Inter-hospital variations in health outcomes in childbirth care in Sweden: a register-based study

Mesterton, Johan; Brommels, Mats; Ladfors, Lars; Lindgren, Peter; Amer‐Wåhlin, Isis

doi:10.1093/intqhc/mzy153

Cited by 6 publications

(5 citation statements)

References 78 publications

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“…This research resulted in the launch of a crossregional analytics tool for case mix adjusted benchmarking of outcomes, resource use, and care processes. Birth care was one of the six patient groups initially addressed in the project [22][23][24]. Moreover the government decided to put improvement of birth care and women's health on the political agenda and pledged significant national funds to the regions between 2015 and 2022 [25].…”

Section: Settingmentioning

confidence: 99%

“…The tool included data from several sources, one of which is the cross-regional benchmarking tool used by the regions participating in Sveus. This data was primarily based on information from patient administrative systems and included algorithms for case mix adjustment [23]. This data was combined with clinical data from local EMRs to enable tracking of local performance on a wide array of different indicators.…”

Section: The Technology Supported Qi Programmementioning

confidence: 99%

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How can technology support quality improvement? Lessons learned from the adoption of an analytics tool for advanced performance measurement in a hospital unit

Tolf

Mesterton

Söderberg

et al. 2020

BMC Health Serv Res

Self Cite

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Background: Technology 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 technologysupported QI programme in an obstetric unit through a complexity informed framework. Methods: This 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 (NASS S) framework guided the data collection and analysis. Results: Staff 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. Conclusions: Technology 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.

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Section: Settingmentioning

confidence: 99%

Section: The Technology Supported Qi Programmementioning

confidence: 99%

How can technology support quality improvement? Lessons learned from the adoption of an analytics tool for advanced performance measurement in a hospital unit

Tolf

Mesterton

Söderberg

et al. 2020

BMC Health Serv Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…This group of injuries consisted predominantly of medical errors, as well as diagnostic errors and heterogenous, unclassifiable injuries. A Swedish study 30 across 21 hospitals estimates that, if all hospitals performed as well as the top 5 hospitals for each indicator, we could expect the following reductions in adverse outcomes: 23% for OASIS (obstetric anal sphincter injuries), 26% for postpartum haemorrhage, 30% for infections, and 42% for low 5-min Apgar score. This suggests that features of the healthcare system are important for mitigating the risk of complications during delivery.…”

Section: Discussionmentioning

confidence: 99%

Maternal and pregnancy predictive risk factors for having a compensated maternal injury claim: a Swedish nationwide cohort study

Ivert,

Holowko,

Liu

et al. 2023

Sci Rep

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To describe trends and identify maternal and pregnancy predictive risk factors for having a compensated claim for a maternal injury during delivery, as a proxy for having received suboptimal care. This nationwide retrospective cohort study included 1 754 869 births in Sweden between 2000 and 2016, including 4488 maternal injury claims filed with The National Swedish Patient Insurance Company (Löf), of which 1637 were compensated. Descriptive statistics on maternal and pregnancy characteristics, trends in filed/compensated claims over time, and distribution of compensated claims by clinical classification are presented. Characteristics associated with suboptimal care were identified using multivariable logistic regression, with mutual adjustment in the final model. Compensated claims were sorted into 14 clinical classifications (ICD-10 codes for main condition, injury, and causality). Overall, there was a two-fold increase in filed claims from 2000 to 2016, peaking in 2014. The rate of compensated claims only increased marginally, and 36.5% of filed claims were deemed avoidable. Perineal and pelvic floor injuries, as well as medical and diagnostic errors, were responsible for the majority of compensated claims. Women with a previous caesarean section, post term delivery, chronic or gestational disease, > 13 antenatal visits, or a multiple pregnancy had increased risk of having a compensated claim for a maternal injury during delivery. Understanding the risk factors for having a compensated maternal injury claim may guide health workers and maternity wards in improving the quality and organisation of care to reduce the risk of childbirth related injuries.

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“…This research resulted in the launch of a cross-regional analytics tool for case mix adjusted benchmarking of outcomes, resource use, and care processes. Birth care was one of the six patient groups initially addressed in the project (18)(19)(20). Moreover, the government decided to put improvement of birth care and women's health on the political agenda and pledged significant national funds to the regions between 2015 and 2022 (21).…”

Section: Settingmentioning

confidence: 99%

“…The tool included data from several sources, one of which is the cross-regional benchmarking tool used by the regions participating in Sveus. This data was primarily based on information from patient administrative systems and included algorithms for case mix adjustment (19). This data was combined with clinical data from local EMRs to enable tracking of local performance on a wide array of different indicators.…”

Section: The Technology Supported Qi Programmementioning

confidence: 99%

How Can Technology Support Quality Improvement? Lessons Learned from the adoption of an analytics tool for advanced performance measurement in a hospital unit.

Tolf

Mesterton

Söderberg

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background Technology 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 . Methods This 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. Results Staff 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. Conclusions Technology 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.

show abstract

Inter-hospital variations in health outcomes in childbirth care in Sweden: a register-based study

Cited by 6 publications

References 78 publications

How can technology support quality improvement? Lessons learned from the adoption of an analytics tool for advanced performance measurement in a hospital unit

How can technology support quality improvement? Lessons learned from the adoption of an analytics tool for advanced performance measurement in a hospital unit

Maternal and pregnancy predictive risk factors for having a compensated maternal injury claim: a Swedish nationwide cohort study

How Can Technology Support Quality Improvement? Lessons Learned from the adoption of an analytics tool for advanced performance measurement in a hospital unit.

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