Buurtzorg is a nurse-led, nurse-run organization of self-managed teams that provide home care to patients in their neighborhoods. Championing humanity over bureaucracy, autonomous teams work with primary care providers, community supports, and family resources to bring patients to optimal functioning as quickly as possible. The award-winning organization grew out of a common sense approach based on principles of trust, autonomy, creativity, simplicity, and collaboration. These organizational principles translate into highly effective and efficient care, satisfied patients, and enthusiastic nurses. The model is being replicated worldwide, with teams starting in Minnesota, Sweden, Japan, and other countries.

Background: Electronic health records (EHRs) provide a clinical view of patient health. EHR data are becoming available in large data sets and enabling research that will transform the landscape of healthcare research. Methods are needed to incorporate wellbeing dimensions and strengths in large data sets. The purpose of this study was to examine the potential alignment of the Wellbeing Model with a clinical interface terminology standard, the Omaha System, for documenting wellbeing assessments. Objective: To map the Omaha System and Wellbeing Model for use in a clinical EHR wellbeing assessment and to evaluate the feasibility of describing strengths and needs of seniors generated through this assessment. Methods: The Wellbeing Model and Omaha System were mapped using concept mapping techniques. Based on this mapping, a wellbeing assessment was developed and implemented within a clinical EHR. Strengths indicators and signs/symptoms data for 5 seniors living in a residential community were abstracted from wellbeing assessments and analyzed using standard descriptive statistics and pattern visualization techniques. Results: Initial mapping agreement was 93.5%, with differences resolved by consensus. Wellbeing data analysis showed seniors had an average of 34.8 (range=22–49) strengths indicators for 22.8 concepts. They had an average of 6.4 (range=4–8) signs/symptoms for an average of 3.2 (range=2–5) concepts. The ratio of strengths indicators to signs/symptoms was 6:1 (range 2.8–9.6). Problem concepts with more signs/symptoms had fewer strengths. Conclusion: Together, the Wellbeing Model and the Omaha System have potential to enable a whole-person perspective and enhance the potential for a wellbeing perspective in big data research in healthcare.

A nurse-led model of care has revolutionized home care in the Netherlands.

Visualization is a Big Data method for detecting and validating previously unknown and hidden patterns within large data sets. This study used visualization techniques to discover and test novel patterns in public health nurse (PHN)–client–risk–intervention–outcome relationships. To understand the mechanism underlying risk reduction among high risk mothers, data representing complex social interventions were visualized in a series of three steps, and analyzed with other important contextual factors using standard descriptive and inferential statistics. Overall, client risk decreased after clients received personally tailored PHN services. Clinically important and unique PHN–client–risk–intervention–outcome patterns were discovered through pattern detection using streamgraphs, heat maps, and parallel coordinates techniques. Statistical evaluation validated that PHN intervention tailoring leads to improved client outcomes. The study demonstrates the importance of exploring data to discover ways to improve care quality and client outcomes. Further research is needed to examine additional factors that may influence PHN–client–risk–intervention–outcome patterns, and to test these methods with other data sets.