes the most impact for clinicians and patients.
While machine-learning technology has demonstrated its impact on the treatment of stroke, emergency departments have the opportunity to leverage this technology in other areas of life-threatening care, to overcome environmental inefficiencies and improve patient outcomes in a meaningful way.
About Debi Taylor
Debi Taylor, MSN, RN, SCRN, is the Director of Clinical Learning and Development, and former Clinical Program Workflow Leader, at RapidAI. At RapidAI, Debi serves as the clinical workflow subject matter expert for stroke and pulmonary embolism product lines, leads disease-specific pilots, and develops and executes disease-specific education and training both internally and externally. Prior to joining RapidAI, Debi held clinical roles at Sutter Health, Kaiser Permanente, and Cleveland Clinic.
