Use Of Data In Research Research Articles

Artificial intelligence-driven approach for patient-focused drug development.

Patients' increasing digital participation provides an opportunity to pursue patient-centric research and drug development by understanding their needs. Social media has proven to be one of the most useful data sources when it comes to understanding a company's potential audience to drive more targeted impact. Navigating through an ocean of information is a tedious task where techniques such as artificial intelligence and text analytics have proven effective in identifying relevant posts for healthcare business questions. Here, we present an enterprise-ready, scalable solution demonstrating the feasibility and utility of social media-based patient experience data for use in research and development through capturing and assessing patient experiences and expectations on disease, treatment options, and unmet needs while creating a playbook for roll-out to other indications and therapeutic areas.

An online survey on user perceptions of natural science collections in South Africa

Background: In South Africa, and globally, the value of natural science collections for scientific research is not widely recognised and has led to its marginalisation, which in turn has resulted in low funding, staffing and use of the collections. Aim & objectives: To this end, as part of the effort to increase understanding and appreciation of the collections, a cross-sectional web-based survey was administered to users of natural science collections (NSCs) in South Africa. The objectives of the study were to identify the perceived value of NSCs to the research community; perceived or experienced barriers in accessing NSCs and associated data for use in research; perceptions of NSCs’ current performance in serving the needs of stakeholders; and how performance is judged and what the expectations are to improve future performance of NSCs to better serve the needs of stakeholders. Methods: The survey consisted of 26 questions, distributed by email to relevant researcher community mailing lists, and posted on relevant social media groups. The survey was completed by 131 respondents. Results & conclusion: The study indicated the overall perception of the importance of NSCs and their accessibility to the student and researcher community in South Africa and internationally to be extremely important to their research. Lack of funding for operations and staff impedes the ability of researchers and other users alike in using NSCs to optimise their research and contribute to issues of societal concern. A sustained commitment is required from NSC institutions to work together to solve various challenges, including improvement in serving stakeholder needs, which will in turn assist with demonstrating the value of NSCs to policymakers, in order to lobby for support and funding. Improved recognition of the importance of NSCs for research by the scientific community will assist NSCs in demonstrating their impact. Political priority should also be given to the long-term upkeep and ongoing assistance of institutional infrastructures.

From intention to action: The factors affecting health data sharing intention and action

IntroductionEffective prevention and treatment of diseases requires utilization of health-related lifestyle data, which has thus become increasingly important. According to some studies, participants were willing to share their health data for use in medical care and research. Although intention does not always accurately reflect action, few studies have examined the question of whether data-sharing intention leads to data-sharing action. ObjectiveThe aim of this study was to examine the extent of actualizing data-sharing intention to data-sharing action and to identify the factors that influence data-sharing intention and action. MethodsA web-based survey of members of a university examined the data-sharing intention and issues of concern when making decisions on data sharing. The participants were asked to deposit their armband data for use in research at the end of the survey. A comparison of data-sharing intention and action in relation to the participants’ characteristics was performed. Factors having a significant effect on data-sharing intention and action were identified using logistic regressions. ResultsOf 386 participants, 294 expressed willingness to share health data. However, only 73 participants deposited their armband data. The primary reason for refusal to deposit armband data was the inconvenience of the data transfer process (56.3%). Appropriate compensation had a significant effect on data-sharing intention (OR: 3.3, CI: 1.86–5.75) and action (OR: 2.8, CI: 1.14–8.21). The compensation for data sharing (OR:2.8, CI:1.14–8.21) and familiarity with data (OR:3.1, CI:1.36–8.21) were significant predictors of data sharing action, however, data-sharing intention was not (OR: 1.5, CI:0.65–3.72). ConclusionDespite expressing willingness to share their health data, the participants’ intention was not actualized to data-sharing behavior for depositing armband data. Implementation of a streamlined data transfer process and providing appropriate compensation might facilitate data-sharing. These findings could be useful in development of strategies to facilitate sharing and reuse of health data.

Differential willingness for genetic testing to target treatment in older Danish citizens

Abstract Background Studies from different countries have shown that population majorities are willing to accept genetic tests for treatment personalisation and that considerable proportions are ready to donate their data to research. However, it has also been shown that concerns, for example about data use and confidentiality or treatment rationing, are common. To enable a more targeted communication process with the public about personalized medicine, more knowledge is needed on views in different sub-populations. In the present study, a hypothetical scenario was used to investigate differential readiness to accept a genetic test for treatment targeting and to permit use of personal data for research. Methods A cross-sectional survey was conducted with 50-80-year-old Danish citizens (n = 6807) who were sampled to represent the Danish population in that age segment. Socio-demographic data were added from a national registry. Data were analyzed by multivariable logistic regression analysis. Results Preliminary results showed that a majority was willing to be tested (78.3%). Readiness was lower in women [OR = .67; CI = .59-.77] and those 70-80 [OR = .72; CI = 61-.86], while it was higher in those with better income [OR = 1.29; CI = 1.09-1.52]. Further, those less satisfied with their health, the obese and those with a perceived genetic vulnerability were more willing to be tested. Over 90% of those ready to be tested were also willing to permit use of their data for research. Rates were higher in men, older segments, those with higher income/education as well as those with current pain experience and those aware of a personal genetic vulnerability. Conclusions Findings indicate group differences in acceptance of a genetic test for personalisation of medicine and data use for research. Further research should investigate group-specific benefit perceptions versus concerns in population subgroups to inform implementation and enable targeted communication strategies. Key messages • Acceptance of genetic testing for personalisation of treatment as well as willingness to contribute data to research may differ between population subgroups. • Women and those with lower income are less willing to accept genetic testing for treatment personalisation and accept research use of data while health vulnerabilities increase acceptance.

Deep-Hist: Breast cancer diagnosis through histopathological images using convolution neural network

Breast cancer diagnosis utilizes histopathological images to get best results as per standards. For detailed diagnosis of breast cancer, microscopic analysis is necessary. During analysis, pathologists examine breast cancer tissues under different magnification levels and it takes a long time, can be hampered by human interpretation and requires expertise of different magnifications. A single patient usually requires dozens of such images during examination. Since, labelling the data is a computationally expensive task, it is assumed that the images for all patients have the same label in conventional image-based classification and is not usually tested practically. In this study, we are intending to investigate the significance of machine learning techniques in computer aided diagnostic systems based on analysis of histopathological breast cancer images. Publicly available BreakHis data set containing around 8,000 histopathological images of breast tumours is used for conducting experiments. The recently proposed non-parametric approach is proven to show interesting results when compared in detail with machine learning approaches. Our proposed model ’Deep-Hist’ is magnification independent and achieves > 92.46% accuracy with Stochastic Gradient Descent (SGD) which is better than the pretrained models for image classification. Hence, our approach can be used in processing data for use in research and clinical environments to provide second opinions very close to the experts’ intuition.

Evaluation of freely available data profiling tools for health data research application: a functional evaluation review

ObjectivesTo objectively evaluate freely available data profiling software tools using healthcare data.DesignData profiling tools were evaluated for their capabilities using publicly available information and data sheets. From initial assessment, several...

Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study.

BackgroundWearable devices that are used for observational research and clinical trials hold promise for collecting data from study participants in a convenient, scalable way that is more likely to reach a broad and diverse population than traditional research approaches. Amazon Mechanical Turk (MTurk) is a potential resource that researchers can use to recruit individuals into studies that use data from wearable devices.ObjectiveThis study aimed to explore the characteristics of wearable device users on MTurk that are associated with a willingness to share wearable device data for research. We also aimed to determine whether compensation was a factor that influenced the willingness to share such data.MethodsThis was a secondary analysis of a cross-sectional survey study of MTurk workers who use wearable devices for health monitoring. A 19-question web-based survey was administered from March 1 to April 5, 2018, to participants aged ≥18 years by using the MTurk platform. In order to identify characteristics that were associated with a willingness to share wearable device data, we performed logistic regression and decision tree analyses.Results A total of 935 MTurk workers who use wearable devices completed the survey. The majority of respondents indicated a willingness to share their wearable device data (615/935, 65.8%), and the majority of these respondents were willing to share their data if they received compensation (518/615, 84.2%). The findings from our logistic regression analyses indicated that Indian nationality (odds ratio [OR] 2.74, 95% CI 1.48-4.01, P=.007), higher annual income (OR 2.46, 95% CI 1.26-3.67, P=.02), over 6 months of using a wearable device (OR 1.75, 95% CI 1.21-2.29, P=.006), and the use of heartbeat and pulse tracking monitoring devices (OR 1.60, 95% CI 0.14-2.07, P=.01) are significant parameters that influence the willingness to share data. The only factor associated with a willingness to share data if compensation is provided was Indian nationality (OR 0.47, 95% CI 0.24-0.9, P=.02). The findings from our decision tree analyses indicated that the three leading parameters associated with a willingness to share data were the duration of wearable device use, nationality, and income.ConclusionsMost wearable device users indicated a willingness to share their data for research use (with or without compensation; 615/935, 65.8%). The probability of having a willingness to share these data was higher among individuals who had used a wearable for more than 6 months, were of Indian nationality, or were of American (United States of America) nationality and had an annual income of more than US $20,000. Individuals of Indian nationality who were willing to share their data expected compensation significantly less often than individuals of American nationality (P=.02).

Operational Challenges in the Use of Structured Secondary Data for Health Research.

Background: In Brazil, secondary data for epidemiology are largely available. However, they are insufficiently prepared for use in research, even when it comes to structured data since they were often designed for other purposes. To date, few publications focus on the process of preparing secondary data. The present findings can help in orienting future research projects that are based on secondary data.Objective: Describe the steps in the process of ensuring the adequacy of a secondary data set for a specific use and to identify the challenges of this process.Methods: The present study is qualitative and reports methodological issues about secondary data use. The study material was comprised of 6,059,454 live births and 73,735 infant death records from 2004 to 2013 of children whose mothers resided in the State of São Paulo - Brazil. The challenges and description of the procedures to ensure data adequacy were undertaken in 6 steps: (1) problem understanding, (2) resource planning, (3) data understanding, (4) data preparation, (5) data validation and (6) data distribution. For each step, procedures, and challenges encountered, and the actions to cope with them and partial results were described. To identify the most labor-intensive tasks in this process, the steps were assessed by adding the number of procedures, challenges, and coping actions. The highest values were assumed to indicate the most critical steps.Results: In total, 22 procedures and 23 actions were needed to deal with the 27 challenges encountered along the process of ensuring the adequacy of the study material for the intended use. The final product was an organized database for a historical cohort study suitable for the intended use. Data understanding and data preparation were identified as the most critical steps, accounting for about 70% of the challenges observed for data using.Conclusion: Significant challenges were encountered in the process of ensuring the adequacy of secondary health data for research use, mainly in the data understanding and data preparation steps. The use of the described steps to approach structured secondary data and the knowledge of the potential challenges along the process may contribute to planning health research.

GENE2D: A NoSQL Integrated Data Repository of Genetic Disorders Data.

There are few sources from which to obtain clinical and genetic data for use in research in Saudi Arabia. Numerous obstacles led to the difficulty of integrating these data from silos and scattered sources to provide standardized access to large data sets for patients with common health conditions. To this end, we sought to contribute to this area and offer a practical and easy-to-implement solution. In this paper, we aim to design and implement a “not only SQL” (NoSQL) based integration framework to generate an Integrated Data Repository of Genetic Disorders Data (GENE2D) to integrate data from various genetic clinics and research centers in Saudi Arabia and provide an easy-to-use query interface for researchers to conduct their studies on large datasets. The major components involved in the GENE2D architecture consists of the data sources, the integrated data repository (IDR) as a central database, and the application interface. The IDR uses a NoSQL document store via MongoDB (an open source document-oriented database program) as a backend database. The application interface called Query Builder provides multiple services for data retrieval from the database using a custom query to answer simple or complex research questions. The GENE2D system demonstrates its potential to help grow and develop a national genetic disorders database in Saudi Arabia.

Use of web-based species occurrence information systems by academics and government professionals.

Web-based information systems designed to increase access to species occurrence data for use in research and natural resource decision-making have become more prevalent over the past few decades. The effectiveness of these systems depends on their usability and extent of use by their intended audiences. We conducted an online survey of academics and government professionals in the United States to compare their species occurrence data needs and their perceptions and use of web-based species occurrence information systems. Our results indicate that although views and perceptions held by academics and government professionals about the importance, usefulness, and ease of use of these information systems tend to be similar, there were differences in their use of species occurrence data and web-based species occurrence information systems. The baseline information obtained in this study will help inform future directions for improvements in species occurrence information systems.

CART-WHEEL.org: An Ethically Approved Online Database for Patient-Entered Data to Facilitate Rare Cancer Research.

PURPOSERare cancers are challenging for researchers, as clinicians and scientists have difficulty recruiting sufficient patient cases to power studies appropriately. Likewise, patients often are frustrated by a lack of specific information or evidence base for their cancer and, although eager to participate in research, have limited opportunities. We established CART-WHEEL.org, an online patient-entered database, to directly engage patients in the research process, collect rare cancer data, and facilitate their entry into additional research.PATIENTS AND METHODSPatients access CART-WHEEL.org directly online. Clinical information is collected from users via a streamlined questionnaire developed collaboratively with consumer groups to ensure accessibility and relevance. Data collected include the following: patient demographics, comorbidities, and risk factors and tumor diagnostic, biomarker, and treatment history. Patients can download a medical summary for personal use; consent for research use of data; and indicate willingness to be contacted about other research or clinical trials. We describe data collected to date and its validation, and we provide examples of how CART-WHEEL.org can facilitate rare cancer research.RESULTSFrom January 2010 to March 2018, 558 patients provided consent and entered their rare cancer data. One hundred distinct rare tumor types and patients from 22 countries were included. Validation of data entered by 21 patients with sarcoma against a hospital database demonstrated accuracy sufficient to facilitate future research in key fields, such as tumor site (95%) and histopathologic diagnosis (90%). Examples of CART-WHEEL–based disease-specific projects, subsequent recruitment to other rare cancer projects, and rare cancer patient cases of interest are described.CONCLUSIONSOnline platforms like CART-WHEEL.org can engage consumers directly, facilitating collection of patient-entered rare cancer data for hypothesis generation, and connect patients with researchers to enable specific rare cancer research and clinical trials.

Abstract P2-18-03: Breast cancer treatment and association with clinicopathologic features in TCGA

Abstract Background: For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data were analyzed and a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource was created. However, TCGA treatment data are not systematically analyzed. Here we focus on TCGA primary breast cancer (TCGA-BC) treatment data to assess their completeness, regimen patterns, and association with clinicopathologic features. Method: 814 TCGA-BC patients with treatment data diagnosed from 2001 to 2013 were selected for this study. The treatment data were prepared and classified to be adjuvant Chemotherapy (CT), adjuvant Radiation Therapy (RT), and adjuvant Hormone Therapy (HT). An in-house Clinical Breast Care Project (CBCP) treatment dataset (n=1051 from 2001 to 2017), which were relatively complete, were used as a reference for data completeness. Multinomial logistic regression was used to analyze the associations between different therapies and clinical features. Results: There is no consistent treatment data difference between TCGA-BC and CBCP. In TCGA-BC, 68.7%, 64.4%, and 64.5% of patients received CT, RT, and HT respectively, whereas in CBCP, the corresponding percentages were 59.0%, 71.2%, and 77.1%. The percentages of patients receiving combined therapies are even more comparable between the two cohorts (data not shown due to many combinations). The associations between treatment and clinicopathologic features were analyzed using multinomial logistic regressions with age, race, menopausal status, AJCC stage, and PAM50 subtype as covariates. Several of these covariates significantly associate with the use of therapies. Compared to patients with age < 50, patients with age ≥ 65 were significantly more likely to receive HT, HT+RT, or RT than CT only (OR=11.34, 9.25, 4.82; p=0.001, 0.002, and 0.024 respectively); Compared to pre-menopausal patients, post-menopausal patients were significantly more likely to receive HT+RT than CT only (OR=4.12, p=0.034); Compared to patients with early stages (I, II), patients with advanced stages (III, IV) were more likely to receive CT+HT+RT (OR=1.92, p=0.038) or CT+RT (OR=2.38, p=0.010) than CT only, and less likely to receive CT+HT (OR=0.39, p=0.033) or HT (OR=0.30, p=0.008) than CT only. PAM50 subtype also significantly associates with the use of therapies. Compared to Luminal A patients, patients with Basal subtype were significantly less likely to receive CT+HT (OR=0.14, p=1.3e-05), CT+HT+RT (OR=0.06, p=3.0e-10), HT+RT (OR=0.01, p=9.2e-06), or HT (OR=0.02, p=3.8e-07) than CT alone. Patients with HER2+ subtype showed similar patterns (note targeted therapies were classified as CT). In addition, patients with Basal subtype were significantly more likely to receive CT+RT (OR=2.47, p=0.01) than CT only. Conclusion: TCGA-BC patient treatment data are relatively as complete as our in-house CBCP patient treatment data, enabling us to perform a preliminary analysis of the former for association with clinicopathologic features of the patients. The significant associations of age, menopausal status, AJCC stage, and PAM50 subtype with treatment regimens are consistent with clinical knowledge, suggesting potential validity of TCGA BC treatment data for research use. Disclaimer: The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions or policies of Uniformed Services University of the Health Sciences (USUHS), The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., the Department of Defense (DoD), the Departments of the Army, Navy, or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. Citation Format: Jianfang Liu, J. Leigh Fantacone-Campbell, Albert J. Kovatich, Brenda Deyarmin, Bradley J. Mostoller, Jeffrey A. Hooke, Hallgeir Rui, Craig D. Shriver, Hai Hu. Breast cancer treatment and association with clinicopathologic features in TCGA [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-18-03.

Investigating the Comparative Suitability of Traditional and Task-Specific Think Aloud Training.

The think aloud (TA) protocol is used to capture conscious cognition for wide ranging applications. However, the methods used to train the TA technique have been inconsistent, involving a mixture of both traditional guidelines and task-specific examples. This study aimed to examine how best to train the TA process. We recruited 20 competitive golfers as research participants, and we randomly assigned them to equal sized groups of traditional TA training as described by Ericsson and Simon and task-specific training in which participants were familiarized with TA via task-specific examples. Following training, all participants performed a golf task and were asked to TA. We transcribed audiotapes of their verbatim TA content and analyzed them using a deductive framework. We also collected various social validation self-report measures to assess participant perceptions of TA training. Overall, we found no significant differences in the frequency or type of TA verbalizations when comparing traditional and task-specific TA training groups. However, participants in the task-specific training group reported more favorable perceptions of training and found training significantly clearer than did participants in the traditional training group. We suggest that these findings support traditional TA training following Ericsson and Simon's training guidelines, but adding task-specific examples seems to increase the familiarity of TA use and facilitate more reliable and accurate cognition data for research use.

Attitudes of blood donors to their sample and data donation for biobanking

Modern biomedical and genetic studies require large study cohorts; blood donors have been suggested to represent an appropriate group for recruiting healthy cohorts. The Blood Service Biobank (BSB) in Finland was recently established to recruit blood donors willing to give broad biobank consent. The aim of the present study is to understand how the blood bank context influences views on donating samples and health data. We organised 61 interviews and 10 group discussions with current and potential blood donors. Using qualitative content analysis, we identified three discussion frameworks that summarise the results. We found that frequent blood donors associated the voluntary act of donation with caring for patients. The blood donation experience was considered to accommodate biobank participation, but also allowed critical observations on the integration of research data collection into blood donation. Research participants identified an important difference between the blood bank and biobank contexts. In the biobank context, the focus shifts from donating blood to patients into donating personal and genetic data for research use. Blood donors’ anxiety over data use was balanced with their experience of the trustworthiness of the Blood Service. These experiences indicated that the new biobanking activity could be trusted to a familiar organisation. To build donors’ trust, biobanks should invest in their institutional reputation, donor experience and dialogue with donors. These findings can be applied to other institutions that are considering setting up biobanks with broad consent for personal data use.

Real-World Data for Pediatric Pharmacometrics: Can We Upcycle Clinical Data for Research Use?

Real-World Data for Pediatric Pharmacometrics: Can We Upcycle Clinical Data for Research Use?

Linkage between Utah All Payers Claims Database and Central Cancer Registry.

To evaluate the linkage of claims from the Utah All Payers Claims Database (APCD) and Utah Cancer Registry (UCR). Secondary data from 2013 and 2014 Utah APCD and 2013 UCR cases. This is a descriptive analysis of the quality of linkage between APCD claims data and cancer registry cases. We used the LinkPlus software to link Utah APCD and UCR data. We were able to link 82.4 percent (9441/11453) of the UCR reportable cancer cases with APCD claims. Of those linked, 66 percent were perfect matches. The quality of identifiers is high, evidence that claims data can potentially supplement cancer registry data for use in research.

Time for a change: considering the rights of study participants to ownership of their personal research-grade genomic data

Determining the ownership of a patient's personal genomic data is important because it impacts how data is governed and shared, which has both clinical and research implications for precision oncology. The 21st Century Cures Act enacted in December 2016 defined the ownership of clinical genomic data, but the governance of research-grade genomic data remains a hotly contested topic. The many stakeholders often have competing perspectives about ownership of raw and processed genomic data derived in research settings and how to weigh risks versus benefits of sharing this data with study participants. A growing number of research studies, policy recommendations, and ethics reviews have not been enough to influence changes in practice. Most genomic research is conducted in academia, which is guided by Institutional Review Board-approved protocols to protect study participants. The current standard is to limit the return of research-grade data to study participants, and give data ownership solely to the researchers or the research institution, since this data is not vetted for clinical purposes and is meant for research use only. However, these practices conflict not only with recommendations from peer-reviewed literature on best practices for addressing research study participants' needs but might indeed run counter to legal and ethical guidelines about data ownership. For example, patient-participants faced with poorly understood or incurable diseases such as certain cancers want, and could potentially benefit from, having access to their personal genomic data in this rapidly evolving field. This commentary highlights the gap between the status quo as approved by the IRB and the literature suggesting that study participants should be given access to their personal genomic data. There is an opportunity to facilitate a more effective and ethical way to collect genomic data for research use across institutions.

Coding Agreement on Identification of Main Resource Use Using ICD-10 and ICD-11

IntroductionMain condition coding definitions in the International Classification of Disease (ICD) 10th and 11th versions are broadly defined in the current and upcoming versions of ICD, and coding health data can involve subjective coding specialist interpretation. Inconsistent coding can lead to inaccurate reporting, and lower quality data for research use.
 Objectives and ApproachMain condition coding agreement was compared between ICD-10 and ICD-11. 730 hospital charts were randomly selected from Foothills Medical Centre in Calgary, Alberta. These charts were previously coded using ICD-10, and six professional coding specialists recoded them using ICD-11. To compare frequencies of ICD-10 to ICD-11, we used current WHO crosswalk tables to match codes. For any missing codes, manual comparison by done by a qualified reviewer. In Canada, the “main condition” is the clinically significant reason for the hospital visit. If multiple problems were present, the diagnosis using the greatest amount of resources is coded, “main resource use”.
 ResultsOverall, 730 ICD-10 coded charts were analyzed. Of these charts, 79% (577) had matching resource coding between ICD-10 and ICD-11, and 21% (153) had mismatching coding. Matching coding was either considered an exact match between definitions (23.2%, 134), or similar but not identical (often one code has greater detail, 76.8%, 443). Mismatching codes were either due to different codes for similar conditions (13.1%, 20), different codes for not similar but related conditions (43.8%, 67), or completely different codes for unrelated conditions (43.1%, 66).
 Conclusion/ImplicationsICD-10 and ICD-11 main resource codes had a high match frequency indicating consistency between coding practices and ICD definitions (577/730, 79%). Future research will aim to understand underlying causes of mismatched main resource use codes. This research will help us understand issues in coding and contribute to future ICD-11 revisions.

Is commercial/private sector access to administrative data inevitable?

BackgroundCurrently in the UK, access to publicly collected administrative data for use in research, is only available to accredited researchers. However, administrative data may be of interest to commercial or private sector organisations, some of whom have already controversially been partnering with universities and health researchers in the recent case of an NHS Trust partnering with Google Deep Mind.
 ObjectivesHow do we deal with the issue of commercial/private sector access openly and critically as a society?
 MethodThis work will present the current landscape of commercial access to administrative data use and the results of a focus group with members of the public. Presented will be the views of the focus group in relation to different categories of commercial organisations and whether they should or should not be allowed access to administrative data and what conditions (if any) should be in place.
 FindingsInitial findings indicate commercial access may be acceptable, but this raises new issues for society to consider about the role and extent of commercial/private sector reach.

Research Transparency and Data Privacy – a fine balance

Preserving the confidentiality of information is of paramount importance to data controllers sharing data for research use. Work within the Administrative Data Research Network is built upon strict information governance and assurance protocols to minimise any privacy risks and maximise public benefit. However, research funders and publishers are ever more requiring research to be as transparent and open as possible, with increasing pressure to publish datasets underpinning the research. This is seen as crucial for both verification and to maintain the integrity of the research.
 While Safe Havens and data repositories provide some reassurance in terms of storage and citation, how can researchers prepare to walk this fine line between publisher and funder requirements and the need to protect privacy?
 This presentation will explore the issue of privacy vs research transparency from the viewpoints of researchers, safe haven providers and research co-ordinators. Examining such issues as disclosure control, statistical packages updating, public opinion and the potential impact of the new European data protection legislation, it will outline challenges and provide potential guidelines for researchers and administrators involved in administrative data research.