Predicting future state for adaptive clinical pathway management

Optimized continuous homecare provisioning through distributed data-driven semantic services and cross-organizational workflows

BACKGROUND

In healthcare, an increasing collaboration can be noticed between different caregivers, especially considering the shift to homecare. To provide optimal patient care, efficient coordination of data and workflows between these different stakeholders is required. To achieve this, data should be exposed in a machine-interpretable, reusable manner. In addition, there is a need for smart, dynamic, personalized and performant services provided on top of this data. Flexible workflows should be defined that realize their desired functionality, adhere to use case specific quality constraints and improve coordination across stakeholders. User interfaces should allow configuring all of this in an easy, user-friendly way.

METHODS

A distributed, generic, cascading reasoning reference architecture can solve the presented challenges. It can be instantiated with existing tools built upon Semantic Web technologies that provide data-driven semantic services and constructing cross-organizational workflows. These tools include RMLStreamer to generate Linked Data, DIVIDE to adaptively manage contextually relevant local queries, Streaming MASSIF to deploy reusable services, AMADEUS to compose semantic workflows, and RMLEditor and Matey to configure rules to generate Linked Data.

RESULTS

A use case demonstrator is built on a scenario that focuses on personalized smart monitoring and cross-organizational treatment planning. The performance and usability of the demonstrator's implementation is evaluated. The former shows that the monitoring pipeline efficiently processes a stream of 14 observations per second: RMLStreamer maps JSON observations to RDF in 13.5 ms, a C-SPARQL query to generate fever alarms is executed on a window of 5 s in 26.4 ms, and Streaming MASSIF generates a smart notification for fever alarms based on severity and urgency in 1539.5 ms. DIVIDE derives the C-SPARQL queries in 7249.5 ms, while AMADEUS constructs a colon cancer treatment plan and performs conflict detection with it in 190.8 ms and 1335.7 ms, respectively.

CONCLUSIONS

Existing tools built upon Semantic Web technologies can be leveraged to optimize continuous care provisioning. The evaluation of the building blocks on a realistic homecare monitoring use case demonstrates their applicability, usability and good performance. Further extending the available user interfaces for some tools is required to increase their adoption.

Impact of clinical pathway implementation satisfaction, work engagement, and hospital-patient relationship on quality of care in Chinese nurses

AIMS

This study aimed to investigate how clinical pathway implementation satisfaction, work engagement, and hospital-patient relationship impact the quality of care that is provided by nurses in public hospitals.

BACKGROUND

Clinical pathways are recommended as a form of quality improvement by broader healthcare systems and are widely used in the world. Nurses are the most involved group of healthcare professionals in the implementation of clinical pathways in public hospitals. So, it is important to investigate how their satisfaction with the process affects the quality of care they provide and influencing factors.

METHODS

This descriptive cross-sectional study surveyed nurses practicing across seven tertiary public hospitals in Sichuan Province, China, online. The survey consisted of a questionnaire for the general characteristics of the participants and four Chinese maturity scales validated by previous studies: clinical pathway implementation satisfaction scale, work engagement scale, hospital-patient relationship perception scale, and quality of care scale. The bootstrap method was used to test a moderated mediation model using Hayes' PROCESS macro models 4 and 8. We followed STROBE guidelines to prepare the study report.

RESULTS

A total of 880 nurses filled out the questionnaires, 821 of which were regarded as valid. Clinical pathway implementation satisfaction had a positive effect on quality of care (B = 0.873, P < 0.001). Work engagement played a mediation role between nurses' clinical pathway implementation satisfaction and the quality of care (effect = 0.080, Boot 95% CI = [0.023, 0.142]). This mediation model was moderated by the hospital-patient relationship (P < 0.01).

CONCLUSION

Clinical pathway implementation satisfaction may enhance the quality of care by work engagement of nurses. Moreover, a good hospital-patient relationship can enhance the positive impact of nurses' satisfaction on work engagement and health service quality.

IMPLICATIONS FOR NURSING AND NURSING POLICY

Public hospital managers need to pay attention to nurses' evaluation of and perceptions toward clinical pathway implementation and then take corresponding measures to improve their satisfaction to enhance the quality of care. At the same time, the government, society, and hospitals also need to foster good hospital-patient relationships to ensure that nurses have a high level of work engagement that aids in providing high-quality care services.

Satisfaction with clinical pathway implementation versus job performance of clinicians: empirical evidence on the mediating role of work engagement from public hospitals in Sichuan, China

BACKGROUND

The job performance of clinicians is a clear indicator of both hospital capacity and the level of hospital service. It plays a crucial role in maintaining the effectiveness and quality of medical care. Clinical pathways are a systematic method of quality improvement successfully recommended by broader healthcare systems. Since clinicians play a key role in implementing clinical pathways in public hospitals, this study aims to investigate the effect of the satisfaction of clinicians in public hospitals with clinical pathway implementation on their job performance.

METHODS

A cross-sectional study design was used. Questionnaires were administered online. A total of 794 clinicians completed the questionnaires in seven tertiary public hospitals in Sichuan Province, China, of which 723 were valid for analysis. Questionnaires contained questions on social demographic characteristics, satisfaction with clinical pathway implementation, work engagement, and job performance. Structural Equation Model (SEM) was used to test the hypotheses.

RESULTS

The satisfaction of clinicians in public hospitals with clinical pathway implementation was significantly positively correlated with work engagement (r = 0.570, P < 0.01) and job performance (r = 0.522, P < 0.01). A strong indirect effect of clinicians' satisfaction with clinical pathway implementation on job performance mediated by work engagement was observed, and the value of this effect was 0.383 (boot 95%CI [0.323, 0.448]).

CONCLUSION

The satisfaction of clinicians in public hospitals with clinical pathway implementation not only directly influences their job performance, but also indirectly affects it through the mediating variable of work engagement. Therefore, managers of public hospitals need to pay close attention to clinicians' evaluation and perception of the clinical pathway implementation. This entails taking adequate measures, such as providing strong organizational support and creating a favorable environment for the clinical pathway implementation. Additionally, focusing on teamwork to increase clinicians' satisfaction can further enhance job performance. Furthermore, managers should give higher priority to increasing employees' work engagement to improve clinicians' job performance.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study

INTRODUCTION

Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models.

MATERIAL AND METHODS

We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed.

RESULTS

A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods.

CONCLUSIONS

The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.