Dataset Information

Machine Learning-Based Analysis of Encrypted Medical Data in the Cloud: Qualitative Study of Expert Stakeholders' Perspectives.

ABSTRACT:

Background

Third-party cloud-based data analysis applications are proliferating in electronic health (eHealth) because of the expertise offered and their monetary advantage. However, privacy and security are critical concerns when handling sensitive medical data in the cloud. Technical advances based on "crypto magic" in privacy-preserving machine learning (ML) enable data analysis in encrypted form for maintaining confidentiality. Such privacy-enhancing technologies (PETs) could be counterintuitive to relevant stakeholders in eHealth, which could in turn hinder adoption; thus, more attention is needed on human factors for establishing trust and transparency.

Objective

The aim of this study was to analyze eHealth expert stakeholders' perspectives and the perceived tradeoffs in regard to data analysis on encrypted medical data in the cloud, and to derive user requirements for development of a privacy-preserving data analysis tool.

Methods

We used semistructured interviews and report on 14 interviews with individuals having medical, technical, or research expertise in eHealth. We used thematic analysis for analyzing interview data. In addition, we conducted a workshop for eliciting requirements.

Results

Our results show differences in the understanding of and in trusting the technology; caution is advised by technical experts, whereas patient safety assurances are required by medical experts. Themes were identified with general perspectives on data privacy and practices (eg, acceptance of using external services), as well as themes highlighting specific perspectives (eg, data protection drawbacks and concerns of the data analysis on encrypted data). The latter themes result in requiring assurances and conformance testing for trusting tools such as the proposed ML-based tool. Communicating privacy, and utility benefits and tradeoffs with stakeholders is essential for trust. Furthermore, stakeholders and their organizations share accountability of patient data. Finally, stakeholders stressed the importance of informing patients about the privacy of their data.

Conclusions

Understanding the benefits and risks of using eHealth PETs is crucial, and collaboration among diverse stakeholders is essential. Assurances of the tool's privacy, accuracy, and patient safety should be in place for establishing trust of ML-based PETs, especially if used in the cloud.

SUBMITTER: Alaqra AS

PROVIDER: S-EPMC8485196 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundEven though research integrity (RI) training programs have been developed in the last decades, it is argued that current training practices are not always able to increase RI-related awareness within the scientific community. Defining and understanding the capacities and lacunas of existing RI training are becoming extremely important for developing up-to-date educational practices to tackle present-day challenges. Recommendations on how to implement RI education have been primarily made by selected people with specific RI-related expertise. Those recommendations were developed mainly without consulting a broader audience with no specific RI expertise. Moreover, the academic literature lacks qualitative studies on RI training practices. For these reasons, performing in-depth focus groups with non-RI expert stakeholders are of a primary necessity to understand and outline how RI education should be implemented.MethodsIn this qualitative analysis, different focus groups were conducted to examine stakeholders' perspectives on RI training practices. Five stakeholders' groups, namely publishers and peer reviewers, researchers on RI, RI trainers, PhDs and postdoctoral researchers, and research administrators working within academia, have been identified to have a broader overview of state of the art.ResultsA total of 39 participants participated in five focus group sessions. Eight training-related themes were highlighted during the focus group discussions. The training goals, timing and frequency, customisation, format and teaching approach, mentoring, compulsoriness, certification and evaluation, and RI-related responsibilities were discussed. Although confirming what was already proposed by research integrity experts in terms of timing, frequency, duration, and target audience in organising RI education, participants proposed other possible implementations strategies concerning the teaching approach, researchers' obligations, and development an evaluation-certification system.ConclusionsThis research aims to be a starting point for a better understanding of necessary, definitive, and consistent ways of structuring RI education. The research gives an overview of what has to be considered needed in planning RI training sessions regarding objectives, organisation, and teaching approach.

Project description:ObjectivesWhile studies at the undergraduate level have begun to explore healthcare students' safety and dignity dilemmas, none have explored such dilemmas with multiple stakeholders at the postgraduate level. The current study therefore explores the patient and staff safety and dignity narratives of multiple stakeholders to better understand the healthcare workplace learning culture.DesignA qualitative interview study using narrative interviewing.SettingTwo sites in the UK ranked near the top and bottom for raising concerns according to the 2013 General Medical Council National Training Survey.ParticipantsUsing maximum variation sampling, 39 participants were recruited representing four different groups (10 public representatives, 10 medical trainees, 8 medical trainers and 11 nurses and allied health professionals) across the two sites.MethodsWe conducted 1 group and 35 individual semistructured interviews. Data collection was completed in 2015. Framework analysis was conducted to identify themes. Theme similarities and differences across the two sites and four groups were established.ResultsWe identified five themes in relation to our three research questions (RQs): (1) understandings of safety and dignity (RQ1); (2) experiences of safety and dignity dilemmas (RQ2); (3) resistance and/or complicity regarding dilemmas encountered (RQ2); (4) factors facilitating safety and/or dignity (RQ3); and (5) factors inhibiting safety and/or dignity (RQ3). The themes were remarkably similar across the two sites and four stakeholder groups.ConclusionsWhile some of our findings are similar to previous research with undergraduate healthcare students, our findings also differ, for example, illustrating higher levels of reported resistance in the postgraduate context. We provide educational implications to uphold safety and dignity at the level of the individual (eg, stakeholder education), interaction (eg, stakeholder communication and teamwork) and organisation (eg, institutional policy).

Project description:BackgroundRecently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML.ObjectiveThis study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems.MethodsTo gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care-specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process.ResultsWith the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process.ConclusionsMany clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.

Project description:BackgroundReal-world data (RWD) and real-world evidence (RWE) are playing increasingly important roles in clinical research and health care decision-making. To leverage RWD and generate reliable RWE, data should be well defined and structured in a way that is semantically interoperable and consistent across stakeholders. The adoption of data standards is one of the cornerstones supporting high-quality evidence for the development of clinical medicine and therapeutics. Clinical Data Interchange Standards Consortium (CDISC) data standards are mature, globally recognized, and heavily used by the pharmaceutical industry for regulatory submissions. The CDISC RWD Connect Initiative aims to better understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance needed to more easily implement them.ObjectiveThe aim of this study is to understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance that may be needed to implement CDISC standards more easily for this purpose.MethodsWe conducted a qualitative Delphi survey involving an expert advisory board with multiple key stakeholders, with 3 rounds of input and review.ResultsOverall, 66 experts participated in round 1, 56 in round 2, and 49 in round 3 of the Delphi survey. Their inputs were collected and analyzed, culminating in group statements. It was widely agreed that the standardization of RWD is highly necessary, and the primary focus should be on its ability to improve data sharing and the quality of RWE. The priorities for RWD standardization included electronic health records, such as data shared using Health Level 7 Fast Health care Interoperability Resources (FHIR), and the data stemming from observational studies. With different standardization efforts already underway in these areas, a gap analysis should be performed to identify the areas where synergies and efficiencies are possible and then collaborate with stakeholders to create or extend existing mappings between CDISC and other standards, controlled terminologies, and models to represent data originating across different sources.ConclusionsThere are many ongoing data standardization efforts around human health data-related activities, each with different definitions, levels of granularity, and purpose. Among these, CDISC has been successful in standardizing clinical trial-based data for regulation worldwide. However, the complexity of the CDISC standards and the fact that they were developed for different purposes, combined with the lack of awareness and incentives to use a new standard and insufficient training and implementation support, are significant barriers to setting up the use of CDISC standards for RWD. The collection and dissemination of use cases, development of tools and support systems for the RWD community, and collaboration with other standards development organizations are potential steps forward. Using CDISC will help link clinical trial data and RWD and promote innovation in health data science.