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Abstract
Objective To co-design artificial intelligence (AI)-based clinical informatics workflows to routinely analyse patient-reported experience measures (PREMs) in hospitals.
Methods The context was public hospitals (n=114) and health services (n=16) in a large state in Australia serving a population of ~5 million. We conducted a participatory action research study with multidisciplinary healthcare professionals, managers, data analysts, consumer representatives and industry professionals (n=16) across three phases: (1) defining the problem, (2) current workflow and co-designing a future workflow and (3) developing proof-of-concept AI-based workflows. Co-designed workflows were deductively mapped to a validated feasibility framework to inform future clinical piloting. Qualitative data underwent inductive thematic analysis.
Results Between 2020 and 2022 (n=16 health services), 175 282 PREMs inpatient surveys received 23 982 open-ended responses (mean response rate, 13.7%). Existing PREMs workflows were problematic due to overwhelming data volume, analytical limitations, poor integration with health service workflows and inequitable resource distribution. Three potential semiautomated, AI-based (unsupervised machine learning) workflows were developed to address the identified problems: (1) no code (simple reports, no analytics), (2) low code (PowerBI dashboard, descriptive analytics) and (3) high code (Power BI dashboard, descriptive analytics, clinical unit-level interactive reporting).
Discussion The manual analysis of free-text PREMs data is laborious and difficult at scale. Automating analysis with AI could sharpen the focus on consumer input and accelerate quality improvement cycles in hospitals. Future research should investigate how AI-based workflows impact healthcare quality and safety.
Conclusion AI-based clinical informatics workflows to routinely analyse free-text PREMs data were co-designed with multidisciplinary end-users and are ready for clinical piloting.
Introduction
Patient experience is a key indicator of healthcare quality and safety. Understanding the importance of measuring patient experience of healthcare has grown steadily over time and is now widely reflected in key healthcare performance frameworks, such as the Quadruple Aim.1 Improving patient experience has been associated with positive patient safety outcomes across diverse settings (eg, emergency care provision, paediatrics, elective surgery) and conditions (eg, chronic heart failure, mental health, stroke).2 In a hospital setting, patient satisfaction is commonly surveyed via patient-reported experience measures (PREMs). There are many validated PREMs that invite patients to rate elements of care using a simple numerical scale, which often overlook the richness of the patient narrative.3 Open-ended questions are now commonly included in PREMs surveys, such as ‘what was good about your care?’ and ‘what could be improved?’. Free-text (unstructured) data from patient experience surveys allow clinicians and administrators to learn from the narrative experiences (rather than just quantitative ranking data) of patients to conduct meaningful continuous quality improvement activity cycles in healthcare settings.4
Analysing free-text data at scale is time and resource intensive and increasingly unsustainable as acute care populations continue to grow. The complexities of large-scale manual analysis of free-text patient-generated health data are driving a new era of digital and AI-based analytical solutions. Natural language processing (NLP) and other machine learning (ML) techniques have emerged as contemporary tools to analyse free-text data from patient experience feedback.3 To date, most applications of NLP and ML for patient experience in health services have focused on analysing publicly sourced data from social media sites (eg, Twitter, Facebook) and healthcare forums (eg, National Health Service Choices, Yelp).3 There is limited research that has investigated how to integrate automation via NLP or ML techniques into routine practice to create a semi-automated (human and AI) workflow that analyses free-text PREMs data. Our study addressed this research and practice gap across three research questions (RQs):
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RQ1: What are the current problems of analysing free-text PREMs data in the state public healthcare system?
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RQ2: What is the current workflow, and how can we co-design an optimal future workflow to routinely analyse free-text PREMs in the state public healthcare system?
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RQ3: What are the requirements of a proof-of-concept AI-based clinical informatics workflow to routinely analyse free-text PREMs data in the state public healthcare system, and what is the theoretical feasibility of translating this workflow into practice?
Our overall aim was to co-produce a AI-based (semi-automated) clinical informatics workflow to routinely analyse PREMs in hospitals and health services in a statewide public healthcare system in Australia that delivers inpatient care to more than 1.3 million patients across 114 hospitals each year.
Conclusions
Currently, health services rely on manual analysis and interpretation of free-text data collected from PREMs questionnaires that is difficult to translate into healthcare quality improvement activities. This limits the capacity for routinely incorporating the rich patient narrative into routine quality improvement. This study co-produced AI-based clinical workflows to routinely analyse free-text PREMs data to address this problem. Future research into the implementation of AI-based PREMs analysis must be prioritised as PREMs data are increasingly part of global health system infrastructure. Healthcare improvement will likely accelerate by integrating the consumer voice into routine clinical governance activities.
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