SBIR-STTR Award

This Small Business Innovation Research (SBIR) Phase I project seeks to address the most significant and challenging software need in healthcare: Cohort identification. A cohort is a group of patients with a common medical condition. Cohorts underpin modern medical care, defining treatment algorithms, measuring quality improvement, supporting government initiatives, and representing the core organization for research trials. While manual techniques have been developed to identify a cohort within a healthcare organization's electronic medical record (EMR), all rely on a physician or coder identifying and marking every record for every applicable medical condition. This manual process is inaccurate and only addresses the most common conditions. The suggested novel and revolutionary approach is to use big data techniques, utilizing the detailed unstructured narrative notes recorded on every patient for every encounter in every healthcare institution. The core technology required to extract and make unstructured data usable in healthcare is natural language processing (NLP) combined with coded representations of clinical concepts (ontologies). This proposal brings together industry leading teams and technologies to tackle the greatest data problem in healthcare, which offers a unique opportunity to significantly influence care for decades to come. The broader impact/commercial potential of this project includes creating the foundational infrastructure for the next generation of data-driven healthcare. Just as Google and Yahoo required advanced information extraction and search indexing techniques to make the vast amount of internet data usable, healthcare requires similar enabling technology. The healthcare challenge is even more complex given the multitude of natural language descriptions used by physicians and the complex logic that defines potential cohorts and algorithms. To address these issues, healthcare requires the category of technologies used in Google and Yahoo, but specialized for the healthcare domain. In healthcare, quality improvement requires recognizing at risk cohorts in a population. Missing these cohorts and inadequately treating them can increase mortality by an order of magnitude, as in the case of deep vein thrombosis (DVT) in acute care. For quality measures being implemented by the federal government, defining and identifying cohorts is always the first step of tracking and reporting. Current processes are manual, limited, and inaccurate. By bringing evidence derived from clinical documentation which is created in current workflow to real-time and population based treatment decisions, this intervention will form a foundation for data-driven care, supporting improved outcomes, shorter hospitalizations, and reduced direct medical costs

The innovation presented in this proposal is a new paradigm for capturing critical quality information at the point-of-care using advanced technologies in an intuitive workflow. Healthcare quality and cost represent top national priorities. An increasingly common strategy to improve outcomes and value of care is performance assessment to promote best practices. Over decades, the most successful quality improvement (QI) programs have been heavily data-reliant. These programs require identifying patients that fit specific quality measures and assuring their care meets national guidelines. Linking patients to quality measures is the rate limiting step, involving an overwhelming amount of manual labor to review narrative notes one at a time and link them to an appropriate subset of hundreds of known quality measures. Leveraging a robust platform proven in Phase I, the proposed Phase II solution offers an automated approach to capturing a set of quality measures in real-time. The output will provide rich and compliant documentation enhanced with quality measures that feed the electronic health record (EHR) and downstream clinical, operational, and financial hospital systems through standard protocols. The goal is a disruptive change that will fast-track national initiatives and enable a safer and more efficient healthcare system.The broader/commercial impact of this program is to further national healthcare goals of reducing cost and improving quality in care. The approach leverages increased breadth, depth, and accuracy of patient data captured at the point of care. The most aggressive national initiatives encourage capturing a small portion of the hundreds of known quality measures. Accelerating capture and use of quality measures is an opportunity to meaningfully improve a healthcare system that lags in quality and cost. Impact must also be considered at a personal level. There is a cost to care within a system where quality is not documented and tracked. A typical example out of the hundreds of defined measures is ventilator associated pneumonia (VAP). Multiple studies on manual programs to document VAP and leverage care algorithms demonstrate greater than 40% reduction in mortality and 20% reduction in cost. VAP, though common, did not make the top 15 list of measures required by the government in 2014 because it is too difficult to capture. There is currently no automated approach to capture this quality measure. Addressing technical limitations in documenting quality measures will expand QI reach and save lives.