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Table 1 Description of the dataset

From: A NLP-based semi-automatic identification system for delays in follow-up examinations: an Italian case study on clinical referrals

Variable

Source

Description

Anonymized Doctor ID

(1)

Unique ID of the prescribing physician

Anonymized Referral ID

(1)

Unique ID of the referral

Date of referral

(1)

Date when the physician filled out the referral

Date of booking

(2)

Date when the patient booked the examination

First proposed date

(2)

First date proposed to the patient by the booking system

Accepted date

(2)

Date accepted by the patient to perform the examination (may be later than the first proposed date)

Type of examination

(1)

Type of specialist or instrumental examination requested

O/Z Flag

(2)

Flag indicating if the examination is subject to waiting time monitoring (O) or not (Z)

Healthcare facility

(2)

Hospital or outpatient clinic where the examination is performed

ATS (LHA - Local Health Authority)

(1)

LHA of the prescribing physician

Clinical question

(1)

Free text containing the reason for the referral and possibly its timing

  1. List of variables included in the analyzed dataset, with source and description. Sources: (1) = Anonymized Electronic Referrals, (2) = Anonymized Performed Examinations