Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care Professionals

Abstract

Background One reason for the introduction of digital technologies into health care has been to try to improve safety and patient outcomes by providing real-time access to patient data and enhancing communication among health care professionals. However, the adoption of such technologies into clinical pathways has been less examined, and the impacts on users and the broader health system are poorly understood. We sought to address this by studying the impacts of introducing a digitally enabled care pathway for patients with acute kidney injury (AKI) at a tertiary referral hospital in the United Kingdom. A dedicated clinical response team—comprising existing nephrology and patient-at-risk and resuscitation teams—received AKI alerts in real time via Streams, a mobile app. Here, we present a qualitative evaluation of the experiences of users and other health care professionals whose work was affected by the implementation of the care pathway. Objective The aim of this study was to qualitatively evaluate the impact of mobile results viewing and automated alerting as part of a digitally enabled care pathway on the working practices of users and their interprofessional relationships. Methods A total of 19 semistructured interviews were conducted with members of the AKI response team and clinicians with whom they interacted across the hospital. Interviews were analyzed using inductive and deductive thematic analysis. Results The digitally enabled care pathway improved access to patient information and expedited early specialist care. Opportunities were identified for more constructive planning of end-of-life care due to the earlier detection and alerting of deterioration. However, the shift toward early detection also highlighted resource constraints and some clinical uncertainty about the value of intervening at this stage. The real-time availability of information altered communication flows within and between clinical teams and across professional groups. Conclusions Digital technologies allow early detection of adverse events and of patients at risk of deterioration, with the potential to improve outcomes. They may also increase the efficiency of health care professionals’ working practices. However, when planning and implementing digital information innovations in health care, the following factors should also be considered: the provision of clinical training to effectively manage early detection, resources to cope with additional workload, support to manage perceived information overload, and the optimization of algorithms to minimize unnecessary alerts.

Implementation of a Digitally Enabled Care Pathway (Part 1): Impact on Clinical Outcomes and Associated Health Care Costs

“…An explanation for the possible effect of the intervention on rates of cardiac arrest emerged from qualitative data provided in our parallel paper [30]. Here, users suggested the care pathway not only enhanced early access to specialist care for deteriorating patients but also informed treatment escalation plans.…”

“…Implementation of the digitally enabled care pathway for these patients was associated with significant improvement in the reliability of AKI recognition, a reduction in time to recognize and adjust potentially nephrotoxic medications [18]. Our qualitative analysis [30] found that care pathway improved access to patient information and expedited early specialist care. Our results concur with other research findings: a recent study from Korea [31] suggested that e-alerting for inpatients improves AKI recognition and the number of patients receiving specialist review [31].…”

Information Overload in Emergency Medicine Physicians: A Multisite Case Study Exploring the Causes, Impact, and Solutions in Four North England National Health Service Trusts

“…In addition, remembering patient diagnostics and medical history becomes difficult throughout a shift because of the large amount of information acquired about other cases and other confounding factors [ 28 ]. The fast turnover of emergency departments implies that there can often be a queue of people waiting to speak to a physician, who frequently does not manage to action one of these requests before others come in, hence compromising patient safety [ 32 ]. Among the effects of information overload mentioned by the respondents, distraction and tiredness play a key role.…”

“…Digital technologies are proving to be both a curse and a blessing. On the one hand, they represent yet another source of—often unwanted information [ 31 ]—but, on the other hand, they can support physicians in the detection of adverse events and patients at risk of deterioration with the potential to improve health outcomes [ 32 ]. This is only achievable if, when planning and implementing digital information solutions in hospitals and clinics, factors such as the provision of clinical training, resources to cope with the additional workload, and optimization of algorithms to reduce superfluous alerts are taken into consideration [ 32 ].…”

Developing a delivery science for artificial intelligence in healthcare

“…Current efforts to use AI in healthcare often begin with “I have a ML model that can accurately predict or classify X”, but then get stuck at “how do I use it and for whom?” 4 As a result, libraries of ML models remain on the shelf without finding appropriate use cases, or models are implemented but deemed to not be as valuable as initially imagined 5 . A recently published ML model that predicts acute kidney injury with high accuracy 6 was assumed by the authors to provide valuable information to clinicians, but when implemented in a real clinical environment, did not significantly improve patient care and in fact resulted in additional work for the physicians that was of unclear value 7 . This example highlights the importance of understanding the complexities of care delivery associated with the clinical use case before building the ML model; just focusing on the capability to accurately perform a prediction task is not sufficient for improving care.…”