LABORATORY MANAGEMENT Service support availability 24/7 System flexibility (increasing workload adaptability,


different workflows capability, physical re-configuration) Reliability (downtime)


Low hands-on time for maintenance Middleware functionalities / LIS interface


System redundancy (back-up) or ‘self-recovery’ capabilities Automatic sample storage


Automatic sample retrieval (reflex, re-run, add-on tests) Reagents and disposable traceability System walk-away time


Third-party analyser connectivity


Different samples tube size management Total TLA footprint


Reagents inventory tracking Stat samples management Solid / liquid waste production


8 0% 2% 4% 6% 8% 10% 12% 14% 16% 0 Fig 2. Main requirements of a laboratory automation system.


the new activities or the changes to those already in use;


4. description of individual activity and operations: to detail ‘what-to-do’, roles and responsibilities of all people involved in the process.


The workflow analysis can be supported by specialised software tools for data collection, analysis of operational trends, and estimation of expected results by simulating possible intervention scenarios and technological configurations. A typical workflow example, as defined during the planning of an automation project, may involve up to 25 separate steps, as in Figure 1.


One of the objectives of automation


is to increase the number of automated operations (shown in blue), reduce those that will necessarily remain manual, (shown in orange), and identify those related to data management that can be entrusted to software and middleware systems (shown in green). When automation becomes a reality, among the potential risks of failure, the acceptance by operators and their relationship with the new instrumentation is the most critical. A large number of people view the implementation of automated systems as a direct threat to their role.


Success depends on how the change for technical staff, resulting from automation, is managed and how its concerns are identified and addressed. Therefore gathering the needs and expectations of personnel who will be


34


directly involved in the change process is among the most important activities on which the success of the project depends. Through a simple survey, as an example, conducted among internal staff, both operational and managerial, it is possible to identify the main requirements that the new working model will have to meet, and consequently which aspects will have to be carefully evaluated in order to select the right design from different suppliers.


In this way it is also possible to bring out clearly which selection criteria are considered most important by laboratory personnel, criteria that the project will necessarily have to take into account. Figure 2 shows some of the main


requirements, listed in descending order of importance, that are most frequently mentioned.


Opportunities to further increase the efficiency of a laboratory must be sought in pursuing organisational models that lead to an efficient and effective response to meet clinical needs. This goal can certainly be achieved with automation, provided that the following requirements have already been met: n adoption of systems to control and contain the cost of analytical service delivery through the elimination of ‘wastes’, with more rational use of reagents and consumables, for example, by managing the test frequency of low-demand processes;


n integration and consolidation of analytical activities, based mainly


2 4 6 8 10 12 14 16 6%


5% 5% 5%


4% 4% 4%


3% 3% 3%


2%


8% 8%


12% 15% 14%


on the use of ‘homogeneous instrumental platforms’ rather than instruments dedicated to different analytical disciplines; it is increasingly common within laboratories to adopt organisational models based on areas by delivery intensity and technological homogeneity;


n integration between emergency and routine requests, with the aim of better utilisation of laboratory staff and equipment, decreasing improper requests.


Before determining what type of automation is needed, it is therefore important that the laboratory has already carried out a ‘reengineering process’, for example through well- proven methodologies such as ‘Lean’ or ‘Six-Sigma’, to achieve significant improvements in process quality, reduce most of the manual workload and eliminate unnecessary and redundant activities, thus paving the way for the next stage of automation. For the clinical laboratory, efficiency first equates to reducing the number of steps in the analytical process itself. First simplify and then automate.


Post-implementation monitoring


The benefits of automation are now widely documented with various examples of successful installations around the world. However, references to less successful projects and especially documentation of the first few months after implementation are more limited. Many of the most common reasons


for failure of an automation project can be traced back to a number of mistakes made during the initial planning phase, such as: n failure to optimise current processes prior to automation;


n loss of operational flexibility due to rigid automation and limited extra capacity;


n unrealistic expectations; n oversized and overly complex design (both hardware and software);


n inadequate technical support from suppliers;


n absence of a credible and realistic analysis of the impact of automation on the entire laboratory process; n hidden or disregarded costs.


Verifying the objectives of an automation project is a crucial phase that the laboratory must conduct together with the suppliers of the adopted technologies, both to confirm compliance with the project specifications and the achievement of the expected objectives.


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