<abstract xmlns="http://www.w3.org/1999/xhtml"><sec><h3>Introduction</h3><p>The Slovenian Resolution on the National Healthcare Plan notes that the country’s medical laboratory activities are fragmented, which may result in cost-inefficiency and a reduction in the quality of the services provided. Defining the efficiency of laboratory service providers can therefore help us to pursue the objectives of the Resolution, i.e. to consolidate and integrate laboratory activities.</p></sec><sec><h3>Methods</h3><p>Using the DEA method, we conducted an analysis of the efficiency of 20 biomedical laboratories in Slovenia, and made a comparison with a “virtual” laboratory, i.e. a merger of laboratories within a selected organisational unit. By testing different DEA models, we sought to determine whether the use of different input variables caused significant differences in the laboratories’ efficiency scores.</p></sec><sec><h3>Results</h3><p>The research results show that inefficiency resulting from the size of the units is 1.5 times greater than process inefficiency. Using a non-parametric Wilcoxon Signed Rank test, we determined, at a risk level of 0.05, that there was no difference between the efficiency results when using two different technical efficiency DEA models. When evaluating the virtually merged laboratory, we determined that, under all three models, the virtual laboratory achieved 100% VRS efficiency. However, when the CRS methodology was used, the laboratory showed a certain degree of scale inefficiency.</p></sec><sec><h3>Conclusions</h3><p>When evaluating merger of medical laboratories we note that the DEA method is methodologically suitable for evaluating the effects of health policy implementation, and is an appropriate tool for identifying where the field of laboratory medicine might be further developed and improved.</p></sec></abstract>

IntroductionThe Slovenian Resolution on the National Healthcare Plan notes that the country’s medical laboratory activities are fragmented, which may result in cost-inefficiency and a reduction in the quality of the services provided. Defining the efficiency of laboratory service providers can therefore help us to pursue the objectives of the Resolution, i.e. to consolidate and integrate laboratory activities.MethodsUsing the DEA method, we conducted an analysis of the efficiency of 20 biomedical laboratories in Slovenia, and made a comparison with a “virtual” laboratory, i.e. a merger of laboratories within a selected organisational unit. By testing different DEA models, we sought to determine whether the use of different input variables caused significant differences in the laboratories’ efficiency scores.ResultsThe research results show that inefficiency resulting from the size of the units is 1.5 times greater than process inefficiency. Using a non-parametric Wilcoxon Signed Rank test, we determined, at a risk level of 0.05, that there was no difference between the efficiency results when using two different technical efficiency DEA models. When evaluating the virtually merged laboratory, we determined that, under all three models, the virtual laboratory achieved 100% VRS efficiency. However, when the CRS methodology was used, the laboratory showed a certain degree of scale inefficiency.ConclusionsWhen evaluating merger of medical laboratories we note that the DEA method is methodologically suitable for evaluating the effects of health policy implementation, and is an appropriate tool for identifying where the field of laboratory medicine might be further developed and improved.

Evaluation of Biomedical Laboratory Performance Optimisation Using the DEA Method

Slovenian Journal of Public Health

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

OVREDNOTENJE OPTIMIZACIJE DELOVANJA BIOMEDICINSKIH LABORATORIJEV Z METODO DEA

IntroductionThe Slovenian Resolution on the National Healthcare Plan notes that the country’s medical laboratory activities are fragmented, which may result in...

	Model 1				Model 2				Model 3
DMU	CRS (%)	VRS (%)	RTS	SE (%)	CRS (%)	VRS (%)	RTS	SE (%)	CRS (%)	VRS (%)	RTS	SE (%)
B1	34.93	100	1	34.93	39.86	100	1	39.86	37.26	100	1	37.26
B2	38.78	68.93	1	56.26	45.25	71.79	1	63.03	40.36	66.74	1	60.47
B3	100	100	-	100	100	100	-	100	100	100	-	100
B4	100	100	-	100	100	100	-	100	100	100	-	100
B5	69.81	88.59	1	78.80	70.54	80.64	1	87.48	64.05	73.40	1	87.26
B6	100	100	-	100	100	100	-	100	100	100	-	100
B7	100	100	-	100	78.08	86.62	1	90.14	55.75	60.32	1	92.42
B8	75.14	86.14	1	87.23	80.15	90.16	1	88.90	71.74	74.01	1	96.93
B9	64.53	77.56	1	83.20	66.66	79.06	1	84.32	63.83	75.18	1	84.90
B10	88.82	97.37	1	91.22	99.94	100	1	99.94	89.61	100	1	89.61
B11	100	100	-	100	81.03	96.73	1	83.77	97.54	100	1	97.54
Z1	100	100	-	100	100	100	-	100	87.36	100	−1	87.36
Z2	76.90	77.56	−1	99.15	86.97	87.34	1	99.58	76.9	77.56	−1	99.15
Z3	66.40	96.40	1	68.88	75.73	100	1	75.73	66.4	100	1	66.40
T1	100	100	-	100	100	100	-	100	100	100	-	100
T2	72.28	100	1	72.28	92.33	100	1	92.33	77.62	100	1	77.62
T3	100	100	-	100	100	100	-	100	100	100	-	100
T4	80.87	87.49	1	92.43	77.59	79.57	1	97.51	46.14	52.87	1	87.27
T5	60.04	72.64	1	82.65	60.04	72.64	1	82.65	58.44	67.17	1	87
T6	100	100	-	100	100	100	-	100	100	100	-	100
x(%)	81.43	92.63		87.35	82.71	92.23		89.26	76.65	87.36		87.56
SD	21.01	10.60		17.79	18.87	10.29		15.42	21.78	16.68		16.38

		Min.	Max.	Average	SD
INPUTS	Number of working hours	7,458	157,746	42,604.85	35,947.78
	Number of biomedical analyzers	6	68	21	16
	Cost of laboratory equipment	49,616	2,437,057	601,112.50	590,995.57
	Labour costs	97,332.54	2,290,144	657,896.07	591,933.26
	Cost of laboratory reagents and material	134,683	4,520,071	942,724.43	945,991.67

OUTPUTS	Number of basic tests	49,985	5,616,624	941,786.90	1,277,335.30
	Number of special tests	2,651	784,245	121,054.15	170,071.16
	Number of reference tests	24	111,940	18,066.60	26,016.38

	DMU	CRS (%)	VRS (%)	RTS	SE (%)
MODEL 1	Pseudo 1	93.33	100	−1	93.33
MODEL 2	Pseudo 2	95.89	100	−1	95.89
MODEL 3	Pseudo 3	89.60	100	−1	89.60
$\bar{x}$ {\rm{\bar x}}		92.94	100		92.94
SD		3.16	0		3.16

Evaluation of Biomedical Laboratory Performance Optimisation Using the DEA Method

Article Category: Original scientific article

Published Online: Jun 25, 2020

Page range: 172 - 179

Received: Nov 13, 2019

Accepted: Jun 01, 2020

DOI: https://doi.org/10.2478/sjph-2020-0022

Keywords
optimisation, biomedical laboratory, DEA method, efficiency, consolidation

© 2020 Nejc Lamovšek et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Organisational chart 1

Efficiency results.

Statistical analysis.

Efficiency of the virtually merged laboratory.

Evaluation of Biomedical Laboratory Performance Optimisation Using the DEA Method

Article Category: Original scientific article

Published Online: Jun 25, 2020

Page range: 172 - 179

Received: Nov 13, 2019

Accepted: Jun 01, 2020

DOI: https://doi.org/10.2478/sjph-2020-0022

Keywordsoptimisation, biomedical laboratory, DEA method, efficiency, consolidation

© 2020 Nejc Lamovšek et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Organisational chart 1

Efficiency results.

Statistical analysis.

Efficiency of the virtually merged laboratory.

Keywords
optimisation, biomedical laboratory, DEA method, efficiency, consolidation