Tuesday, 30 April 2019


Written by Daniel Pineda-Tenor | Santiago Prieto Menchero | Nicolás Prieto García, Posted in Volumen10

Figure 1. FIFO, LIFO and RISO modes. Different options and priorities. There are two urgent samples, one detected at reception5 and another that goes unnoticed as routine3.

The terms FIFO, LIFO and FEFO are commonly used in management (Table 1).

Can be applied to samples and response times, when considering "expiration" as "commitment of response". Every laboratory follows different procedures. However, FIFO-routine and LIFO-urgency approach are frequent. Specific manual entries for emergencies in the analyzers are applied. There is a coexistence of two laboratories: routine and urgency.
In this paper, we introduce a new concept (RISO) that redefines these approaches from a perspective that combines automation, intelligent systems and orientation to results on the patient.

Table 1. Some terms used that can be applied in sample management.
Improvements in information technologies (EHR – Electronic Health records and LIS – Laboratory Information System) and robotics/analytical chains (LAS - Laboratory Automation System) would allow these schemes to become a game changer. The sophistication of the systems increases the possibilities of real effectiveness; requests are categorized by their delivery priority as a block (achievement of the required criteria) or individually according to specific needs.

We suggest a new approach called RISO (Random In, Smart Out), which prioritizes processing, verification and validation according to the date/time of delivery needed.

What happens when there is a single entry for all the requests (routine, emergency, hospitalization, day hospital, high-resolution consultations, etc.)? If the samples are received randomly in the laboratory, their processing will not be efficient or adequate to the needs of the patient because there is not a smart management system. We need to create a system that differentiates the priority of the sample result on the clinician side right when the samples are received. This system needs to process the samples because of their delivery priority, not because of their entry date.

We propose a paradigm shift in workflows. Instead increasing the speed of processing as an axis, the objective should not be to stratify samples and / or reduce overall response times, but to adapt them to the real needs of the patient, minimizing times when they have an effect on the attention provided.
mplementing RISO implies:
  1. Define result priority. Consensus on response times and priority according to the real need of each request.
  2. Laboratory information system (LIS). It must be able to define categories and manage workflows according to different factors and to adapt each request to the real needs of the patient. The date/time in which the result is required (date/time of visit or consultation) must be included and updated through HL7 messaging or web-service.
  3. Laboratory automation system (LAS). The pre-analytical system and/or LAS will be able to reorganize samples in order to process them according to priority (by category or sample`s request). The analyzers will queue the processing order according to the samples priority.
Some laboratories try to solve inefficiency by increasing the equipment or personnel. However, it is possible to increase technological performance and improve response with process analysis and the implementation of effective management systems. The model does not impose special requirements for the applicant (Random In) but allows prioritizing the issuance of reports based on clinical needs and/or consensual proceedings (Smart Out).
It is not always about the equipment, sometimes it is just about intelligent processing.

Instead of measuring response times, it is better to measure the percentage of available requests within the committed response time for each one.

The fixed response time is not a valid indicator of its modalities, but the percentage of reports delivered in the agreed time or before (negative slack time) is.

  1. Knowles S, Barnes I. Lean laboratories: laboratory medicine needs to learn from other industries how to deliver more for less. J Clin Pathol. 2013;66(8):635-7.
  2. José Ramón Vilana Arto Gestión de Stocks  (acceso 10/01/2019).
  3. Leaven LT. Improving Hospital Laboratory Performance: Implications for Healthcare Managers. Hosp Top. 2015;93(2):19-26.
  4. Armbruster DA, Overcash DR, Reyes J. Clinical Chemistry Laboratory Automation in the 21st Century -Amat Victoria curam (Victory loves careful preparation). Clin Biochem Rev. 2014;35(3):143-53.
  5. Dolci A, Giavarina D, Pasqualetti S, Szőke D, Panteghini M. Total laboratory automation: Do stat tests still matter?. Clin Biochem. 2017;50(10-11):605-611.
  6. Goswami B, Singh B, Chawla R, Gupta VK, Mallika V. Turn Around Time (TAT) as a Benchmark of Laboratory Performance. Indian J Clin Biochem. 2010;25(4):376-9.
  7. Pati HP, Singh G. Turnaround Time (TAT): Difference in Concept for Laboratory and Clinician. Indian J Hematol Blood Transfus. 2014;30(2):81-4.

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