Towards an evaluation framework for Laboratory Information Systems.

BackgroundReducing laboratory errors presents a significant opportunity for both cost reduction and healthcare quality improvement. This is particularly true in low-resource settings where laboratory errors are further exacerbated by poor infrastructure and shortages in a trained workforce. Informatics interventions can be used to address some of the sources of laboratory errors.ObjectivesThis article describes the development process for a clinical laboratory information system (LIS) that leverages informatics interventions to address problems in the laboratory testing process at a hospital in a low-resource setting.MethodsWe designed interventions using informatics methods for previously identified problems in the laboratory testing process at a clinical laboratory in a low-resource setting. First, we reviewed a pre-existing LIS functionality assessment toolkit and consulted with laboratory personnel. This provided requirements that were developed into a LIS with interventions designed to address the problems that had been identified. We piloted the LIS at the Kamuzu Central Hospital in Lilongwe, Malawi.ResultsWe implemented a series of informatics interventions in the form of a LIS to address sources of laboratory errors and support the entire laboratory testing process. Custom hardware was built to support the ordering of laboratory tests and review of laboratory test results.ConclusionOur experience highlights the potential of using informatics interventions to address systemic problems in the laboratory testing process in low-resource settings. Implementing these interventions may require innovation of new hardware to address various contextual issues. We strongly encourage thorough testing of such innovations to reduce the risk of failure when implemented.

Efficient Interaction between Hospital information (HIS) and laboratory information systems (LIS) provide a smooth laboratory testing process and data consistency. The current software ecosystem can be characterized by its rapid changes that can lead to breaks in HIS-LIS interaction and problems with semantic interoperability of the systems. To avoid such problems developers can clusterize software applications into small, easily supportable functional units that can be changed on demand without effecting other pieces of software. This approach commonly referred to as microservice architecture. The goal the research is to develop a FHIR based microservice platform that connects HIS, LIS and a Clinical decision support system (CDSS) into unified information space. A microservice platform has been implemented and now is in the production operation processing around 15000 orders a day.

BackgroundThe aim of this study was to determine the impact of COVID-19 pandemic on the total testing process using six sigma metrics based on a comparison of pre and during pandemic periods. Material & methodsThe study duration was over 12 months, 6 months before and 6 months after the COVID-19 onset in Pakistan in March 2020 after the recognition of the first case, using quality indicators (QIs). QIs were chosen from a model of QIs recommended by the CAP. Analysis was performed using Six Sigma calculators and QIs were expressed in percentage (%) and Sigma value were computed. Three levels of performance quality i.e. 25th, 50th, and 75th percentile were derived, being best, common and worst performance respectively. Between-group differences were tested using the Mann-Whitney's U test. ResultsThe median defect percentages of these QIs ranged from 0% to 0.27% for the pre-pandemic period and 0% to 0.13% for the during pandemic period. Meanwhile, sigma values of the majority of the QIs were all above 4.0σ during the pre and the pandemic times. For the pre-analytical phase, sigma scores declined for 1 QI, improved for 3 QIs and remained same for 2. In the post analytical phase, no change in sigma metrics was noted for critical values notification. Considerable increase in defect percentage of inappropriate turnaround times was noted. ConclusionThe emergency preparedness proved to be fruitful as depicted by exceptional performance on the sigma metrics for most Qis both prior to and during the pandemic. The pre-analytical and the post analytical phases, being the most error sensitive requires strict vigilance.

In this paper, we present the results of case studies conducted in three software companies in order to identify the improvement targets in their test processes. The test processes were modeled using the LAPPI (A Light-weight Technique to Practical Process Modeling and Improvement Target Identification) technique. Identifying improvement targets quickly and in a cost-effective way is important to maximize the value of SPI (Software Process Improvement) initiatives. The most significant problems identified in this study were the low level of unit testing, unknown repercussions of code changes, and missing exit criteria for testing. These findings support the results of earlier studies. Further, a less reported problem was identified. It appears that software companies find it difficult to integrate test automation with their manual test processes. Making the recurring test related problems known among the software industry helps to justify the need for test process improvement and standardization of test processes.

Background: Clinical Laboratory testing is a highly complex process that entails numerous procedures. Although it has been known that laboratory testing services are safe, it is increasingly becoming a common knowledge that they are not that safe. Studies have indicated that there are a number of errors that occur due to laboratory testing processes. These errors may not be realized easily during the testing process, but they make significant impact on the results given.Aims and Objective: To determine the levels of pre-analytical, analytical, and post analytical errors found in the analysis of Clinical Laboratory specimen at Kenyatta National Hospital.Materials and Methods: A prospective and descriptive study was carried out at Clinical Chemistry Laboratory, Department of Laboratory Medicine, Kenyatta National Hospital. A total of 346 request forms, specimens/samples and dispatched results were scrutinized and errors documented as per the different variables in the different phases, over a period of three months and the findings were analyzed.Results: Results of the study showed that Preanalytical errors were most common with a frequency of 148(42.8%), followed by analytical errors 114 (32.9%) and post analytical errors 84 (24.3%), respectively.Conclusions: The study concludes that pre-analytical, analytical, and post analytical errors are errors that compromise the quality of laboratory service delivery, which impacts on the patient management and diagnosis. Clinical laboratory errors can be minimized if due diligence and professionalism is adhered in the laboratory.Asian Journal of Medical Sciences Vol.8(1) 2017 64-70

Issues and Initiatives in the Testing Process in Primary Care Physician Offices

Chapter 18 - Laboratory information management

Effective monitoring and testing during each step are essential for document verification in research and development (R&D) projects. In software development, proper testing is required to verify it carefully and constantly because of the invisibility features of software. However, not enough studies on test processes for R&D projects have been done. Thus, in this paper, we introduce a Test Maturity Model integration (TMMi)-based software field R&D test process that offers five integrity levels and makes the process compatible for different types of projects. The Software & Systems Process Engineering Metamodel (SPEM) is used widely in the software process-modeling context, but it lacks built-in enactment capabilities, so there is no tool or process engine that enables one to execute the process models described in SPEM. Business Process Model and Notation (BPMN)-based workflow engines can be a solution for process execution, but process models described in SPEM need to be converted to BPMN models. Thus, we propose an approach to support enactment of SPEM-based process models by converting them into business processes. We show the effectiveness of our approach through converting software R&D test processes specified in SPEM in a case study.

Since at least 15 years, software testing has been one of the areas of software engineering that is increasing its importance in many organizations around the world. Due to this, guides, standards, and maturity models, such as the Brazilian Test Process Improvement model (MPT.Br), are gaining visibility to ensure that a company or organization, even a small one, has a quality level in their testing processes. Such models are important to ensure customer satisfaction and quality analysis of business processes in the production of a system. Given this context, this research aims to present the implementation of the MPT.br (in the first two maturity levels) in a telecommunications company in Brazil to present the benefits of defining the software test process according to a maturity model. To achieve the research goal, we are using a research method called action-research, which is based on empirical studies in software engineering. As results, we expect that this deployment in the company will contribute to a significant improvement in its testing processes, thus contributing to the improvement of the entire organization.

Pre-analytical workstations: A tool for reducing laboratory errors

Background: Diagnostics in Clinical chemistry laboratory is a pivotal part of clinical decision-making but is not exempt from ‘human errors’. Scientific innovations such as automation and electronic order test requesting have contributed to substantial improvements in the field of laboratory science, but errors still occur. One major example of such failing is connected to the prevalence of errors occurring in pre-analytical phase of the Total Testing Process (TTP). Preanalytical errors can occur at the time of patient assessment, test order entry, patient identification, sample collection, sample transport, or sample receipt in the laboratory. Previous work and clinical insights suggest that most errors in the TTP are extra-laboratory (i.e. they occur before the samples reach the laboratory for analysis). Such errors are frequently the results of human mistakes during phlebotomy practice. Therefore to reduce these errors the pre-analytical phase of the TTP must be prioritised. Study objective: To investigate the sources of pre-analytical errors in the TTP, categorize these errors in order to identify the error prone steps, and evaluate errorreporting frequencies, with the aim of improving service. Methods: The first part of the study was a query of the laboratory information management system (LIMS) for samples rejected due to pre-analytical errors. Data collection was done retrospectively to cover two periods from 2007-2008 (manual paper test requesting) and 2012-2013 (after implementation of electronic test ordering, Anglia-ICE). Pre- and post- implementation Anglia-ICE error data were transferred to excel spreadsheets and compared by chi-squared test. The contribution of each error category to total sample error received in the laboratory was also determined. The second part of the study was a questionnaire survey of pre-analytical procedures to capture the attitudes of phlebotomists towards current practice in Sheffield Teaching Hospitals NHS Foundation Trust (STH NHS FT). Results: The results of the first part of the study indicated that of the 416,703 specimens collected pre-Anglia ICE, 2,055 (0.49%) were recorded as errors compared with 1,616 errors (0.11%) of 903,814 specimens collected post-Anglia ICE implementation, which represents a 0.31% (p<0.05) absolute error reduction rate, although more samples were received post-Anglia-ICE. The results of the second part (questionnaire survey) indicate that recommended procedure for phlebotomy practice was not strictly followed by a large percentage of the staff surveyed. Conclusion: This study is the first inquiry linking venous blood sampling (VBS) practices in phlebotomy to retrospective LIMS pre-analytical data in a UK NHS Hospital. The results suggest low compliance by staff with recommended practice, which may be responsible for the prevalence of certain categories of pre-analytical errors in the TTP and may also be associated with increased risks to attending patients. It is suggested that the development of a local guideline for VBS and compliance to this guideline will improve phlebotomy practice, improve the quality of sample testing in the clinical chemistry laboratory, reduce pre-analytical errors in TTP and consequently improve the safety of patients.

In laboratory medicine, the terms “standardization” and “harmonization” are frequently used interchangeably as the final goal is the same: the equivalence of measurement results among different routine measurement procedures over time and space according to defined analytical and clinical quality specifications. However, the terms define two distinct, albeit closely linked, concepts based on traceability principles. The word “standardization” is used when results for a measurement are equivalent and traceable to the International System of Units (SI) through a high-order primary reference material and/or a reference measurement procedure (RMP). “Harmonization” is generally used when results are equivalent, but neither a high-order primary reference material nor a reference measurement procedure is available. Harmonization is a fundamental aspect of quality in laboratory medicine as its ultimate goal is to improve patient outcomes through the provision of accurate and actionable laboratory information. Patients, clinicians and other healthcare professionals assume that clinical laboratory tests performed by different laboratories at different times on the same sample and specimen can be compared, and that results can be reliably and consistently interpreted. Unfortunately, this is not necessarily the case, because many laboratory test results are still highly variable and poorly standardized and harmonized. Although the initial focus was mainly on harmonizing and standardizing analytical processes and methods, the scope of harmonization now also includes all other aspects of the total testing process (TTP), such as terminology and units, report formats, reference intervals and decision limits as well as tests and test profiles, requests and criteria for interpretation. Several projects and initiatives aiming to improve standardization and harmonization in the testing process are now underway. Laboratory professionals should therefore step up their efforts to provide interchangeable and comparable laboratory information in order to ultimately assure better diagnosis and treatment in patient care.

The Impact of a Pneumatic Tube and Computerized Physician Order Management on Laboratory Turnaround Time

Web application testing is concerned with numerous and complicated testing objects, methods and processes. So a testing framework fitting for the properties of Web application is needed to guide and organize all the testing tasks. Based on the analysis for Web application characters and traditional software testing process, the process for Web application testing is modeled, which describes a series of testing flows such as the testing requirement analysis, test cases generation and selection, testing execution, and testing results analysis and measurement. Furthermore, the realization techniques are also investigated so as to integrate each testing step and implement the whole testing process harmoniously and effectively. Thus the framework is suitable for the Internet environment and can guide the Web application testing actively and availably.

Software test maturity assessment and test process improvement: A multivocal literature review