BACKGROUND: The global Human Resources for Health (HRH) strategy emphasizes the need to invest in HRH to meet population needs and improve the provision of quality health care services. In South Africa, dietitians are recognized as registered professionals who provide nutrition services. In this paper, we used 2 key steps (3 and 4) of the eight step World Health Organization (WHO) Workload Indicators of Staffing Need (WISN) methodology to determine the workload components and activity standards for dietitians at South African central and tertiary public hospitals.
METHODS: All (9) provincial nutrition managers (phase one) and 21 out of a total 22 head dietitians at central and tertiary public hospitals (phase two) participated in an online survey. In phase one, the provincial managers provided the job descriptions (JDs) of dietitians in their provinces, and the JDs were analyzed to determine the baseline workload components. In phase two, dietitians participated in a multi-stage Delphi process to reach consensus on workload components and activity standards. Consensus was deemed to be agreement of 70% or more, while the median of participants\' responses was used to obtain consensus on the activity standards.
RESULTS: The JDs of dietitians were a useful baseline for the consensus exercise as there were no other suitable source documents. The response rate was 100% for all three rounds of the Delphi survey. Dietitians reached agreement (consensus ≥ 70%) on 92% of proposed workload components and activity standards. Following the removal of duplicate and certain administrative activities, a total of 15 health, 15 support and 15 additional service activities with aligned activity standards resulted from the consensus exercise.
CONCLUSIONS: The Delphi technique was a suitable method for reaching agreement on workload components and activity standards for dietitians at South African central and tertiary public hospitals. The findings from this study can now be used to compile a standardized list of workload components and activity standards and ultimately to determine dietetic staffing needs for the central and tertiary public hospital level of care.

BACKGROUND: We evaluated the applicability of automated citation screening in developing clinical practice guidelines.
METHODS: We prospectively compared the efficiency of citation screening between the conventional (Rayyan) and semi-automated (ASReview software) methods. We searched the literature for five clinical questions (CQs) in the development of the Japanese Clinical Practice Guidelines for the Management of Sepsis and Septic Shock. Objective measurements of the time required to complete citation screening were recorded. Following the first screening round, in the primary analysis, the sensitivity, specificity, positive predictive value, and overall screening time were calculated for both procedures using the semi-automated tool as index and the results of the conventional method as standard reference. In the secondary analysis, the same parameters were compared between the two procedures using the final list of included studies after the second screening session as standard reference.
RESULTS: Among the five CQs after the first screening session, the highest and lowest sensitivity, specificity, and positive predictive values were 0.241 and 0.795; 0.991 and 1.000; and 0.482 and 0.929, respectively. In the secondary analysis, the highest sensitivity and specificity in the semi-automated citation screening were 1.000 and 0.997, respectively. The overall screening time per 100 studies was significantly shorter with semi-automated than with conventional citation screening.
CONCLUSIONS: The potential advantages of the semi-automated method (shorter screening time and higher discriminatory rate for the final list of studies) warrant further validation.

OBJECTIVE: To identify and prioritize a list of factors that contribute to the workload of the hospital at home (HaH) professionals.
METHODS: A qualitative methodology study performed between January and December 2019 in the 10 HAH units of the Basque Country. The data were obtained in 4phases: 1. Systematic literature search and review; 2. Expert group meeting; 3. Consensus method: Delphi technique (2 survey rounds) and nominal group meeting; 4. Meeting of the research team.
RESULTS: In the systematic literature search and review 85 factors were initially identified. These were reduced to 38 after the 8-person expert group meeting, in which 10 new factors were added. After the 2 Delphi rounds (106 and 57 professionals, respectively), 17 factors were maintained and 12 remained in doubt. The latter were evaluated at the nominal group meeting, consisting of 13 professionals who decided to eliminate 5 factors, include 3, and keep 3 as doubt. After the 8-person research team meeting, 14 potential factors were finally selected. They are related to the place of residence, the health state and social situation of the patients, as well as the health care provided at home.
CONCLUSIONS: The identified factors could serve for improving the organization and optimize the daily word of the HaH professionals.

Greater evaluations are needed to identify barriers or facilitators in nurses\' guidelines adherence. The current review aims to explore extrinsic and intrinsic factors impacting nurses\' compliance.
Mixed-method systematic review with a convergent approach, following the PRISMA checklist and the JBI Mixed Methods Review Methodological Guidance was conducted. MEDLINE, Embase, CINAHL were systematically searched, to find studies published between 2010 and 2021, including qualitative, quantitative or mixed-methods articles.
Sixty studies were included, and the major findings were analysed by aggregating them in two main themes: intrinsic and extrinsic factors. The intrinsic factors were: a) knowledge and skills; b) attitudes of health personnel; c) sense of belonging towards guidelines. The extrinsic factors were: a) organizational and environmental factors; b) workload; c) guidelines structure; d) patients and caregivers\' attitude.
The included studies report lack of resources, among environmental factors, as the main barrier perceived. Nurses, who are at the forefront in addressing the direct application of knowledge and skills to ensure patient safety, have a higher perception of this kind of barriers than other healthcare personnel. Potential facilitators emerged in the review are positive feedback and reinforcements at the workplace, either from the members of the team or from the leaders. Moreover, the level of active participation of the patient and caregiver could have a positive impact on nurses\' guidelines adherence. Guidelines implementation remains a complex process, resulting in a strong recommendation to support health policymakers and nursing leaders in implementing continuing education programs.

Industry 4.0 is the concept used to summarize the ongoing fourth industrial revolution, which is profoundly changing the manufacturing systems and business models all over the world. Collaborative robotics is one of the most promising technologies of Industry 4.0. Human-robot interaction and human-robot collaboration will be crucial for enhancing the operator\'s work conditions and production performance. In this regard, this enabling technology opens new possibilities but also new challenges. There is no doubt that safety is of primary importance when humans and robots interact in industrial settings. Nevertheless, human factors and cognitive ergonomics (i.e. cognitive workload, usability, trust, acceptance, stress, frustration, perceived enjoyment) are crucial, even if they are often underestimated or ignored. Therefore, this work refers to cognitive ergonomics in the design of human-robot collaborative assembly systems. A set of design guidelines has been developed according to the analysis of the scientific literature. Their effectiveness has been evaluated through multiple experiments based on a laboratory case study where different participants interacted with a low-payload collaborative robotic system for the joint assembly of a manufacturing product. The main assumption to be tested is that it is possible to improve the operator\'s experience and efficiency by manipulating the system features and interaction patterns according to the proposed design guidelines. Results confirmed that participants improved their cognitive response to human-robot interaction as well as the assembly performance with the enhancement of workstation features and interaction conditions by implementing an increasing number of guidelines.

In 2011, National Institute for Health and Care Excellence (NICE) guidelines recommended the routine use of out-of-office blood pressure (BP) monitoring for the diagnosis of hypertension. These changes were predicted to reduce unnecessary treatment costs and workload associated with misdiagnosis.
To assess the impact of guideline change on rates of hypertension-related consultation in general practice.
A retrospective open cohort study in adults registered with English general practices contributing to the Clinical Practice Research Datalink between 1 April 2006 and 31 March 2017.
The primary outcome was the rate of face-to-face, telephone, and home visit consultations related to hypertension with a GP or nurse. Age- and sex-standardised rates were analysed using interrupted time-series analysis.
In 3 937 191 adults (median follow-up 4.2 years) there were 12 253 836 hypertension-related consultations. The rate of hypertension-related consultation was 71.0 per 100 person-years (95% confidence interval [CI] = 67.8 to 74.2) in April 2006, which remained flat before 2011. The introduction of the NICE hypertension guideline in 2011 was associated with a change in yearly trend (change in trend -3.60 per 100 person-years, 95% CI = -5.12 to -2.09). The rate of consultation subsequently decreased to 59.2 per 100 person-years (95% CI = 56.5 to 61.8) in March 2017. These changes occurred around the time of diagnosis, and persisted when accounting for wider trends in all consultations.
Hypertension-related workload has declined in the last decade, in association with guideline changes. This is due to changes in workload at the time of diagnosis, rather than reductions in misdiagnosis.

Artificial intelligence (AI) has made impressive progress over the past few years, including many applications in medical imaging. Numerous commercial solutions based on AI techniques are now available for sale, forcing radiology practices to learn how to properly assess these tools. While several guidelines describing good practices for conducting and reporting AI-based research in medicine and radiology have been published, fewer efforts have focused on recommendations addressing the key questions to consider when critically assessing AI solutions before purchase. Commercial AI solutions are typically complicated software products, for the evaluation of which many factors are to be considered. In this work, authors from academia and industry have joined efforts to propose a practical framework that will help stakeholders evaluate commercial AI solutions in radiology (the ECLAIR guidelines) and reach an informed decision. Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services. KEY POINTS: • Numerous commercial solutions based on artificial intelligence techniques are now available for sale, and radiology practices have to learn how to properly assess these tools. • We propose a framework focusing on practical points to consider when assessing an AI solution in medical imaging, allowing all stakeholders to conduct relevant discussions with manufacturers and reach an informed decision as to whether to purchase an AI commercial solution for imaging applications. • Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services.

Diagnosis of heart failure with preserved ejection fraction (HFpEF) is associated with certain difficulties since many patients with HFpEF have a slight left ventricular diastolic dysfunction and normal filling pressure at rest. Diagnosis of HFpEF is improved by using diastolic transthoracic stress-echocardiography with dosed exercise (or diastolic stress test), which allows detection of increased filling pressure during the exercise. The present expert consensus explains the requirement for using the diastolic stress test in diagnosing HFpEF from clinical and pathophysiological standpoints; defines indications for the test with a description of its methodological aspects; and addresses issues of using the test in special patient groups.

Performing systematic reviews is a time-consuming and resource-intensive process.
We investigated whether a machine learning system could perform systematic reviews more efficiently.
All systematic reviews and meta-analyses of interventional randomized controlled trials cited in recent clinical guidelines from the American Diabetes Association, American College of Cardiology, American Heart Association (2 guidelines), and American Stroke Association were assessed. After reproducing the primary screening data set according to the published search strategy of each, we extracted correct articles (those actually reviewed) and incorrect articles (those not reviewed) from the data set. These 2 sets of articles were used to train a neural network-based artificial intelligence engine (Concept Encoder, Fronteo Inc). The primary endpoint was work saved over sampling at 95% recall (WSS@95%).
Among 145 candidate reviews of randomized controlled trials, 8 reviews fulfilled the inclusion criteria. For these 8 reviews, the machine learning system significantly reduced the literature screening workload by at least 6-fold versus that of manual screening based on WSS@95%. When machine learning was initiated using 2 correct articles that were randomly selected by a researcher, a 10-fold reduction in workload was achieved versus that of manual screening based on the WSS@95% value, with high sensitivity for eligible studies. The area under the receiver operating characteristic curve increased dramatically every time the algorithm learned a correct article.
Concept Encoder achieved a 10-fold reduction of the screening workload for systematic review after learning from 2 randomly selected studies on the target topic. However, few meta-analyses of randomized controlled trials were included. Concept Encoder could facilitate the acquisition of evidence for clinical guidelines.

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