Authors: Alvin Rajkomar, MD and Sara Murray, MD
Background: As many as 70% of emergency room visits may be preventable, with a proportion of these resulting in hospitalizations that may have also been avoided. These preventable escalations of care, which we define as decompensations, cause personal and financial stress to patients and use of costly services by health systems which are increasingly focused on high-value care. There is a pressing need to be able to identify patients at greatest risk for imminent decompensation, with the intent on intervening prior to the need for emergency or hospital care. Prior to use of our unified electronic health record (EHR), the data that contained the clinical status of patients was locked up in paper charts or disparate electronic databases that could not be analyzed without time-consuming manual chart review. Our EHR now houses a wealth of clinical data in a single database that can be used for to help clinicians identify these high-risk patients, through high-throughput algorithms. Much of the assessment of clinical status and risk of decompensation is contained within the clinical notes as unstructured free text (e.g. “The patient is calling to report worsening fever and chest pain.”). Therefore, algorithms must not only able to quickly gather information about patients but also draw upon modern machine learning techniques to extract meaning from structured and unstructured data to assess patient status. Here we propose developing a novel algorithm that leverages the EHR to improve outcomes for our highest risk patients across the medical center.
Proposal: We propose building a machine-learning model that employs deep learning (e.g. deep neural networks), including processing of free text from clinical notes/encounters, to predict a given patient’s risk of emergency room visit or hospitalization within the next 7 days. To do this, we will first assemble a data repository of all patients receiving care at UCSF by extracting the subset of the EHR that includes clinically relevant structured data (demographics, laboratory data, encounters, problem lists, etc.) as well as all of the clinical notes for each individual patient. Using this data repository, we will develop an algorithm that accounts for the patterns and content of an individual’s interactions with the healthcare system. We plan to deploy this algorithm on a bi-weekly basis to identify UCSF patients at greatest risk for decompensation and feed that information back to key stake-holders (including primary care clinics, the accountable care organization, and ideally a call system to check-in on these patients).
Feasibility: This project is feasible for our team, as it builds upon and synthesizes prior work we have done in validating data extraction algorithms from the EHR, streamlining storage and processing of large amounts of EHR data, and building machine-learning algorithms to be used in predictive modeling. Both primary investigators are Clarity certified and have direct access to generate this data repository. Dr. Murray has already built a similar data repository containing structured data and unstructured free text for use with machine learning algorithms in lupus patients. Dr. Rajkomar has already built and is using a computational server that pulls and analyzes EHR data in real-time and has created deep learning algorithms on high-throughput computational clusters. Both primary investigators have collaborated with Epic build-team members in prior projects and understand how to push data from an algorithm back into the EHR.
This would be a highly novel application of health IT in which we synthesize big data - nearly the entirety of the EHR - and use the machine learning to directly improve clinical care. We anticipate that this project will not only help us reduce emergency room visits and hospitalizations at UCSF, but also serve as a model that fundamentally changes how we use EHR data to affect patient care. The fundamental premise of real-time processing of clinical data at scale has multiple applications for the Department of Medicine and UCSF Health, as the same pipeline could be used to predict an infinite number of outcomes that are important to our health system and the patients we serve.
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