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Transforming Kidney Care in the Emergency Department Using Artificial Intelligence-Driven Clinical Decision Support

Successful development and implementation of an artificial intelligence-driven clinical decision support system for detection and treatment of acute kidney injury in the emergency department may improve the quality of kidney care and generate best-practice methods to advance the application of artificial intelligence and develop a scalable CDS product.

Project Details -
Ongoing

Emergency departments are a primary source of healthcare for many individuals and used for a variety of conditions, spanning non-urgent to time-sensitive, life-threatening conditions and illnesses. Providers delivering care in the emergency department are faced with making high-stakes clinical decisions in a high-volume, time-sensitive, and hectic environment. Artificial intelligence (AI) holds promise for extracting and analyzing relevant electronic health record (EHR) data for use in emergency point-of-care clinical decision support (CDS). However, methods for incorporating AI-derived insights into clinical care delivery are underdeveloped.

Acute kidney injury (AKI) is prevalent, associated with adverse clinical outcomes, and one of the most costly conditions in U.S. healthcare. Studies suggest detection and assessment of the severity of AKI in the emergency department setting is limited due to diagnostic criteria and lack of syndrome recognition. However, it is well suited for automated detection by predictive modeling and risk stratification. Diagnosis criteria and severity staging can be applied using data from the EHR. Using AKI as a reference case, a Johns Hopkins University research team will develop an AI-driven algorithm to detect AKI. The algorithm will be developed into a CDS tool in the EHR and optimized for use in the emergency department setting.

The specific aims of this research are as follows:

  • Develop an AI-driven algorithm for promotion of AKI-focused clinical decision making in the emergency department. 
  • Translate the AI algorithm to an AKI-CDS system to enable indepth study of clinician-AI interactions in the emergency department. 
  • Perform a multisite effectiveness implementation-evaluation of the AKI-CDS system in the emergency department. 

Building on previously developed AKI surveillance and prediction tools, the research team will develop an EHR-based algorithm to estimate AKI risk and flag patients for AKI CDS in the EHR. The algorithm will be validated using two groups of discharged patients. The AI algorithm will be translated into an AKI-CDS system and pilot tested among emergency department clinicians to examine AI trustworthiness and explainability. Findings will inform establishment of end-user requirements for the AKI-CDS system.

Lastly, the research team will evaluate the AKI-CDS system implemented at three study sites to understand the drivers of end-user trust and adoption of AI in the emergency department setting. In doing so, the team will develop methods that increase reliability of AI-driven predictions and promote trustworthiness, transparency, explainability, and usability of AI in the emergency department setting.

It’s Not Just for Sci-Fi: Using Artificial Intelligence to Identify Kidney Disease

It’s Not Just for Sci-Fi: Using Artificial Intelligence to Identify Kidney Disease

Successful development and implementation of an artificial intelligence-driven clinical decision support system for detection and treatment of acute kidney injury in the emergency department may improve the quality of kidney care and generate best practice methods to advance the application of artificial intelligence as well as develop a scalable CDS product.

Principal Investigator: Hinson, Jeremiah S., Levin, Scott R.
Organization: Johns Hopkins University
Research Profile: Transforming Kidney Care in the Emergency Department Using Artificial Intelligence-Driven Clinical Decision Support
Funded Amount: $1,963,619

Kidney disease is common but difficult to detect

Providers delivering care in the emergency department (ED) are faced with making high-stakes clinical decisions in a high-volume, time-sensitive, and hectic environment. Acute kidney injury (AKI) is a very common condition for patients presenting in the ED and is associated with adverse clinical outcomes. Unlike conditions such as heart attack, where a provider witnesses a patient wincing in pain, or stroke, where a provider can rapidly measure a patient’s strength and assess their speech, it is difficult to identify kidney injury quickly. Detection and assessment of the severity of AKI in the ED setting is limited due to laboratory-based diagnostic criteria and a general lack of syndrome recognition. Diagnosis of AKI currently relies on detection of changes in serum creatinine (sCr) concentration and urine output, both of which may take several days to manifest and identify.

Oftentimes, the signal that is used to detect kidney injury is delayed. So, no matter what we would do in response to that signal, we don't have a way to know as emergency physicians that this person's going to develop acute kidney injury. There is a protein product that builds up in the blood and it can be measured, but that doesn't happen for days after the initial injury, so we can totally miss it.”
– Dr. Hinson

Complex pattern matching

To address this diagnosing challenge, Drs. Jeremiah Hinson and Scott Levin and a Johns Hopkins University-based research team are betting that artificial intelligence (AI) techniques will be able to extract and analyze relevant electronic health record (EHR) data for AKI detection and treatment in emergency point-of-care clinical decision support (CDS). They are developing an EHR-based algorithm to estimate AKI risk and flag patients at high risk for AKI. This algorithm will then be translated into an AKI-CDS system and pilot-tested among emergency department providers. While it sounds very complicated, Dr. Hinson describes it as pattern matching that is very complex.

We have a database with over 300,000 patient encounters from people who have come into the emergency department in the past, some of whom developed AKI. We can use computers and algorithms to really mine all of these data, and look at patterns in patients who developed AKI. We can leverage the information that we've gained over time, to identify patients who are at really high risk, based on other patients in the past, and develop an estimate of how sure we are about that, or what their probability is of developing AKI. All the data come together and they tell a story, but it can be too complex for us to do that pattern matching using a human brain.”
– Dr. Hinson

Innovative research that can be scaled and disseminated

Dr. Hinson and team believe this research is highly innovative and will have an impact, as it will generate important knowledge and tools to advance the study and application of AI in the ED; this will result in a CDS product that is scalable via distribution platforms such as AHRQ’s CDS Connect. This research has the capacity to improve the quality of kidney care delivered to more than 1 million patients affected by AKI in the United States every year.