De-identified Health Information Research Articles

Surveillance of Severe Acute Respiratory Syndrome Coronavirus 2 and Variants Using Digital Droplet Polymerase Chain Reaction at a Large University and Healthcare System in California.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with different infectivity, transmission potential, and morbidity change the characteristics of local epidemics and affect vaccine effectiveness. As part of the University of Southern California COVID-19 Pandemic Research Center's efforts to understand, control, and inform local community on coronavirus disease 2019 (COVID-19), we implemented a SARS-CoV-2 surveillance program among students, employees, and USC Keck Medical Center patients. We present the epidemiology and distribution of SARS-CoV-2 and its variants among the population. We used digital droplet reverse-transcriptase polymerase chain reaction (PCR) to analyze in real-time remnant SARS-CoV-2 PCR-positive saliva specimens stored at the USC Keck Medicine laboratory between September 2020 and April 2022. Samples were tested for the original strain (A20) and 9 SARS-CoV-2 variants: α(B.1.1.7, Q.1-Q.8), β(B.1.351, B.1.351.2, B.1.351.3), γ(P.1, P.1.1, P.1.2), δ(B.1.617.2), δ+(or δ417N), ε(B.1.427 and B.1.429), η(B.1.525), λ(C.37) and ο(B.1.1.529, ΒΑ.1, BA.2). We reviewed deidentified health information from positive cases including demographics, history of COVID-19 (eg, symptoms, hospitalizations, and repeat infections), and COVID-19 vaccination status. We reviewed 1169 cases and determined the variant type of 482 specimens: 77 specimens were original strain, 119 "Delta", 165 "Omicron". The original strain was detected during the third and fourth quarters of 2020. The Delta variant appeared during the second quarter of 2021, whereas Omicron appeared in the fourth quarter of 2021. Prospectively tracking SARS-CoV-2 variants in a university population and a hospital system, utilizing a low-cost, high-throughput PCR assay, was feasible. Local variant monitoring remains important to inform prevention and control efforts among university and clinical settings.

Educational attainment and willingness to use technology for health and to share health information – The reimagining healthcare survey

BackgroundAustralia has seen a rapid uptake of virtual care since the start of the COVID-19 pandemic. We aimed to describe the willingness of consumers to use digital technology for health and to share their health information; and explore differences by educational attainment and area of remoteness. MethodsWe conducted an online survey on consumer preferences for virtual modes of healthcare delivery between June and September 2021. Participants were recruited through the study’s partner organisations and an online market research company. Australian residents aged ≥18 years who provided study consent and completed the survey were included in the analysis. We reported the weighted percentages of participants who selected negative response to the questions to understand the size of the population that were unlikely to adopt virtual care. Age-adjusted Poisson regression models were used to estimate the prevalence ratios for selecting negative response associated with education and remoteness. ResultsOf the 1778 participants included, 29% were not aware of digital technologies for monitoring/supporting health, 22% did not have access to technologies to support their health, and 19% were not willing to use technologies for health. Over a fifth of participants (range: 21–34%) were not at all willing to use seven of the 15 proposed alternative methods of care. Between 21% and 36% of participants were not at all willing to share de-identified health information tracked in apps/devices with various not-for-profit organisations compared to 47% with private/for-profit health businesses. Higher proportions of participants selected negative response to the questions in the lower educational attainment groups than those with bachelor’s degree or above. No difference was observed between area of remoteness. ConclusionsImproving the digital health literacy of people, especially those with lower educational attainment, will be required for virtual care to become an equitable part of normal healthcare delivery in Australia.

Posttraumatic pneumonectomy and management of severely contaminated pleural space.

Posttraumatic pneumonectomy and management of severely contaminated pleural space.

Mining the Data: Exploring Rural Patients’ Attitudes about the Use of Their Personal Information in Research

Background This study examines rural patients’ perceived importance of knowing or being consulted about researchers’ access and use of their personal data (identifiable and de-identified health information, and identifiable and de-identified non-health information) across five scenarios. This study also examines their views on stewardship or governance of their personal information by researchers in their healthcare systems. Methods We conducted a survey by mail. Data were analyzed using descriptive statistics. Multivariable regression analyses were conducted across each scenario and type of personal data with the same variables included in each model. Results The majority of participants said it was “very important/absolutely essential” to know the purpose of the study, to be asked every time, and to know the policies governing researcher access and use of their identifiable health information. Just over two-thirds of respondents thought it “very important/absolutely essential” to know who serves on the data governance committee and to have a community member serve. Distrust in healthcare organizations was positively correlated with the scenarios while willingness to give permission to donate leftover biological specimens was negatively correlated. Conclusion Our study findings indicate that the type of personal information being accessed and used generally matters to 1,407 patients living in rural Pennsylvania. We also demonstrate that knowing their healthcare organizations’ governance policies and practices for managing their personal data is important to many rural Pennsylvania patients. Biomedical researchers need to recognize and attend to those differences as much as possible in order to expand opportunities for and participation in research by residents of these rural communities. Supplemental data for this article is available online at

Big data or big risk: general practitioner, practice nurse and practice manager attitudes to providing de-identified patient health data from electronic medical records to researchers

Research utilising de-identified patient health information extracted from electronic medical records (EMRs) from general practices has steadily grown in recent years in response to calls to increase use of health data for research and other secondary purposes in Australia. Little is known about the views of key primary care personnel on this issue, which are important, as they may influence whether practices agree to provide EMR data for research. This exploratory qualitative study investigated the attitudes and beliefs of general practitioners (GPs), practice managers (PMs) and practice nurses (PNs) around sharing de-identified EMR patient health information with researchers. Semi-structured interviews were conducted with 11 participants (6 GPs, 3 PMs and 2 PNs) recruited via purposive sampling from general practices in Victoria, Australia. Transcripts were coded and thematically analysed. Participants were generally enthusiastic about research utilising de-identified health information extracted from EMRs for altruistic reasons, including: positive effects on primary care research, clinical practice and population health outcomes. Concerns raised included patient privacy and data breaches, third-party use of extracted data and patient consent. These findings can provide guidance to researchers and policymakers in designing and implementing projects involving de-identified health information extracted from EMRs.

Achieving quality primary care data: a description of the Canadian Primary Care Sentinel Surveillance Network data capture, extraction, and processing in Alberta.

IntroductionElectronic medical record (EMR) databases have become increasingly popular for secondary purposes, such as health research. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is the first and only pan-Canadian primary care EMR data repository, with de-identified health information for almost two million Canadians. Comprehensive and freely available documentation describing the data ‘lifecycle’ is important for assessing potential data quality issues and appropriate interpretation of research findings. Here, we describe the flow and transformation of CPCSSN data in the province of Alberta.ApproachIn Alberta, the data originate from 54 publicly-funded primary care settings, including one community pediatric clinic, with 318 providers contributing de-identified EMR data for 410,951 patients (as of December 2018). Data extraction methods have been developed for five different EMR systems, and include both backend and automated frontend extractions. The raw EMR data are transformed according to specific rules, including trimming implausible values, converting values and free text to standard terminologies or classification systems, and structuring the data into a common CPCSSN format. Following local data extraction and processing, the data are transferred to a central repository and made available for research and disease surveillance.ConclusionThis paper aims to provide important contextual information to future CPCSSN data users.

Effect of Public Deliberation on Patient Attitudes Regarding Consent and Data Use in a Learning Health Care System for Oncology

We sought to generate informed and considered opinions regarding acceptable secondary uses of deidentified health information and consent models for oncology learning health care systems. Day-long democratic deliberation sessions included 217 patients with cancer at four geographically and sociodemographically diverse sites. Patients completed three surveys (at baseline, immediately after deliberation, and 1-month follow-up). Participants were 67.3% female, 21.7% black, and 6.0% Hispanic. The most notable changes in perceptions after deliberation related to use of deidentified medical-record data by insurance companies. After discussion, 72.3% of participants felt comfortable if the purpose was to make sure patients receive recommended care (v 79.5% at baseline; P = .03); 24.9% felt comfortable if the purpose was to determine eligibility for coverage or reimbursement (v 50.9% at baseline; P < .001). The most notable change about secondary research use related to believing it was important that doctors ask patients at least once whether researchers can use deidentified medical-records data for future research. The proportion endorsing high importance decreased from baseline (82.2%) to 68.7% immediately after discussion (P < .001), and remained decreased at 73.1% (P = .01) at follow-up. At follow-up, non-Hispanic whites were more likely to consider it highly important to be able to conduct medical research with deidentified electronic health records (96.8% v 87.7%; P = .01) and less likely to consider it highly important for doctors to get a patient's permission each time deidentified medical record information is used for research (23.2% v 51.6%; P < .001). This research confirms that most patients wish to be asked before deidentified medical records are used for research. Policies designed to realize the potential benefits of learning health care systems can, and should be, grounded in informed and considered public opinion.

Clinical genomics, big data, and electronic medical records: reconciling patient rights with research when privacy and science collide.

Widespread use of medical records for research, without consent, attracts little scrutiny compared to biospecimen research, where concerns about genomic privacy prompted recent federal proposals to mandate consent. This paper explores an important consequence of the proliferation of electronic health records (EHRs) in this permissive atmosphere: with the advent of clinical gene sequencing, EHR-based secondary research poses genetic privacy risks akin to those of biospecimen research, yet regulators still permit researchers to call gene sequence data ‘de-identified’, removing such data from the protection of the federal Privacy Rule and federal human subjects regulations. Medical centers and other providers seeking to offer genomic ‘personalized medicine’ now confront the problem of governing the secondary use of clinical genomic data as privacy risks escalate. We argue that regulators should no longer permit HIPAA-covered entities to treat dense genomic data as de-identified health information. Even with this step, the Privacy Rule would still permit disclosure of clinical genomic data for research, without consent, under a data use agreement, so we also urge that providers give patients specific notice before disclosing clinical genomic data for research, permitting (where possible) some degree of choice and control. To aid providers who offer clinical gene sequencing, we suggest both general approaches and specific actions to reconcile patients’ rights and interests with genomic research.

Generalization Technique for Privacy Preserving of Medical information

 Abstract—the development in health information technology have increased the need share data and knowledge especially medical information, researchers, public health organizations, others interested in medical data. Some medical records are often added and deleted in the practical applications. The leakage of privacy information caused by re-publishing datasets with multiple sensitive attributes becomes more likely than any other publication styles This paper attempts to fill the above gaps, presents a framework for de-identifying health information .we used the generalization technique and K- Anonymization model to protecting privacy of patient data. After the protecting privacy applicable prepare the data for analysis and extract the knowledge that supports decision- making, we offer a range of initial assessments show the effectiveness-of our-approach.

Erratum: Personal medicine—the new banking crisis

Nat. Biotechnol. 30, 141–147 (2012); published online 8 February 2012; corrected after print 24 February 2012 In the version of the article originally published, the citation in Figure 1 was given as ref. 14; it should be ref. 2. In Table 1, CARTaGENE was misspelled, and the descriptions in column 3of this repository, BioVu's and the International HapMap were incorrect: CARTaGENE should be described as “A repository of socio-demographic, health data and biological samples from 20,000 citizens of the province of Quebec in Canada”; BioVu's description should read “Repository of DNA samples and de-identified health information from the Vanderbilt University Medical Center's electronic system”; and the International HapMap description should read “International collaboration with the ultimate goal of developing a haplotype map of the human genome.

The Collaborative Experience of Creating the National Capital Region Disease Surveillance Network

The Johns Hopkins University Applied Physics Laboratory (JHU/APL) implemented state and district surveillance nodes in a central aggregated node in the National Capital Region (NCR). Within this network, de-identified health information is integrated with other indicator data and is made available to local and state health departments for enhanced disease surveillance. Aggregated data made available to the central node enable public health practitioners to observe abnormal behavior of health indicators spanning jurisdictions and view geographical spread of outbreaks across regions.Forming a steering committee, the NCR Enhanced Surveillance Operating Group (ESOG), was key to overcoming several data-sharing issues. The committee was composed of epidemiologists and key public health practitioners from the 3 jurisdictions. The ESOG facilitated early system development and signing of the cross-jurisdictional data-sharing agreement. This agreement was the first of its kind at the time and provided the legal foundation for sharing aggregated health information across state/district boundaries for electronic disease surveillance.Electronic surveillance system for the early notification of community-based epidemics provides NCR users with a comprehensive regional view to ascertain the spread of disease, estimate resource needs, and implement control measures. This article aims to describe the creation of the NCR Disease Surveillance Network as an exceptional example of cooperation and potential that exists for regional surveillance activities.

Managing Patient Expectations About Deidentification

Mark Rothstein (2010) convincingly illustrates that deidentification is an insufficient method for protecting patients’ private medical information. The current regulatory distinction between ident...

The Illinois Women'S Health Registry: Advancing Women'S Health Research and Education in Illinois, USA

To achieve the goal of personalized medicine, we must first improve our understanding of the differences in health and illness between men and women. The purpose of the Illinois Women's Health Registry (USA) is to provide a research and education tool that advances scientific knowledge of sex- and gender-based differences in health and disease. Specifically, the Registry is a confidential 30-min health and lifestyle survey for female residents of Illinois over the age of 18 years. The survey includes questions regarding health, environment, health-related behaviors, symptoms and illnesses or conditions that a participant may have now or has had in the past. By enrolling in the Registry, women throughout the state are provided with information and access to clinical research studies that they may be eligible for, based on their self-reported health information. The Registry not only serves as a platform for recruitment into pivotal research studies, but also represents the beginning of a state-wide database that enables researchers to examine the collective de-identified health information provided by women living in Illinois. Ultimately, a cross-sectional and longitudinal analysis of these data will help to clarify the issues that women themselves identify as their main health concerns. In response to these concerns, specific research studies can be designed and launched, allowing us to eventually deliver tailored treatment and prevention options to women. Finally, by creating a reliable state-focused research tool, developed by staff that are trained in women's health research, we can compare health issues across the state and apply strategies for improvement where it is needed most. This article will provide examples of sex differences in disease, the lack of federal enforcement for inclusion of women in studies, researcher-perceived burdens and sex-based reasons as to why recruitment of women is considered to be more challenging. In addition, this article will discuss what a women's health registry is and why we need one in Illinois, how we have recruited women and our successes and challenges. Our goal is to inform the reader about the utility of a state-based tool and to provide a discussion regarding the lessons learned in order to aid other states in implementing this kind of program.

An integrated framework for de-identifying unstructured medical data

While there is an increasing need to share medical information for public health research, such data sharing must preserve patient privacy without disclosing any information that can be used to identify a patient. A considerable amount of research in data privacy community has been devoted to formalizing the notion of identifiability and developing techniques for anonymization but are focused exclusively on structured data. On the other hand, efforts on de-identifying medical text documents in medical informatics community rely on simple identifier removal or grouping techniques without taking advantage of the research developments in the data privacy community. This paper attempts to fill the above gaps and presents a framework and prototype system for de-identifying health information including both structured and unstructured data. We empirically study a simple Bayesian classifier, a Bayesian classifier with a sampling based technique, and a conditional random field based classifier for extracting identifying attributes from unstructured data. We deploy a k-anonymization based technique for de-identifying the extracted data to preserve maximum data utility. We present a set of preliminary evaluations showing the effectiveness of our approach.