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Responding to COVID-19 with real-time general practice data in Australia.
Pearce, Christopher; McLeod, Adam; Supple, Jamie; Gardner, Karina; Proposch, Amanda; Ferrigi, Jason.
  • Pearce C; Director of Research, Outcome Health, Adjunct Associate Professor in General Practice, Monash University, 1 Chapel Street, Blackburn 3130, Australia. Electronic address: drchrispearce@mac.com.
  • McLeod A; Chief Executive Officer, Outcome Health, Australia.
  • Supple J; Director of Business Intelligence, Outcome Health, Australia.
  • Gardner K; Research Manager, Outcome Health, Australia.
  • Proposch A; Chief Executive Officer, Gippsland Primary Health Network, Australia.
  • Ferrigi J; Chief Information Officer, Outcome Health, Australia.
Int J Med Inform ; 157: 104624, 2022 01.
Article in English | MEDLINE | ID: covidwho-1506596
ABSTRACT

INTRODUCTION:

As SARS-CoV-2 spread around the world, Australia was no exception. Part of the Australian response was a robust primary care approach, involving changes to care models (including telehealth) and the widespread use of data to inform the changes. This paper outlines how a large primary care database responded to provide real-time data to inform policy and practice. Simply extracting the data is not sufficient. Understanding the data is. The POpulation Level Analysis and Reporting (POLAR) program is designed to use GP data for multiple objectives and is built on a pre-existing engagement framework established over a fifteen-year period. Initially developed to provide QA activities for general practices and population level data for General Practice support organisations, the POLAR platform has demonstrated the critical ability to design and deploy real-time data analytics solutions during the COVID-19 pandemic for a variety of stakeholders including state and federal government agencies.

METHODS:

The system extracts and processes data from over 1,300 general practices daily. Data is de-identified at the point of collection and encrypted before transfer. Data cleaning for analysis uses a variety of techniques, including Natural Language Processing and coding of free text information. The curated dataset is then distilled into several analytic solutions designed to address specific areas of investigation of interest to various stakeholders. One such analytic solution was a model we created that used multiple data inputs to rank patient geographic areas by the likelihood of a COVID-19 outbreak. The model utilised pathology ordering, COVID-19 related diagnoses, indication of COVID-19 related concern (via progress notes) and also incorporated state based actual confirmed case figures.

RESULTS:

Using the methods described, we were able to deliver real-time data feeds to practices, Primary Health Networks (PHN) and other agencies. In addition, we developed a COVID-19 geographic risk stratification based on local government areas (LGAs) to pro-actively inform the primary care response. Providing PHNs with a list of geographic priority hotspots allowed for better targeting and response of Personal Protective Equipment allocation and pop-up clinic placement.

CONCLUSIONS:

The program summarised here demonstrates the ability of a well-designed system underpinned by accurate and reliable data, to respond in real-time to a rapidly evolving public health emergency in a way which supports and enhances the health system response.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: General Practice / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Oceania Language: English Journal: Int J Med Inform Journal subject: Medical Informatics Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: General Practice / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Oceania Language: English Journal: Int J Med Inform Journal subject: Medical Informatics Year: 2022 Document Type: Article