title: Improving the Efficiency and Ease of Healthcare Analysis Through Use of Data Visualization Dashboards.
abstract: The digitization of a patient's health record has profoundly impacted medicine and healthcare. The compilation and accessibility of medical history has provided clinicians an unprecedented, holistic account of a patient's conditions, procedures, medications, family history, and social situation. In addition to the bedside benefits, this level of information has opened the door for population-level monitoring and research, the results of which can be used to guide initiatives that are aimed at improving quality of care. Cerner Corporation partners with health systems to help guide population management and quality improvement projects. With such an enormous and diverse client base-varying in geography, size, organizational structure, and analytic needs-discerning meaning in the data and how they fit with that particular hospital's goals is a slow, difficult task that requires clinical, statistical, and technical literacy. This article describes the development of dashboards for efficient data visualization at the healthcare facility level. Focusing on two areas with broad clinical importance, sepsis patient outcomes and 30-day hospital readmissions, dashboards were developed with the goal of aggregating data and providing meaningful summary statistics, highlighting critical performance metrics, and providing easily digestible visuals that can be understood by a wide range of personnel with varying levels of skill and areas of expertise. These internal-use dashboards have allowed associates in multiple roles to perform a quick and thorough assessment on a hospital of interest by providing the data to answer necessary questions and to identify important trends or opportunities. This automation of a previously manual process has greatly increased efficiency, saving hours of work time per hospital analyzed. Additionally, the dashboards have standardized the analysis process, ensuring use of the same metrics and processes so that overall themes can be compared across hospitals and health systems.
authors.affiliation: Population Health, Cerner Corporation, North Kansas City, Missouri.
authors.forename: Jennifer G
authors.initials: JG
authors.lastname: Stadler
authors.affiliation: Population Health, Cerner Corporation, North Kansas City, Missouri.
authors.forename: Kipp
authors.initials: K
authors.lastname: Donlon
authors.affiliation: Population Health, Cerner Corporation, North Kansas City, Missouri.
authors.forename: Jordan D
authors.initials: JD
authors.lastname: Siewert
authors.affiliation: Population Health, Cerner Corporation, North Kansas City, Missouri.
authors.forename: Tessa
authors.initials: T
authors.lastname: Franken
authors.affiliation: Population Health, Cerner Corporation, North Kansas City, Missouri.
authors.forename: Nathaniel E
authors.initials: NE
authors.lastname: Lewis
mesh_terms: D003627:Data Interpretation, Statistical; D003695:Delivery of Health Care; D011785:Quality Assurance, Health Care
keywords: big data analytics; business intelligence; data distribution; healthcare; structured data