Secondary Use Of Clinical Data Research Articles

Do Physicians Have a Duty to Support Secondary Use of Clinical Data in Biomedical Research? An Inquiry into the Professional Ethics of Physicians.

Secondary use of clinical data in research or learning activities (SeConts) has the potential to improve patient care and biomedical knowledge. Given this potential, the ethical question arises whether physicians have a professional duty to support SeConts. To investigate this question, we analyze prominent international declarations on physicians' professional ethics to determine whether they include duties that can be considered as good reasons for a physicians' professional duty to support SeConts. Next, we examine these documents to identify professional duties that might conflict with a potential duty of physicians to support SeConts.

Abstract P4-02-27: Preliminary Attainability Assessment of Real-World Data for Answering Clinical Questions about Breast Cancer Brain Metastases

Abstract Background Brain metastases is one of breast cancer’s leading causes of death, and the incidence is increasing with advances in diagnosis and treatment techniques. Nevertheless, these patients have fewer opportunities to participate in prospective randomized clinical trials because of the design challenges of increasing target population heterogeneity or differentiating the definition of endpoints and how the endpoints are evaluated. Real-world data has the potential that reaches the information at a blinded area of RCT approaches. In recent years, observational studies using real-world data have gotten attention for their capability to enhance an understanding of hard-to-reach RCT approaches. However, the yet incomplete gold standard of the study protocol and the unpredictable ’hidden labor’ of secondary use of clinical data become barriers to clinical researchers. In the study, we demonstrate data attainability assessment focused on clinicians’ clinical unmet needs in breast cancer brain metastases (BCBM) as a preliminary step of the observational study for screening study feasibility on a certain data source. Methods A breast cancer registry based on the clinical data warehouse (CDW) of Samsung Medical Center has been used as the data source (N=45,219, up to 31 Dec 2021). A total of 5 clinical questions (CQ) were presented as the result of the interview of two breast cancer experts about BCBM for the study (Table 1). Additionally, we conducted an attainability assessment for the population size and core variables constrained by a clinical question and a survey of 7 breast cancer clinicians for evaluation of the recognized clinical significance and research methods’ suitability for each CQ. A working group was formed for data attainability tests withal a person with experience in clinical research for at least several years across interdisciplinary areas, including clinical expertise, medical informatics, and epidemiology. For the first step, we declare the operational definition for the study population and core variables in which the research question is inherent conceptually. And appropriate data fields and value sets are figured out from the data set. Data attainability is examined via RWD extraction on the population size and availability of core variables. Result The collected five CQs were mostly about the relationship between brain metastases, systemic metastases, and systemic treatments. Other CQs were related to the relationship between brain metastases incidence and the type of neoadjuvant treatment or patients’ described symptoms. Assessment of the importance of clinical questions aligned well, especially for the questions with higher scores. (r=- · 98) For CQ E, filtered out not to fit the SMC BCR dataset at the early stage of the data attainability screening process due to lack of explicit data field or semantically related value sets for one of the core variables, ‘neurological symptom.’ For CQ A, B, and D, we could confirm enough population size and dataset for conducting the study, but additional logical reconstruction of data elements had needed. For example, we used the ‘clinical subtype’ variable, which is provided from SMC BCR, as pre-processed feature variable using an expert-knowledge-based algorithm. For CQ C, we were able to obtain pertinent data for the study only with direct data field match and explicit code set definition. Conclusion To effectively use CDW, the variables have to be interpreted and defined in a clinically meaningful way with the cooperation of clinicians and data science experts. For observational studies based on RWD, understanding the data source contents and clarifying the research question enough to translate to the data level is essential. The step-by-step protocol presented in the study could be applied to the other clinical researcher using RWD at preliminary feasibility screening. Table 1. List of clinical questions from expert interviews Citation Format: Min-Jeong Kim, Hyo Jung Kim, Danbee Kang, Hee Kyung Ahn, Soo-Yong Shin, Seri Park, Juhee Cho, Yeon Hee Park. Preliminary Attainability Assessment of Real-World Data for Answering Clinical Questions about Breast Cancer Brain Metastases [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-02-27.

Do Physicians Have a Duty to Support Secondary Use of Clinical Data in Biomedical Research? An Inquiry into the Professional Ethics of Physicians

Do Physicians Have a Duty to Support Secondary Use of Clinical Data in Biomedical Research? An Inquiry into the Professional Ethics of Physicians

Patients' Willingness to Provide Their Clinical Data for Research Purposes and Acceptance of Different Consent Models: Findings From a Representative Survey of Patients With Cancer.

BackgroundSecondary use of clinical data for biomedical research purposes holds great potential for various types of noninterventional, data-driven studies. Patients’ willingness to support research with their clinical data is a crucial prerequisite for research progress.ObjectiveThe aim of the study was to learn about patients’ attitudes and expectations regarding secondary use of their clinical data. In a next step, our results can inform the development of an appropriate governance framework for secondary use of clinical data for research purposes.MethodsA questionnaire was developed to assess the willingness of patients with cancer to provide their clinical data for biomedical research purposes, considering different conditions of data sharing and consent models. The Cancer Registry of the German federal state of Baden-Württemberg recruited a proportionally stratified random sample of patients with cancer and survivors of cancer based on a full census.ResultsIn total, 838 participants completed the survey. Approximately all participants (810/838, 96.7%) showed general willingness to make clinical data available for biomedical research purposes; however, they expected certain requirements to be met, such as comparable data protection standards for data use abroad and the possibility to renew consent at regular time intervals. Most participants (620/838, 73.9%) supported data use also by researchers in commercial companies. More than half of the participants (503/838, 60%) were willing to give up control over clinical data in favor of research benefits. Most participants expressed acceptance of the broad consent model (494/838, 58.9%), followed by data use by default (with the option to opt out at any time; 419/838, 50%); specific consent for every study showed the lowest acceptance rate (327/838, 39%). Patients expected physicians to share their data (763/838, 91.1%) and their fellow patients to support secondary use with their clinical data (679/838, 81%).ConclusionsAlthough patients’ general willingness to make their clinical data available for biomedical research purposes is very high, the willingness of a substantial proportion of patients depends on additional requirements. Taking these perspectives into account is essential for designing trustworthy governance of clinical data reuse and sharing. The willingness to accept the loss of control over clinical data to enhance the benefits of research should be given special consideration.

Establishing a Data Quality Baseline in the AKTIN Emergency Department Data Registry - A Secondary Use Perspective.

Secondary use of clinical data is an increasing application that is affected by the data quality (DQ) of its source systems. Techniques such as audits and risk-based monitoring for controlling DQ often rely on source data verification (SDV). SDV requires access to data generating systems. We present an approach to a targeted SDV based on manual input and synthetic data that is applicable in low resource settings with restricted system access. We deployed the protocol in the DQ management of the AKTIN Emergency Department Data Registry. Our targeted approach has shown to be feasible to form a DQ baseline that can be used for different DQ monitoring processes such as the identification of different error sources.

Current situation of electronic medical records in ophthalmological departments in Germany

Electronic medical records are required in ophthalmology clinics to be integrated into digital care networks and efficient clinical registry databases. To assess the prevalence and methods of electronic medical recordkeeping in inpatient ophthalmological care in Germany. An online questionnaire was sent to all German university eye hospitals and ophthalmology departments in June 2021. It included13 open and closed option questions concerning current practices of digital recordkeeping, including the structure of data storage and the recording of billing-relevant codes in the departments. A total of 44 (44%) out of 100 clinics responded. Patient documentation was completely digital in15 (34%) clinics and partly digital and paper-based in the remaining29 (66%). A total of 16different constellations of documentation programs were specified. The most frequently used programs were Orbis (27%) (Dedalus HealthCare, Bonn, Germany), FIDUS (18%) (Arztservice Wente, Darmstadt, Germany), and SAP/i.s.h.med (16%) (SAP Deutschland, Walldorf, Germany; Cerner Deutschland, Berlin, Germany) and 3 clinics indicated primary use of paper records. Structured documentation of findings was performed in 61% of the departments, while 23% used asemistructured manner and 15% used anonstructured format. Electronic documents are stored as DICOM (Digital Imaging and Communications in Medicine) documents 20% of the clinics and as PDF (Portable Document Format) files in 34% of the clinics while 23% store scanned printouts. Methods of medical record keeping in German eye clinics are heterogeneous, with paper-based documentation continuing to play an important role. This, as well as the high number of different electronic medical record software pose important challenges in terms of interoperability and secondary use of clinical data.

A Privacy-Preserving and Standard-Based Architecture for Secondary Use of Clinical Data

The heterogeneity of the formats and standards of clinical data, which includes both structured, semi-structured, and unstructured data, in addition to the sensitive information contained in them, require the definition of specific approaches that are able to implement methodologies that can permit the extraction of valuable information buried under such data. Although many challenges and issues that have not been fully addressed still exist when this information must be processed and used for further purposes, the most recent techniques based on machine learning and big data analytics can support the information extraction process for the secondary use of clinical data. In particular, these techniques can facilitate the transformation of heterogeneous data into a common standard format. Moreover, they can also be exploited to define anonymization or pseudonymization approaches, respecting the privacy requirements stated in the General Data Protection Regulation, Health Insurance Portability and Accountability Act and other national and regional laws. In fact, compliance with these laws requires that only de-identified clinical and personal data can be processed for secondary analyses, in particular when data is shared or exchanged across different institutions. This work proposes a modular architecture capable of collecting clinical data from heterogeneous sources and transforming them into useful data for secondary uses, such as research, governance, and medical education purposes. The proposed architecture is able to exploit appropriate modules and algorithms, carry out transformations (pseudonymization and standardization) required to use data for the second purposes, as well as provide efficient tools to facilitate the retrieval and analysis processes. Preliminary experimental tests show good accuracy in terms of quantitative evaluations.

Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic.

This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019 (COVID-19). We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital's COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process. Among 233 patients with COVID-19, our EHR Report identified 4116 patient-HCW pairs, including 2365 (30.0%) of the 7890 pairs detected by the CTT. The EHR Report also revealed 1751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall. Automated methods to screen HCWs for potential exposures to patients with COVID-19 using clinical event data from the EHR (1) are likely to improve epidemiological surveillance by contact tracing programs and (2) represent a viable and readily available strategy that should be considered by other institutions.

Potential Uses of Blockchain Technology for Outcomes Research on Opioids.

The scale and severity of the opioid epidemic call for innovative, multipronged solutions. Research and development is key to accelerate the discovery and evaluation of interventions that support pain and substance use disorder management. In parallel, the use and integration of blockchain technology within research networks holds the potential to address some of the unique challenges facing opioid research. This paper discusses the applications of blockchain technology and illustrates potential ways in which it could be applied to strengthen the validity of outcomes research on the opioid epidemic. We reviewed published and gray literature to identify useful applications of blockchain, specifically those that address the challenges faced by opioid research networks and programs. We then convened a panel of experts to discuss the strengths, limitations, and feasibility of each application. Blockchain has the potential to address some of the issues surrounding health data management, including data availability, data sharing and interoperability, and privacy and security. We identified five primary applications of blockchain to opioids: clinical trials and pharmaceutical research, incentivizing data donation and behavior change, secure exchange and management of e-prescriptions, supply chain management, and secondary use of clinical data for research and public health surveillance. The published literature was limited, leading us to rely on gray literature, which was also limited in its discussion of the technical aspects of implementation. The technical expert panel provided additional context and an assessment of feasibility that was lacking in the literature. Research on opioid use and misuse is challenging because of disparate data stored across different systems, data and system interoperability issues, and legal requirements. These areas must be navigated to make data accessible, timely, and useful to researchers. Blockchain technologies have the potential to act as a facilitator in this process, offering a more efficient, secure, and privacy-preserving solution for data exchange. Among the 5 primary applications, we found that clinical trial research, supply chain management, and secondary use of data had the most examples in practice and the potential effectiveness of blockchain. More discussions and studies should focus on addressing technical questions concerning scalability and tackling practical concerns such as cost, standards, and governance around the implementation of blockchain in health care. Policy concerns related to balancing the need for data accessibility that also protects patient privacy and autonomy in revoking consent should also be examined.

A section identification tool: Towards HL7 CDA/CCR standardization in Spanish discharge summaries

Background.Nowadays, with the digitalization of healthcare systems, huge amounts of clinical narratives are available. However, despite the wealth of information contained in them, interoperability and extraction of relevant information from documents remains a challenge. Objective.This work presents an approach towards automatically standardizing Spanish Electronic Discharge Summaries (EDS) following the HL7 Clinical Document Architecture. We address the task of section annotation in EDSs written in Spanish, experimenting with three different approaches, with the aim of boosting interoperability across healthcare systems and hospitals. Methods.The paper presents three different methods, ranging from a knowledge-based solution by means of manually constructed rules to supervised Machine Learning approaches, using state of the art algorithms like the Perceptron and transfer learning-based Neural Networks. Results.The paper presents a detailed evaluation of the three approaches on two different hospitals. Overall, the best system obtains a 93.03% F-score for section identification. It is worth mentioning that this result is not completely homogeneous over all section types and hospitals, showing that cross-hospital variability in certain sections is bigger than in others. Conclusions.As a main result, this work proves the feasibility of accurate automatic detection and standardization of section blocks in clinical narratives, opening the way to interoperability and secondary use of clinical data.

Secondary Use of Clinical Data in Data-Gathering, Non-Interventional Research or Learning Activities: Definition, Types, and a Framework for Risk Assessment.

BackgroundThe secondary use of clinical data in data-gathering, non-interventional research or learning activities (SeConts) has great potential for scientific progress and health care improvement. At the same time, it poses relevant risks for the privacy and informational self-determination of patients whose data are used.ObjectiveSince the current literature lacks a tailored framework for risk assessment in SeConts as well as a clarification of the concept and practical scope of SeConts, we aim to fill this gap.MethodsIn this study, we analyze each element of the concept of SeConts to provide a synthetic definition, investigate the practical relevance and scope of SeConts through a literature review, and operationalize the widespread definition of risk (as a harmful event of a certain magnitude that occurs with a certain probability) to conduct a tailored analysis of privacy risk factors typically implied in SeConts.ResultsWe offer a conceptual clarification and definition of SeConts and provide a list of types of research and learning activities that can be subsumed under the definition of SeConts. We also offer a proposal for the classification of SeConts types into the categories non-interventional (observational) clinical research, quality control and improvement, or public health research. In addition, we provide a list of risk factors that determine the probability or magnitude of harm implied in SeConts. The risk factors provide a framework for assessing the privacy-related risks for patients implied in SeConts. We illustrate the use of risk assessment by applying it to a concrete example.ConclusionsIn the future, research ethics committees and data use and access committees will be able to rely on and apply the framework offered here when reviewing projects of secondary use of clinical data for learning and research purposes.

Developing a systematic approach to assessing data quality in secondary use of clinical data based on intended use.

IntroductionSecondary use of electronic health record (EHR) data for research requires that the data are fit for use. Data quality (DQ) frameworks have traditionally focused on structural conformance and completeness of clinical data extracted from source systems. In this paper, we propose a framework for evaluating semantic DQ that will allow researchers to evaluate fitness for use prior to analyses.MethodsWe reviewed current DQ literature, as well as experience from recent multisite network studies, and identified gaps in the literature and current practice. Derived principles were used to construct the conceptual framework with attention to both analytic fitness and informatics practice.ResultsWe developed a systematic framework that guides researchers in assessing whether a data source is fit for use for their intended study or project. It combines tools for evaluating clinical context with DQ principles, as well as factoring in the characteristics of the data source, in order to develop semantic DQ checks.ConclusionsOur framework provides a systematic process for DQ development. Further work is needed to codify practices and metadata around both structural and semantic data quality.

Cross-Border Exchange of Clinical Data Using Archetype Concepts Compatible with the International Patient Summary.

This paper proposes an approach and demonstrates its application for cross-border exchange of clinical documents oriented towards the use of archetype concepts and international patient summary standards adopted in the European Union. A novelty in this approach is the management of native XML instances of an archetype concept in the CEN 13606 standard by means of a native XML database and XML technologies. The computer experiments demonstrate that it is suitable for representing relatively small clinical datasets such as those describing rare diseases like the Acromegaly illness, where the semantic context in the relatively small number of symptoms is practicable to tag in terms of SNOMED-CT terminology codes. Additionally, we demonstrate that the semantically enriched information model can facilitate secondary use of clinical data by visualizing the execution of queries based on standard terminologies. Finally, the compatibility of the information model with the IPS standard enables sharing of clinical data among different information models.

Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework.

In this article, the authors propose an ethical framework for using and sharing clinical data for the development of artificial intelligence (AI) applications. The philosophical premise is as follows: when clinical data are used to provide care, the primary purpose for acquiring the data is fulfilled. At that point, clinical data should be treated as a form of public good, to be used for the benefit of future patients. In their 2013 article, Faden et al argued that all who participate in the health care system, including patients, have a moral obligation to contribute to improving that system. The authors extend that framework to questions surrounding the secondary use of clinical data for AI applications. Specifically, the authors propose that all individuals and entities with access to clinical data become data stewards, with fiduciary (or trust) responsibilities to patients to carefully safeguard patient privacy, and to the public to ensure that the data are made widely available for the development of knowledge and tools to benefit future patients. According to this framework, the authors maintain that it is unethical for providers to "sell" clinical data to other parties by granting access to clinical data, especially under exclusive arrangements, in exchange for monetary or in-kind payments that exceed costs. The authors also propose that patient consent is not required before the data are used for secondary purposes when obtaining such consent is prohibitively costly or burdensome, as long as mechanisms are in place to ensure that ethical standards are strictly followed. Rather than debate whether patients or provider organizations "own" the data, the authors propose that clinical data are not owned at all in the traditional sense, but rather that all who interact with or control the data have an obligation to ensure that the data are used for the benefit of future patients and society.

Challenges Frequently Encountered in the Secondary Use of Electronic Medical Record Data for Research.

The wide adoption of electronic medical records and subsequent availability of large amounts of clinical data provide a rich resource for researchers. However, the secondary use of clinical data for research purposes is not without limitations. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review to identify current issues related to secondary use of electronic medical record data via MEDLINE and CINAHL databases. All articles published until June 2018 were included. Sixty articles remained after title and abstract review, and four domains of potential limitations were identified: (1) data quality issues, present in 91.7% of the articles reviewed; (2) data preprocessing challenges (53.3%); (3) privacy concerns (18.3%); and (4) potential for limited generalizability (21.7%). Researchers must be aware of the limitations inherent to the use of electronic medical record data for research and consider the potential effects of these limitations throughout the entire study process, from initial conceptualization to the identification of adequate sources that can provide data appropriate for answering the research questions, analysis, and reporting study results. Consideration should also be given to using existing data quality assessment frameworks to facilitate use of standardized data quality definitions and further efforts of standard data quality reporting in publications.

Artificial Intelligence Assisted Standard White Light Endoscopy Accurately Characters Early Colorectal Cancer: A Multicenter Diagnostic Study

Background: Early detection and treatment under endoscopy have effectively decreased the mortality of colorectal cancer (CRC). But it is difficult to detect early CRC under white light endoscopy (WLE). Artificial intelligence (A.I.) assisted diagnose system are accumulated, but early CRC assisting diagnosis system is lack due to clinical design and sample size limitation. We propose to establish a propagable AI-assisted early CRC diagnosis and characterization system (ECRC-CAD). Methods: Establishing algorithms were implemented in four (i.e., municipal, provincial, rural) hospitals of China. All patients who completed WLE from January 2016 to July 2019 were enrolled. Early CRC is diagnosed by pathological examination. Images of intraepithelial neoplasm (IN-ECRC) were included in the case group, and precancerous conditions and serrate lesion without intraepithelial neoplasm (NIN-ECRC) were randomly sampled in corresponding quantity included to control group. These images were randomly assigned (8:1:1) to the training, optimizing datasets and internal datasets. The evaluating indexes of diagnostic performance include sensitivity, specificity, positive predictive value, and negative predictive value. Findings: The largest new CRC endoscopic image database to date were established, which included 4,390 images of IN-ECRC. The diagnostic accuracy was 0·963 (95% CI, 0·941-0·978) in the internal validation set, and 0·835 (95% CI, 0·805-0·862) of identifying IN-ECRC in external datasets. ECRC-CAD achieved better diagnostic accuracy than the expert endoscopist (0·885 [95% CI, 0·859-0·907] vs 0.505 [95% CI, 0·411–0·598]; p<0.0001). The sensitivity, specificity, positive, and negative predictive value were significantly higher for ECRC-CAD than the expert endoscopist (p<0.0001). Interpretation: ECRC-CAD can identify early CRC and characterize neoplasms with high malignant potential; the performance is better comparing with expert endoscopists. Spreading of ECRC-CAD to regions with different medical levels can assist in CRC screening and prevention. Funding Statement: This research was supported by Major Science and Technology Project of Zhejiang Provincial (2020C03030), Natural Science Foundation of Fujian Province (2019-CXB-31), Science and technology program of Guangdong Province (2016A020213002), and National Natural Science Foundation of China (81773956). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Each participating institute has ethical approval by independent ethics Committee according to the Helsinki declaration. The informed consent of retrospective cohort was exempted by the institutional review boards as the secondary use of clinical data without any intervention. In the prospective study, each patient was informed and signed informed consent.

Big data techniques for the secondary use of clinical data in the generation of medical knowledge. The MIMIC solution

Both Cryptococcus neoformans and Cryptococcus gattii have been isolated from a variety of environmental sources in Colombia.To determine the viability of C. neoformans/C. gattii isolates in stored soil samples, filtrates and bird droppings from which these yeasts were previously recovered.A total of 964 samples collected between 2003 and 2009, and kept at room temperature were processed. From them, 653 samples were from trees decaying wood, 274 from soil filtrates and 37 from bird droppings. When C. neoformans or C. gattii were recovered, the molecular type of each isolate was established by PCR fingerprinting using the single primer (GTG)5.Among the processed samples, 161 isolates were recovered. From those, 81 (50.3%) corresponded to C. gattii recovered from decaying wood of Eucalyptus spp., Corymbia ficifolia, Terminalia catappa and Ficus spp. trees, and 80 (49.7%) corresponded to C. neoformans recovered from Ficus spp. and eucalyptus trees, as well as from bird droppings. The most prevalent molecular type among the C. gattii and C. neoformans isolates was VGII and VNI, respectively.The re-isolation of C. neoformans/C. gattii from 10-year stored samples suggests that these yeasts are able to keep viable in naturally colonized samples.Cryptococcus neoformans y Cryptococcus gattii han sido aislados de diversas fuentes ambientales en Colombia.Determinar la viabilidad de C. neoformans/C. gattii en muestras de suelo, filtrados y excrementos de aves en las que se habían aislado las levaduras previamente.Se procesaron 964 muestras recogidas entre los años 2003 y 2009, y almacenadas a temperatura ambiente. De estas, 653 muestras provenían de detritos, 274 de filtrados de suelo y 37 de excrementos de aves. Una vez recuperados C. neoformans y C. gattii, se determinó el patrón molecular mediante la técnica de PCR huella digital con el iniciador (GTG)5.Entre las muestras procesadas, se recuperaron 161 aislamientos. De estos, 81 (50,3%) fueron C. gattii recuperados de detritos de Eucalyptus spp., Corymbia ficifolia, Terminalia catappa y Ficus spp., y 80 (49,7%) eran C. neoformans recuperados de Ficus spp. y eucaliptos, así como de excrementos de aves. El patrón molecular más prevalente entre C. gattii y C. neoformans fue VGII y VNI, respectivamente.El reaislamiento de C. neoformans/C. gattii de muestras almacenadas durante 10 años evidencia que estas levaduras son capaces de mantenerse viables en muestras colonizadas naturalmente.

Leaf: an open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research.

ObjectiveAcademic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of clinical data. Enterprise data warehouses have emerged as central resources for these data, but often require an informatician to extract meaningful information, limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying clinical data from heterogeneous data models and sources.Materials and MethodsLeaf utilizes a flexible biomedical concept system to define hierarchical concepts and ontologies. Each Leaf concept contains both textual representations and SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries.ResultsLeaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an enterprise data warehouse with over 300 active users. Through the support of UW Medicine (https://uwmedicine.org), the Institute of Translational Health Sciences (https://www.iths.org), and the National Center for Data to Health (https://ctsa.ncats.nih.gov/cd2h/), Leaf source code has been released into the public domain at https://github.com/uwrit/leaf.DiscussionLeaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication.ConclusionsLeaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly leverage existing user authentication systems and clinical databases in situ. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine biobanks, and clinical studies involving large patient cohorts.

A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems.

Natural language processing (NLP) has become essential for secondary use of clinical data. Over the last two decades, many clinical NLP systems were developed in both academia and industry. However, nearly all existing systems are restricted to specific clinical settings mainly because they were developed for and tested with specific datasets, and they often fail to scale up. Therefore, using existing NLP systems for one's own clinical purposes requires substantial resources and long-term time commitments for customization and testing. Moreover, the maintenance is also troublesome and time-consuming. This research presents a lightweight approach for building clinical NLP systems with limited resources. Following the design science research approach, we propose a lightweight architecture which is designed to be composable, extensible, and configurable. It takes NLP as an external component which can be accessed independently and orchestrated in a pipeline via web APIs. To validate its feasibility, we developed a web-based prototype for clinical concept extraction with six well-known NLP APIs and evaluated it on three clinical datasets. In comparison with available benchmarks for the datasets, three high F1 scores (0.861, 0.724, and 0.805) were obtained from the evaluation. It also gained a low F1 score (0.373) on one of the tests, which probably is due to the small size of the test dataset. The development and evaluation of the prototype demonstrates that our approach has a great potential for building effective clinical NLP systems with limited resources.

Transforming Data into Knowledge: How to Improve the Efficiency of Clinical Care?

Summary Objectives: To provide an introduction to the 2017 IMIA Yearbook of Medical Informatics by the editors. Methods: We present a brief overview of the 2017 special topic “Learning from experience: Secondary use of patient data”. We review our choice of special topic section editors, present the new section “Health Information Management”, and discuss transitions in the editorial team. Results: In this edition of the Yearbook, we focused on one of the most important issues for the medical informatics community: The secondary use of clinical data. With the ubiquitous adoption of electronic health records (EHRs) and the increasing availability of genomic and environmental data, as well as the accessibility of unstructured data in social media, issues related to data integration, storage, and management, as well as the need for novel analytic approaches are clear challenges. The paradigm of Learning Health Systems (LHSs) is presented in the keynote paper and survey papers review the significant developments in allied fields such as clinical research, clinical systems, translational informatics, and public health over the past two years. IMIA Working Groups also contributed to this topic. Conclusion: The 2017 issue of the IMIA yearbook focuses on the secondary use of patient data and presents the difficulties that still need to be solved before witnessing the actual development of LHSs.