Co-Design as Enabling Factor for Patient-Centred Healthcare: A Bibliometric Literature Review

The development of a family-led novel intervention for delirium prevention and management in the adult intensive care unit: A co-design qualitative study

AIM

The aim of this study was to codesign a Family Members' Voice Reorientation Intervention (FAMVR) for delirium prevention and management in critically ill adult patients through collaborative process with previous patients, families, and clinical staff.

BACKGROUND

Delirium is a common consequence of intensive care admission, and there is limited evidence to support family-led interventions to prevent or minimise delirium in intensive care. People with lived experience of intensive care are seldom involved in codesigning delirium prevention and management interventions despite the identified benefits of their involvement in delirium care.

DESIGN

Codesign qualitative study.

METHODS

The process of co-designing was undertaken using the four stages of the Double Diamond model. Participants included people with lived experience of the intensive care unit, family members, and intensive care clinicians. The codesign approach was utilised, and data were gathered from a series of focus groups and individual interviews. Data were digitally recorded, transcribed verbatim, and analysed using thematic analysis.

FINDINGS

Of the 26 people who indicated their interest in participating, 12 (46%) completed the first and second stages, and nine (35%) completed the third and fourth stages of the Family Members' Voice Reorientation Intervention development. All participant groups were represented in the fourth stage: patients (n = 4), family members (n = 1), nurses (n = 2), and medical staff (n = 2). Four themes were identified: message content, wording, reactions, and tone, all of which informed the prototype of the intervention and its associated domains.

CONCLUSION

A codesign approach was important for developing a delirium management intervention. This process enabled participants to provide their feedback in the context of their unique experiences, which in turn enhanced the authenticity and appropriateness of this unique intervention.

Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

Despite significant technical advances in machine learning (ML) over the past several years, the tangible impact of this technology in healthcare has been limited. This is due not only to the particular complexities of healthcare, but also due to structural issues in the machine learning for healthcare (MLHC) community which broadly reward technical novelty over tangible, equitable impact. We structure our work as a healthcare-focused echo of the 2012 paper "Machine Learning that Matters", which highlighted such structural issues in the ML community at large, and offered a series of clearly defined "Impact Challenges" to which the field should orient itself. Drawing on the expertise of a diverse and international group of authors, we engage in a narrative review and examine issues in the research background environment, training processes, evaluation metrics, and deployment protocols which act to limit the real-world applicability of MLHC. Broadly, we seek to distinguish between machine learning ON healthcare data and machine learning FOR healthcare-the former of which sees healthcare as merely a source of interesting technical challenges, and the latter of which regards ML as a tool in service of meeting tangible clinical needs. We offer specific recommendations for a series of stakeholders in the field, from ML researchers and clinicians, to the institutions in which they work, and the governments which regulate their data access.

Co-designing care for multimorbidity: a systematic review

BACKGROUND

The co-design of health care enables patient-centredness by partnering patients, clinicians and other stakeholders together to create services.

METHODS

We conducted a systematic review of co-designed health interventions for people living with multimorbidity and assessed (a) their effectiveness in improving health outcomes, (b) the co-design approaches used and (c) barriers and facilitators to the co-design process with people living with multimorbidity. We searched MEDLINE, EMBASE, CINAHL, Scopus and PsycINFO between 2000 and March 2022. Included experimental studies were quality assessed using the Cochrane risk of bias tool (ROB-2 and ROBINS-I).

RESULTS

We screened 14,376 reports, with 13 reports meeting the eligibility criteria. Two reported health and well-being outcomes: one randomised clinical trial (n = 134) and one controlled cohort (n = 1933). Outcome measures included quality of life, self-efficacy, well-being, anxiety, depression, functional status, healthcare utilisation and mortality. Outcomes favouring the co-design interventions compared to control were minimal, with only 4 of 17 outcomes considered beneficial. Co-design approaches included needs assessment/ideation (12 of 13), prototype (11 of 13), pilot testing (5 of 13) (i.e. focus on usability) and health and well-being evaluations (2 of 13). Common challenges to the co-design process include poor stakeholder interest, passive participation, power imbalances and a lack of representativeness in the design group. Enablers include flexibility in approach, smaller group work, advocating for stakeholders' views and commitment to the process or decisions made.

CONCLUSIONS

In this systematic review of co-design health interventions, we found that few projects assessed health and well-being outcomes, and the observed health and well-being benefits were minimal. The intensity and variability in the co-design approaches were substantial, and challenges were evident. Co-design aided the design of novel services and interventions for those with multimorbidity, improving their relevance, usability and acceptability. However, the clinical benefits of co-designed interventions for those with multimorbidity are unclear.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Background

Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods

We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion

Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.