Keeping psychologists in the driver’s seat: Four perspectives on quality improvement and clinical data registries.

135 Background: Data is essential to achieve meaningful quality improvement. A variety of commercial products are currently available for sophisticated data collection. However, data systems alone are not sufficient to improve quality. Additional resources are required in order to leverage electronic clinical data for meaningful improvement. This abstract outlines the necessary requirements and available methods for data-based oncology quality improvement using the experience of Intermountain Healthcare. Methods: The organizational components required for quality improvement are complex. Successful quality improvement begins with project feasibility, data availability and clinical leadership. Clinical processes are reviewed, data accuracy and availability are confirmed and clinical goals are established. Data collection, validation, and analysis are standardized across multiple facilities and providers. Data must be analyzed and presented in a way that clearly illustrates differences in current performance compared to quality goals, should be tracked over time to ensure real and sustainable progress, and must be combined with other improvement strategies to maximize impact. Results: Once quality reports are generated, a physician champion presents them to clinical staff along with education materials, national guidelines and current evidence from peer-reviewed literature. Clinicians are presented with individualized data comparing their personal performance to de-identified performance of their peers, the facility and the system. Providers are given updated data on a regular basis, the data system is monitored for outliers and the need for subsequent interventions, and additional metrics are tracked to ensure process changes don’t negatively impact quality in other areas. Conclusions: Oncology quality improvement requires both clinical and data infrastructure. Electronic clinical data systems are essential for quality improvement, but are not sufficient by themselves. Additional resources are required to capture, extract, validate and analyze clinical data. Appropriate use of these resources transforms existing electronic data systems into a powerful quality improvement tool.

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The DEDUCE Guided Query tool: Providing simplified access to clinical data for research and quality improvement

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2010.11.008 ◽

2011 ◽

Vol 44 (2) ◽

pp. 266-276 ◽

Cited By ~ 108

Author(s):

Monica M. Horvath ◽

Stephanie Winfield ◽

Steve Evans ◽

Steve Slopek ◽

Howard Shang ◽

...

Keyword(s):

Quality Improvement ◽

Clinical Data ◽

Query Tool

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A Standardized Implementation of Multicenter Quality Improvement Program of Very Low Birth Weight Newborns Could Significantly Reduce Admission Hypothermia and Improve Outcomes Within a Certain Region in China

10.21203/rs.3.rs-962766/v1 ◽

2021 ◽

Author(s):

Shu-yu Bi ◽

Yong-hui Yu ◽

Cong Li ◽

Ping Xu ◽

Hai-yan Xu ◽

...

Keyword(s):

Quality Improvement ◽

Birth Weight ◽

Clinical Data ◽

Gestational Age ◽

Clinical Characteristics ◽

Very Low Birth Weight ◽

Late Onset ◽

Improvement Program ◽

Two Phases ◽

The Impact

Abstract Background: Admission hypothermia (AH, <36.5℃) remains a major challenge for global neonate survival, especially in China. Due to high incidence of reginal AH, we developed a prospective multicenter quality improvement (QI) initiative to reduce regional AH and evaluate the impact on outcome among VLBW neonates.Methods: The study used sequential Plan - Do - Study - Act (PDSA) approach. Clinical data were collected prospectively with 5 NICUs from Sino-Northern Neonatal Network (SNN) in China. Bundle come into practice since January 1, 2019. The clinical data in pre-QI phase (January 1, 2018– December 31, 2018) were compared with post-QI phase (January 1, 2019–December 31, 2020). Clinical characteristics and outcomes data were analysed.Results: A total of 750 in-born VLBW infants were enrolled in the study, 270 in pre-QI period and 480 in post- QI period, respectively. There had no significant differences in clinical characteristics in two phases. Compared with pre-QI period, the percentage of AH decreased in the QI period (95.9 %vs 71.3%, P < 0.01). Admission mod-severe hypothermia (AMSH) was improved significantly, reduced by 38.5% after QI (68.5 %vs 30%, P < 0.01). Average admission temperature improved after QI [36.0 ˚C（35.8˚C，36.5˚C）vs 35.5 ˚C（35.2 ˚C，36.0 ˚C）, P < 0.01 ]. No significant increase in AH rate and thermal burns (0.4%VS 0%). Risks of mortality and late-onset neonatal sepsis (LOS) were significantly lower in post-QI period as compared to pre-QI period (aRR 0.19, 95% CI 0.09–0.39; aRR 0.55, 95% CI 0.41–0.80) whether adjusting for birth weight (BW), gestational age (GA)，small for gestational age (SGA), Apgar score at 5 min < 7.Conclusion：Implementation of multicenter thermoregulatory QI help in significant reduction of AH and AMSH of VLBW neonates within a certain area, which in turn can help to improve reginal neonatal outcomes. We gained a lot from QI, learned and explored a suitable method to continuous QI, this may provide reference for similar developing countries.

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