The ideal laboratory information system

QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories

OBJECTIVES

Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices.

METHODS

QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively.

RESULTS

Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size and offering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results.

CONCLUSIONS

In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

Data flow in clinical laboratories: could metadata and peridata bridge the gap to new AI-based applications?

In the last decades, clinical laboratories have significantly advanced their technological capabilities, through the use of interconnected systems and advanced software. Laboratory Information Systems (LIS), introduced in the 1970s, have transformed into sophisticated information technology (IT) components that integrate with various digital tools, enhancing data retrieval and exchange. However, the current capabilities of LIS are not sufficient to rapidly save the extensive data, generated during the total testing process (TTP), beyond just test results. This opinion paper discusses qualitative types of TTP data, proposing how to divide laboratory-generated information into two categories, namely metadata and peridata. Being both metadata and peridata information derived from the testing process, it is proposed that the first is useful to describe the characteristics of data, while the second is for interpretation of test results. Together with standardizing preanalytical coding, the subdivision of laboratory-generated information into metadata or peridata might enhance ML studies, also by facilitating the adherence of laboratory-derived data to the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Finally, integrating metadata and peridata into LIS can improve data usability, support clinical utility, and advance AI model development in healthcare, emphasizing the need for standardized data management practices.

Utilizing the open-source programming language Python to create interactive Quality Assurance dashboards for diagnostic and screening performance in Cytology

INTRODUCTION

Effective feedback on cytology performance relies on navigating complex laboratory information system data, which is prone to errors and lacks flexibility. As a comprehensive solution, we used the Python programming language to create a dashboard application for screening and diagnostic quality metrics.

MATERIALS AND METHODS

Data from the 5-year period (2018-2022) were accessed. Versatile open-source Python libraries (user developed program code packages) were used from the first step of LIS data cleaning through the creation of the application. To evaluate performance, we selected 3 gynecologic metrics: the ASC/LSIL ratio, the ASC-US/ASC-H ratio, and the proportion of cytologic abnormalities in comparison to the total number of cases (abnormal rate). We also evaluated the referral rate of cytologists/cytotechnologists (CTs) and the ratio of thyroid AUS interpretations by cytopathologists (CPs). These were formed into colored graphs that showcase individual results in established, color-coded laboratory "goal," "borderline," and "attention" zones based on published reference benchmarks. A representation of the results distribution for the entire laboratory was also developed.

RESULTS

We successfully created a web-based test application that presents interactive dashboards with different interfaces for the CT, CP, and laboratory management (https://drkvcsstvn-dashboards.hf.space/app). The user can choose to view the desired quality metric, year, and the anonymized CT or CP, with an additional automatically generated written report of results.

CONCLUSIONS

Python programming proved to be an effective toolkit to ensure high-level data processing in a modular and reproducible way to create a personalized, laboratory specific cytology dashboard.

Survey of Laboratory Medicine's national response to the HSE cyberattack in the Republic of Ireland

BACKGROUND

On Friday 14 May 2021, the Health Service Executive (HSE) was subjected to a serious cyberattack on their information technology (IT) infrastructure. Healthcare workers lost access to HSE-provided clinical and non-clinical IT systems, including laboratory systems.

AIM

The aim of this national survey was to capture Laboratory Medicine's response across the Republic of Ireland during the HSE cyberattack.

METHOD

An electronic survey developed using Microsoft Forms® was emailed on 24 September 2021 to 58 local representatives of the PeriAnalytic and Laboratory Medicine Society (PALMSoc).

RESULTS

The survey was sent to 43 clinical laboratories across the Republic of Ireland. A total of 41 responses from 43 laboratories across all laboratory disciplines were received (95% response rate). From these, 55% did not have access to a functioning LIS, with 56% of these not having access to a LIS for greater than 2 weeks. A decrease in specimen requests received during this period was reported by 74% of laboratories, with 32% experiencing a reduction that lasted in excess of one month. Over half of the laboratories (55%) experienced a reduction of > 30% in requests, indicating that clinicians stopped investigating patients (87% reduction in primary care), further escalating the disruption to healthcare.

CONCLUSION

The cyberattack burdened the HSE and laboratories at a time when healthcare staffs were coming to terms with the impact of the COVID-19 pandemic. Despite this, the survey confirms the agility of laboratory staff in meeting the demands placed on it during this time.

Comprehensive improvements in the emergency laboratory test process based on information technology

OBJECTIVE

To explore the application effects of information technology (IT) on emergency laboratory testing procedures.

METHODS

In this study, IT-based optimisation of the emergency laboratory testing process was implemented between October and December 2021. Thus, the emergency laboratory test reports from January to September 2021 were placed into the pre-optimised group, while those from January to September 2022 were categorised into the post-optimised group. Besides, the emergency laboratory test report time, emergency laboratory test report time limit coincidence rate, error rate, and employee and patient satisfaction levels in individual months and across the whole period were described. Moreover, changes in the above indicators before and after the implementation of IT-based optimisation were explored and the application effects of IT-based optimisation were also evaluated.

RESULTS

The emergency laboratory test report times after the implementation of IT-based optimisation were shorter than those before IT-based optimisation (P < 0.05). The total number of laboratory test items before and after information optimization amounted to 222,139 and 259,651, respectively. Also, IT-based optimisation led to an increase in the emergency laboratory test report time limit coincidence rate from 98.77% to 99.03% (P < 0.05), while the emergency laboratory test report error rate fell from 0.77‱ to 0.15‱ (P < 0.05). Additionally, IT-based optimisation resulted in increases in both employee satisfaction, from 80.65% to 93.55% (N = 31, P > 0.05), and patient satisfaction, from 93.06% to 98.44% (P < 0.05).

CONCLUSION

The automation and IT-based optimisation of the emergency laboratory testing process significantly reduces the emergency laboratory test report time and error rate. Additionally, IT-driven optimization enhances the alignment of emergency laboratory test report deadlines and enhances the overall quality and safety of emergency laboratory testing.

Digitization of Pathology Labs: A Review of Lessons Learned

Pathology laboratories are increasingly using digital workflows. This has the potential of increasing laboratory efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, information technology decision makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.

Antimicrobial susceptibility testing and reporting practices of public hospital microbiology laboratories in Greece, 2022: A national observational survey and call for action

Antimicrobial resistance (AMR) in Greece is among the highest across the European Union/European Economic Area (EU/EEA), with high AMR rates even to last-line antibiotics. To better understand the clinical microbiology laboratory practices and capacities in species identification and antimicrobial susceptibility testing (AST) across public healthcare establishments in Greece, we sent a questionnaire to 98 of 128 public hospital microbiology laboratories between 1 February and 1 April 2022. Of the 73.5% (72/98) that responded, 51.4% (37/72) reported using EUCAST guidelines. Two of three laboratories used an automated instrument for species identification and AST for all laboratory samples. Broth microdilution for colistin susceptibility testing was used by 46 of the laboratories, more frequently in larger (> 400 beds) versus smaller (< 400 beds) hospitals (90.5% (19/21) vs 52.9% (27/51) respectively, p = 0.011). MALDI-TOF mass spectrometry was available in one of 10 laboratories, and more often in larger compared to smaller hospitals (p = 0.035). Although the majority of laboratories had a laboratory information system (LIS) in place, only half had the capacity to extract data directly from the LIS for the purpose of AMR surveillance; 73.6% (53/72) used restrictive antibiograms. Public microbiology laboratory AMR capacities in Greece require improvement, prioritising interventions for EUCAST guidelines implementation.

Pathologists Overseas: A volunteer-based model for building sustainable, high-quality pathology and laboratory medicine services in low- and middle-income countries

For thirty years Pathologists Overseas (PO) has worked in low- and middle-income countries (LMICs) to provide affordable, sustainable, and high-quality pathology and laboratory medicine (PALM) services through strategic partnerships and the efforts of our large volunteer network. We address low quality diagnostic services by targeting the 3 pillars of PALM quality: human resources, systems, and quality and accreditation. To improve human resource capacity, PO and our partnering organizations provide virtual continuing education to pathologists and laboratory professionals in these countries. To improve systems, we provide laboratory information system installation and implementation support. Lastly, to improve quality and help laboratories progress toward accreditation, we support an external quality assurance program for laboratories in LMICs. As a relatively small organization, PO demonstrates that a network of dedicated volunteers, in partnership with corporations and professional organizations, can initiate sustainable change in the quality of PALM services in LMICs by focusing efforts on the core components of laboratory quality.

Using text mining techniques to extract prostate cancer predictive information (Gleason score) from semi-structured narrative laboratory reports in the Gauteng province, South Africa

Background

Prostate cancer (PCa) is the leading male neoplasm in South Africa with an age-standardised incidence rate of 68.0 per 100,000 population in 2018. The Gleason score (GS) is the strongest predictive factor for PCa treatment and is embedded within semi-structured prostate biopsy narrative reports. The manual extraction of the GS is labour-intensive. The objective of our study was to explore the use of text mining techniques to automate the extraction of the GS from irregularly reported text-intensive patient reports.

Methods

We used the associated Systematized Nomenclature of Medicine clinical terms morphology and topography codes to identify prostate biopsies with a PCa diagnosis for men aged > 30 years between 2006 and 2016 in the Gauteng Province, South Africa. We developed a text mining algorithm to extract the GS from 1000 biopsy reports with a PCa diagnosis from the National Health Laboratory Service database and validated the algorithm using 1000 biopsies from the private sector. The logical steps for the algorithm were data acquisition, pre-processing, feature extraction, feature value representation, feature selection, information extraction, classification, and discovered knowledge. We evaluated the algorithm using precision, recall and F-score. The GS was manually coded by two experts for both datasets. The top five GS were reported, with the remaining scores categorised as “Other” for both datasets. The percentage of biopsies with a high-risk GS (≥ 8) was also reported.

Results

The first output reported an F-score of 0.99 that improved to 1.00 after the algorithm was amended (the GS reported in clinical history was ignored). For the validation dataset, an F-score of 0.99 was reported. The most commonly reported GS were 5 + 4 = 9 (17.6%), 3 + 3 = 6 (17.5%), 4 + 3 = 7 (16.4%), 3 + 4 = 7 (14.7%) and 4 + 4 = 8 (14.2%). For the validation dataset, the most commonly reported GS were: (i) 3 + 3 = 6 (37.7%), (ii) 3 + 4 = 7 (19.4%), (iii) 4 + 3 = 7 (14.9%), (iv) 4 + 4 = 8 (10.0%) and (v) 4 + 5 = 9 (7.4%). A high-risk GS was reported for 31.8% compared to 17.4% for the validation dataset.

Conclusions

We demonstrated reliable extraction of information about GS from narrative text-based patient reports using an in-house developed text mining algorithm. A secondary outcome was that late presentation could be assessed.

Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP)

The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.

Moroccan Digital Health Response to the COVID-19 Crisis

The COVID-19 pandemic and the draconian measures applied to limit its spread have accelerated the process of digitalizing many activities, including those within the health sector. In Morocco, a developing country in northern Africa, digital health has been deployed extensively, and in a remarkable way, to support the management of the current health crisis. Morocco is taking significant measures to become a key player in the process of achieving Sustainable Development Goals (SDG) goal 3. The government has comprehensively integrated digital technology throughout its coordinated containment and mitigation processes. These processes encompass testing and diagnostics; virus genomic surveillance; telecare of suspected and chronic patients; COVID-19 patient contact tracing and tracking; a laboratory information system for medical material dispatching, biological sample collection, and data processing nationwide; and smart vaccination management. Moreover, the pace of amending legislation for enabling efficient telemedicine practice has been achieved at a record-breaking. The successful implementation of all of these digital health strategies testify to the effectiveness of digitalization for managing the health aspects of the pandemic and for the future development of health systems in Morocco and in the African continent, where digital health and telemedicine is set to become the cornerstone of medical practice.

A model to establish autoverification in the clinical laboratory

OBJECTIVES

Autoverification is the process of evaluating and validating laboratory results using predefined computer-based algorithms without human interaction. By using autoverification, all reports are validated according to the standard evaluation criteria with predefined rules, and the number of reports per laboratory specialist is reduced. However, creating and validating these rules are the most demanding steps for setting up an autoverification system. In this study, we aimed to develop a model for helping users establish autoverification rules and evaluate their validity and performance.

DESIGN & METHODS

The proposed model was established by analyzing white papers, previous study results, and national/international guidelines. An autoverification software (myODS) was developed to create rules according to the model and to evaluate the rules and autoverification rates. The simulation results that were produced by the software were used to demonstrate that the determined framework works as expected. Both autoverification rates and step-based evaluations were performed using actual patient results. Two algorithms defined according to delta check usage (Algorithm A and B) and three review limits were used for the evaluation.

RESULTS

Six hundred seventeen rules were created according to the proposed model. 1,976 simulation results were created for validation. Our results showed that manual review limits are the most critical step in determining the autoverification rate, and delta check evaluation is especially important for evaluating inpatients. Algorithm B, which includes consecutive delta check evaluation, had higher AV rates.

CONCLUSIONS

Systemic rule formation is a critical factor for successful AV. Our proposed model can help laboratories establish and evaluate autoverification systems. Rules created according to this model could be used as a starting point for different test groups.

Evaluation of Hematocrit in Adults with Dengue by a Laboratory Information System

The implementation of a laboratory information system (LIS) at the Hospital for Tropical Diseases in Thailand provides valuable medical resources, particularly for dengue. Hematocrit (Hct), which is often derived from hemoglobin (Hgb), is important in the diagnosis and management of dengue. This study aimed to evaluate the Hct value obtained from the LIS automated analyzer. We prospectively enrolled 163 hospitalized adults with dengue, for whom 1,141 real-time complete blood count (CBC) results were obtained via a hematology analyzer and updated in the LIS database. The median (interquartile range (IQR)) duration of analytic turnaround times (TATs) was 40.0 (30.0–53.0) minutes. Linear regression analysis indicated a significant relationship between Hgb and Hct with a coefficient of determination (Pearson's R²) of 0.92 at red blood cell distribution width (RDW) ≤18, but Pearson's R² decreased to 0.78 at RDW >18. The Hct calculated from the three-fold conversion method and from the analyzer had a Pearson's R² of 0.92. At Hgb <12 g/dl and ≥16 g/dl, a greater difference between the two Hct values was observed, with median (IQR) differences of −0.8% (−1.9%–0.2%) and 0.8% (−0.1%–1.7%), respectively (P value <0.05). In conclusion, the Hgb and Hct of patients with dengue were highly correlated at RDW ≤18. The Hct calculated from the three-fold conversion method and from the analyzer had an excellent relationship, except when the Hgb was <12 g/dl or ≥16 g/dl. Apart from routine CBC evaluation, the LIS could help for accurate data collection in clinical research and development.

Considerations for Group Testing: A Practical Approach for the Clinical Laboratory

Group testing, also known as pooled sample testing, was first proposed by Robert Dorfman in 1943. While sample pooling has been widely practiced in blood-banking, it is traditionally seen as anathema for clinical laboratories. However, the ongoing COVID-19 pandemic has re-ignited interest for group testing among clinical laboratories to mitigate supply shortages. We propose five criteria to assess the suitability of an analyte for pooled sample testing in general and outline a practical approach that a clinical laboratory may use to implement pooled testing for SARS-CoV-2 PCR testing. The five criteria we propose are: (1) the analyte concentrations in the diseased persons should be at least one order of magnitude (10 times) higher than in healthy persons; (2) sample dilution should not overly reduce clinical sensitivity; (3) the current prevalence must be sufficiently low for the number of samples pooled for the specific protocol; (4) there is no requirement for a fast turnaround time; and (5) there is an imperative need for resource rationing to maximise public health outcomes. The five key steps we suggest for a successful implementation are: (1) determination of when pooling takes place (pre-pre analytical, pre-analytical, analytical); (2) validation of the pooling protocol; (3) ensuring an adequate infrastructure and archival system; (4) configuration of the laboratory information system; and (5) staff training. While pool testing is not a panacea to overcome reagent shortage, it may allow broader access to testing but at the cost of reduction in sensitivity and increased turnaround time.

Quality Control of Next-Generation Sequencing-Based HIV-1 Drug Resistance Data in Clinical Laboratory Information Systems Framework

Next-generation sequencing (NGS) in HIV drug resistance (HIVDR) testing has the potential to improve both clinical and public health settings, however it challenges the normal operations of quality management systems to be more flexible due to its complexity, massive data generation, and rapidly evolving protocols. While guidelines for quality management in NGS data have previously been outlined, little guidance has been implemented for NGS-based HIVDR testing. This document summarizes quality control procedures for NGS-based HIVDR testing laboratories using a laboratory information systems (LIS) framework. Here, we focus in particular on the quality control measures applied on the final sequencing product aligned with the recommendations from the World Health Organization HIV Drug Resistance Laboratory Network.

Essential role of laboratory physicians in transformation of laboratory practice and management to a value-based patient-centric model

The laboratory is a vital part of the continuum of patient care. In fact, there are few programs in the healthcare system that do not rely on ready access and availability of complex diagnostic laboratory services. The existing transactional model of laboratory "medical practice" will not be able to meet the needs of the healthcare system as it rapidly shifts toward value-based care and precision medicine, which demands that practice be based on total system indicators, clinical effectiveness, and patient outcomes. Laboratory "value" will no longer be focused primarily on internal testing quality and efficiencies but rather on the relative cost of diagnostic testing compared to direct improvement in clinical and system outcomes. The medical laboratory as a "business" focused on operational efficiency and cost-controls must transform to become an essential clinical service that is a tightly integrated equal partner in direct patient care. We would argue that this paradigm shift would not be necessary if laboratory services had remained a "patient-centric" medical practice throughout the last few decades. This review is focused on the essential role of laboratory physicians in transforming laboratory practice and management to a value-based patient-centric model. Value-based practice is necessary not only to meet the challenges of the new precision medicine world order but also to bring about sustainable healthcare service delivery.

Quality and Safety in eHealth: The Need to Build the Evidence Base

Research in the area of health technology safety has demonstrated that technology may both improve patient safety and introduce new types of technology-induced errors. Thus, there is a need to publish safety science literature to develop an evidence-based research base, on which we can continually develop new, safe technologies and improve patient safety. The aim of this viewpoint is to argue for the need to advance evidence-based research in health informatics, so that new technologies can be designed, developed, and implemented for their safety prior to their use in health care. This viewpoint offers a historical perspective on the development of health informatics and safety literature in the area of health technology. I argue for the need to conduct safety studies of technologies used by health professionals and consumers to develop an evidence base in this area. Ongoing research is necessary to improve the quality and safety of health technologies. Over the past several decades, we have seen health informatics emerge as a discipline, with growing research in the field examining the design, development, and implementation of different health technologies and new challenges such as those associated with the quality and safety of technology use. Future research will need to focus on how we can continually extend safety science in this area. There is a need to integrate evidence-based research into the design, development, and implementation of health technologies to improve their safety and reduce technology-induced errors.

Designing and evaluating functional laboratory information system requirements integrated to hospital information systems

RATIONALE, AIMS, AND OBJECTIVES

Laboratory information sub-systems play an important role in diagnosis and treatment of patients. This study aimed to determine functional requirements of users and assess the existence of these requirements in the laboratory information system.

METHOD

This descriptive cross-sectional study was conducted in 2016 in two phases. The first phase was done through three stages. First, based on an unsystematic review of related literature, an outline of functional laboratory information system requirements was identified. In the second stage, these requirements were identified in group meetings in the form of a semi-structured questionnaire and given to experts. Then, modified Delphi technique was used to reach agreement on each item. Then, based on experts' comments, the final version of the questionnaire was presented including 61 closed-ended items using Likert scale and an open-ended item. It was surveyed by 50 experts using Delphi technique. Responses were scored, and the requirements whose mean final score was 3 and above were finally confirmed. In the second phase, based on the confirmed requirements, a checklist comprising 68 requirements was prepared and adopted hospital information systems were evaluated through researcher observation. Data were analysed using descriptive statistics.

RESULTS

The final list of functional laboratory information system requirements was prepared with 68 items. The results of the evaluation revealed that confirmed requirements existed in 58.8% of hospital information systems.

CONCLUSION

Laboratory information system requirements were designed with 68 items. Evaluation results showed that the systems were moderate in terms of compliance with the requirements.

Machine Learning and Other Emerging Decision Support Tools

Emerging applications of machine learning and artificial intelligence offer the opportunity to discover new clinical knowledge through secondary exploration of existing patient medical records. This new knowledge may in turn offer a foundation to build new types of clinical decision support (CDS) that provide patient-specific insights and guidance across a wide range of clinical questions and settings. This article will provide an overview of these emerging approaches to CDS, discussing both existing technologies as well as challenges that health systems and informaticists will need to address to allow these emerging approaches to reach their full potential.

Building a Newborn Screening Information Management System from Theory to Practice

Information Management Systems are the central process management and communication hub for many newborn screening programs. In late 2014, Newborn Screening Ontario (NSO) undertook an end to end assessment of its information management needs which resulted in a project to develop a flexible IS Information Systems (IS) ecosystem and related process changes. This enabled NSO to better manage its current and future work-flows and communication needs. An idealized vision of a Screening Information Management System (SIMS) was developed that was refined into enterprise and functional architectures. This was followed by the development of technical specifications, user requirements and procurement. In undertaking a holistic full product lifecycle redesign approach, a number of change management challenges were faced by NSO across the entire program. Strong leadership support and full program engagement are key for overall project success. It is anticipated that improvements in program flexibility and the ability to innovate will outweigh the efforts and costs.

Recent Advancement of Clinical Information Systems: Opportunities and Challenges

Objectives:  Clinical information systems (CISs) have generated opportunities for meaningful improvements both in patient care and workflow but there is still a long way to perfection. Healthcare providers are still facing challenges of data exchange, management, and integration due to lack of functionality among these systems. Our objective here is to systematically review, synthesize, and summarize the literature that describes the current stage of clinical information systems, so as to assess the current state of knowledge, and identify benefits and challenges.

Methods:  PubMed, EMBASE, and the bibliographies of articles were searched for studies published until September 1, 2017, which reported on significant advancement of clinical information systems, as well as problems and opportunities in this field. Studies providing the most detailed information were included and the others were kept only as references.

Results:  We selected 23 papers out of 1,026 unique abstracts for full-text review using our selection criteria, and 20 out of these 23 studies met all of our inclusion criteria. We focused on three major areas: 1) Ambulatory and inpatients clinical information systems; 2) Specialty information systems; and 3) Ancillary information systems. As CIS can support evidence-based practices that, in turn, improve patient's safety, quality and efficacy of care, advancement, acceptability, and adaptability of CIS have increased worldwide. Although, the demand for CIS functionality is rising fast, current CISs still have data integration challenges and lack of functionality to exchange patient information from all or some parts of the healthcare system. These limitations can be attributed to technical, human, and organizational factors

Conclusion:  Clinical information systems provide tremendous opportunities to reduce clinical errors such as medication errors and diagnostic errors and to support healthcare professionals by offering up-to-date patient information. They promise to improve workflow and efficiency of care, thus boosting the overall quality of healthcare.

Patient facing decision support system for interpretation of laboratory test results

Background

In some healthcare systems, it is common that patients address laboratory test centers directly without a physician’s recommendation. This practice is widely spread in Russia with about 28% of patients who visiting laboratory test centers for diagnostics. This causes an issue when patients get no help from the physician in understanding the results.

Computer decision support systems proved to efficiently solve a resource consuming task of interpretation of the test results. So, a decision support system can be implemented to rise motivation and empower the patients who visit a laboratory service without a doctor’s referral.

Methods

We have developed a clinical decision support system for patients that solves a classification task and finds a set of diagnoses for the provided laboratory tests results.

The Wilson and Lankton’s assessment model was applied to measure patients’ acceptance of the solution.

Results

A first order predicates-based decision support system has been implemented to analyze laboratory test results and deliver reports in natural language to patients. The evaluation of the system showed a high acceptance of the decision support system and of the reports that it generates.

Conclusions

Detailed notification of the laboratory service patients with elements of the decision support is significant for the laboratory data management, and for patients’ empowerment and safety.

Electronic supplementary material

The online version of this article (10.1186/s12911-018-0648-0) contains supplementary material, which is available to authorized users.

Health Informatics Tools to Improve Utilization of Laboratory Tests

Herein, we discuss improper test utilization practices and their implications on delivery of health care, as well as providing a brief explanation of the means to reduce such practices by improvement of personnel factors, particularly involving physicians. The article also elaborates on ways to mitigate improperly utilized test practices using appropriate health informatics technologies to their maximum possible capacities.

Making sense of a haemolysis monitoring and reporting system: a nationwide longitudinal multimethod study of 68 Australian laboratory participant organisations

Background:

The key incident monitoring and management systems (KIMMS) quality assurance program monitors incidents in the pre- and postanalytical phases of testing in medical laboratories. Haemolysed specimens have been found to be the most frequent preanalytical error and have major implications for patient care. The aims of this study were to assess the suitability of KIMMS for quality reporting of haemolysis and to devise a meaningful method for reporting and monitoring haemolysis.

Methods:

A structured survey of 68 Australian KIMMS laboratory participant organisations was undertaken. Quarterly haemolysis reports (2011–2014) were analysed.

Results:

Among 110 million accessions reported, haemolysis rates varied according to the reporting methods that participants used for assigning accessions (16% of participants reported haemolysis by specimen and 83% reported by episode) and counting haemolysis rejections (61% by specimen, 35% by episode and 3% by test). More than half of the participants (56%) assigned accessions by episode and counted rejections by specimen. For this group, the average haemolysis rate per 100,000 episodes was 177 rejected specimens with the average rate varying from 100 to 233 over time. The majority of participants (91%) determined rejections using the haemolysis index. Two thirds of participants (66%) recorded the haemolysis manually in laboratory information systems.

Conclusions:

KIMMS maintains the largest longitudinal haemolysis database in the world. However, as a means of advancing improvements in the quality of the preanalytical laboratory process, there is a need to standardise reporting methods to enable robust comparison of haemolysis rejection rates across participant laboratories.

Implementation of Automated Calculation of Free and Bioavailable Testosterone in Epic Beaker Laboratory Information System

BACKGROUND

Automated calculations by laboratory information system (LIS) are efficient and accurate ways of providing calculated laboratory test results. Due to the lack of established advanced mathematical functions and equation logic in LIS software, calculations beyond simple arithmetic functions require a tedious workaround. Free and bioavailable testosterone (BT) calculations require a quadratic solver currently unavailable as ready to use the function on most commercial LIS platforms. We aimed to develop a module within the Epic Beaker LIS to enable automatic quadratic equation solving capability and real-time reporting of calculated free and BT values.

MATERIALS AND METHODS

We developed and implemented an advanced calculation module from the ground up using existing basic calculation programming functions in the Epic Beaker LIS. A set of calculation variables were created, and mathematical logic and functions were used to link the variables and perform the actual quadratic equation based calculations. Calculations were performed in real-time during result entry events, and calculated results populated the result components in LIS automatically.

RESULTS

Free and BT were calculated using instrument measured results of total testosterone, sex hormone binding globulin, and/or serum albumin, by applying equations widely adopted in laboratory medicine for endocrine diseases and disorders. Calculated results in Epic Beaker LIS were then compared and confirmed by manual calculations using Microsoft Excel spreadsheets and scientific calculators to have no discrepancies.

CONCLUSIONS

Automated calculations of free and BT were successfully implemented and validated, the first of such implementation for the Epic Beaker LIS platform, eliminating the need of offline manual calculations, potential transcription error, and with improved turnaround time. It may serve as a model to build similarly complex equations when the clinical need arises.

Laboratory Information Systems

Laboratory information systems (LISs) supply mission-critical capabilities for the vast array of information-processing needs of modern laboratories. LIS architectures include mainframe, client-server, and thin client configurations. The LIS database software manages a laboratory's data. LIS dictionaries are database tables that a laboratory uses to tailor an LIS to the unique needs of that laboratory. Anatomic pathology LIS (APLIS) functions play key roles throughout the pathology workflow, and laboratories rely on LIS management reports to monitor operations. This article describes the structure and functions of APLISs, with emphasis on their roles in laboratory operations and their relevance to pathologists.

Failure to review STAT clinical laboratory requests and its economical impact

Background

Failure to follow-up laboratory test results has been described as one of the major processes contributing to unsafe patient care. Currently, most of the laboratories do not know with certainty not only their rate of missed (or unreviewed) requests but the economical cost and impact that this issue implies. The aim of our study was to measure that rate and calculate the resulting costs.

Material and methods

In January 2015, we checked in our Laboratory Information Management System (LIMS) for every emergency request from 1^st July 2011 to 30^th June 2014, if they had been reviewed by any allowed user or not. 319,064 requests were ordered during that period of time. Results were expressed as “ordered requests”, “missed requests” and its percentage. Additionally, total cost of missed requests was calculated in euros (€). “Non-productive days” were theorised (as the days producing requests that were not reviewed) based on these results.

Results

7924 requests (2.5%) were never reviewed by clinicians. This represented a total cost of 203,039 € and 27 “non-productive” days in three years. Significant differences between inpatients, outpatients and emergency department as well as different emergencies units were found after application of statistical analysis.

Conclusions

In terms of resources, never reviewed or missed requests appear to be a not negligible problem for the clinical laboratory management. Electronic result delivery, with electronic endorsement to indicate follow-up of requests along with better systems of electronic requesting should be investigated as a way of improving patient outcomes and save unnecessary expenses.

Laboratory Information Systems in Molecular Diagnostics: Why Molecular Diagnostics Data are Different

Molecular diagnostic testing presents new challenges to information management that are yet to be sufficiently addressed by currently available information systems for the molecular laboratory. These challenges relate to unique aspects of molecular genetic testing: molecular test ordering, informed consent issues, diverse specimen types that encompass the full breadth of specimens handled by traditional anatomic and clinical pathology information systems, data structures and data elements specific to molecular testing, varied testing workflows and protocols, diverse instrument outputs, unique needs and requirements of molecular test reporting, and nuances related to the dissemination of molecular pathology test reports. By satisfactorily addressing these needs in molecular test data management, a laboratory information system designed for the unique needs of molecular diagnostics presents a compelling reason to migrate away from the current paper and spreadsheet information management that many molecular laboratories currently use. This paper reviews the issues and challenges of information management in the molecular diagnostics laboratory.

A Map for Clinical Laboratories Management Indicators in the Intelligent Dashboard

INTRODUCTION

management challenges of clinical laboratories are more complicated for educational hospital clinical laboratories. Managers can use tools of business intelligence (BI), such as information dashboards that provide the possibility of intelligent decision-making and problem solving about increasing income, reducing spending, utilization management and even improving quality. Critical phase of dashboard design is setting indicators and modeling causal relations between them. The paper describes the process of creating a map for laboratory dashboard.

METHODS

the study is one part of an action research that begins from 2012 by innovation initiative for implementing laboratory intelligent dashboard. Laboratories management problems were determined in educational hospitals by the brainstorming sessions. Then, with regard to the problems key performance indicators (KPIs) specified.

RESULTS

the map of indicators designed in form of three layered. They have a causal relationship so that issues measured in the subsequent layers affect issues measured in the prime layers.

CONCLUSION

the proposed indicator map can be the base of performance monitoring. However, these indicators can be modified to improve during iterations of dashboard designing process.

Stand-alone laboratory information systems versus laboratory modules incorporated in the electronic health record

The increasing availability of laboratory information management modules within enterprise electronic health record solutions has resulted in some institutional administrators deciding which laboratory information system will be used to manage workflow within the laboratory, often with minimal input from the pathologists. This article aims to educate pathologists on many of the issues and implications this change may have on laboratory operations, positioning them to better evaluate and represent the needs of the laboratory during this decision-making process. The experiences of the authors, many of their colleagues, and published observations relevant to this debate are summarized. There are multiple dimensions of the interdependency between the pathology laboratory and its information system that must be factored into the decision. Functionality is important, but management authority and gap-ownership are also significant elements to consider. Thus, the pathologist must maintain an active role in the decision-making process to ensure the success of the laboratory.

Laboratory Information Systems

Laboratory information systems (LISs) supply mission-critical capabilities for the vast array of information-processing needs of modern laboratories. LIS architectures include mainframe, client-server, and thin client configurations. The LIS database software manages a laboratory's data. LIS dictionaries are database tables that a laboratory uses to tailor an LIS to the unique needs of that laboratory. Anatomic pathology LIS (APLIS) functions play key roles throughout the pathology workflow, and laboratories rely on LIS management reports to monitor operations. This article describes the structure and functions of APLISs, with emphasis on their roles in laboratory operations and their relevance to pathologists.

Reqscan: An open source solution for laboratory requisition scanning, archiving and retrieval

Requisition storage and retrieval are an integral part of the outpatient laboratory testing process. It is frequently necessary to review an original requisition to confirm the ordering physician, patient demographics, diagnostic information, and requested tests. Manual retrieval of a paper requisition is time-consuming and tedious. Although commercial solutions exist for the scanning and archiving of barcoded paper requisitions, the tools to accomplish this are freely available from the open source software community. We present a simple dedicated piece of software, Reqscan, for scanning patient laboratory requisitions, finding all barcode information, and saving the requisition as a portable document format named according the barcode(s) found. This Python application offers a simple solution to patient requisition digitization. Reqscan has been successfully tested and implemented into routine practice for storage and retrieval of outpatient requisitions at St. Paul's Hospital, Department of Pathology and Laboratory Medicine in Vancouver, British Columbia, Canada.

Enquiry time as part of turnaround time: when do our clinicians really consult our results?

BACKGROUND

Traditionally, laboratories' turnaround times (TAT) have been calculated by only considering analytical or intralaboratory steps. The measure of the postanalytical impact in TAT has barely been studied and, more specifically, the running time from when finalised results are available to when clinicians make their first enquiry with an electronic medical record.

METHODS

During May-June of 2013, two 'Times' were collected from our laboratory information system for all the priority requests coming from our day hospitals: 'Validation time' (TV), as the request report time with full verified results and 'Enquiry time' (TQ), as the time when the first consult was made via electronic medical record. We classified requests in groups depending on time results, and TQ-TV (percentiles) were calculated for each group.

RESULTS

654 (69%) requests were consulted by clinicians before 15 : 00 on the same day with available results. 191 (20%) were consulted after 15 : 00 and had complete results as well (p50 (TQ-TV): 5 days) while 61 (7%) were never consulted (up to 31/12/2013). 39 (4%) requests were finally consulted before 15 : 00 h with no available results, but the average time difference between validation and enquiry was 31 min.

CONCLUSIONS

The results obtained lead us to reconsider the TAT established with our day hospitals in order to know if priority has to be reviewed or if there are failures in follow-up results. 'Enquiry time' appears to be a powerful tool in detecting these issues and shows that TATs are no longer just a 'laboratory problem'.

SeqReporter: automating next-generation sequencing result interpretation and reporting workflow in a clinical laboratory

A wide repertoire of bioinformatics applications exist for next-generation sequencing data analysis; however, certain requirements of the clinical molecular laboratory limit their use: i) comprehensive report generation, ii) compatibility with existing laboratory information systems and computer operating system, iii) knowledgebase development, iv) quality management, and v) data security. SeqReporter is a web-based application developed using ASP.NET framework version 4.0. The client-side was designed using HTML5, CSS3, and Javascript. The server-side processing (VB.NET) relied on interaction with a customized SQL server 2008 R2 database. Overall, 104 cases (1062 variant calls) were analyzed by SeqReporter. Each variant call was classified into one of five report levels: i) known clinical significance, ii) uncertain clinical significance, iii) pending pathologists' review, iv) synonymous and deep intronic, and v) platform and panel-specific sequence errors. SeqReporter correctly annotated and classified 99.9% (859 of 860) of sequence variants, including 68.7% synonymous single-nucleotide variants, 28.3% nonsynonymous single-nucleotide variants, 1.7% insertions, and 1.3% deletions. One variant of potential clinical significance was re-classified after pathologist review. Laboratory information system-compatible clinical reports were generated automatically. SeqReporter also facilitated quality management activities. SeqReporter is an example of a customized and well-designed informatics solution to optimize and automate the downstream analysis of clinical next-generation sequencing data. We propose it as a model that may envisage the development of a comprehensive clinical informatics solution.

The role of informatics and decision support in utilization management

Information systems provide a critical link between clinical laboratories and the clinicians and patients they serve. Strategic deployment of informatics resources can enable a wide array of utilization initiatives and can substantially improve the appropriateness of test selection and results interpretation. In this article, we review information systems including computerized provider order entry (CPOE) systems, laboratory information systems (LISs), electronic health records (EHRs), laboratory middleware, knowledge management systems and systems for data extraction and analysis, and describe the role that each can play in utilization management. We also discuss specific utilization strategies that laboratories can employ within these systems, citing examples both from our own institution and from the literature. Finally, we review how emerging applications of decision support technologies may help to further refine test utilization, "personalize" laboratory diagnosis, and enhance the diagnostic value of laboratory testing.