scholarly journals Elimination of clinical biochemistry laboratory tests through artificial intelligence programs to increase cost-effectiveness

2018 ◽  
Vol 9 (4) ◽  
Author(s):  
Ataman Gönel
Keyword(s):  
Artificial Intelligence ◽  
Cost Effectiveness ◽  
Laboratory Tests ◽  
Clinical Biochemistry ◽  
Clinical Biochemistry Laboratory

Effect of feedback to clinical staff of information on clinical biochemistry requesting patterns.

1981 ◽  
Vol 27 (10) ◽  
pp. 1717-1720 ◽  
Author(s):  
A R Grivell ◽  
H J Forgie ◽  
C G Fraser ◽  
M N Berry
Keyword(s):  
Medical Students ◽  
Laboratory Tests ◽  
Clinical Biochemistry ◽  
Reliable Information ◽  
Medical Specialist ◽  
Related Information ◽  
Number Of Patients ◽  
Standard Parameter ◽  
Continuous Feedback ◽  
Clinical Biochemistry Laboratory

Abstract Attempting to reduce the number of unnecessary tests requested, we initiated a program providing continuous feedback to clinicians concerning their requesting patterns of clinical biochemistry laboratory tests. At four-week intervals each medical specialist received data showing the number and costs of tests requested by all members of his team, the number of patients who had tests, and various related information. Comparative summaries, in frequency-histogram format, of the number of specimens submitted and the costs involved allowed each medical specialist to rate his own performance against that of his peers. The program appeared to have no effect on laboratory use, although reliable information concerning workload patterns was accrued. The degree of education of medical students in clinical biochemistry did not appear to influence the use they made of the laboratory after qualification. The specimens per bed-day ratio is suggested as a useful parameter in the identification of inappropriate laboratory use and as a standard parameter for comparing laboratory use in different hospitals.

scholarly journals High preanalytical non-compliance and samples rejection rate in clinical biochemistry laboratory are decreased by nurse staff training in phlebotomy and sample handling

2020 ◽  
Vol 13 (3) ◽  
pp. 113-118
Author(s):  
Modibo Coulibaly ◽  
Abdelaye Keita ◽  
Moussa Diawara ◽  
Valentin Sagara ◽  
Brehima Traoré ◽  
...  
Keyword(s):  
Staff Training ◽  
Clinical Biochemistry ◽  
Statistical Significance ◽  
Venous Blood ◽  
Diagnostic Errors ◽  
Hospital Staff ◽  
Clinical Samples ◽  
Sample Handling ◽  
Significant Difference ◽  
Clinical Biochemistry Laboratory

Background: Preanalytical phase of biomedical analysis remains an important source of diagnostic errors that deserves special attention. This study aims to evaluate the training in phlebotomy and sample handling impact on the preanalytical non-compliances. Material and Methods: we performed a prospective study before and after staff training in phlebotomy and sample handling by systematically recording all clinical samples non-compliances. First, we assessed and describe the non-compliance baseline rate from January to December 2017 in the clinical biochemistry laboratory of Hôpital Sominé DOLO de Mopti. After two sessions of one week staff training in January 2018, we performed the same study from January to December 2018. We compared the proportions of non-compliances between the two assessments. Data were collected on the case report forms, captured in Excel and analyzed by R software for (Mac) OS X version 4.0.3. Pearson Ch2 or Fisher exact tests were used for the comparison of proportions. The statistical significance was set at p < 5%. Results: a total of 27,810 venous blood samples were received during the study period; 48% was for biochemistry, 41% for immuno-serology, 9% for blood cell count and 2% for coagulation tests. There were 3,826 instances of preanalytical non-compliances (13.76%) identified that led to sample rejection. Out of the 11 types of non-compliances investigated, 5 (45.4%) accounted for nearly 91% of the problems: insufficient sample volume (28.9%), hemolyzed samples (20.5%), inappropriate collection time (17.8%), sample clot (12.9%), and inappropriate sample collection tube (10.8%). We observed a significant difference in rates of non-compliance between inpatients and outpatients samples (44.4% vs 7.3%; p < 0.001). The proportion of non-compliance have significatively decreased after the two training sessions of hospital staff in phlebotomy and sample handling 3,826/27,810 (13.8%) vs 3,009/32,476 (9.3%); p < 0.001. Conclusion: we report a significantly higher rate of non-compliance in inpatients. Hospital staff training in phlebotomy and sample handling reduce the proportion of preanalytical non-compliance and thereby improve patient management and safety.

scholarly journals Application of Sigma Metrics for Evaluating the Analytical Performance of Thyroid Profile and Cortisol in Clinical Biochemistry Laboratory

2020 ◽  
Author(s):  
Smita Natvarbhai Vasava ◽  
Roshni Gokaldas Sadaria
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Clinical Laboratory ◽  
Clinical Biochemistry ◽  
Poor Performance ◽  
Analytical Performance ◽  
Internal Quality ◽  
Six Sigma Methodology ◽  
Clinical Biochemistry Laboratory ◽  
Thyroid Profile

Introduction: Now-a-days quality is the key aspect of clinical laboratory services. The six sigma metrics is an important quality measurement method for evaluating the performance of the clinical laboratory. Aim: To assess the analytical performance of clinical biochemistry laboratory by utilising thyroid profile and cortisol parameters from Internal Quality Control (IQC) data and to calculate sigma values. Materials and Methods: Study was conducted at Clinical Biochemistry Laboratory, Dhiraj General Hospital, Piparia, Gujarat, India. Retrospectively, IQC data of thyroid profile and cortisol were utilised for six subsequent months (July to December 2019). Coefficient of Variation (CV%) and bias were calculated from IQC data, from that the sigma values were calculated. The sigma values <3, >3 and >6 were indicated by poor performance procedure, good performance and world class performance, respectively. Results: The sigma values were estimated by calculating mean of six months. The mean sigma value of Thyroid Stimulating Hormone (TSH) and Cortisol were >3 for six months which indicated the good performance. However, sigma value of Triiodothyronine (T3), Tetraiodothyronine (T4) were found to be <3 which indicated poor performance. Conclusion: Six sigma methodology applications for thyroid profile and cortisol was evaluated, it was generally found as good. While T3 and T4 parameters showed low sigma values which requires detailed root cause analysis of analytical process. With the help of six sigma methodology, in clinical biochemistry laboratories, an appropriate Quality Control (QC) programming should be done for each parameter. To maintain six sigma levels is challenging to quality management personnel of laboratory, but it will be helpful to improve quality level in the clinical laboratories.

Cost-effectiveness of AI in medicine from a clinical, technical, and economic perspective: A scoping review and a framework of analysis (Preprint)

2021 ◽  
Author(s):  
Jesus Gomez Rossi ◽  
Ben Feldberg ◽  
Joachim Krois ◽  
Falk Schwendicke
Keyword(s):  
Artificial Intelligence ◽  
Cost Effectiveness ◽  
Scientific Literature ◽  
Economic Aspects ◽  
Inclusion Criteria ◽  
Economic Perspective ◽  
Health Policy Analysis ◽  
The Cost ◽  
The Relationship ◽  
Potential Use

BACKGROUND Research and Development (R&D) of Artificial Intelligence (AI) in medicine involve clinical, technical and economic aspects. Better understanding the relationship between these dimensions seems necessary to coordinate efforts of R&D among stakeholders. OBJECTIVE To assess systematically existing literature on the cost-effectiveness of Artificial Intelligence (AI) from a clinical, technical and economic perspective. METHODS A systematic literature review was conducted to study the cost-effectiveness of AI solutions and summarised within a scoping framework of health policy analysis developed to study clinical, technical and economic dimensions. RESULTS Of the 4820 eligible studies, 13 met the inclusion criteria. Internal medicine and emergency medicine were the most studied clinical disciplines. Technical R&D aspects have not been uniformly disclosed in the studies we analysed. Monetisation aspects such as payment models assumed have not been reported in the majority of cases. CONCLUSIONS Existing scientific literature on the cost-effectiveness of AI currently does not allow to draw conclusive recommendations. Further research and improved reporting on technical and economic aspects seem necessary to assess potential use-cases of this technology, as well as to secure reproducibility of results. CLINICALTRIAL Not applicable

Innovation in Laboratory Medicine

2019 ◽  
pp. 117-137
Author(s):  
Carlos Lemos
Keyword(s):  
Cost Effectiveness ◽  
Resource Management ◽  
Outcome Measures ◽  
Laboratory Tests ◽  
Laboratory Medicine ◽  
Process Change ◽  
Medical Test ◽  
A Value ◽  
Improve Patient

Laboratory medicine has a unique capability to evaluate the correct management of a medical test, its results, and the decisions it can determine. Therefore, laboratory medicine should try to improve patient outcomes, while improving quality and productivity, so that innovation in healthcare may proceed. Innovation in laboratory medicine demands an adequate identification of the unmet clinical need, evidence of clinical and cost-effectiveness of laboratory tests, and a managed implementation that takes into account the process change, appropriate resource management, and monitoring of outcomes. The main objectives of this chapter are to elucidate the role of innovation in laboratory medicine, identifying its main issues and the barriers it faces; to define a value proposition for laboratory tests and to point out several outcome measures that can be adopted in laboratory medicine.

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