The number of patients with cirrhosis and advanced liver disease has been growing in the VA system and general population of the US. As of 2008, the prevalence of chronic liver disease in the US reached 15%. Complications of cirrhosis frequently require hospital admission, and each year, cirrhosis is responsible for >150,000 hospitalizations at a cost of approximately $4 billion. Among patients who survive the initial hospitalization, nearly half are rehospitalized within 1 year. The VA is facing an increasing burden of chronic liver disease due to substance use disorders, chronic viral hepatitis, and increasing numbers of patients with non-alcoholic steatohepatitis, and is the largest single provider of hepatitis C (HCV) care in the US. It was recently estimated that cirrhosis prevalence among VHA HCV patients will exceed 50% over the next decade.45 Alcohol use accelerates fibrosis progression, and current active high risk alcohol use among consecutive HCV patients in some VA clinics has been shown to be 25-34%. The use of clinical decision support (CDSS) in clinical dashboards has great potential for facilitating more robust risk stratification and tailored clinical care interventions as well as providing a platform for effective use of NLP technologies in clinical care, but has been disseminated into general clinical practice slowly because of the sophisticated underlying data requirements and a lack of focus on clinical workflow and efficiency optimization. The overall objective of this project is to develop the informatics infrastructure and tools to facilitate improved evidence based quality care delivery to patients with cirrhosis that will impact readmission and mortality rates. More specifically, we will 1) develop and validate near real-time natural language processing (NLP) tools in order to extract information that is relevant for case finding and risk factor modification among these patients, 2) develop and validate a robust family of logistic regression prediction models for readmission and mortality following hospitalization for use in the identification of high risk patients, 3) development of a clinical dashboard with imbedded clinical decision support and patient data visualization tools to support clinical care delivery, and 4) conduct a pre-post clinical pilot to evaluate the efficacy and adoption of the dashboard when used. This proposal will analyze national retrospective cohort data among adult hospitalized patients for Specific Aims 1 and 2, beginning with all hospitalized patients and identifying the cohort of patients with cirrhosis in order to develop predictive models for readmission and mortality. All variables will be extracted from structured data in the CDW, Medical SAS, and Medicare files, with real time NLP used to extract risk factors from unstructured data. Augmented case finding will be used in Aim 1 to detect additional cirrhotic patients, and the discussed risk factors, social history factors, and modifiable clinical variables will be used to generate logistic regression models for readmission and mortality, internally validated with bootstrapping. The NLP pipeline and prediction models will be incorporated into a clinical dashboard that will be developed to support the clinical care delivery needs of these patients, as described in Specific Aim 3 using established implementation science framework methods through observation and interview of the providers using the dashboard. Finally, a pre- post clinical pilot will be conducted to evaluate efficacy and adoption of the dashboard for use in inpatient and outpatient cirrhotic patient care at the San Diego and TVHS VA facilities. This proposal will improve veterans' care in a number of areas. This work has the potential to provide robust risk stratification tools for the identification of high risk patients during hospitalization and allow improvements in the receipt of evidence based quality care and reductions in mortality and readmission through the use of the clinical dashboard. Finally, the NLP and dashboard development has the potential to be applied in a wide variety of clinical domains beyond that of advanced liver disease.
| Koola, Jejo D; Davis, Sharon E; Al-Nimri, Omar et al. (2018) Development of an automated phenotyping algorithm for hepatorenal syndrome. J Biomed Inform 80:87-95 |
| Ho, Samuel B; Matheny, Michael E; Schnabl, Bernd E (2016) Changes in Hospital Admissions and Mortality for Complications of Cirrhosis: Implications for Clinicians and Health Systems. Gut Liver 10:8-9 |
