Hidden labour: the skilful work of clinical audit data collection and its implications for secondary use of data via integrated health IT

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.

Automated audit of hospital oxygen use devised during the COVID-19 pandemic

BACKGROUND

The British Thoracic Society (BTS) has organised intermittent audits of hospital oxygen use in UK hospitals since 2008. Manual audits are time-consuming and subject to human errors. Oxygen prescribing and bedside observations including National Early Warning Scores (NEWS2 scores) are undertaken within an integrated electronic medical record (EMR) at this hospital.

METHODS

The hospital's Business Information team were commissioned in late 2019 to devise a bespoke automated audit of oxygen prescribing and use. A summary report displays the oxygen saturation alongside the oxygen prescription status of every patient in the hospital except for critical care units which do not use NEWS2. The display has a 'traffic-light' colour scheme (green within target range, amber or red if below range or if above range on supplemental oxygen), with a graph showing oxygen use and saturation levels for patients with each prescribed target range. Clinicians can access raw data including oxygen saturation, oxygen device and flow rate for each individual patient.

RESULTS

Over 51 audits involving 34 352 sets of observations, an average of 6.0% involved use of oxygen and 88.6% of these had a valid oxygen prescription. During the first wave of the COVID-19 pandemic in spring 2020, the monthly percentage of observations involving oxygen use increased to a peak of 10.4% followed by a rise to 10.6% during the second wave and 7.4% during the third (Omicron) wave. Oxygen use returned to baseline after each wave.

CONCLUSIONS

In hospitals with integrated EMRs, it is possible to automate all fundamental aspects of the BTS oxygen audits and to monitor oxygen use at individual patient level and a hospital-wide level. This could be particularly valuable during major events such as the COVID-19 pandemic. This methodology could be extended to other clinical audits where the audit questions relate to routinely collected EMR data.

Preliminary Attainability Assessment of Real-World Data for Answering Major Clinical Research Questions in Breast Cancer Brain Metastasis: Framework Development and Validation Study

BACKGROUND

In recent decades, real-world evidence (RWE) in oncology has rapidly gained traction for its potential to answer clinical questions that cannot be directly addressed by randomized clinical trials. Integrating real-world data (RWD) into clinical research promises to contribute to more sustainable research designs, including extension, augmentation, enrichment, and pragmatic designs. Nevertheless, clinical research using RWD is still limited because of concerns regarding the shortage of best practices for extracting, harmonizing, and analyzing RWD. In particular, pragmatic screening methods to determine whether the content of a data source is sufficient to answer the research questions before conducting the research with RWD have not yet been established.

OBJECTIVE

We examined the PAR (Preliminary Attainability Assessment of Real-World Data) framework and assessed its utility in breast cancer brain metastasis (BCBM), which has an unmet medical need for data attainability screening at the preliminary step of observational studies that use RWD.

METHODS

The PAR framework was proposed to assess data attainability from a particular data source during the early research process. The PAR framework has four sequential stages, starting with clinical question clarification: (1) operational definition of variables, (2) data matching (structural/semantic), (3) data screening and extraction, and (4) data attainability diagramming. We identified 5 clinical questions to be used for PAR framework evaluation through interviews and validated them with a survey of breast cancer experts. We used the Samsung Medical Center Breast Cancer Registry, a hospital-based real-time registry implemented in March 2021, leveraging the institution's anonymized and deidentified clinical data warehouse platform. The number of breast cancer patients in the registry was 45,129; it covered the period from June 1995 to December 2021. The registry consists of 24 base data marts that represent disease-specific breast cancer characteristics and care pathways. The outcomes included screening results of the clinical questions via the PAR framework and a procedural diagram of data attainability for each research question.

RESULTS

Data attainability was tested for study feasibility according to the PAR framework with 5 clinical questions for BCBM. We obtained data sets that were sufficient to conduct studies with 4 of 5 clinical questions. The research questions stratified into 3 types when we developed data fields for clearly defined research variables. In the first, only 1 question could be answered using direct data variables. In the second, the other 3 questions required surrogate definitions that combined data variables. In the third, the question turned out to be not feasible for conducting further analysis.

CONCLUSIONS

The adoption of the PAR framework was associated with more efficient preliminary clinical research using RWD from BCBM. Furthermore, this framework helped accelerate RWE generation through clinical research by enhancing transparency and reproducibility and lowering the entry barrier for clinical researchers.

The practical ethics of repurposing health data: how to acknowledge invisible data work and the need for prioritization

Throughout the Global North, policymakers invest in large-scale integration of health-data infrastructures to facilitate the reuse of clinical data for administration, research, and innovation. Debates about the ethical implications of data repurposing have focused extensively on issues of patient autonomy and privacy. We suggest that it is time to scrutinize also how the everyday work of healthcare staff is affected by political ambitions of data reuse for an increasing number of purposes, and how different purposes are prioritized. Our analysis builds on ethnographic studies within the Danish healthcare system, which is internationally known for its high degree of digitalization and well-connected data infrastructures. Although data repurposing ought to be relatively seamless in this context, we demonstrate how it involves costs and trade-offs for those who produce and use health data. Even when IT systems and automation strategies are introduced to enhance efficiency and reduce data work, they can end up generating new forms of data work and fragmentation of clinically relevant information. We identify five types of data work related to the production, completion, validation, sorting, and recontextualization of health data. Each of these requires medical expertise and clinical resources. We propose that the implications for these forms of data work should be considered early in the planning stages of initiatives for large-scale data sharing and reuse, such as the European Health Data Space. We believe that political awareness of clinical costs and trade-offs related to such data work can provide better and more informed decisions about data repurposing.