Data work and practices in healthcare: A scoping review

CONTEXT

In healthcare, digitization has been widespread and profound, entailing a deluge of data. This has spurred ambitions for healthcare to become data-driven to improve efficiency and quality, and within medicine itself to improve diagnosing and treating diseases. The generation and processing of data requires human intervention and work, though this is often not acknowledged.

PURPOSE

The paper investigates who, where, by which means, and for which purposes data work is conducted which is crucial for healthcare managers and policy makers if ambitions to become data-driven are to succeed. To guide further research, it also provides an overview of existing research on data work and practices.

METHODS

We conducted a scoping review based on a search for papers including the terms healthcare or health care combined with at least one of the following terms: data work, data worker*, data practice*, data practitioner* in Scopus and Web of Science. 74 papers on data work or practices in healthcare were included.

ANALYSIS

The 74 papers were coded and analyzed regarding the following themes: the kind of data workers and practitioners, organizational settings, involved technologies, purposes, data work tasks, theories and concepts, and definitions of data work and practice.

RESULTS

Data work is pervasive in healthcare and conducted by various professions and people and in various contexts. The field researching data work and practices is emerging, with publications spread across multiple venues. and there is a need for more precise definitions of data work. Further, data work and practices are useful concepts that have enabled the exploration of those efforts and tasks in detail.

CONCLUSION

The research on data work and practices in healthcare is emerging and promising. We call for more research to consolidate the field and to better understand and support the work needed for healthcare to become data-driven.

Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes

Organizations are increasingly introducing data science initiatives to support decision-making. However, the decision outcomes of data science initiatives are not always used or adopted by decision-makers, often due to uncertainty about the quality of data input. It is, therefore, not surprising that organizations are increasingly turning to data governance as a means to improve the acceptance of data science decision outcomes. In this paper, propositions will be developed to understand the role of data governance in creating trust in data science decision outcomes. Two explanatory case studies in the asset management domain are analyzed to derive boundary conditions. The first case study is a data science project designed to improve the efficiency of road management through predictive maintenance, and the second case study is a data science project designed to detect fraudulent usage of electricity in medium and low voltage electrical grids without infringing privacy regulations. The duality of technology is used as our theoretical lens to understand the interactions between the organization, decision-makers, and technology. The results show that data science decision outcomes are more likely to be accepted if the organization has an established data governance capability. Data governance is also needed to ensure that organizational conditions of data science are met, and that incurred organizational changes are managed efficiently. These results imply that a mature data governance capability is required before sufficient trust can be placed in data science decision outcomes for decision-making.

Data science and its relationship to library and information science: a content analysis

Purpose

The purpose of this paper is to present the results of a study exploring the emerging field of data science from the library and information science (LIS) perspective.

Design/methodology/approach

Content analysis of research publications on data science was made of papers published in the Web of Science database to identify the main themes discussed in the publications from the LIS perspective.

Findings

A content analysis of 80 publications is presented. The articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences. The category of tools, techniques and applications of data science was most addressed by the authors, followed by data science from the perspective of health sciences, data science education and training and knowledge and skills of the data professional. However, several publications fell into several categories because these topics were closely related.

Research limitations/implications

Only publication recorded in the Web of Science database and with the term “data science” in the topic area were analyzed. Therefore, several relevant studies are not discussed in this paper that either were related to other keywords such as “e-science”, “e-research”, “data service”, “data curation”, “research data management” or “scientific data management” or were not present in the Web of Science database.

Originality/value

The paper provides the first exploration by content analysis of the field of data science from the perspective of the LIS.

Caught in the middle?

Purpose

Distributed trust technologies, such as blockchain, propose to permit peer-to-peer transactions without trusted third parties. Yet not all implementations of such technologies fully decentralize. Information professionals make strategic choices about the level of decentralization when implementing such solutions, and many organizations are taking a hybrid (i.e. partially decentralized) approach to the implementation of distributed trust technologies. This paper conjectures that while hybrid approaches may resolve some challenges of decentralizing information governance, they also introduce others. To better understand these challenges, this paper aims first to elaborate a framework that conceptualizes a centralized–decentralized information governance continuum along three distinct dimensions: custody, ownership and right to access data. This paper then applies this framework to two illustrative blockchain case studies – a pilot Brazilian land transfer recording solution and a Canadian health data consent sharing project – to exemplify how the current transition state of blockchain pilots straddles both the old (centralized) and new (decentralized) worlds. Finally, this paper outlines the novel challenges that hybrid approaches introduce for information governance and what information professionals should do to navigate this thorny transition period. Counterintuitively, it may be much better for information professionals to embrace decentralization when implementing distributed trust technologies, as hybrid models could offer the worst of both the centralized and future decentralized worlds when consideration is given to the balance between information governance risks and new strategic business opportunities.

Design/methodology/approach

This paper illustrates how blockchain is transforming organizations and societies by highlighting new strategic information governance challenges using our original analytic framework in two detailed blockchain case studies – a pilot solution in Brazil to record land transfers (Flores et al., 2018) and another in Canada to handle health data sharing consent (Hofman et al., 2018). The two case studies represent research output of the first phase of an ongoing multidisciplinary research project focused on gaining an understanding of how blockchain technology generates organizational, societal and data transformations and challenges. The analytic framework was developed inductively from a thematic synthesis of the findings of the case studies conducted under the auspices of this research project. Each case discussed in detail in this paper was chosen from among the project's case studies, as it represents a desire to move away from the old centralized world of information governance to a new decentralized one. However, each case study also represents and embodies a transition state between the old and new worlds and highlights many of the associated strategic information governance challenges.

Findings

Decentralization continues to disrupt organizations and societies. New emerging distributed trust technologies such as blockchain break the old rules with respect to the trust and authority structures of organizations and how records and data are created, managed and used. While governments and businesses around the world clearly see value in this technology to drive business efficiency, open up new market opportunities and create new forms of value, these advantages will not come without challenges. For information executives then, the question is not if they will be disrupted, but how. Understanding the how as will be discussed in this paper provides the business know how to leverage the incredible innovation and transformation that decentralized trust technology enables before being leapfrogged by another organization. It requires a change of mindset to consider an organization as one part of a broader ecosystem, and for those who successfully do so, this paper views this as a strategic opportunity for those responsible for strategic information governance to design the future instead of being disrupted by it.

Research limitations/implications

This paper presents a novel analytic framework for strategic information governance challenges as we transition from a traditional world of centralized records and information management to a new decentralized world. This paper analyzes these transitions and their implications for strategic information governance along three trajectories: custody, ownership and right to access records and data, illustrating with reference to our case studies.

Practical implications

This paper predicts a large number of organizations will miss the opportunities of the new decentralized trust world, resulting in a rather major churning of organizations, as those who successfully participate in building the new model will outcompete those stuck in the old world or the extremely problematic hybrid transition state. Counterintuitively, this paper argues that it may be much less complex for information executives to embrace decentralization as fast as they can, as in some ways the hybrid model seems to offer the worst of both the centralized and future decentralized worlds with respect to information governance risks.

Social implications

This paper anticipates broader societal consequences of the predicted organization churn, in particular with respect to uncertainty about the evidence that records provide for public accountability and contractual rights and entitlements.

Originality/value

Decentralized trust technologies, such as blockchain, permit peer-to-peer transactions without trusted third parties. Of course, such radical shifts do not happen overnight. The current transition state of blockchain pilots straddles both the old and new worlds. This paper presents a theoretical framework categorizing strategic information governance challenges on a spectrum of centralized to decentralized in three primary areas: custody, ownership and right to access records and data. To illustrate how decentralized trust is transforming organizations and societies, this paper presents these strategic information governance challenges in two blockchain case studies – a pilot Brazilian land transfer recording solution and a Canadian health data consent sharing project. Drawing on the theoretical framework and case studies, this paper outlines what information executives should do to navigate this thorny transition period.

Quality of Open Research Data: Values, Convergences and Governance

This paper focuses on the characteristics of research data quality, and aims to cover the most important issues related to it, giving particular attention to its attributes and to data governance. The corporate word’s considerable interest in the quality of data is obvious in several thoughts and issues reported in business-related publications, even if there are apparent differences between values and approaches to data in corporate and in academic (research) environments. The paper also takes into consideration that addressing data quality would be unimaginable without considering big data.

Data science from a library and information science perspective

Purpose

Data science is a relatively new field which has gained considerable attention in recent years. This new field requires a wide range of knowledge and skills from different disciplines including mathematics and statistics, computer science and information science. The purpose of this paper is to present the results of the study that explored the field of data science from the library and information science (LIS) perspective.

Design/methodology/approach

Analysis of research publications on data science was made on the basis of papers published in the Web of Science database. The following research questions were proposed: What are the main tendencies in publication years, document types, countries of origin, source titles, authors of publications, affiliations of the article authors and the most cited articles related to data science in the field of LIS? What are the main themes discussed in the publications from the LIS perspective?

Findings

The highest contribution to data science comes from the computer science research community. The contribution of information science and library science community is quite small. However, there has been continuous increase in articles from the year 2015. The main document types are journal articles, followed by conference proceedings and editorial material. The top three journals that publish data science papers from the LIS perspective are the Journal of the American Medical Informatics Association, the International Journal of Information Management and the Journal of the Association for Information Science and Technology. The top five countries publishing are USA, China, England, Australia and India. The most cited article has got 112 citations. The analysis revealed that the data science field is quite interdisciplinary by nature. In addition to the field of LIS the papers belonged to several other research areas. The reviewed articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences.

Research limitations/implications

The limitations of this research are that this study only analyzed research papers in the Web of Science database and therefore only covers a certain amount of scientific papers published in the field of LIS. In addition, only publications with the term “data science” in the topic area of the Web of Science database were analyzed. Therefore, several relevant studies are not discussed in this paper that are not reflected in the Web of Science database or were related to other keywords such as “e-science,” “e-research,” “data service,” “data curation” or “research data management.”

Originality/value

The field of data science has not been explored using bibliographic analysis of publications from the perspective of the LIS. This paper helps to better understand the field of data science and the perspectives for information professionals.