Public Preferences for Digital Health Data Sharing: Discrete Choice Experiment Study in 12 European Countries

BACKGROUND

With new technologies, health data can be collected in a variety of different clinical, research, and public health contexts, and then can be used for a range of new purposes. Establishing the public's views about digital health data sharing is essential for policy makers to develop effective harmonization initiatives for digital health data governance at the European level.

OBJECTIVE

This study investigated public preferences for digital health data sharing.

METHODS

A discrete choice experiment survey was administered to a sample of European residents in 12 European countries (Austria, Denmark, France, Germany, Iceland, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) from August 2020 to August 2021. Respondents answered whether hypothetical situations of data sharing were acceptable for them. Each hypothetical scenario was defined by 5 attributes ("data collector," "data user," "reason for data use," "information on data sharing and consent," and "availability of review process"), which had 3 to 4 attribute levels each. A latent class model was run across the whole data set and separately for different European regions (Northern, Central, and Southern Europe). Attribute relative importance was calculated for each latent class's pooled and regional data sets.

RESULTS

A total of 5015 completed surveys were analyzed. In general, the most important attribute for respondents was the availability of information and consent during health data sharing. In the latent class model, 4 classes of preference patterns were identified. While respondents in 2 classes strongly expressed their preferences for data sharing with opposing positions, respondents in the other 2 classes preferred not to share their data, but attribute levels of the situation could have had an impact on their preferences. Respondents generally found the following to be the most acceptable: a national authority or academic research project as the data user; being informed and asked to consent; and a review process for data transfer and use, or transfer only. On the other hand, collection of their data by a technological company and data use for commercial communication were the least acceptable. There was preference heterogeneity across Europe and within European regions.

CONCLUSIONS

This study showed the importance of transparency in data use and oversight of health-related data sharing for European respondents. Regional and intraregional preference heterogeneity for "data collector," "data user," "reason," "type of consent," and "review" calls for governance solutions that would grant data subjects the ability to control their digital health data being shared within different contexts. These results suggest that the use of data without consent will demand weighty and exceptional reasons. An interactive and dynamic informed consent model combined with oversight mechanisms may be a solution for policy initiatives aiming to harmonize health data use across Europe.

Future steps for improving the secondary use of data for research in Switzerland

Introduction

Ensuring that the health data infrastructure and governance permits an efficient secondary use of data for research is a policy priority for many countries. Switzerland is no exception and many initiatives have been launched to improve its health data landscape. The country now stands at an important crossroad, debating the right way forward. We aimed to explore which specific elements of data governance can facilitate - from ethico-legal and socio-cultural perspectives - the sharing and reuse of data for research purposes in Switzerland.

Methods

A modified Delphi methodology was used to collect and structure input from a panel of experts via successive rounds of mediated interaction on the topic of health data governance in Switzerland.

Results

First, we suggested techniques to facilitate data sharing practices, especially when data are shared between researchers or from healthcare institutions to researchers. Second, we identified ways to improve the interaction between data protection law and the reuse of data for research, and the ways of implementing informed consent in this context. Third, we put forth ideas on policy changes, such as the steps necessary to improve coordination between different actors of the data landscape and to win the defensive and risk-adverse attitudes widespread when it comes to health data.

Conclusions

After having engaged with these topics, we highlighted the importance of focusing on non-technical aspects to improve the data-readiness of a country (e.g., attitudes of stakeholders involved) and of having a pro-active debate between the different institutional actors, ethico-legal experts and society at large.

Open access and its potential impact on public health - A South African perspective

Traditionally, access to research information has been restricted through journal subscriptions. This means that research entities and individuals who were unable to afford subscription costs did not have access to journal articles. There has however been a progressive shift toward electronic access to journal publications and subsequently growth in the number of journals available globally. In the context of electronic journals, both open access and restricted access options exist. While the latter option is comparable to traditional, subscription-based paper journals, open access journal publications follow an "open science" publishing model allowing scholarly communications and outputs to be publicly available online at no cost to the reader. However, for readers to enjoy open access, publication costs are shifted elsewhere, typically onto academic institutions and authors. SARS-CoV-2, and the resulting COVID-19 pandemic have highlighted the benefits of open science through accelerated research and unprecedented levels of collaboration and data sharing. South Africa is one of the leading open access countries on the African continent. This paper focuses on open access in the South African higher education research context with an emphasis on our Institution and our own experiences. It also addresses the financial implications of open access and provides possible solutions for reducing the cost of publication for researchers and their institutions. Privacy in open access and the role of the Protection of Personal Information Act (POPIA) in medical research and secondary use of data in South Africa will also be discussed.

The Secondary Use of Data to Support Medication Safety in the Hospital Setting: A Systematic Review and Narrative Synthesis

OBJECTIVES

To conduct a systematic review and narrative synthesis of interventions based on secondary use of data (SUD) from electronic prescribing (EP) and electronic hospital pharmacy (EHP) systems and their effectiveness in secondary care, and to identify factors influencing SUD.

METHOD

The search strategy had four facets: 1. Electronic databases, 2. Medication safety, 3. Hospitals and quality/safety, and 4. SUD. Searches were conducted within EMBASE, Medline, CINAHL, and International Pharmaceutical Abstracts. Empirical SUD intervention studies that aimed to improve medication safety and/or quality, and any studies providing insight into factors affecting SUD were included.

RESULTS

We identified nine quantitative studies of SUD interventions and five qualitative studies. SUD interventions were complex and fell into four categories, with 'provision of feedback' the most common. While heterogeneous, the majority of quantitative studies reported positive findings in improving medication safety but little detail was provided on the interventions implemented. The five qualitative studies collectively provide an overview of the SUD process, which typically comprised nine steps from data identification to analysis. Factors influencing the SUD process were electronic systems implementation and level of functionality, knowledge and skills of SUD users, organisational context, and policies around data reuse and security.

DISCUSSION AND CONCLUSION

The majority of the SUD interventions were successful in improving medication safety, however, what contributes to this success needs further exploration. From synthesis of research evidence in this review, an integrative framework was developed to describe the processes, mechanisms, and barriers for effective SUD.

International Standard Accident Number: A Master Case Index Linking Accident & Emergency with Medical Data

Unconnected data silos make it difficult to view a patient's accident and emergency case in a comprehensive way, i.e. across all sectors. This includes the emergency medical services (EMS), medical data in a hospital's electronic health record (EHR) as well as event data recorders (EDRs) collecting information about the circumstances of the accident and emergency event. In this paper, we propose a conceptual architecture which introduces a novel case-based record linkage approach and the international standard accident number (ISAN) as a master case index for linking data from EDR, EMS, and EHR.

Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data

Conventional data mining algorithms are unable to satisfy the current requirements on analyzing big data in some fields such as medicine, policy making, judicial, and tax records. However, applying diverse datasets from different institutes (both healthcare and non-healthcare related) can enrich information and insights. So far, analyzing this data in an automated, privacy-preserving manner does not exist to our knowledge. In this work, we propose an infrastructure, and proof-of-concept for privacy-preserving analytics on vertically partitioned data.

Secondary Use of Data: Non-Interventional Study Best Practices in Planning and Protocol Development

Well established guidelines already exist that address best practices for Non-Interventional Study (NIS) design and methods. These guidelines provide advice on things to consider while designing a study and developing a protocol, but do not necessarily capture specific details related to the implementation of NIS. The intent of this paper is to propose a best practice for conducting secondary use of data NIS. We propose that the ideal implementation of a NIS should include the development of a strong Study Concept, followed by a detailed Protocol, Analysis Plan, Report, and considerations for Dissemination. We review and discuss common mistakes/pitfalls and key considerations at each step from concept to publication. In many cases in this review, we have also provided suggestions or accessible resources that researchers can apply as a "best practices" guide when planning, conducting, or reviewing this investigative method.

Exploring the Potential for Secondary uses of Dementia Care Mapping (DCM) Data for Improving the Quality of Dementia Care

The reuse of existing datasets to identify mechanisms for improving healthcare quality has been widely encouraged. There has been limited application within dementia care. Dementia Care Mapping is an observational tool in widespread use, predominantly to assess and improve quality of care in single organisations. Dementia Care Mapping data have the potential to be used for secondary purposes to improve quality of care. However, its suitability for such use requires careful evaluation. This study conducted in-depth interviews with 29 Dementia Care Mapping users to identify issues, concerns and challenges regarding the secondary use of Dementia Care Mapping data. Data were analysed using modified Grounded Theory. Major themes identified included the need to collect complimentary contextual data in addition to Dementia Care Mapping data, to reassure users regarding ethical issues associated with storage and reuse of care related data and the need to assess and specify data quality for any data that might be available for secondary analysis.

Electronic Support for Retrospective Analysis in the Field of Radiation Oncology: Proof of Principle Using an Example of Fractionated Stereotactic Radiotherapy of 251 Meningioma Patients

Introduction

The purpose of this study is to verify the possible benefit of a clinical data warehouse (DWH) for retrospective analysis in the field of radiation oncology.

Material and methods

We manually and electronically (using DWH) evaluated demographic, radiotherapy, and outcome data from 251 meningioma patients, who were irradiated from January 2002 to January 2015 at the Department of Radiation Oncology of the Erlangen University Hospital. Furthermore, we linked the Oncology Information System (OIS) MOSAIQ^® to the DWH in order to gain access to irradiation data. We compared the manual and electronic data retrieval method in terms of congruence of data, corresponding time, and personal requirements (physician, physicist, scientific associate).

Results

The electronically supported data retrieval (DWH) showed an average of 93.9% correct data and significantly (p = 0.009) better result compared to manual data retrieval (91.2%). Utilizing a DWH enables the user to replace large amounts of manual activities (668 h), offers the ability to significantly reduce data collection time and labor demand (35 h), while simultaneously improving data quality. In our case, work time for manually data retrieval was 637 h for the scientific assistant, 26 h for the medical physicist, and 5 h for the physician (total 668 h).

Conclusion

Our study shows that a DWH is particularly useful for retrospective analysis in the radiation oncology field. Routine clinical data for a large patient group can be provided ready for analysis to the scientist and data collection time can be significantly reduced. Furthermore, linking multiple data sources in a DWH offers the ability to improve data quality for retrospective analysis, and future research can be simplified.

'You can't just hit a button': an ethnographic study of strategies to repurpose data from advanced clinical information systems for clinical process improvement

BACKGROUND

Current policies encourage healthcare institutions to acquire clinical information systems (CIS) so that captured data can be used for secondary purposes, including clinical process improvement. Such policies do not account for the extra work required to repurpose data for uses other than direct clinical care, making their implementation problematic. This paper aims to analyze the strategies employed by clinical units to use data effectively for both direct clinical care and clinical process improvement.

METHODS

Ethnographic methods were employed. A total of 54 contextual interviews with health professionals spanning various disciplines and 18 hours of observation were carried out in 5 intensive care units in England using an advanced CIS. Case studies of how the extra work was achieved in each unit were derived from the data and then compared.

RESULTS

We found that extra work is required to repurpose CIS data for clinical process improvement. Health professionals must enter data not required for clinical care and manipulation of this data into a machine-readable form is often necessary. Ambiguity over who should be responsible for this extra work hindered CIS data usage for clinical process improvement. We describe 11 strategies employed by units to accommodate this extra work, distributing it across roles. Seven of these motivated data entry by health professionals and four addressed the machine readability of data. Many of the strategies relied heavily on the skill and leadership of local clinical customizers.

CONCLUSIONS

To realize the expected clinical process improvements by the use of CIS data, clinical leaders and policy makers need to recognize and support the redistribution of the extra work that is involved in data repurposing. Adequate time, funding, and appropriate motivation are needed to enable units to acquire and deliver the necessary skills in CIS customization.

Using routinely gathered data to empower locally led health improvements

Data are routinely used throughout the NHS to report on and monitor performance. For example, detailed information regarding hospital episodes is reported via the Secondary Use Services (SUS) programme. Local commissioners use this data to monitor hospital contracts. In primary care, data such as glycaemic control of diabetes patients is extracted from general practice clinical systems to calculate practice payments for the 'Quality and Outcomes Framework' (QOF). We suggest that this routinely gathered data should also be used to help clusters of practices to learn from locally led innovation and to motivate long-term partnerships for interorganisational health improvement. Following the recent NHS reforms, the number of data sources that could facilitate this is likely to increase in size, variety and complexity. In this paper, we describe some of the existing data sources that could be used to do this; we also describe some of the dangers of using data in this way, and our conclusions about the best way forward.

Using routinely gathered data to empower locally led health improvements

Data are routinely used throughout the NHS to report on and monitor performance. For example, detailed information regarding hospital episodes is reported via the Secondary Use Services (SUS) programme. Local commissioners use this data to monitor hospital contracts. In primary care, data such as glycaemic control of diabetes patients is extracted from general practice clinical systems to calculate practice payments for the 'Quality and Outcomes Framework' (QOF). We suggest that this routinely gathered data should also be used to help clusters of practices to learn from locally led innovation and to motivate long-term partnerships for interorganisational health improvement. Following the recent NHS reforms, the number of data sources that could facilitate this is likely to increase in size, variety and complexity. In this paper, we describe some of the existing data sources that could be used to do this; we also describe some of the dangers of using data in this way, and our conclusions about the best way forward.

Evaluating the Risk of Re-identification of Patients from Hospital Prescription Records

BACKGROUND

Pharmacies often provide prescription records to private research firms, on the assumption that these records are de-identified (i.e., identifying information has been removed). However, concerns have been expressed about the potential that patients can be re-identified from such records. Recently, a large private research firm requested prescription records from the Children's Hospital of Eastern Ontario (CHEO), as part of a larger effort to develop a database of hospital prescription records across Canada.

OBJECTIVE

To evaluate the ability to re-identify patients from CHEO'S prescription records and to determine ways to appropriately de-identify the data if the risk was too high.

METHODS

The risk of re-identification was assessed for 18 months' worth of prescription data. De-identification algorithms were developed to reduce the risk to an acceptable level while maintaining the quality of the data.

RESULTS

The probability of patients being re-identified from the original variables and data set requested by the private research firm was deemed quite high. A new de-identified record layout was developed, which had an acceptable level of re-identification risk. The new approach involved replacing the admission and discharge dates with the quarter and year of admission and the length of stay in days, reporting the patient's age in weeks, and including only the first character of the patient's postal code. Additional requirements were included in the data-sharing agreement with the private research firm (e.g., audit requirements and a protocol for notification of a breach of privacy).

CONCLUSIONS

Without a formal analysis of the risk of re-identification, assurances of data anonymity may not be accurate. A formal risk analysis at one hospital produced a clinically relevant data set that also protects patient privacy and allows the hospital pharmacy to explicitly manage the risks of breach of patient privacy.