Effects of routine collection of patient-reported outcomes on patient health outcomes in oncology settings: A systematic review

Patient-Reported Outcome Measures as an Intervention: A Comprehensive Overview of Systematic Reviews on the Effects of Feedback

OBJECTIVES

Patient-reported outcome measures (PROMs) have emerged as a promising approach to involve patients in their treatment process. Beyond serving as outcome measures, PROMs can be applied to provide feedback to healthcare providers and patients, thereby offering valuable insights that can improve health outcomes and care processes. This overview offers a comprehensive synthesis of the effects of PROM feedback, contributing to the evidence-based discussion on PROMs' potential to enhance patient care.

METHODS

Following Cochrane Collaboration recommendations, this overview included literature reviews across diverse treatment areas, investigating the impact of PROM feedback on patient health outcomes (including quality of life, symptoms, or survival) and care process outcomes (including communication, symptom identification, or clinical practice). The methodological quality of the evidence was assessed with a modified version of A Measurement Tool to Assess Systematic Reviews 2, and the potential overlap of primary studies was quantified. Results were narratively synthesized.

RESULTS

Forty reviews grouped into 4 categories of treatment areas were included. Overall, their methodological quality was low. The overall overlap of primary studies was 2.2%, reaching up to 15.7% within specific treatment areas. The results indicate that PROM feedback may enhance the quality-of-care processes, whereas its effects on patient health outcomes remained less conclusive.

CONCLUSIONS

PROM feedback positively influences the interaction between physicians and patients across the included treatment areas. Further research is needed to comprehend the trickle-down effects of PROM feedback and how to enhance its potential in yielding health benefits for patients.

Analysis of the Use of Sample Size and Effect Size Calculations in a Temporomandibular Disorders Randomised Controlled Trial-Short Narrative Review

BACKGROUND/OBJECTIVES

Temporomandibular disorder (TMD) is the term used to describe a pathology (dysfunction and pain) in the masticatory muscles and temporomandibular joint (TMJ). There is an apparent upward trend in the publication of dental research and a need to continually improve the quality of research. Therefore, this study was conducted to analyse the use of sample size and effect size calculations in a TMD randomised controlled trial.

METHODS

The period was restricted to the full 5 years, i.e., papers published in 2019, 2020, 2021, 2022, and 2023. The filter article type-"Randomized Controlled Trial" was used. The studies were graded on a two-level scale: 0-1. In the case of 1, sample size (SS) and effect size (ES) were calculated.

RESULTS

In the entire study sample, SS was used in 58% of studies, while ES was used in 15% of studies.

CONCLUSIONS

Quality should improve as research increases. One factor that influences quality is the level of statistics. SS and ES calculations provide a basis for understanding the results obtained by the authors. Access to formulas, online calculators and software facilitates these analyses. High-quality trials provide a solid foundation for medical progress, fostering the development of personalized therapies that provide more precise and effective treatment and increase patients' chances of recovery. Improving the quality of TMD research, and medical research in general, helps to increase public confidence in medical advances and raises the standard of patient care.

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Backgrounds

With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers.

Aim

To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward.

Methods

Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords.

Results

According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement.

Conclusion

Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.