Pressure injuries, which can lead to a patient safety indicator (PSI), require clear documentation and coordination among coding, CDI, and clinical departments. Katherine Siemens, RN, BSN, CMSRN, CCDS , evaluates how poor coordination could result in a PSI being incorrectly reported.
This article reviews malware basics and covers tips that healthcare employees can implement to avoid cyberattacks that could put protected health information (PHI) at risk.
Q: A 64-year-old female inpatient has hepatocellular cancer with an orthotropic liver transplant with bile duct obstruction and is immunosuppressed due to drugs. Which ICD-10-CM codes would be reported?
A study published in the Journal of the American Medical Association found that four popular pretest risk assessment models for evaluating risk of hospital-acquired venous thromboembolism in inpatients did “not perform particularly well.”
Kathy Dorich, MSN, RN, CCDS, CPHQ , explains two types of DRG reconciliation processes that she has implemented to alleviate conflict between coding and CDI departments.
JoAnn Baker, CCS, CPC, COC , defines sepsis and septic shock, and delves into the emerging initiative to integrate AI into the diagnosis and treatment process.
Verbal conversations with providers regarding reportable conditions and procedures are considered verbal queries. Refresh how they should be memorialized within the record to maintain compliance. Note : To access this free article, make sure you first register here if you do not have a paid subscription.
Q: A patient has acute renal failure due to dehydration, a history of Type 1 diabetes mellitus causing end-stage renal disease, a kidney transplant two years ago, and chronic kidney disease stage 3a, immunosuppressed by their drugs. How would this be reported in ICD-10-CM?
A Journal of the American Medical Association study found that ICD-10-CM influenza codes accurately represented cases of positive diagnoses in pediatric patients, but their sensitivity was modest.
Brandi Hutcheson, RN, MSN, CCM, CCDS, CCA , examines the coding and clinical literature on malnutrition and obesity to see how coders can reconcile these seemingly disparate diagnoses.
