Please use this identifier to cite or link to this item: http://hdl.handle.net/11434/1164
Title: Better data, better outcomes and better value with analytics.
Epworth Authors: Wickramasinghe, Nilmini
McConchie, Steven
Haddad, Peter
Other Authors: Eigner, Isabella
Bodendorf, Freimut
Schaffer, Jonathan
Keywords: Data
Clinical Data
Data Science
Predictive Analysis
Readmission
Usability of Data
Data Science
Quality of Healthcare
Reduce Cost of Care
Data Wrangling
Data Modelling
Data Evaluation
Data Sets Structures
Deployment
Improving Healthcare Outcome
Chair of Health Informatics Management, Epworth HealthCare, Victoria, Australia
Issue Date: Jun-2017
Citation: Epworth Research Institute Research Week 2017; Poster 57: pp 81
Conference: Epworth Research Institute Research Week 2017
Conference Location: Epworth Research Institute, Victoria, Australia
Abstract: BACKGROUND/ INTRODUCTION: Healthcare executives and administrators have a wealth of data accessible through several data sources. Yet they typically remain unsure as to how much of that data is usable or even how they can use that data to improve outcomes. Data science is now a widely adopted approach to leveraging such data sets in the healthcare domain to enhance quality and reduce cost of care. This project develops a model to predict readmission occurrences across various clinical specialties. It starts with understanding the data and their dictionaries as applicable, then will work on cleaning the data and preparing it for the third stage which involves the use of data science techniques and tools. METHODLOGY: This research is exploratory in nature using a multiple case study approach. The chosen cases focus on spinal surgery, colorectal, urology, cardiothoracic, and orthopaedic. Further, the study consists of 6 key phases as follows: Phase 1: Understanding the structures of the various data sets, Phase 2: Data wrangling, Phase 3: Modelling, Phase 4: Evaluations, Phase 5: deployment and Phase 6: Final evaluation and feedback exchange. RESULTS AND CONCLUSIONS: This is a work in progress. To date, we have developed the appropriate conceptual model which identifies the important key data elements that must be incorporated, from where and how they will be collected as well as key aspects of their respective data structures. In addition, we are fine turning the necessary data preparation strategies. given the changes to the healthcare environment and the likelihood of a bundles payment structure to be adopted, similar to the US healthcare environment, this study represents a strategic necessity so that healthcare providers can be prepared and ready to operate effectively and efficiently in a new value-based healthcare environment; ie so they have better data, better outcomes and better value.
URI: http://hdl.handle.net/11434/1164
Type: Conference Poster
Affiliated Organisations: Faculty of Health, Deakin University
FAU University Erlangen-Nuremberg, Germany
Type of Clinical Study or Trial: Predictive Test
Appears in Collections:Health Informatics

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