A comparison of machine learning algorithms in predicting COVID-19 prognostics.

ML algorithms are used to develop prognostic and diagnostic models and so to support clinical decision-making. This study uses eight supervised ML algorithms to predict the need for intensive care, intubation, and mortality risk for COVID-19 patients. The study uses two datasets: (1) patient demographics and clinical data (n = 11,712), and (2) patient demographics, clinical data, and blood test results (n = 602) for developing the prediction models, understanding the most significant features, and comparing the performances of eight different ML algorithms. Experimental findings showed that all prognostic prediction models reported an AUROC value of over 0.92, in which extra tree and CatBoost classifiers were often outperformed (AUROC over 0.94). The findings revealed that the features of C-reactive protein, the ratio of lymphocytes, lactic acid, and serum calcium have a substantial impact on COVID-19 prognostic predictions. This study provides evidence of the value of tree-based supervised ML algorithms for predicting prognosis in health care.

A system safety approach to assessing risks in the sepsis treatment process.

In healthcare, most accidents occur as a result of inadequate interactions between system components rather than component failures. In such cases traditional risk analysis methods are of limited use for analysing system safety, so methods such as Systems Theoretic Process Analysis (STPA) and the Functional Resonance Analysis Method (FRAM) have been developed. This study uses STPA to assess risks in the sepsis treatment process, discusses the potential value STPA adds and compares the results of STPA with the results of another study that used FRAM. The findings indicate that STPA and FRAM have different strengths which reflect the different scientific approaches behind these two methods. FRAM facilitates an in-depth understanding of a system, while STPA allows for more comprehensive risk analysis by identifying more risks, scenarios and safety recommendations. Nevertheless, it is reasonable to say that not only does STPA provide more comprehensive risk analysis; its terminology and philosophy are also closer to the current safety management applications employed in complex systems.

Systems approach to health service design, delivery and improvement: a systematic review and meta-analysis.

OBJECTIVES: To systematically review the evidence base for a systems approach to healthcare design, delivery or improvement. DESIGN: Systematic review with meta-analyses. METHODS: Included were studies in any patients, in any healthcare setting where a systems approach was compared with usual care which reported quantitative results for any outcomes for both groups. We searched Medline, Embase, HMIC, Health Business Elite, Web of Science, Scopus, PsycINFO and CINAHL from inception to 28 May 2019 for relevant studies. These were screened, and data extracted independently and in duplicate. Study outcomes were stratified by study design and whether they reported patient and/or service outcomes. Meta-analysis was conducted with Revman software V.5.3 using ORs-heterogeneity was assessed using I2 statistics. RESULTS: Of 11 405 records 35 studies were included, of which 28 (80%) were before-and-after design only, five were both before-and-after and concurrent design, and two were randomised controlled trials (RCTs). There was heterogeneity of interventions and wide variation in reported outcome types. Almost all results showed health improvement where systems approaches were used. Study quality varied widely. Exploratory meta-analysis of these suggested favourable effects on both patient outcomes (n=14, OR=0.52 (95% CI 0.38 to 0.71) I2=91%), and service outcomes (n=18, OR=0.40 (95% CI 0.31 to 0.52) I2=97%). CONCLUSIONS: This study suggests that a systems approaches to healthcare design and delivery results in a statistically significant improvement to both patient and service outcomes. However, better quality studies, particularly RCTs are needed.PROSPERO registration numberCRD42017065920.

Evaluating inputs of failure modes and effects analysis in identifying patient safety risks.

PURPOSE: There is a growing awareness on the use of systems approaches to improve patient safety and quality. While earlier studies evaluated the validity of such approaches to identify and mitigate patient safety risks, so far only little attention has been given to their inputs, such as structured brainstorming and use of system mapping approaches (SMAs), to understand their impact in the risk identification process. To address this gap, the purpose of this paper is to evaluate the inputs of a well-known systems approach, failure modes and effects analysis (FMEA), in identifying patient safety risks in a real healthcare setting. DESIGN/METHODOLOGY/APPROACH: This study was conducted in a newly established adult attention deficit hyperactivity disorder service at Cambridge and Peterborough Foundation Trust in the UK. Three stakeholders of the chosen service together with the facilitators conducted an FMEA exercise along with a particular system diagram that was initially found as the most useful SMA by eight stakeholders of the service. FINDINGS: In this study, it was found that the formal structure of FMEA adds value to the risk identification process through comprehensive system coverage with the help of the system diagram. However, results also indicates that the structured brainstorming refrains FMEA participants from identifying and imagining new risks since they follow the process predefined in the given system diagram. ORIGINALITY/VALUE: While this study shows the potential contribution of FMEA inputs, it also suggests that healthcare organisations should not depend solely on FMEA results when identifying patient safety risks; and therefore prioritising their safety concerns.

A framework to support risk assessment in hospitals.

QUALITY PROBLEM OR ISSUE: A number of challenges have been identified with current risk assessment practice in hospitals, including: a lack of consultation with a sufficiently wide group of stakeholders; a lack of consistency and transparency; and insufficient risk assessment guidance. Consequently, risk assessment may not be fully effective as a means to ensure safety. INITIAL ASSESSMENT: We used a V system developmental model, in conjunction with mixed methods, including interviews and document analysis to identify user needs and requirements. CHOICE OF SOLUTION: One way to address current challenges is through providing good guidance on the fundamental aspects of risk assessment. We designed a risk assessment framework, comprising: a risk assessment model that depicts the main risk assessment steps; risk assessment explanation cards that provide prompts to help apply each step; and a risk assessment form that helps to systematize the risk assessment and document the findings. IMPLEMENTATION: We conducted multiple group discussions to pilot the framework through the use of a representative scenario and used our findings for the user evaluation. EVALUATION: User evaluation was conducted with 10 participants through interviews and showed promising results. LESSONS LEARNED: While the framework was recommended for use in practice, it was also proposed that it be adopted as a training tool. With its use in risk assessment, we anticipate that risk assessments would lead to more effective decisions being made and more appropriate actions being taken to minimize risks. Consequently, the quality and safety of care delivered could be improved.

A Review of Risk Matrices Used in Acute Hospitals in England.

In healthcare, patient safety has received substantial attention and, in turn, a number of approaches to managing safety have been adopted from other high-risk industries. One of these has been risk assessment, predominantly through the use of risk matrices. However, while other industries have criticized the design and use of these risk matrices, the applicability of such criticism has not been investigated formally in healthcare. This study examines risk matrices as used in acute hospitals in England and the guidance provided for their use. It investigates the applicability of criticisms of risk matrices from outside healthcare through a document analysis of the risk assessment policies, procedures, and strategies used in English hospitals. The findings reveal that there is a large variety of risk matrices used, where the design of some might increase the chance of risk misprioritization. Additionally, findings show that hospitals may provide insufficient guidance on how to use risk matrices as well as what to do in response to the existing criticisms of risk matrices. Consequently, this is likely to lead to variation in the quality of risk assessment and in the subsequent deployment of resources to manage the assessed risk. Finally, the article outlines ways in which hospitals could use risk matrices more effectively.