Factors driving differences in the adoption of quality management practices among hospitals: A two-phase, sequential mixed-methods analysis

BACKGROUND

Although all hospitals aim to deliver high-quality care, there is considerable variation in their adoption of quality management (QM) practices. Organizational and environmental factors are known to drive strategic decision-making in hospitals, but their impact on the adoption of QM practices remains unclear.

PURPOSE

Our study aims to identify multiple organizational and environmental factors that explain variation in the adoption of QM practices among hospitals and to explore mechanisms underlying these relationships.

METHODOLOGY

We conducted a two-phase, sequential mixed-methods study of German acute care hospitals. The quantitative phase used between-effects regressions to identify factors explaining variation in the number of QM practices adopted by hospitals from 2015 to 2019. The qualitative phase used semistructured interviews with quality managers to gain in-depth insights.

RESULTS

The number of QM practices adopted by a hospital was significantly associated with factors like hospital size and the presence of an emergency department or QM steering committee. Our qualitative findings highlighted potential mechanisms such as the presence of an emergency department serving as a proxy for organizational complexity or urgency of case-mix.

CONCLUSION

We provide an overview of factors driving QM adoption in hospitals, extending beyond the focus on single factors in previous research. Future studies could explore additional factors highlighted by our interviewees.

PRACTICE IMPLICATIONS

Our results can inform interventions to strengthen QM in hospitals and guide future research on this topic.

How social networks influence the local implementation of initiatives developed in quality improvement collaboratives in health care: A qualitative process study

BACKGROUND

Quality improvement collaboratives (QICs) have facilitated cross-organizational knowledge exchange in health care. However, the local implementation of many quality improvement (QI) initiatives continues to fail, signaling a need to better understand the contributing factors. Organizational context, particularly the role of social networks in facilitating or hindering implementation within organizations, remains a potentially critical yet underexplored area to addressing this gap.

PURPOSE

We took a dynamic process perspective to understand how QI project managers' social networks influence the local implementation of QI initiatives developed through QICs.

METHODOLOGY

We explored the case of a QIC by triangulating data from an online survey, semistructured interviews, and archival documents from 10 organizations. We divided implementation into four stages and employed qualitative text analysis to examine the relationship between three characteristics of network structure (degree centrality, network density, and betweenness centrality) and the progress of each QI initiative.

RESULTS

The progress of QI initiatives varied considerably among organizations. The transition between stages was influenced by all three network characteristics to varying degrees, depending on the stage. Project managers whose QI initiatives progressed to advanced stages of implementation had formed ad hoc clusters of colleagues passionate about the initiatives.

CONCLUSION

Implementing QI initiatives appears to be facilitated by the formation of clusters of supportive individuals within organizations; this formation requires high betweenness centrality and high network density.

PRACTICE IMPLICATIONS

Flexibly modifying specific network characteristics depending on the stage of implementation may help project managers advance their QI initiatives, achieving more uniform results from QICs.

The impact of integrated care on health care utilization and costs in a socially deprived urban area in Germany: A difference-in-differences approach within an event-study framework

We investigated the impact of an integrated care initiative in a socially deprived urban area in Germany. Using administrative data, we empirically assessed the causal effect of its two sub-interventions, which differed by the extent to which their instruments targeted the supply and demand side of healthcare provision. We addressed confounding using propensity score matching via the Super Learner machine learning algorithm. For our baseline model, we used a two-way fixed-effects difference-in-differences approach to identify causal effects. We then employed difference-in-differences analyses within an event-study framework to explore the heterogeneity of treatment effects over time, allowing us to disentangle the effects of the sub-interventions and improve causal interpretation and generalizability. The initiative led to a significant increase in hospital and emergency admissions and non-hospital outpatient visits, as well as inpatient, non-hospital outpatient, and total costs. Increased utilization may indicate that the intervention improved access to care or identified unmet need.

What is the impact of introducing a non-clinical community health advice and navigation service on the demand for primary care in socially deprived areas? Evidence from an observational panel study with difference-in-differences design

OBJECTIVES

To examine the effect of introducing a non-clinical community health advice and navigation service on the demand for primary care in a socially deprived area.

DESIGN

Observational panel study with difference-in-differences design. We conducted fixed-effects negative binomial regressions to compare changes in the number of visits to general practitioners (GPs) in individuals who visited the health advice and navigation service and a matched control group of individuals who did not visit the service. In addition, we analysed the effects of visiting the service multiple times.

SETTING AND PARTICIPANTS

Our empirical setting is a socially deprived urban area in Germany with a multicultural population of about 110 000 people. Our analyses are based on patient data (N=1044) from a non-clinical community health advice and navigation service and from two statutory health insurers.

OUTCOME MEASURES

Patient demand for primary care measured as the number of visits to GPs before and after the first visit to the health advice and navigation service.

RESULTS

Visiting the service for the first time significantly decreased the number of GP visits compared with the control group (β=-0.113, p<0.1). Each additional visit to the service, however, significantly decreased the effect of the first visit (β=0.037, p<0.05).

CONCLUSIONS

Our findings suggest that non-clinical community health advice and navigation services can serve as a low-threshold first point of contact. As first point contact, such services might possibly reduce the burden of primary care physicians in socially deprived areas. At the same time, such services might function as a gateway to accessing the health system, reducing unmet care needs and stimulate demand. Ongoing counselling in the service can identify medical needs that require a physician visit. Our findings may be useful for policymakers and healthcare leaders seeking to reduce the demand on the primary care workforce and can stimulate further research in this area.

Social connections at work and mental health during the first wave of the COVID-19 pandemic: Evidence from employees in Germany

Empirical evidence on the social and psychological impact of the COVID-19 pandemic in the workplace and the resulting consequences for the mental health of employees is lacking. As a result, research on this subject is urgently needed to develop appropriate countermeasures. This study builds on Person-Environment fit theory to investigate social connections at work and mental health during the first wave of the COVID-19 pandemic. It analyses employees’ needs for social connections and how social connections affect different mental health measures. Data were collected in May 2020 in an online survey of employees across Germany and analysed using response surface analysis. Mental health was measured as positive mental health and mental health disorders. Social connections were measured as social support and social interactions. 507 employees participated in the survey and more than one third reported having less social support and social interaction at work than they desired (p < .001). This was associated with a decrease in mental health. In contrast, having more than the desired amount of social support was associated with a decrease, and having more than the desired amount of social interaction with an increase, in mental health. This study provides important early evidence on the impact of the first wave of the COVID-19 pandemic in the workplace. With it, we aim to stimulate further research in the field and provide early evidence on the potential mental health consequences of social distancing–while also opening avenues to combat them.

Factors associated with patient activation in a socially deprived population: Evidence from Germany

OBJECTIVE

Patient activation has been identified as a crucial determinant of health, but little is known about its own determinants, particularly in low socioeconomic status populations. To address this research gap, we analyzed factors that might explain variation in patient activation in such a population.

METHODS

We conducted a cross-sectional patient survey (n = 582) in a low socioeconomic status urban district in Germany in 2017. Using multivariate linear regressions, we examined the association between patient activation and a range of psychological, sociodemographic, and health-related factors. To assess the relative importance of these factors, we used dominance analysis.

RESULTS

Our results suggest that age, employment status, linguistic acculturation, health status, and self-efficacy were significantly associated with patient activation. Dominance analysis indicated that self-efficacy was the most important factor explaining variation in patient activation.

CONCLUSIONS

Age, employment status, linguistic acculturation, health status, and self-efficacy are important determinants of patient activation.

PRACTICE IMPLICATIONS

Our results can inform decision makers about approaches for more targeted and effective interventions to improve patient activation in low socioeconomic status populations. Much might be gained by investing in interventions that focus on age, employment status, linguistic acculturation, and health status. Interventions that improve self-efficacy may represent a particularly promising approach.

Once is rarely enough: can social prescribing facilitate adherence to non-clinical community and voluntary sector health services? Empirical evidence from Germany

Background

Non-clinical health interventions provided by the voluntary and community sector can improve patients’ health and well-being and reduce pressure on primary and secondary care, but only if patients adhere to them. This study provides novel insights into the impact of doctor referrals to such services, known as social prescribing, on patients’ adherence to them.

Methods

Using a negative binomial model, we analysed electronic visitor records from a community health advice and navigation service in Germany between January 2018 and December 2019 to determine whether social prescribing was associated with greater adherence to the service (measured in terms of return visits) compared to patients who self-referred. We also explored whether this effect differed according to patient characteristics.

Results

Based on 1734 observations, we found that social prescribing was significantly associated with a greater number of return visits compared to patient self-referrals (p < 0.05). For patients who visited the service because of psychological concerns, the effect of social prescribing was lower. For all other patient characteristics, the effect remained unchanged, suggesting relevance to all other patient groups.

Conclusions

The results of our study indicate that social prescribing may be an effective way to facilitate adherence to non-clinical community and voluntary sector health services. This knowledge is important for policy makers who are deciding whether to implement or expand upon social prescribing schemes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-020-09927-4.