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Anonymization for outputs of population health and health services research conducted via an online data center.

ABSTRACT: Objective:Online data centers (ODCs) are becoming increasingly popular for making health-related data available for research. Such centers provide good privacy protection during analysis by trusted researchers, but privacy concerns may still remain if the system outputs are not sufficiently anonymized. In this article, we propose a method for anonymizing analysis outputs from ODCs for publication in academic literature. Methods:We use as a model system the Secure Unified Research Environment, an online computing system that allows researchers to access and analyze linked health-related data for approved studies in Australia. This model system suggests realistic assumptions for an ODC that, together with literature and practice reviews, inform our solution design. Results:We propose a two-step approach to anonymizing analysis outputs from an ODC. A data preparation stage requires data custodians to apply some basic treatments to the dataset before making it available. A subsequent output anonymization stage requires researchers to use a checklist at the point of downloading analysis output. The checklist assists researchers with highlighting potential privacy concerns, then applying appropriate anonymization treatments. Conclusion:The checklist can be used more broadly in health care research, not just in ODCs. Ease of online publication as well as encouragement from journals to submit supplementary material are likely to increase both the volume and detail of analysis results publicly available, which in turn will increase the need for approaches such as the one suggested in this paper.

SUBMITTER: O'Keefe CM

PROVIDER: S-EPMC7651952 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Anonymization for outputs of population health and health services research conducted via an online data center.

O'Keefe Christine M CM Westcott Mark M O'Sullivan Maree M Ickowicz Adrien A Churches Tim T

Journal of the American Medical Informatics Association : JAMIA 20170501 3

<h4>Objective</h4>Online data centers (ODCs) are becoming increasingly popular for making health-related data available for research. Such centers provide good privacy protection during analysis by trusted researchers, but privacy concerns may still remain if the system outputs are not sufficiently anonymized. In this article, we propose a method for anonymizing analysis outputs from ODCs for publication in academic literature.<h4>Methods</h4>We use as a model system the Secure Unified Research ...[more]

PMID: 28011594

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Project description:BACKGROUND:Health services researchers spend a substantial amount of time performing integration, cleansing, interpretation, and aggregation of raw data from multiple public or private data sources. Often, each researcher (or someone in their team) duplicates this effort for their own project, facing the same challenges and experiencing the same pitfalls discovered by those before them. OBJECTIVE:This paper described a design process for creating a data warehouse that includes the most frequently used databases in health services research. METHODS:The design is based on a conceptual iterative process model framework that utilizes the sociotechnical systems theory approach and includes the capacity for subsequent updates of the existing data sources and the addition of new ones. We introduce the theory and the framework and then explain how they are used to inform the methodology of this study. RESULTS:The application of the iterative process model to the design research process of problem identification and solution design for the Healthcare Research and Analytics Data Infrastructure Solution (HRADIS) is described. Each phase of the iterative model produced end products to inform the implementation of HRADIS. The analysis phase produced the problem statement and requirements documents. The projection phase produced a list of tasks and goals for the ideal system. Finally, the synthesis phase provided the process for a plan to implement HRADIS. HRADIS structures and integrates data dictionaries provided by the data sources, allowing the creation of dimensions and measures for a multidimensional business intelligence system. We discuss how HRADIS is complemented with a set of data mining, analytics, and visualization tools to enable researchers to more efficiently apply multiple methods to a given research project. HRADIS also includes a built-in security and account management framework for data governance purposes to ensure customized authorization depending on user roles and parts of the data the roles are authorized to access. CONCLUSIONS:To address existing inefficiencies during the obtaining, extracting, preprocessing, cleansing, and filtering stages of data processing in health services research, we envision HRADIS as a full-service data warehouse integrating frequently used data sources, processes, and methods along with a variety of data analytics and visualization tools. This paper presents the application of the iterative process model to build such a solution. It also includes a discussion on several prominent issues, lessons learned, reflections and recommendations, and future considerations, as this model was applied.

Project description:BackgroundData science offers an unparalleled opportunity to identify new insights into many aspects of human life with recent advances in health care. Using data science in digital health raises significant challenges regarding data privacy, transparency, and trustworthiness. Recent regulations enforce the need for a clear legal basis for collecting, processing, and sharing data, for example, the European Union's General Data Protection Regulation (2016) and the United Kingdom's Data Protection Act (2018). For health care providers, legal use of the electronic health record (EHR) is permitted only in clinical care cases. Any other use of the data requires thoughtful considerations of the legal context and direct patient consent. Identifiable personal and sensitive information must be sufficiently anonymized. Raw data are commonly anonymized to be used for research purposes, with risk assessment for reidentification and utility. Although health care organizations have internal policies defined for information governance, there is a significant lack of practical tools and intuitive guidance about the use of data for research and modeling. Off-the-shelf data anonymization tools are developed frequently, but privacy-related functionalities are often incomparable with regard to use in different problem domains. In addition, tools to support measuring the risk of the anonymized data with regard to reidentification against the usefulness of the data exist, but there are question marks over their efficacy.ObjectiveIn this systematic literature mapping study, we aim to alleviate the aforementioned issues by reviewing the landscape of data anonymization for digital health care.MethodsWe used Google Scholar, Web of Science, Elsevier Scopus, and PubMed to retrieve academic studies published in English up to June 2020. Noteworthy gray literature was also used to initialize the search. We focused on review questions covering 5 bottom-up aspects: basic anonymization operations, privacy models, reidentification risk and usability metrics, off-the-shelf anonymization tools, and the lawful basis for EHR data anonymization.ResultsWe identified 239 eligible studies, of which 60 were chosen for general background information; 16 were selected for 7 basic anonymization operations; 104 covered 72 conventional and machine learning-based privacy models; four and 19 papers included seven and 15 metrics, respectively, for measuring the reidentification risk and degree of usability; and 36 explored 20 data anonymization software tools. In addition, we also evaluated the practical feasibility of performing anonymization on EHR data with reference to their usability in medical decision-making. Furthermore, we summarized the lawful basis for delivering guidance on practical EHR data anonymization.ConclusionsThis systematic literature mapping study indicates that anonymization of EHR data is theoretically achievable; yet, it requires more research efforts in practical implementations to balance privacy preservation and usability to ensure more reliable health care applications.

Project description:BackgroundVisual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots.ObjectiveThis scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR).MethodsUsing the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences.ResultsAfter screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard.ConclusionsWith the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage.International registered report identifier (irrid)RR2-10.2196/14019.

Project description:ObjectivesUK National Health Service/Health and Social Care (NHS/HSC) data are variably shared between healthcare organisations for direct care, and increasingly de-identified for research. Few large-scale studies have examined public opinion on sharing, including of mental health (MH) versus physical health (PH) data. We measured data sharing preferences.Design/setting/interventions/outcomesPre-registered anonymous online survey, measuring expressed preferences, recruiting February to September 2020. Participants were randomised to one of three framing statements regarding MH versus PH data.ParticipantsOpen to all UK residents. Participants numbered 29 275; 40% had experienced an MH condition.ResultsMost (76%) supported identifiable data sharing for direct clinical care without explicit consent, but 20% opposed this. Preference for clinical/identifiable sharing decreased with geographical distance and was slightly less for MH than PH data, with small framing effects. Preference for research/de-identified data sharing without explicit consent showed the same small PH/MH and framing effects, plus greater preference for sharing structured data than de-identified free text. There was net support for research sharing to the NHS, academic institutions, and national research charities, net ambivalence about sharing to profit-making companies researching treatments, and net opposition to sharing to other companies (similar to sharing publicly). De-identified linkage to non-health data was generally supported, except to data held by private companies. We report demographic influences on preference. A majority (89%) supported a single NHS mechanism to choose uses of their data. Support for data sharing increased during COVID-19.ConclusionsSupport for healthcare data sharing for direct care without explicit consent is broad but not universal. There is net support for the sharing of de-identified data for research to the NHS, academia, and the charitable sector, but not the commercial sector. A single national NHS-hosted system for patients to control the use of their NHS data for clinical purposes and for research would have broad support.Trial registration numberISRCTN37444142.

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.

Project description:BackgroundLumps and soft tissue tumors (STT) are frequent reasons for consulting a physician. Most STT are benign, and lumps are not always associated with a tumor. MRI is the most advanced imaging modality to assist a provisional diagnosis of STT. Only a small fraction of STT is malignant, these soft tissue sarcomas are known for their aggressive growth. The study aims to analyze the influence of the MRI report on the speed of treatment of patients with suspected STT.MethodsThis was a retrospective, longitudinal, single-center study from 2011-2020. We included adult patients who had biopsies or resections of masses suspicious for STT in MRI exams. MRI reports were classified as benign (I), intermediate/unclear (II), or malignant (III). For these cohorts, time was statistically analyzed from MRI scan to first contact with the University cancer center (UCC) and surgery. Furthermore, distance in kilometers from the patients´ home to the UCC was examined and compared to age and suspected malignancy.ResultsThree hundred two patients (♀130; ♂172) were included. Histologic analyses revealed 286 tumors. The average age of the patients was 54.7(SD: 16.2) years. Malignant tumors were more often suspected in older patients (p = 0.0098). Patients with a benign diagnosed tumor in MRI contacted the UCC after an average of 31.3 (SD: 47.8) days. In contrast, patients with suspicion of a malignant tumor contacted the UCC significantly earlier, after 14.1 days (SD: 17.1); p = 0.0098. Likewise, the time between first contact and biopsy/resection was 32.8 days (SD: 35.7) for suspiciously benign tumors, and potentially malignant tumors were treated significantly faster 14.8 (SD: 16.0) days; (p = 0.028). Patients traveled on average 47.5 km (range: 0.5-483) to contact a specialized physician at the UCC. Suspected degree of malignancy or patient´s age had no statistical influence on traveled distance.DiscussionThe treatment speed depended to a great extent on the suspected malignancy of the STT in the MRI report. The provisional diagnoses from the radiologist highly influenced the time delay between MRI scan and first contact to the UCC and surgical treatment. No discrimination of age or distance to the UCC was observed in this study.

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Anonymization for outputs of population health and health services research conducted via an online data center.

Publications

Anonymization for outputs of population health and health services research conducted via an online data center.

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