Management of laboratory data and information exchange in the electronic health record

Analysis of laboratory data transmission between two healthcare institutions using a widely used point-to-point health information exchange platform: a case report

Objective

The objective was to identify information loss that could affect clinical care in laboratory data transmission between 2 health care institutions via a Health Information Exchange platform.

Materials and Methods

Data transmission results of 9 laboratory tests, including LOINC codes, were compared in the following: between sending and receiving electronic health record (EHR) systems, the individual Health Level Seven International (HL7) Version 2 messages across the instrument, laboratory information system, and sending EHR.

Results

Loss of information for similar tests indicated the following potential patient safety issues: (1) consistently missing specimen source; (2) lack of reporting of analytical technique or instrument platform; (3) inconsistent units and reference ranges; (4) discordant LOINC code use; and (5) increased complexity with multiple HL7 versions.

Discussion and Conclusions

Using an HIE with standard messaging, SHIELD (Systemic Harmonization and Interoperability Enhancement for Laboratory Data) recommendations, and enhanced EHR functionality to support necessary data elements would yield consistent test identification and result value transmission.

Evaluation of three protocols for direct susceptibility testing for gram negative-Enterobacteriaceae from patient samples in Uganda with SMS reporting

In Uganda, the challenge of generating and timely reporting essential antimicrobial resistance (AMR) data has led to overreliance on empirical antibiotic therapy, exacerbating the AMR crisis. To address this issue, this study aimed to adapt a one-step AMR testing protocol alongside an SMS (Short Message Service) result relay system (SRRS), with the potential to reduce the turnaround time for AMR testing and result communication from 4 days or more to 1 day in Ugandan clinical microbiology laboratories. Out of the 377 samples examined, 54 isolates were obtained. Notably, E. coli (61%) and K. pneumoniae (33%) were the most frequently identified, majority testing positive for ESBL. Evaluation of three AMR testing protocols revealed varying sensitivity and specificity, with Protocol A (ChromID ESBL-based) demonstrating high sensitivity (100%) but no calculable specificity, Protocol B (ceftazidime-based) showing high sensitivity (100%) and relatively low specificity (7.1%), and Protocol C (cefotaxime-based) exhibiting high sensitivity (97.8%) but no calculable specificity. ESBL positivity strongly correlated with resistance to specific antibiotics, including cefotaxime, ampicillin, and aztreonam (100%), cefuroxime (96%), ceftriaxone (93%), and trimethoprim sulfamethoxazole (87%). The potential of integrating an SRRS underscored the crucial role this could have in enabling efficient healthcare communication in AMR management. This study underscores the substantial potential of the tested protocols for accurately detecting ESBL production in clinical samples, potentially, providing a critical foundation for predicting and reporting AMR patterns. Although considerations related to specificity warrant careful assessment before widespread clinical adoption.

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare's path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

Big Data in Nephrology

A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.

Use, Impact, Weaknesses, and Advanced Features of Search Functions for Clinical Use in Electronic Health Records: A Scoping Review

Objective  Although vast amounts of patient information are captured in electronic health records (EHRs), effective clinical use of this information is challenging due to inadequate and inefficient access to it at the point of care. The purpose of this study was to conduct a scoping review of the literature on the use of EHR search functions within a single patient's record in clinical settings to characterize the current state of research on the topic and identify areas for future study.

Methods  We conducted a literature search of four databases to identify articles on within-EHR search functions or the use of EHR search function in the context of clinical tasks. After reviewing titles and abstracts and performing a full-text review of selected articles, we included 17 articles in the analysis. We qualitatively identified themes in those articles and synthesized the literature for each theme.

Results  Based on the 17 articles analyzed, we delineated four themes: (1) how clinicians use search functions, (2) impact of search functions on clinical workflow, (3) weaknesses of current search functions, and (4) advanced search features. Our review found that search functions generally facilitate patient information retrieval by clinicians and are positively received by users. However, existing search functions have weaknesses, such as yielding false negatives and false positives, which can decrease trust in the results, and requiring a high cognitive load to perform an inclusive search of a patient's record.

Conclusion  Despite the widespread adoption of EHRs, only a limited number of articles describe the use of EHR search functions in a clinical setting, despite evidence that they benefit clinician workflow and productivity. Some of the weaknesses of current search functions may be addressed by enhancing EHR search functions with collaborative filtering.

Hybrid collaborative filtering methods for recommending search terms to clinicians

With increasing and extensive use of electronic health records (EHR), clinicians are often challenged in retrieving relevant patient information efficiently and effectively to arrive at a diagnosis. While using the search function built into an EHR can be more useful than browsing in a voluminous patient record, it is cumbersome and repetitive to search for the same or similar information on similar patients. To address this challenge, there is a critical need to build effective recommender systems that can recommend search terms to clinicians accurately. In this study, we developed a hybrid collaborative filtering model to recommend search terms for a specific patient to a clinician. The model draws on information from patients' clinical encounters and the searches that were performed during them. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. In one variation of the model (Hybrid Collaborative Filtering Method for Healthcare, or HCFMH), we use only the most recent ICD codes assigned to the patient, and in the other (Co-occurrence Pattern based HCFMH, or cpHCFMH), all ICD codes. We have conducted comprehensive experiments to evaluate the proposed model. These experiments demonstrate that our model outperforms state-of-the-art baseline methods for top-N search term recommendation on different data sets.

Iron studies and transferrin, a source of test ordering confusion highly amenable to clinical decision support

INTRODUCTION

An important cause of laboratory test misordering and overutilization is clinician confusion between tests with similar sounding names or similar indications. We identified an area of test ordering confusion with iron studies that involves total iron binding capacity (TIBC), transferrin, and transferrin saturation. We observed concurrent ordering of direct transferrin along with TIBC at many hospitals within our health system and suspected this was unnecessary.

METHODS

We extracted patient test results for transferrin, TIBC and other biomarkers. Using these data, we evaluated both patterns of test utilization and test result concordance. We implemented a clinical decision support (CDS) alert to discourage unnecessary orders for direct transferrin.

RESULTS

Using linear regression, we were able to predict transferrin from either TIBC alone or TIBC with other analytes with a high degree of accuracy, demonstrating that in most cases, direct transferrin in combination with TIBC provides little if any additional diagnostic information beyond TIBC alone. The CDS alert proved highly effective in reducing transferrin test utilization at four different hospitals.

CONCLUSIONS

Concurrent ordering of direct transferrin and TIBC should usually be avoided. Removal of transferrin or TIBC from the test menu or implementation of CDS may improve utilization of these tests.

The Effect of Information Technology on the Information Exchange between Laboratories and Ambulatory Care Centers: A Systematic Review

Laboratory services form an integral part of medical care in the decision-making of physicians, including those working at ambulatory care centers. Information exchange is essential between ambulatory care centers and laboratories. Inevitable errors have always existed in the exchange of such information on paper, which can be to some extent avoided by developing appropriate computer-based interfaces. Therefore, this review aimed to examine studies conducted to determine the effect of electronic communication between ambulatory care centers and laboratories. This systematic review was conducted on the basis of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Studies were searched in the PubMed, Embase, Cochrane, and Web of Science, and those written in English and published between 2000 and February 2019 with full texts available were selected. From a total of 3898 papers retrieved from the studied databases, 24 papers were eligible for entering this study after removing similar and nonrelated studies. Electronic exchanges between ambulatory care centers and laboratories can have numerous benefits in terms of financial, organizational, and quality. This evidence for the value of electronic communications is an important factor contributing to its local investment and adoption.

Advancing laboratory medicine in hospitals through health information exchange: a survey of specialist physicians in Canada

BACKGROUND

Laboratory testing occupies a prominent place in health care. Information technology systems have the potential to empower laboratory experts and to enhance the interpretation of test results in order to better support physicians in their quest for better and safer patient care. This study sought to develop a better understanding of which laboratory information exchange (LIE) systems and features specialist physicians are using in hospital settings to consult their patients' laboratory test results, and what benefit they derive from such use.

METHODS

As part of a broader research program on the use of health information exchange systems for laboratory medicine in Quebec, Canada, this study was designed as on online survey. Our sample is composed of 566 specialist physicians working in hospital settings, out of the 1512 physicians who responded to the survey (response rate of 17%). Respondents are representative of the targeted population of specialist physicians in terms of gender, age and hospital location.

RESULTS

We first observed that 80% of the surveyed physicians used the province-wide interoperable electronic health records (iEHR) system and 93% used a laboratory results viewer (LRV) to consult laboratory test results and most (72%) use both systems to retrieve lab results. Next, our findings reveal important differences in the capabilities available in each type of system and in the use of these capabilities. Third, there are differences in the nature of the perceived benefits obtained from the use of each of these two systems. Last, the extent of use of an LRV is strongly influenced by the IT artefact itself (i.e., the hospital's LRV available capabilities) while the use of the provincial iEHR system is influenced by its organizational context (i.e. the hospital's size and location).

CONCLUSIONS

The main contribution of this study lies in its insights into the role played by context in shaping physicians' choices about which laboratory information exchange systems to adopt and which features to use, and the different perceptions they have about benefits arising from such use. One related implication for practice is that success of LIE initiatives should not be solely assessed with basic usage statistics.

Laboratory Test Utilization and Repeat Testing for Inpatients of Age 80 and Over in Australia: A Retrospective Observational Study

Introduction

Repeat laboratory testing is often necessary in hospitals. However, frequent blood draws can be harmful to older patients. The objective of this study was to identify the most frequently ordered laboratory tests and repeat testing rates for these tests among older inpatients.

Methods

A retrospective observational study of inpatients of age 80 years and over in 4 public hospitals in New South Wales, Australia, was conducted between 2008 and 2013. Proportions of laboratory tests and proportions of repeated tests among the most frequently used tests were reported.

Results

There were 42739 patients with 108003 admissions (56.2% women; 43.2% of ages 80–84). Of these admissions, 95.9% had a laboratory test, with 3012577 tests recorded. Five tests accounted for 62% of all tests and were present in 98.5% of admissions: electrolytes urea and creatinine (EUC; 18% of all tests ordered), complete blood count (CBC; 16.7%), calcium magnesium phosphate (CaMgPhos; 10.2%), liver function test (LFT; 9.0%), and C-reactive protein (CRP; 8.0%). Proportions of repeat tests for this group performed outside recommended minimum repeat intervals were 10.3% EUC, 8.9% CBC, 41.5% CRP, 68.2% CaMgPhos, and 65.2% LFT tests. An exponential increase in repeat testing for all 5 tests was observed around 24 h after a previous test.

Conclusion

Compliance with guidelines on repeat testing intervals among older patients is variable. A better understanding of the underlying reasons for repeat testing would allow targeting of interventions, including decision support, to improve laboratory use for older inpatients.

Implementation of Laboratory Review of Test Builds Within the Electronic Health Record Reduces Errors

CONTEXT.—

As electronic health records (EHRs) become more ubiquitous, physicians have come to expect that laboratory data from a variety of sources will be incorporated into the EHR in a structured format. The Clinical Laboratory Improvement Amendments have standards for data transmission traditionally met by pathologist review of their own hospital laboratory information system transmissions. However, with third-party laboratory data now being sent through external (nonhospital laboratory) interfaces, ownership of this review is less clear. Lack of an expert laboratory review process prior to changes being implemented can result in mapping and interfacing errors that could lead to misinterpretation and diagnostic errors.

OBJECTIVE.—

To determine the impact of retrospective and prospective laboratorian-assisted review on the volume of interface errors and new builds.

DESIGN.—

A seminal event led to a restructuring of the process for review of EHR laboratory builds, using laboratory expertise.

RESULTS.—

A review of 26 500 test result fields found 61 of 4282 (1.4%) unique codes that could have led to misinterpretation. These were corrected and a process for proactive review and maintenance by laboratory experts was implemented. This resulted in monthly decreases in outbound error message from 4270 to 1820 (57.4%), in new test builds from 586 to 274 (53.2%), and in new result builds from 1116 to 552 (50.5%).

CONCLUSIONS.—

Regular review and maintenance of external laboratory test builds in EHRs by a laboratory review team reduces interface error messages and reduces the number of new builds required for results to file into the EHR.

Quality management and accreditation in laboratory hematology: Perspectives from India

Quality management (QM), including quality assurance and quality control, was developed in clinical laboratories in North America and Western Europe, but must be implemented worldwide to ensure accurate, reproducible, and clinically useful results. India, a middle income country with a population of over 1.34 billion, has limited budget allotted to health care. As yet accreditation for clinical laboratories is not mandatory, which contributes to challenges in implementing good laboratory practice. This review provides a summary of internationally laid down QM principles and their application in a middle income country like India.

Development and evaluation of a novel user interface for reviewing clinical microbiology results

Background

Microbiology laboratory results are complex and cumbersome to review. We sought to develop a new review tool to improve the ease and accuracy of microbiology results review.

Methods

We observed and informally interviewed clinicians to determine areas in which existing microbiology review tools were lacking. We developed a new tool that reorganizes microbiology results by time and organism. We conducted a scenario-based usability evaluation to compare the new tool to existing legacy tools, using a balanced block design.

Results

The average time-on-task decreased from 45.3 min for the legacy tools to 27.1 min for the new tool (P < .0001). Total errors decreased from 41 with the legacy tools to 19 with the new tool (P = .0068). The average Single Ease Question score was 5.65 (out of 7) for the new tool, compared to 3.78 for the legacy tools (P < .0001). The new tool scored 88 ("Excellent") on the System Usability Scale.

Conclusions

The new tool substantially improved efficiency, accuracy, and usability. It was subsequently integrated into the electronic health record and rolled out system-wide. This project provides an example of how clinical and informatics teams can innovative alongside a commercial Electronic Health Record (EHR).

The Benefits and Challenges of an Interfaced Electronic Health Record and Laboratory Information System: Effects on Laboratory Processes

Context.—

A recent government regulation incentivizes implementation of an electronic health record (EHR) with computerized order entry and structured results display. Many institutions have also chosen to interface their EHR with their laboratory information system (LIS).

Objective.—

To determine the impact of an interfaced EHR-LIS on laboratory processes.

Design.—

We analyzed several different processes before and after implementation of an interfaced EHR-LIS: the turnaround time, the number of stat specimens received, venipunctures per patient per day, preanalytic errors in phlebotomy, the number of add-on tests using a new electronic process, and the number of wrong test codes ordered. Data were gathered through the LIS and/or EHR.

Results.—

The turnaround time for potassium and hematocrit decreased significantly (P = .047 and P = .004, respectively). The number of stat orders also decreased significantly, from 40% to 7% for potassium and hematocrit, respectively (P &lt; .001 for both). Even though the average number of inpatient venipunctures per day increased from 1.38 to 1.62 (P &lt; .001), the average number of preanalytic errors per month decreased from 2.24 to 0.16 per 1000 specimens (P &lt; .001). Overall there was a 16% increase in add-on tests. The number of wrong test codes ordered was high and it was challenging for providers to correctly order some common tests.

Conclusions.—

An interfaced EHR-LIS significantly improved within-laboratory turnaround time and decreased stat requests and preanalytic phlebotomy errors. Despite increasing the number of add-on requests, an electronic add-on process increased efficiency and improved provider satisfaction. Laboratories implementing an interfaced EHR-LIS should be cautious of its effects on test ordering and patient venipunctures per day.

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.

Electronic Health Records: Then, Now, and in the Future

OBJECTIVES

Describe the state of Electronic Health Records (EHRs) in 1992 and their evolution by 2015 and where EHRs are expected to be in 25 years. Further to discuss the expectations for EHRs in 1992 and explore which of them were realized and what events accelerated or disrupted/derailed how EHRs evolved.

METHODS

Literature search based on "Electronic Health Record", "Medical Record", and "Medical Chart" using Medline, Google, Wikipedia Medical, and Cochrane Libraries resulted in an initial review of 2,356 abstracts and other information in papers and books. Additional papers and books were identified through the review of references cited in the initial review.

RESULTS

By 1992, hardware had become more affordable, powerful, and compact and the use of personal computers, local area networks, and the Internet provided faster and easier access to medical information. EHRs were initially developed and used at academic medical facilities but since most have been replaced by large vendor EHRs. While EHR use has increased and clinicians are being prepared to practice in an EHR-mediated world, technical issues have been overshadowed by procedural, professional, social, political, and especially ethical issues as well as the need for compliance with standards and information security. There have been enormous advancements that have taken place, but many of the early expectations for EHRs have not been realized and current EHRs still do not meet the needs of today's rapidly changing healthcare environment.

CONCLUSION

The current use of EHRs initiated by new technology would have been hard to foresee. Current and new EHR technology will help to provide international standards for interoperable applications that use health, social, economic, behavioral, and environmental data to communicate, interpret, and act intelligently upon complex healthcare information to foster precision medicine and a learning health system.

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.

Customizing Laboratory Information Systems: Closing the Functionality Gap

Highly customizable laboratory information systems help to address great variations in laboratory workflows, typical in Pathology. Often, however, built-in customization tools are not sufficient to add all of the desired functionality and improve systems interoperability. Emerging technologies and advances in medicine often create a void in functionality that we call a functionality gap. These gaps have distinct characteristics—a persuasive need to change the way a pathology group operates, the general availability of technology to address the missing functionality, the absence of this technology from your laboratory information system, and inability of built-in customization tools to address it. We emphasize the pervasive nature of these gaps, the role of pathology informatics in closing them, and suggest methods on how to achieve that. We found that a large number of the papers in the Journal of Pathology Informatics are concerned with these functionality gaps, and an even larger proportion of electronic posters and abstracts presented at the Pathology Informatics Summit conference each year deal directly with these unmet needs in pathology practice. A rapid, continuous, and sustainable approach to closing these gaps is critical for Pathology to provide the highest quality of care, adopt new technologies, and meet regulatory and financial challenges. The key element of successfully addressing functionality gaps is gap ownership—the ability to control the entire pathology information infrastructure with access to complementary systems and components. In addition, software developers with detailed domain expertise, equipped with right tools and methodology can effectively address these needs as they emerge.

Accreditation and regulatory implications of electronic health records for laboratory reporting

The Clinical Laboratory Improvement Amendments of 1988 include strict regulations for reporting content, and it falls on the named director to ensure that this content is available to the caregiver. With the electronic health record serving as the conduit to the end user of the laboratory data, the laboratory generally, and the director specifically, must verify accurate transmission of these content components. An understanding of regulatory and accreditation requirements is essential both to allow the proper discharge of these mandated responsibilities and to enforce the role and authority that the pathologist must have to ensure that these requirements are satisfied by the reporting system. The regulatory requirements will be discussed in the context of the Clinical Laboratory Improvement Amendments of 1988 standards; however, interpretation and expansion on these regulations exist both in Clinical Laboratory Improvement Amendments of 1988 inspection guidelines from the Centers for Medicare and Medicaid Services and in accreditation program requirements. This regulatory expectation both places the laboratory director in a position of risk and provides leverage to ensure meaningful and accurate communication of laboratory information.

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.

Pathologists' place in the electronic health record landscape

With growth spurred by recent federal efforts, electronic health records (EHRs) are transforming the practice of medicine and have important implications for pathologists, their laboratories, and the patients they serve. Beyond new EHR-related regulatory requirements, EHRs fundamentally alter the way clinicians interact with laboratory information, including test order entry and result reviewing. This article is the first in a series of 5 related articles whose goal is to provide a "framework" for empowering pathologists to adapt to, and to succeed in, the era of expanding EHR use. This series aims to describe the environment for EHR uptake, to raise awareness of EHR-related issues that pathologists and laboratories face, and to explore new professional roles for pathologists as stewards of patients' laboratory information in EHRs.