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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia
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Improving the accuracy of stroke clinical coding with open-source software and natural language processing.

Stephen Bacchi1, Sam Gluck2, Simon Koblar1

  • 1Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, Adelaide SA 5005, Australia; South Australian Health and Medical Research Institute, Adelaide SA 5000, Australia.

Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia
|December 5, 2021
PubMed
Summary
This summary is machine-generated.

Natural language processing (NLP) can improve clinical coding accuracy for stroke admissions. This study used NLP and open-source tools to identify misclassified diagnoses and groups, potentially leading to significant financial benefits.

Keywords:
Activity-based fundingCasemixClinical codingDiagnosis related groupsMachine learning

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Area of Science:

  • Medical Informatics
  • Health Services Research

Background:

  • Accurate clinical coding is crucial for activity-based funding in healthcare.
  • Natural Language Processing (NLP) offers potential for enhancing coding efficiency and precision.

Purpose of the Study:

  • To assess the feasibility of using NLP with open-source software for identifying misclassifications in stroke admissions.
  • To detect errors in Adjacent Diagnosis Related Groups (ADRG), International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) diagnoses, and Diagnosis Related Groups (DRG).

Main Methods:

  • Analysis of 152 consecutive stroke admissions at the Royal Adelaide Hospital Stroke Unit.
  • Utilized free-text discharge summaries with a random forest classifier and regular expression-based analysis.
  • Employed open-source software libraries for NLP techniques.

Main Results:

  • Identified two cases misclassified as B70 (Stroke and Other Cerebrovascular Disorders) that should be B02 (endovascular thrombectomy).
  • Detected 33 cases with undocumented ataxia.
  • Found two cases requiring reclassification from B70A/B/C to B70D due to inter-facility transfer within five days.

Conclusions:

  • Various NLP techniques, implemented via open-source libraries, can effectively identify coding misclassifications in ADRG, ICD-10-AM, and DRG.
  • These methods have the potential for substantial financial implications for healthcare providers.
  • Future research should focus on applying these open-source NLP tools to identify all ICD-10-AM diagnosis misclassifications in stroke patients.