Practice Management

Reimbursement

Latest AI and machine learning research in reimbursement for healthcare professionals.

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Contemporary Pure Laparoscopic Robot-Assisted Laparoscopic Radical Nephrectomy: Is the Transition Worth It?

The proportion of robotic procedures continues to rise. The literature reinforces that robotic proc...

Deep learning methods for screening patients' S-ICD implantation eligibility.

Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden card...

Medical code prediction via capsule networks and ICD knowledge.

BACKGROUND: Clinical notes record the health status, clinical manifestations and other detailed info...

Automated ICD coding for primary diagnosis via clinically interpretable machine learning.

BACKGROUND: Computer-assisted clinical coding (CAC) based on automated coding algorithms has been ex...

Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution.

OBJECTIVES: Asthma is a heterogenous condition with significant diagnostic complexity, including var...

Gaining Insights Into Patient Satisfaction Through Interpretable Machine Learning.

Patient satisfaction is a key performance indicator of patient-centered care and hospital reimbursem...

A supervised clustering MCMC methodology for large categorical feature spaces.

There is a well-established tradition within the statistics literature that explores different techn...

Conjunctival Provocation Test With .

Conjunctival provocation test (CPT) is used to demonstrate clinical relevance to a specific allerge...

A comparison of natural language processing to ICD-10 codes for identification and characterization of pulmonary embolism.

INTRODUCTION: The 10th revision of the International Classification of Diseases (ICD-10) codes is fr...

Selection of Clinical Text Features for Classifying Suicide Attempts.

Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (...

Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records.

The International Statistical Classification of Disease and Related Health Problems (ICD) is an inte...

Engaging proactive control: Influences of diverse language experiences using insights from machine learning.

We used insights from machine learning to address an important but contentious question: Is bilingua...

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.

Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known risk fac...

Part 1: Artificial intelligence technology in surgery.

Artificial intelligence (AI) is one of the disruptive technologies of the fourth Industrial Revoluti...

Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital.

BACKGROUND: Hospital readmissions are a key quality metric, which has been tied to reimbursement. On...

ZiMM: A deep learning model for long term and blurry relapses with non-clinical claims data.

This paper considers the problems of modeling and predicting a long-term and "blurry" relapse that o...

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.

The application of machine learning (ML) for use in generating insights and making predictions on ne...

Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports.

Accurate, automated extraction of clinical stroke information from unstructured text has several imp...

A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes.

OBJECTIVE: This study aims to develop and evaluate effective methods that can normalize diagnosis an...

Automated ICD coding via unsupervised knowledge integration (UNITE).

OBJECTIVE: Accurate coding is critical for medical billing and electronic medical record (EMR)-based...

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback.

BACKGROUND: Due to reimbursement tied in part to patients' perception of their care, hospitals conti...

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