Shift in translations: Data work with patient-generated health data in clinical practice

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.

Recommendations for the Quality Management of Patient-Generated Health Data in Remote Patient Monitoring: Mixed Methods Study

BACKGROUND

Patient-generated health data (PGHD) collected from innovative wearables are enabling health care to shift to outside clinical settings through remote patient monitoring (RPM) initiatives. However, PGHD are collected continuously under the patient's responsibility in rapidly changing circumstances during the patient's daily life. This poses risks to the quality of PGHD and, in turn, reduces their trustworthiness and fitness for use in clinical practice.

OBJECTIVE

Using a sociotechnical health informatics lens, we developed a data quality management (DQM) guideline for PGHD captured from wearable devices used in RPM with the objective of investigating how DQM principles can be applied to ensure that PGHD can reliably inform clinical decision-making in RPM.

METHODS

First, clinicians, health information specialists, and MedTech industry representatives with experience in RPM were interviewed to identify DQM challenges. Second, these stakeholder groups were joined by patient representatives in a workshop to co-design potential solutions to meet the expectations of all the stakeholders. Third, the findings, along with the literature and policy review results, were interpreted to construct a guideline. Finally, we validated the guideline through a Delphi survey of international health informatics and health information management experts.

RESULTS

The guideline constructed in this study comprised 19 recommendations across 7 aspects of DQM. It explicitly addressed the needs of patients and clinicians but implied that there must be collaboration among all stakeholders to meet these needs.

CONCLUSIONS

The increasing proliferation of PGHD from wearables in RPM requires a systematic approach to DQM so that these data can be reliably used in clinical care. The developed guideline is an important next step toward safe RPM.

Attitudes and perspectives of nurses and physicians in South Korea towards the clinical use of person-generated health data

This study aimed to explore the adoption of person-generated health data in clinical settings and discern the factors influencing clinicians' willingness to use it. A web-based survey containing 48 questions was developed based on prior research and the Unified Theory of Acceptance and Use of Technology 2 model. The survey was administered to a convenience sample of 486 nurses and physicians in South Korea recruited through an online community and snowball sampling. Of these, 70.7% were physicians. While 65% had used mobile health apps and devices, only 12.8% were familiar with person-generated health data. Still, a promising 73.3% expressed interest in incorporating person-generated health data into patient care, particularly data on blood glucose and vital signs. The findings of the study also indicated that clinicians specializing in internal medicine (OR: 1.9, CI: 1.16-3.19), familiar with person-generated health data (OR: 2.6, CI: 1.58-4.29), with a positive view of information and communication technology adoption (OR: 2.6, CI: 1.65-4.13), and who see the value in person-generated health data (OR: 3.9, CI: 2.55-6.09) showed higher inclination to utilize it. However, those in outpatient settings (OR: 0.4, CI: 0.19-0.73) showed less enthusiasm. The findings of this study suggest that despite the willingness of clinicians to use person-generated health data, various barriers must be addressed first, including a lack of knowledge regarding its use, concerns about data reliability and quality, and a lack of provider incentives. Overcoming these challenges demands concerted organizational or policy support. This research underscores person-generated health data's untapped potential in healthcare and the pressing need for strategies that facilitate its clinical integration.

Strategies prescribers and pharmacists use to identify and mitigate adverse drug reactions in inpatient and outpatient care: a cognitive task analysis at a US Veterans Affairs Medical Center

OBJECTIVE

To develop a descriptive model of the cognitive processes used to identify and resolve adverse drug reactions (ADRs) from the perspective of healthcare providers in order to inform future informatics efforts SETTING: Inpatient and outpatient care at a tertiary care US Veterans Affairs Medical Center.

PARTICIPANTS

Physicians, nurse practitioners and pharmacists who report ADRs.

OUTCOMES

Descriptive model and emerging themes from interviews.

RESULTS

We conducted critical decision method interviews with 10 physicians and 10 pharmacists. No nurse practitioners submitted ADR incidents. We generated a descriptive model of an ADR decision-making process and analysed emerging themes, categorised into four stages: detection of potential ADR, investigation of the problem's cause, risk/benefit consideration, and plan, action and follow-up. Healthcare professionals (HCPs) relied on several confirmatory or disconfirmatory cues to detect and investigate potential ADRs. Evaluating risks and benefits of related medications played an essential role in HCPs' pursuits of solutions CONCLUSIONS: This study provides an illustrative model of how HCPs detect problems and make decisions regarding ADRs. The design of supporting technology for potential ADR problems should align with HCPs' real-world cognitive strategies, to assist fully in detecting and preventing ADRs for patients.

'Is this something I should be worried about?': A study of nurses' recontextualisation work when making clinical decisions based on patient reported outcome data

As clinical care practices are becoming more digitalised, information about patients is increasingly being encoded as quantified data, and the processes of sorting data are often supported by algorithmic computations. One such practice becoming more prevalent across Western countries is the clinical use of Patient Reported Outcome (PRO) data. Drawing on ethnographic fieldwork carried out in a Danish setting among nurses managing PRO-based breast cancer follow-up, we examine how clinical decisions are made on the basis of PRO-data and what this requires from the nurses. By applying the concept of recontextualisation work as an analytical perspective, we shed light on the efforts of nurses when mobilising complementary information about patients in order to recontextualise the otherwise decontextualised data, thereby giving data practical value in clinical decision-making. Recontextualisation work, we show, is shaped by organisational structure, available resources, and nurses' professional capacity. Drawing analytical attention to the work of recontextualisation allows for a nuanced understanding of the efforts required to make data workable and hence what it takes to carry out clinical decisions in today's datafied healthcare system.

Changing Categorical Work in Healthcare: the Use of Patient-Generated Health Data in Cancer Rehabilitation

Categorical work in chronic care is increasingly dependent on digital technologies for remote patient care. However, remote care takes many forms and while various types of digital technologies are currently being used, we lack a nuanced understanding of how to design such technologies for specific novel usages. In this paper, we focus on digital technologies for patient-generated health data and how their use changes categorical work in chronic care. Our aim is to understand how categorical work changes, which novel forms of categorical work emerge and what the implications are for the care relation. This paper is based on an ethnographic study of healthcare professionals’ work at a pelvic cancer rehabilitation clinic and their interactions with patients. In this setting, supportive talks between patients and nurses are central. To understand the complexities of categorical work in chronic care when patient-generated health data are introduced, we contrast the traditional supportive talks with supportive talks where the nurses had access to the patients’ patient-generated health data. We identify and analyze challenges connected to novel forms of categorical work. Specifically, we focus on categorical work and how it can undergo changes. Our empirical findings show how changes occur in the way patients’ lived experience of the chronic disease aligns with the categories from chronic care, as well as in the way the nurse works with clinical categories during the talk. These insights help us further understand the implications of patient generated-data use in supportive talks. We contribute to an improved understanding of the use of patient-generated health data in clinical practice and based on this, we identify design implications for how to make categorical work more collaborative.

Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study

Background

Patient-generated health data (PGHD) have been largely collected through mobile health (mHealth) apps and wearable devices. PGHD can be especially helpful in mental health, as patients’ illness history and symptom narratives are vital to developing diagnoses and treatment plans. However, the extent to which clinicians use mental health–related PGHD is unknown.

Objective

A mixed methods study was conducted to understand clinicians’ perspectives on PGHD and current mental health apps. This approach uses information gathered from semistructured interviews, workflow analysis, and user-written mental health app reviews to answer the following research questions: (1) What is the current workflow of mental health practice and how are PGHD integrated into this workflow, (2) what are clinicians’ perspectives on PGHD and how do they choose mobile apps for their patients, (3) and what are the features of current mobile apps in terms of interpreting and sharing PGHD?

Methods

The study consists of semistructured interviews with 12 psychiatrists and clinical psychologists from a large academic hospital. These interviews were thematically and qualitatively analyzed for common themes and workflow elements. User-posted reviews of 56 sleep and mood tracking apps were analyzed to understand app features in comparison with the information gathered from interviews.

Results

The results showed that PGHD have been part of the workflow, but its integration and use are not optimized. Mental health clinicians supported the use of PGHD but had concerns regarding data reliability and accuracy. They also identified challenges in selecting suitable apps for their patients. From the app review, it was discovered that mHealth apps had limited features to support personalization and collaborative care as well as data interpretation and sharing.

Conclusions

This study investigates clinicians’ perspectives on PGHD use and explored existing app features using the app review data in the mental health setting. A total of 3 design guidelines were generated: (1) improve data interpretation and sharing mechanisms, (2) consider clinical workflow and electronic health record integration, and (3) support personalized and collaborative care. More research is needed to demonstrate the best practices of PGHD use and to evaluate their effectiveness in improving patient outcomes.

Secondary care provider attitudes towards patient generated health data from smartwatches

Wearable devices, like smartwatches, are increasingly used for tracking physical activity, community mobility, and monitoring symptoms. Data generated from smartwatches (PGHD_SW) is a form of patient-generated health data, which can benefit providers by supplying frequent temporal information about patients. The goal of this study was to understand providers' perceptions towards PGHD_SW adoption and its integration with electronic medical records. In-depth, semi-structured qualitative interviews were conducted with 12 providers from internal medicine, family medicine, geriatric medicine, nursing, surgery, rehabilitation, and anesthesiology. Diffusion of Innovations was used as a framework to develop questions and guide data analysis. The constant comparative method was utilized to formulate salient themes from the interviews. Four main themes emerged: (1) PGHD_SW is perceived as a relative advantage; (2) data are viewed as compatible with current practices; (3) barriers to overcome to effectively use PGHD_SW; (4) assessments from viewing sample data. Overall, PGHD_SW was valued because it enabled access to information about patients that were traditionally unattainable. It also can initiate discussions between patients and providers. Providers consider PGHD_SW important, but data preferences varied by specialty. The successful adoption of PGHD_SW will depend on tailoring data, frequencies of reports, and visualization preferences to correspond with the demands of providers.

Digitalization and Physician Learning: Individual Practice, Organizational Context, and Social Norm

INTRODUCTION

The emerging context of online platforms and digitally engaged patients demands new competencies of health care professionals. Although information and communication technologies (ICTs) can strengthen continuous professional development (CPD) and learning at work, more research is needed on ICT for experiential and collegial learning.

METHODS

The study builds on prior qualitative research to identify issues and comprises a quantitative assessment of ICT usage for learning in health care. A survey was administered to Swedish physicians participating in a CPD program as part of specialist medical training. Conclusions focused specifically on learning dimensions are drawn from correlation analyses complemented with multiple regression.

RESULTS

The findings show that physicians' actual use of ICT is related to perceived performance, social influence, and organizational context. Social norm was the most important variable for measured general usage, whereas performance expectancy (perceived usefulness of ICT) was important for ICT usage for learning. The degree of individual digitalization affects performance and, in turn, actual use.

DISCUSSION

The study highlights the need to incorporate ICT effectively into CPD and clinical work. Besides formal training and support for specific systems, there is a need to understand the usefulness of digitalization integrated into practice. Moving beyond instrumentalist views of technology, the model in this study includes contextualized dimensions of ICT and learning in health care. Findings confirm that medical communities are influencers of use, which suggests that an emphasis on collegial expectations for digital collaboration will enhance practitioner adaptation.