Methods for the de-identification of electronic health records for genomic research.
Ontology highlight
ABSTRACT:
SUBMITTER: El Emam K
PROVIDER: S-EPMC3129641 | biostudies-literature | 2011 Apr
REPOSITORIES: biostudies-literature
Publications
Methods for the de-identification of electronic health records for genomic research.
Genome medicine 20110427 4
Electronic health records are increasingly being linked to DNA repositories and used as a source of clinical information for genomic research. Privacy legislation in many jurisdictions, and most research ethics boards, require that either personal health information is de-identified or that patient consent or authorization is sought before the data are disclosed for secondary purposes. Here, I discuss how de-identification has been applied in current genomic research projects. Recent metrics and ...[more]
Similar Datasets
Project description:BackgroundMental health supported housing services are a key component in the rehabilitation of people with severe and complex needs. They are implemented widely in the UK and other deinstitutionalised countries but there have been few empirical studies of their effectiveness due to the logistic challenges and costs of standard research methods. The Clinical Record Interactive Search (CRIS) tool, developed to de-identify and interrogate routinely recorded electronic health records, may provide an alternative to evaluate supported housing services.MethodsThe feasibility of using the Camden and Islington NHS Foundation Trust CRIS database to identify a sample of users of mental health supported accommodation services. Two approaches to data interrogation and case identification were compared; using structured fields indicating individual's accommodation status, and iterative development of free text searches of clinical notes referencing supported housing. The data used were recorded over a 10-year-period (01-January-2008 to 31-December-2017).ResultsBoth approaches were carried out by one full-time researcher over four weeks (150 hours). Two structured fields indicating accommodation status were found, 2,140 individuals had a value in at least one of the fields representative of supported accommodation. The free text search of clinical notes returned 21,103 records pertaining to 1,105 individuals. A manual review of 10% of the notes indicated an estimated 733 of these individuals had used a supported housing service, a positive predictive value of 66.4%. Over two-thirds of the individuals returned in the free text search (768/1,105, 69.5%) were identified via the structured fields approach. Although the estimated positive predictive value was relatively high, a substantial proportion of the individuals appearing only in the free text search (337/1,105, 30.5%) are likely to be false positives.ConclusionsIt is feasible and requires minimal resources to use de-identified electronic health record search tools to identify large samples of users of mental health supported housing using structured and free text fields. Further work is needed to establish the availability and completion of variables relevant to specific clinical research questions in order to fully assess the utility of electronic health records in evaluating the effectiveness of these services.
| S-EPMC7444482 | biostudies-literature
Project description:Electronic health records (EHRs), originally designed to facilitate health care delivery, are becoming a valuable data source for health research. EHR systems have two components, both of which have various components, and points of data entry, management, and analysis. The "front end" refers to where the data are entered, primarily by healthcare workers (e.g. physicians and nurses). The second component of EHR systems is the electronic data warehouse, or "back-end," where the data are stored in a relational database. EHR data elements can be of many types, which can be categorized as structured, unstructured free-text, and imaging data. The Sunrise Clinical Manager (SCM) EHR is one example of an inpatient EHR system, which covers the city of Calgary (Alberta, Canada). This system, under the management of Alberta Health Services, is now being explored for research use. The purpose of the present paper is to describe the SCM EHR for research purposes, showing how this generalizes to EHRs in general. We further discuss advantages, challenges (e.g. potential bias and data quality issues), analytical capacities, and requirements associated with using EHRs in a health research context.
| S-EPMC7473254 | biostudies-literature
Project description:The rise of genomically targeted therapies and immunotherapy has revolutionized the practice of oncology in the last 10-15 years. At the same time, new technologies and the electronic health record (EHR) in particular have permeated the oncology clinic. Initially designed as billing and clinical documentation systems, EHR systems have not anticipated the complexity and variety of genomic information that needs to be reviewed, interpreted, and acted upon on a daily basis. Improved integration of cancer genomic data with EHR systems will help guide clinician decision making, support secondary uses, and ultimately improve patient care within oncology clinics. Some of the key factors relating to the challenge of integrating cancer genomic data into EHRs include: the bioinformatics pipelines that translate raw genomic data into meaningful, actionable results; the role of human curation in the interpretation of variant calls; and the need for consistent standards with regard to genomic and clinical data. Several emerging paradigms for integration are discussed in this review, including: non-standardized efforts between individual institutions and genomic testing laboratories; "middleware" products that portray genomic information, albeit outside of the clinical workflow; and application programming interfaces that have the potential to work within clinical workflow. The critical need for clinical-genomic knowledge bases, which can be independent or integrated into the aforementioned solutions, is also discussed.
| S-EPMC5081968 | biostudies-literature
Project description:BACKGROUND:Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity, and lack of suitable measures in important domains still hinder the progress. OBJECTIVE:The objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas. METHODS:On the basis of a local hospital EMR system in Singapore, we developed a structured framework for EMR data processing, including standardization and phenotyping of diagnosis codes, construction of cohort with multilevel views, and generation of variables and proxy measures to supplement primary data. Disease complexity was estimated by Charlson Comorbidity Index (CCI) and Polypharmacy Score (PPS), whereas socioeconomic status (SES) was estimated by housing type. Validity of modified diagnosis codes and derived measures were investigated. RESULTS:Visit-level (N=7,778,761) and patient-level records (n=549,109) were generated. The International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes were standardized to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) with a mapping rate of 87.1%. In all, 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software by Agency for Healthcare Research and Quality. Diagnosis codes that underwent modification (truncation or zero addition) in standardization and phenotyping procedures had the modification validated by physicians, with validity rates of more than 90%. Disease complexity measures (CCI and PPS) and SES were found to be valid and robust after a correlation analysis and a multivariate regression analysis. CCI and PPS were correlated with each other and positively correlated with health care utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with higher SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity, and health care utilization in those aged above 65 years and those aged 65 years or younger. CONCLUSIONS:The framework proposed in this study would be useful for other researchers working with EMR data for health services research. Further analyses would be needed to better understand differences observed in the cohorts.
| S-EPMC6320424 | biostudies-other
Project description:ObjectivePersistent somatic symptoms (PSSs) are defined as symptoms not fully explained by well-established pathophysiological mechanisms and are prevalent in up to 10% of patients in primary care. The present study aimed to explore methods to identify patients with a recognisable risk of having PSS in routine primary care data.DesignA cross-sectional study to explore four identification methods that each cover part of the broad spectrum of PSS was performed. Cases were selected based on (1) PSS-related syndrome codes, (2) PSS-related symptom codes, (3) PSS-related terminology and (4) Four-Dimensional Symptom Questionnaire scores and all methods combined.SettingCoded electronic health record data were extracted from 76 general practices in the Netherlands.ParticipantsPatients who were registered for at least 1 year during 2014-2018, were included (n=169 138).Outcome measuresIdentification methods were explored based on (1) PSS sample sizes and demographics, (2) presence of chronic conditions and (3) healthcare utilisation (HCU) variables. Overlap between methods and practice specific differences were examined.ResultsThe percentage of cases identified varied between 0.3% and 7.0% across the methods. Over 58.1% of cases had chronic physical condition(s) and over 33.8% had chronic mental condition(s). HCU was generally higher for cases selected by any method compared with the total cohort. HCU was higher for method B compared with the other methods. In 26.7% of cases, cases were selected by multiple methods. Overlap between methods was low.ConclusionsDifferent methods yielded different patient samples which were general practice specific. Therefore, for the most comprehensive data-based selection of PSS cases, a combination of methods A, C and D would be recommended. Advanced (data-driven) methods are needed to create a more sensitive algorithm for identifying the full spectrum of PSS. For clinical purposes, method B could possibly support screening of patients who are currently missed in daily practice.
| S-EPMC8451292 | biostudies-literature
Project description:The presence of personally identifiable information (PII) in natural language portions of electronic health records (EHRs) constrains their broad reuse. Despite continuous improvements in automated detection of PII, residual identifiers require manual validation and correction. Here, we describe an automated de-identification system that employs an ensemble architecture, incorporating attention-based deep-learning models and rule-based methods, supported by heuristics for detecting PII in EHR data. Detected identifiers are then transformed into plausible, though fictional, surrogates to further obfuscate any leaked identifier. Our approach outperforms existing tools, with a recall of 0.992 and precision of 0.979 on the i2b2 2014 dataset and a recall of 0.994 and precision of 0.967 on a dataset of 10,000 notes from the Mayo Clinic. The de-identification system presented here enables the generation of de-identified patient data at the scale required for modern machine-learning applications to help accelerate medical discoveries.
| S-EPMC8212138 | biostudies-literature
Project description:Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets.
| S-EPMC7878193 | biostudies-literature
Project description:BACKGROUND:Poor EHR design adds further challenges, especially in the areas of order entry and information visualization, with a net effect of increased rates of incidents, accidents, and mortality in ICU settings. OBJECTIVE:The purpose of this study was to propose a novel, mixed-methods framework to understand EHR-related information overload by identifying and characterizing areas of suboptimal usability and clinician frustration within a vendor-based, provider-facing EHR interface. METHODS:A mixed-methods, live observational usability study was conducted at a single, large, tertiary academic medical center in the Southeastern US utilizing a commercial, vendor based EHR. Physicians were asked to complete usability patient cases, provide responses to three surveys, and participant in a semi-structured interview. RESULTS:Of the 25 enrolled ICU physician participants, there were 5(20%) attending physicians, 9 (36%) fellows, and 11 (44%) residents; 52% of participants were females. On average, residents were the quickest in completing the tasks while attending physician took the longest to complete the same task. Poor usability, complex interface screens, and difficulty to navigate the EHR significantly correlated with high frustration levels. Significant association were found between the occurrence of error messages and temporal demand such that more error messages resulted in longer completion time (p?=?.03). DISCUSSION:Physicians remain frustrated with the EHR due to difficulty in finding patient information. EHR usability remains a critical challenge in healthcare, with implications for medical errors, patient safety, and clinician burnout. There is a need for scientific findings on current information needs and ways to improve EHR-related information overload.
| S-EPMC6584062 | biostudies-literature
Project description:Motivation:Pedigree analysis is a longstanding and powerful approach to gain insight into the underlying genetic factors in human health, but identifying, recruiting and genotyping families can be difficult, time consuming and costly. Development of high throughput methods to identify families and foster downstream analyses are necessary. Results:This paper describes simple methods that allowed us to identify 173?368 family pedigrees with high probability using basic demographic data available in most electronic health records (EHRs). We further developed and validate a novel statistical method that uses EHR data to identify families more likely to have a major genetic component to their diseases risk. Lastly, we showed that incorporating EHR-linked family data into genetic association testing may provide added power for genetic mapping without additional recruitment or genotyping. The totality of these results suggests that EHR-linked families can enable classical genetic analyses in a high-throughput manner. Availability and implementation:Pseudocode is provided as supplementary information. Contact:HEBBRING.SCOTT@marshfieldresearch.org. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC5860602 | biostudies-literature
Project description:BackgroundEHR phenotyping offers the ability to rapidly assemble a precisely defined cohort of patients prescreened for eligibility to participate in health-related research. Even so, stakeholders in the process must still contend with the practical and ethical challenges associated with research recruitment. Patient perspectives on these matters are particularly important given that the success of research recruitment depends on patients' willingness to participate.MethodsWe conducted 15 focus groups (n =?110 participants) in four counties in diverse regions of the southeastern US: Appalachia, the Mississippi Delta, and the Piedmont area of North Carolina. Based on a hypothetical study of a behavioral intervention for type 2 diabetes, we asked about the acceptability and appropriateness of direct investigator versus physician-mediated contact with patients for research recruitment, and whether patients should be asked to opt in or opt out of further contact in response to recruitment letters.ResultsFor initial contact, nearly all participants said it would be acceptable for researchers to contact patients directly and three-fourths said that it would be acceptable for researchers to contact patients through their physicians. When we asked which would be most appropriate, a substantial majority chose direct contact. Themes that arose in the discussion included trust and transparency, decision-making power, the effect on research, and the effect on patient care. For response expectations, the vast majority of participants said both opt-in and opt-out would be acceptable-typically finding neither especially problematic and noting that both afford patients the opportunity to make their own decisions.ConclusionsExternal validity relies heavily on researchers' success enrolling eligible patients and failure to reach accrual targets is a costly and common barrier to advancing scientific knowledge. Our results suggest that patients recognize multiple advantages and disadvantages of different research recruitment strategies and place value on the implications not just for themselves, but also for researchers and healthcare providers. Our findings, including rich qualitative detail, contribute to the body of empirical and ethical literature on improving research recruitment and suggest specific ways forward as well as important areas for future research.
| S-EPMC6390331 | biostudies-literature