ArticleBMC health services research2020
How can technology support quality improvement? Lessons learned from the adoption of an analytics tool for advanced performance measurement in a hospital unit.
Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The utility of website-based quality improvement tools for health professionals: a systematic review.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2024Pooled it
- The role of medical regulations and medical regulators in fostering the use of eHealth data for strengthened continuing professional development (CPD): a document analysis with key informants' interviews.BMC medical education · 2025Article
- Qualitative Study of the Context of Health Information Technology in Sepsis Care Transitions: Facilitators, Barriers, and Strategies for Improvement.Journal of the American Medical Directors Association · 2025Article
- Organizational characteristics associated with sustained participation in internal quality improvement: Findings from two waves of a national sample of physician practices in the United States.Social science & medicine (1982) · 2025Article
- The NASSS (Non-Adoption, Abandonment, Scale-Up, Spread and Sustainability) framework use over time: A scoping review.PLOS digital health · 2025Article
- Optimizing inpatient bed management in a rural community-based hospital: a quality improvement initiative.BMC health services research · 2023Article
- Article
- The Wolfson Prize: designing the hospital of the future.Future healthcare journal · 2023Article
- Proposing a Scientific and Technological Approach to the Summaries of Clinical Issues of Inpatient Elderly with Delirium: A Viewpoint.Healthcare (Basel, Switzerland) · 2022Article
- Operating room relay strategy for turnover time improvement: a quality improvement project.BMJ open quality · 2022Article
- Improving care for immigrant women before, during, and after childbirth - what can we learn from regional interventions within a national program in Sweden?BMC health services research · 2022Article
- Adopt, adapt, or abandon technology-supported person-centred care initiatives: healthcare providers' beliefs matter.BMC health services research · 2021Article
- District health information system 2 data utilization among health professionals in Amara region private hospitals, Ethiopia.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundTechnology for timely feedback of data has the potential to support quality improvement (QI) in health care. However, such technology may pose difficulties stemming from the complex interaction with the setting in which it is implemented. To enable professionals to use data in QI there is a need to better understand of how to handle this complexity. This study aims to explore factors that influence the adoption of a technology-supported QI programme in an obstetric unit through a complexity informed framework.
methodsThis qualitative study, based on focus group interviews, was conducted at a Swedish university hospital's obstetric unit, which used an analytics tool for advanced performance measurement that gave timely and case mix adjusted feedback of performance data to support QI. Data was collected through three focus group interviews conducted with 16 managers and staff. The Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework guided the data collection and analysis.
resultsStaff and managers deemed the technology to effectively support ongoing QI efforts by providing timely access to reliable data. The value of the technology was associated with a clear need to make better use of existing data in QI. The data and the methodology in the analytics tool reflected the complexity of the clinical conditions treated but was presented through an interface that was easy to access and user friendly. However, prior understanding of statistics was helpful to be able to fully grasp the presented data. The tool was adapted to the needs and the organizational conditions of the local setting through a collaborative approach between the technology supplier and the adopters.
conclusionsTechnology has the potential to enable systematic QI through motivating professionals by providing timely and adequate feedback of performance. The adoption of such technology is complex and requires openness for gradual learning and improvement.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.