Discovering Primary Medical Procedures and their Associations with Other Procedures in HCUP Data

Abstract

In recent years, healthcare spending has risen and become a burden on governments especially in the US. The selection of the primary medical procedure by physicians is the first step in the patient treatment process and is considered to be one of the main causes for hospital readmissions if it is not done correctly. In this paper, we propose a system that can identify with high accuracy the primary medical procedure for a newly admitted patient. We propose three approaches to anticipate which medical procedure should be primary. Additionally, we propose the procedure graph, which shows all possible paths that a new patient may undertake during the course of treatment. Finally, we extract the possible associations between the primary procedure and other procedures in the same hospital visit. The results show the ability of our proposed system to identify which procedure should be primary and extract its associations with other procedures.

Reduction of hospital readmissions

“…The ordered sequence of primary medical procedures representing procedure P followed by all procedures a single patient had after procedure P during the whole course of treatment in a hospital is called a procedure path for P. 13 By a procedure graph, 13 for a medical procedure P, we mean a directed graph representing a collection of all procedure paths for P extracted from the Florida SID, associated with patients during their course of treatment. The procedure graph can be used by physicians to visualize all possible outcomes which took place in Florida state hospitals after performing the initial procedure P. Nodes in the graph represent medical procedures that could be minor, such as breast biopsy, arthroscopy or laparoscopy, or that could be major, such as cesarean section, organ replacement or heart surgeries.…”

“…There are many papers focused on predicting hospital readmissions, [1][2][3][4] especially predicting hospital admissions and readmissions to emergency or intensive care units. [5][6][7][8][9][10][11] This editorial focuses on the basics of a knowledge-based recommendation system 12,13 for decreasing the number of readmissions to hospitals, based on actionable knowledge extracted from medical datasets, using the concept of action rules [14][15][16] to provide recommendations. There is not much research done on decreasing the number of readmissions to hospitals after initial procedure and almost none based on action rules.…”