Improving the quality and reliability of Hospital Acquired Complications (HACs) coded data. A sustainable clinical governance approach to reducing HACs for clinicians and clinical coders.
Nicole Payne a, Jennifer Nobbs b
Introduction
The best hospitals deliver high quality healthcare and improve patient outcomes by reducing preventable harm. The accuracy of clinical coding has a direct relationship with the overall quality of care, hospital funding, benchmarking and clinical decision making. A high priority area for many healthcare organisations is the clinical governance for Hospital Acquired Complications (HACs).
The Australian Commission on Safety and Quality in Health Care (ACSQHC) defines a HAC as a patient complication for which clinical risk mitigation strategies may reduce (but not necessarily eliminate) the risk of that complication occurring. Prevention of HACs is the most effective strategy. To achieve this, HACs data must be of the highest quality to maintain confidence in the data and access to a single source of truth. If the coding of HACs does not accurately reflect true incidence rates, mistrust in the data can build with clinical governance programs then focusing on querying the data rather than the prevention of HACs.
Beamtree has developed a HAC management platform called RISQ. RISQ (Relative Indicators for Safety and Quality) is a comprehensive reporting, benchmarking and management tool to improve the quality and reliability of HAC data. Implementation of RISQ has led to increased trust in coded data by clinicians, an overall reduction in reportable HACs and greater collaboration between clinical coders and clinicians.
Methods
RISQ platform reviews coded episodes in near real time to assess the incidence of HACs and the underlying data quality of condition onset flag (COF) data, providing a method to measure and compare the relative safety and quality of performance for reporting, benchmarking, coding review, and clinical service improvement.
The platform monitors overall HAC rates, drills down into hospitals, specialities and individual clinicians. It reports actual and potential coding errors to the clinical coders. It allows clinicians to identify focus areas and to set targets, allowing health services to monitor improvements over time. It compares HAC performance rates with industry best practice. A key feature within the tool is the RISQ Coder Workflow. This workflow captures HAC documentation within the clinical record. This has resulted in better collaboration between the coders and clinicians by facilitating a seamless way to communicate with the clinicians and clinical coding teams to validate the reportable HAC.
Results
All hospitals within Australia utilising the RISQ platform have reported an improvement in the quality and reliability of coded data and a reportable reduction of HACs of between 16%-28%. This is due to improvements in clinical documentation, enhanced clinical coders knowledge of HACs and clinical service improvements as HAC data is now a trusted source of clinical information. RISQ underpins strong clinical governance processes for HAC management, which is sustainable and engages multidisciplinary teams.
Conclusions
The RISQ platform strengthens clinical governance and clinical coding processes for improved HAC data management. It supports hospital to achieve sustainable reductions in HACs by embedding improved practices for clinicians and clinical coders and a collaborative multidisciplinary approach to HACs which is underpinned by high quality and reliability trusted HAC data.
a Beamtree, Australia
b Beamtree, United Kingdom
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