Best Practices for Implementing Electronic Care Records in Adult Social Care: Rapid Scoping Review
Bibliographic record
Abstract
BACKGROUND: In the past decade, the use of digital or electronic records in social care has risen worldwide, capturing key information for service delivery. The COVID-19 pandemic accelerated digitization in health and social care. For example, the UK government created a fund specifically for adult social care provider organizations to adopt digital social care records. These developments offer valuable learning opportunities for implementing digital care records in adult social care settings. OBJECTIVE: This rapid scoping review aimed to understand what is known about the implementation of digital care records in adult social care and how implementation varies across use cases, settings, and broader contexts. METHODS: A scoping review methodology was used, with amendments made to enable a rapid review. Comprehensive searches based on the concepts of digital care records, social care, and interoperability were conducted across the MEDLINE, EmCare, Web of Science Core Collection, HMIC Health Management Information Consortium, Social Policy and Practice, and Social Services Abstracts databases. Studies published between 2018 and 2023 in English were included. One reviewer screened titles and abstracts, while 2 reviewers extracted data. Thematic analysis mapped findings against the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. RESULTS: Our search identified 2499 references. After screening titles and abstracts, 71 records were selected for full-text review, resulting in 31 references from 29 studies. Studies originated from 11 countries, including 1 multicountry study, with the United Kingdom being the most represented (10/29, 34%). Studies were most often conducted in nursing homes or facilities (7/29, 24%) with older people as the target population (6/29, 21%). Health records were the most investigated record type (12/29, 41%). We identified 45 facilitators and 102 barriers to digital care record implementation across 28 studies, spanning 6 of the 7 NASSS framework domains and aligning with 5 overarching themes that require greater active management regarding implementation. Intended or actual implementation outcomes were reported in 17 (59%) of the 29 studies. CONCLUSIONS: The findings suggest that implementation is complex due to a lack of consensus on what digital care records and expected outcomes and impacts should look like. The literature often lacks clear definitions and robust study designs. To be successful, implementation should consider complexity, while studies should use robust frameworks and mixed methods or quantitative designs where appropriate. Future research should define the target population, gather data on carer or service user experiences, and focus on digital care records specifically used in social care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.198 | 0.394 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.050 | 0.040 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".