Background: Pressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. Objective: The objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. Methods: We conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results: A total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. Conclusions: There is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.

Background: The Normalization MeAsure Development (NoMAD) is a brief quantitative tool based on the Normalization Process Theory (NPT), which can measure the implementation process of new technologies and complex interventions. The aim of our study was to translate and culturally adapt the NoMAD into Chinese, and to evaluate the psychometric properties of the Chinese version of NoMAD. Methods: According to the NoMAD translation guideline, we undertook forward translation, backward translation, and compared these translations to get a satisfactory result, then we performed cognitive interviews to achieve cross-culture adaptation. And the psychometric properties of the final version were evaluated among clinical nurses who used the pressure injuries management system via WeChat mini-program at a tertiary hospital in northwestern China. Results: A total of 258 nurses were enrolled in our study, and the response rate was 92.1%. The Cronbach’s alpha of four dimensions were as follow: Coherence (0.768), Cognitive Participation (0.904), Collective Action (0.820), and Reflexive Monitoring (0.808). The overall internal consistency was 0.941. The confirmatory factor analysis results showed a good fit for its theoretical structure (CFI = 0.924, TLI = 0.910, RMSEA = 0.0079, SRMSR = 0.046, χ²/df = 2.61). The item-level content validity index ranged from 0.857 to 1, and the scale-level content validity index was 0.95. There were positive correlations between four constructs scores and three general normalization scores. Conclusions: The Chinese version of NoMAD is a reliable and valid tool to evaluate the implementation process of innovations.

Background: Falls had been identified as one of the nursing-sensitive indicators for nursing care in hospitals. With technological progress, health information systems make it possible for health care professionals to manage patient care better. However, there is a dearth of research on health information systems used to manage inpatient falls. Objective: This study aimed to design and implement a novel hospital-based fall risk management information system (FRMIS) to prevent inpatient falls and improve nursing quality. Methods: This implementation was conducted at a large academic medical center in central China. We established a nurse-led multidisciplinary fall prevention team in January 2016. The hospital’s fall risk management problems were summarized by interviewing fall-related stakeholders, observing fall prevention workflow and post–fall care process, and investigating patients' satisfaction. The FRMIS was developed using an iterative design process, involving collaboration among health care professionals, software developers, and system architects. We used process indicators and outcome indicators to evaluate the implementation effect. Results: The FRMIS includes a fall risk assessment platform, a fall risk warning platform, a fall preventive strategies platform, fall incident reporting, and a tracking improvement platform. Since the implementation of the FRMIS, the inpatient fall rate was significantly lower than that before implementation (P<.05). In addition, the percentage of major fall-related injuries was significantly lower than that before implementation. The implementation rate of fall-related process indicators and the reporting rate of high risk of falls were significantly different before and after system implementation (P<.05). Conclusions: The FRMIS provides support to nursing staff in preventing falls among hospitalized patients while facilitating process control for nursing managers.