Table 3 Description of subcategories linked to each domain of the NASSS framework

Condition (D1)

Broad patient population including both high and low risk patients with diverse background.

Large birth clinic also accepting patients from other regions.

Technology (D2)

The platform enabled staff to easily understand data and to gain new knowledge; prior understanding of statistics was helpful.

The tool was easily accessible.

Timely feedback of data made it more relevant for QI than historical data previously used.

Case mix adjustment made the data more relevant for QI.

Highly detailed data was important for its use in QI.

Lack of detail in certain data in the tool resulted in the need of additional data collection from other sources.

Adaptation of the platform to meet future needs was deemed possible.

Data was generally considered reliable and credible. However, use of data led to increased awareness of differences in measurement for certain variables which led to discussion about data quality and coding.

Collaboration and a mutual learning process with the platform supplier enabled the continuous adaptation of the platform tool to the clinic’s specific needs.

Value proposition (D3)

The cross-regional Sveus report created an awareness of around differences in performance and the possibility of using data for QI purposes.

Previously used data was not suitable for QI due to large time lag and poor accessibility.

Adopter system (D4)

The clinic had an established culture of working interprofessionally.

Interprofessional teams were thought to create unity around improvements and lead to better care for patients.

Increased use of measurable data could create excessive focus on risk groups over “normal” patient groups, groups over the individual patient, and measurable aspects over unmeasurable aspects of care.

The project did not require any significant changes in staff roles but led to the need of development of some new skills in the clinical work.

There was an ambition of evolving staff’s role in using data independently, but lack of hindered this development.

Data visualized the need for change and improvements in results and triggered a dialogue which motivated staff to further improve.

Data united staff around a common understanding of the current situation and emphasized the need for change.

Better understanding of data could improve information to patients.

Organization (D5)

Data was not available for staff who were not involved in QI.

Staff not involved in QI had little insight into the innovation and the work done within QI teams.

Staff not involved in QI were involved only as recipients of directives decided within QI teams.

Even though the ambition was that anyone should access the data, in practice the managers and staff involved in QI were the ones who used it and then presented it to the staff.

An already established digital way of working facilitated the use of the tool.

A clear mandate was perceived to be needed to conduct QI.

A long learning process was required for managers to understand the tool before beginning its implementation in the clinic.

Use of the tool was promoted by managers through creating a curiosity and demand for the information, which was intended to secure longevity of the initiative

Engaged manager enabled the implementation of the project.

Recruitment to QI teams was based on expressed interest or decided by managers.

Lack of time and difficulty scheduling limited staff’s ability to work with QI and use the data.

Use of data led to improvements in data registration in order to improve data quality.

Wider System (D6)

Data improved communication around patients with other sections of the women’s clinic, the neonatal clinic and external actors.

Medial discussion about the quality of birth care caused concern amongst patients

The Region had a goal to reduce infections, but this was not important for the initiation of the QI programme.

A hospital-wide programme on VBHC supported the implementation of the tool to some extent.