A prototype of knowledge-based patient safety event reporting and learning system

Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review

Objectives

Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases.

Materials and Methods

We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions.

Results

We identified 35 eligible studies and classified in three groups: psychological disorder (n = 22), eye diseases (n = 6), and others (n = 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications.

Conclusion

More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.

Creating a database for health IT events via a hybrid deep learning model

OBJECTIVE

The use of poorly designed and improperly implemented health information technology (HIT) may compound risks because it can disrupt established work patterns and encourage workarounds. Analyzing and learning from HIT events could reduce the risks and improve safety but are limited by accessible HIT event reports. In this study, we propose a hybrid deep learning model to identify HIT event reports from the FDA resource and thus establish the first publicly accessible database for HIT event reports.

MATERIALS AND METHODS

6994 samples (3521 HIT and 3473 non-HIT events) extracted from the FDA MAUDE database were employed to assess nine individual and 120 hybrid models on the task of HIT identification. The optimal model was evaluated on an independent dataset prior to its application for establishing the HIT event database.

RESULTS

The hybrid model consisting of logistic regression, CNN, and Hierarchical RNN (ACC = 0.903, AUC = 0.954, F₁ score = 0.876) is superior to all the other models. The causes of errors include lack of root cause (72.3%), short descriptions (19.7%), and model undertrained (8.0%). The accuracy of the hybrid model on an independent dataset is reported as 0.862. We applied the optimal model to the entire MAUDE database (1991-2018) and generated an HIT event database with 48,997 reports.

CONCLUSION

The first HIT event database contains 48,997 reports with an annual growth rate of 10% (~5000 reports). The strategy of HIT event identification and establishment of the database could help healthcare professionals describe, understand, integrate the events and propose solutions in the context of a fuller spectrum of HIT events.

Process Control Charts in Falls Prevention: The Experience of the Local Healthcare Authority of Romagna, Italy

Patient safety is a great concern of healthcare institutions and the correct reporting and management of adverse events is a key element for supporting safety improvement efforts. Patient falls are the most frequent adverse event in hospitals and often cause serious patient outcomes. In this work, we describe the experience of the Local Healthcare Authority of Romagna, Italy that, within the framework of a quality and safety improvement programme, designed, developed and implemented a desktop application for monitoring inpatient falls. A multidisciplinary team created a software tool based on R, an open source software for statistical computing that, appropriately combined with the existing hospital information system, is used to obtain Shewhart u-control charts for monitoring the monthly fall rates. The tool had been implemented in twenty nine hospital units. The results indicate that the proposed application gave a valuable contribution in the safety improvement activities. Its usefulness extended beyond the “safety problem” as it also enabled hospital managers to identify a number of critical issues in data collection. As a result, where necessary, improvement actions had been implemented. Furthermore, the use of open source software led to a considerable cost reduction and facilitated customization of the software tool.