Using Patient Flow Information to Determine Risk of Hospital Presentation: Protocol for a Proof-of-Concept Study

Responding to COVID-19 with real-time general practice data in Australia

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.

Healthcare inequality in compensation claims concerning acute hospital services: a Danish register-based study

OBJECTIVE

Although citizens' equal right to acute healthcare of appropriate quality is an oft-cited goal for modern societies, healthcare disparities may persist. We aimed to investigate inequality in compensation claims and compensation payments regarding acute healthcare services.

DESIGN AND SETTING

We conducted a cross-sectional study of compensation claim patterns using the Danish Patient Compensation Association (DPCA) registries.

PARTICIPANTS, INTERVENTIONS AND MAIN OUTCOME MEASURES

We used register data on all cases managed by DPCA relating to acute hospital healthcare for adults (aged > 18 years) from 2007 to 2017.

RESULTS

In total, the DPCA had 5556 compensation claims for injuries caused by acute care services during the years 2007-2017. Age group of 50-64 years (odds ratio (OR) = 1.37 compared with those aged 18-49 years; P < 0.001), marriage (OR = 1.14; P < 0.001), higher income (OR = 1.55; P < 0.001) and Danish origin (OR = 1.49; P < 0.001) were statistically associated with higher odds for filing a compensation claim; men (OR = 0.83; P < 0.001) and those with many co-morbidities were much less represented (OR = 0.24; P < 0.001). Male gender (OR = 1.25; P < 0.001) and higher age (OR = 2.55 (80+ years); P < 0.001) were associated with higher odds for a compensation award. Failed diagnosis was also more often at stake in men (OR = 1.38; P < 0.001) and in patients aged 50-64 years (OR = 1.17; P < 0.001) but occurred less often in patients with multiple morbidities (OR = 0.68; P < 0.001).

CONCLUSIONS

Findings from our Danish material suggest some inequality in compensation claims and compensation payments regarding acute healthcare services.

Changes in compensation claim contents following reorganization of emergency hospital care

OBJECTIVES

To study the effects of the reorganization on patient compensation claim contents relating to health care quality and patient safety.

DESIGN AND SETTINGS

Danish emergency care has developed considerably over the past decades, including a major reorganization to improve health care quality and patient safety through ensuring easier access to specialist treatment. Analysis of compensation claim patterns is used to evaluate the effects of such health care system changes.

PARTICIPANTS, INTERVENTIONS AND MAIN OUTCOME MEASURES

A sample of 1613 compensation claims to the Danish Patient Assurance organization was reviewed using a standardized taxonomy (the Healthcare Complaints Analysis Tool [HCAT]). Using trend analysis, we compared the proportions of claims categorized under HCAT domains, problem categories and sub-categories before and after the reorganization, with particular emphasis on the 'Clinical problems' domain covering health care quality and patient safety issues.

RESULTS

We observed a baseline increase in claims relating to clinical problems (P < 0.01), but this increase was less pronounced following the reorganization. This appeared to be driven mainly by a decrease in claims about clinician skills (P = 0.03) and health care neglects (P = 0.01). However, claims about diagnostic errors and patient outcomes showed a tendency (insignificant) to increase.

CONCLUSIONS

Emergency care reorganization apparently has been followed by a shift in claim contents towards fewer claims about health care neglect and staff competencies, although claims about other matters may have become more common. Present analyses of compensation claim trends should be supplemented by effect studies using traditional outcome measures such as mortality and readmission rates.

POLAR Diversion: Using General Practice Data to Calculate Risk of Emergency Department Presentation at the Time of Consultation

OBJECTIVE

This project examined and produced a general practice (GP) based decision support tool (DST), namely POLAR Diversion, to predict a patient's risk of emergency department (ED) presentation. The tool was built using both GP/family practice and ED data, but is designed to operate on GP data alone.

METHODS

GP data from 50 practices during a defined time frame were linked with three local EDs. Linked data and data mapping were used to develop a machine learning DST to determine a range of variables that, in combination, led to predictive patient ED presentation risk scores. Thirteen percent of the GP data was kept as a control group and used to validate the tool.

RESULTS

The algorithm performed best in predicting the risk of attending ED within the 30-day time category, and also in the no ED attendance tests, suggesting few false positives. At 0 to 30 days the positive predictive value (PPV) was 74%, with a sensitivity/recall of 68%. Non-ED attendance had a PPV of 82% and sensitivity/recall of 96%.

CONCLUSION

Findings indicate that the POLAR Diversion algorithm performed better than previously developed tools, particularly in the 0 to 30 day time category. Its utility increases because of it being based on the data within the GP system alone, with the ability to create real-time "in consultation" warnings. The tool will be deployed across GPs in Australia, allowing us to assess the clinical utility, and data quality needs in further iterations.