title: Using Patient Flow Information to Determine Risk of Hospital Presentation: Protocol for a Proof-of-Concept Study.
abstract: BACKGROUND
  Every day, patients are admitted to the hospital with conditions that could have been effectively managed in the primary care sector. These admissions are expensive and in many cases are possible to avoid if early intervention occurs. General practitioners are in the best position to identify those at risk of imminent hospital presentation and admission; however, it is not always possible for all the factors to be considered. A lack of shared information contributes significantly to the challenge of understanding a patient's full medical history. Some health care systems around the world use algorithms to analyze patient data in order to predict events such as emergency presentation; however, those responsible for the design and use of such systems readily admit that the algorithms can only be used to assess the populations used to design the algorithm in the first place. The United Kingdom health care system has contributed data toward algorithm development, which is possible through the unified health care system in place there. The lack of unified patient records in Australia has made building an algorithm for local use a significant challenge.
  
  
  OBJECTIVE
  Our objective is to use linked patient records to track patient flow through primary and secondary health care in order to develop a tool that can be applied in real time at the general practice level. This algorithm will allow the generation of reports for general practitioners that indicate the relative risk of patients presenting to an emergency department.
  
  
  METHODS
  A previously designed tool was used to deidentify the general practice and hospital records of approximately 100,000 patients. Records were pooled for patients who had attended emergency departments within the Eastern Health Network of hospitals and general practices within the Eastern Health Network catchment. The next phase will involve development of a model using a predictive analytic machine learning algorithm. The model will be developed iteratively, testing the combination of variables that will provide the best predictive model.
  
  
  RESULTS
  Records of approximately 97,000 patients who have attended both a general practice and an emergency department have been identified within the database. These records are currently being used to develop the predictive model.
  
  
  CONCLUSIONS
  Records from general practice and emergency department visits have been identified and pooled for development of the algorithm. The next phase in the project will see validation and live testing of the algorithm in a practice setting. The algorithm will underpin a clinical decision support tool for general practitioners which will be tested for face validity in this initial study into its efficacy.
authors.affiliation: Melbourne East General Practice Network, Burwood East, Australia.
authors.forename: Christopher M
authors.identifier: http://orcid.org/0000-0002-3132-6033
authors.initials: CM
authors.lastname: Pearce
authors.affiliation: Melbourne East General Practice Network, Burwood East, Australia.
authors.forename: Adam
authors.identifier: http://orcid.org/0000-0001-8842-6783
authors.initials: A
authors.lastname: McLeod
authors.affiliation: Health Language Analytics, Eveleigh, Australia.
authors.forename: Jon
authors.identifier: http://orcid.org/0000-0001-6234-9740
authors.initials: J
authors.lastname: Patrick
authors.affiliation: Research Information Technology Unit, Health and Biomedical Informatics Centre, The University of Melbourne, Parkville, Australia.
authors.forename: Douglas
authors.identifier: http://orcid.org/0000-0002-4779-7083
authors.initials: D
authors.lastname: Boyle
authors.affiliation: Melbourne East General Practice Network, Burwood East, Australia.|Gippsland Primary Health Network, Moe, Australia.
authors.forename: Marianne
authors.identifier: http://orcid.org/0000-0002-5458-8116
authors.initials: M
authors.lastname: Shearer
authors.affiliation: Melbourne East General Practice Network, Burwood East, Australia.|Eastern Melbourne Primary Health Network, Box Hill, Australia.
authors.forename: Paula
authors.identifier: http://orcid.org/0000-0002-1861-7864
authors.initials: P
authors.lastname: Eustace
authors.affiliation: Melbourne East General Practice Network, Burwood East, Australia.
authors.forename: Mary Catherine
authors.identifier: http://orcid.org/0000-0001-6209-1604
authors.initials: MC
authors.lastname: Pearce
keywords: avoidable presentation; data linkage; electronic health record; machine learning; primary care