Administrative data has poor accuracy for surveillance of Staphylococcus aureus bacteraemia

Post-Stroke Infections: Insights from Big Data Using Clinical Data Warehouse (CDW)

This study analyzed a digitized database of electronic medical records (EMRs) to identify risk factors for post-stroke infections. The sample included 41,236 patients hospitalized with a first stroke diagnosis (ICD-10 codes I60, I61, I63, and I64) between January 2011 and December 2020. Logistic regression analysis was performed to examine the effect of clinical variables on post-stroke infection. Multivariable analysis revealed that post-stroke infection was associated with the male sex (odds ratio [OR]: 1.79; 95% confidence interval [CI]: 1.49-2.15), brain surgery (OR: 7.89; 95% CI: 6.27-9.92), mechanical ventilation (OR: 18.26; 95% CI: 8.49-44.32), enteral tube feeding (OR: 3.65; 95% CI: 2.98-4.47), and functional activity level (modified Barthel index: OR: 0.98; 95% CI: 0.98-0.98). In addition, exposure to steroids (OR: 2.22; 95% CI: 1.60-3.06) and acid-suppressant drugs (OR: 1.44; 95% CI: 1.15-1.81) increased the risk of infection. On the basis of the findings from this multicenter study, it is crucial to carefully evaluate the balance between the potential benefits of acid-suppressant drugs or corticosteroids and the increased risk of infection in patients at high risk for post-stroke infection.

Evaluation of the accuracy of diagnostic coding for influenza compared to laboratory results: the availability of test results before hospital discharge facilitates improved coding accuracy

Background

Assessing the accuracy of diagnostic coding is essential to ensure the validity and reliability of administrative coded data. The aim of the study was to evaluate the accuracy of assigned International Classification of Diseases version 10-Australian Modification (ICD-10-AM) codes for influenza by comparing with patients’ results of their polymerase chain reaction (PCR)-based laboratory tests.

Method

A retrospective study was conducted across seven public hospitals in New South Wales, Australia. A total of 16,439 patients who were admitted and tested by either cartridge-based rapid PCR or batched multiplex PCR between January 2016 and December 2017 met the inclusion criteria. We calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of ICD-10-AM coding using laboratory results as a gold standard. Separate analyses were conducted to determine whether the availability of test results at the time of hospital discharge influenced diagnostic coding accuracy.

Results

Laboratory results revealed 2759 positive influenza cases, while ICD-10-AM coding identified 2527 patients. Overall, 13.7% (n = 378) of test positive patients were not assigned an ICD-10-AM code for influenza. A further 5.8% (n = 146) patients with negative test results were incorrectly assigned an ICD-10-AM code for influenza. The sensitivity, specificity, PPV and NPV of ICD-10-AM coding were 93.1%; 98.9%; 94.5% and 98.6% respectively when test results were received before discharge and 32.7%; 99.2%; 87.8% and 89.8% respectively when test results were not available at discharge. The sensitivity of ICD-10-AM coding varied significantly across hospitals. The use of rapid PCR or hospitalisation during the influenza season were associated with greater coding accuracy.

Conclusion

Although ICD-10-AM coding for influenza demonstrated high accuracy when laboratory results were received before discharge, its sensitivity was substantially lower for patients whose test results were not available at discharge. The timely availability of laboratory test results during the episode of care could contribute to improved coding accuracy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01531-9.

A simple measure to improve sepsis documentation and coding

INTRODUCTION

Sepsis incidence and mortality are increasing, yet sepsis appears to be under-recognised and under-reported. Accurate recognition and coding of sepsis allows for appropriate funding and accurate epidemiological representation.

METHODS

We implemented a discharge summary template for all patients discharged from our infectious diseases service and analysed sepsis documentation and coding before and after its introduction.

RESULTS

Beforehand, we found that 59% of 29 patients had sepsis, yet only 10% had it documented on their discharge summary, and 17% had it coded. Following implementation of the template, 38% of 52 patients had sepsis documented, yet only 20% of these had it coded. After delivery of a training session to the coders regarding the importance of sepsis, 38% of patients with a diagnosis of sepsis had it coded.

DISCUSSION

Despite requiring ongoing education and encouragement of clinicians and coders, implementation of the template was quick, cheap and easy and improved sepsis coding.

The importance of clinical documentation improvement for Australian hospitals

Clinical documentation improvement (CDI) is a recent initiative gaining increased momentum in Australia. The benefits surrounding its success internationally include improved quality and patient safety outcomes and increased reimbursement. The premise of CDI is simple: engage clinicians to improve the clinical documentation in the medical record in “real time” so that it is fit for reporting, analysis and reimbursement. Every country has differing healthcare systems and this article has focused on validating the relevancy of CDI for the Australian healthcare environment.