How to Make the Best Use of the Workload Indicators of Staffing Needs Method in Determining the Proportion of Time Spent in Each of the Workload Components and Its Implication in Decision Making: the Experience of the Sultanate of Oman

BackgroundThe Ministry of Health in the Sultanate of Oman decided to have better distribution of the health workforce among all health facilities through evidenced-based staffing norms. Four directorates worked together to develop the staffing norms through making use of the workload indicators of staffing needs (WISN) method. The aim of this study is to describe the process of applying the WISN method in Primary Health Care institutions and how to make the best use of method in determining the proportion of time spent in each of the workload components and its implication in decision making. MethodsThe WISN was applied for five priority categories namely doctors, nurses, pharmacists, laboratory technicians, and radiology technicians at PHC institutions. The WISN ratio has been translated into workload pressure as a percentage through applying the formula [workload pressure as % (in case of shortage) = (1 - WISN ratio) x 100%]. While the proportion of time spent in each of the workload components was calculated through making use of the category allowance standard, the individual allowance standard to determine the time spent in support and additional activities. The sum is subtracted from 100% to give the time spent in the health service activities.ResultsDetermining the workload pressure as a percent and its interpretation is based on the fact that one cadre or as a group can bear up to 10% of extra workload. Thus, managers can undertake sensible short-term arrangements or decisions in redistributing the cadres among the health facilities on expectation of deploying more staff. DiscussionCareful and detailed analysis of the proportion of time spent in each of the workload components will allow to have better understanding of the context and dynamics of work. ConclusionDecision makers and planners can undertake rational short-term decisions in redistributing the cadres among the health facilities based on the workload pressure. In addition, they can as well as easily decide on the optimal proportions of time for each staff category, and hence choose what activities and tasks to be shifted or delegated to other staff category.

Drug-Related Problems of Patients in Primary Health Care Institutions: A Systematic Review

Introduction: Drug-related problems (DRPs) are not only detrimental to patients' physical health and quality of life but also lead to a serious waste of health care resources. The condition of DRPs might be more severe for patients in primary health care institutions.Objective: This systematic review aims to comprehensively review the characteristics of DRPs for patients in primary health care institutions, which might help find effective strategies to identify, prevent, and intervene with DRPs in the future.Methods: We searched three English databases (Embase, The Cochrane Library, and PubMed) and four Chinese databases (CNKI, CBM, VIP, and Wanfang). Two of the researchers independently conducted literature screening, quality evaluation, and data extraction. Qualitative and quantitative methods were combined to analyze the data.Results: From the 3,368 articles screened, 27 met the inclusion criteria and were included in this review. The median (inter-quartile range, IQR) of the incidences of DRPs was 70.04% (59%), and the median (IQR) of the average number of DRPs per patient was 3.4 (2.8). The most common type of DRPs was “treatment safety.” The causes of DRPs were mainly in the prescribing section, including “drug selection” and “dose selection”, while patients' poor adherence in the use section was also an important cause of DRPs. Risk factors such as the number of medicines, age, and disease condition were positively associated with the occurrence of DRPs. In addition, the medians (IQR) of the rate of accepted interventions, implemented interventions, and solved DRPs were 78.8% (22.3%), 64.15% (16.85%), and 76.99% (26.09%), respectively.Conclusion: This systematic review showed that the condition of DRPs in primary health care institutions was serious. In pharmaceutical practice, the patients with risk factors of DRPs should be monitored more closely. Pharmacists could play important roles in the identification and intervention of DRPs, and more effective intervention strategies need to be established in the future.

Opportunities for Competitive Advantage in the Value Created by Primary Health Care Institutions in Lithuania: Is Competitiveness Dependent on the Form of Ownership?

Background.In the face of competition between primary health care institutions (PHCIs), attempts are made to gain a competitive advantage in the market by creating greater value for patients. Applying and developing the professional skills of medical staff (family physicians and nurses) is important both in providing value to patients and in pursuit of the competitiveness of the institution. Little research has been conducted on whether the form of a PHCI’s ownership is a factor of its competitive advantage. The aim of the study.To determine opportunities for competitive advantage in the management of value creation in public and private primary health care institutions by using the method of focus group discussion with managers.Methods.Focus group discussions were held in 10 Lithuanian counties, and 10 focus group sessions were carried out overall. A total of 48 primary health care managers were interviewed.Results.The competitive advantage of a PHCI depends on its form of ownership, which makes for unfair competitive conditions. A competitive advantage is created by factors such as: the variety of services available; the health care policy in action; the function of the manager; the professionalism of the staff; and the location of the institution. Medical staff have the same opportunities to express and develop their professional skills in public and private PHCIs, but private institutions attract the most skilled staff because they have the resources to increase their motivation.Conclusions.The managers of PHCIs indicated that the competitive advantage depends on the form of ownership. In management science this study and the results can be a basis for the health care reform development and the foundation for new theories.

The effect of population distribution measures on evaluating spatial accessibility of primary health-care institutions: A case study from China

Improvement of the equality of geographical allocation of limited health-care resources requires an accurate evaluation of spatial accessibility of the facilities. The adoption of appropriate population distribution measures is one of the leading factors affecting such an evaluation. Using primary health-care institutions in Hainan, China as an example, this study aimed to explore the disparities embedded in spatial accessibility evaluations based on six common measures of population distribution, namely community/ village population (VillagePop), average population distribution (AveragePop), population distribution by night-time light intensity (NighttimelightPop) together with the public population databases LandScan, WorldPop and PoiPop for construction of the weights. The enhanced two-step floating catchment area method, two-way analysis of variance (ANOVA), Dunnett test, root mean square error and the mean absolute error were employed to assess and compare spatial accessibilities based on these different population distribution measures. The spatial accessibility of primary health-care institutions in Hainan was found to vary when plotted using the various population distribution measures mentioned. As indicated by the statistical outcomes of both ANOVA and the Dunnett test, using the spatial accessibility calculated by VillagePop as reference, those calculated by AveragePop and PoiPop were found to be significantly different. In addition, the spatial accessibilities calculated by AveragePop and PoiPop demonstrated higher error rates in the identification of underserved areas compared with the reference. Considering the limitations of public population databases, the adoption of night-time light data is highly recommended for estimating population distribution in the absence of high-resolution data.

Automatic detection of pituitary microadenoma from magnetic resonance imaging using deep learning algorithms

Pituitary microadenoma (PM) is often difficult to detect by MR imaging alone. We employed a computer-aided PM diagnosis (PM-CAD) system based on deep learning to assist radiologists in clinical workflow. We enrolled 1,228 participants and stratified into 3 non-overlapping cohorts for training, validation and testing purposes. Our PM-CAD system outperformed 6 existing established convolutional neural network models for detection of PM. In test dataset, diagnostic accuracy of PM-CAD system was comparable to radiologists with > 10 years of professional expertise (94% versus 95%). The diagnostic accuracy in internal and external dataset was 94% and 90%, respectively. Importantly, PM-CAD system detected the presence of PM that had been previously misdiagnosed by radiologists. This is the first report showing that PM-CAD system is a viable tool for detecting PM. Our results suggest that PM-CAD system is applicable to radiology departments, especially in primary health care institutions.

The perspectives of patients with type 2 diabetes mellitus on marketing mix elements in primary health care: a quantitative study from Lithuania

Background: A competitive advantage in health care institutions can be cultivated by marketing activities and value creation for patients with chronic diseases in primary health care. Type 2 diabetes mellitus (T2DM) is a major challenge in primary health care, as managing risk factors and managing patient knowledge can help to prevent a number of major of complications. This study reveals the expectations and attitudes of patients with T2DM regarding marketing mix elements in the management of their condition. Aim of the study: To explore the perspectives of patients with T2DM on marketing mix elements in the primary health care institutions of Lithuania. Materials and methods: The design of the national study was based on a survey of patients with T2DM that was conducted after consultation with a family physician in primary health care institutions in Lithuania. The survey was conducted from October 2017 to January 2018, and involved 510 patients with T2DM. Data analysis included factor analysis and linear logistic regression. A hypothetical model was built, defining the relationships between marketing mix elements and both perceived value (emotional, functional, and social) and satisfaction with primary health care services. Results: The marketing mix element of ‘Service’ is statistically significantly dependent on the gender of the respondents, and is expressed more frequently by women (rcr = 0.118, P = 0.007). The occupation of respondents with T2DM (rcr = 0.151, P = 0.009) and affiliation to primary health care institution (rcr = 0.091, P = 0.040) statistically positively affect the marketing mix element of ‘Price’. The marketing mix elements of ‘Promotion’ and ‘People’ do not statistically significantly depend on the sociodemographic characteristics of the respondents. Only a weak correlation between the sociodemographic characteristics of the respondents and the marketing element of ‘Place’ was found. The ‘Process’ element is statistically significantly more relevant to patients with an average monthly income of €350 (rcr = 0.104, P = 0.019). The element of ‘Physical evidence’ is more statistically significantly related to respondents with an average monthly income of €350 (rcr = 0.092, P = 0.038). Conclusions: Marketing mix analysis provides information about patients’ expectations of primary health care services and identifies areas of improvement for the health services provided by primary health care institutions. The competitiveness of primary health care services is strengthened by enhancing value for patients, by using elements of the health care marketing, and by increasing patient satisfaction.

Automatic detection of pituitary microadenoma from magnetic resonance imaging using deep learning algorithms

The risks of misdiagnosed pituitary microadenoma is high. We designed a convolutional neural network (CNN) based computer-aided diagnosis (CAD) system to retrospectively diagnose patients with pituitary microadenoma. A total 5,540 pituitary magnetic resonance (MR) images from 1,108 participants were recruited. MRI images were randomly stratified into non-overlapping cohorts (training set, validation set and test set) to establish five different CNN models. The best CNN model is the ResNet with a diagnostic accuracy of 94%, which outperforms the diagnosis accuracy of our radiologists (64%-85%). The accuracy of our CAD system is further confirmed in additional MR datasets. The diagnostic accuracy of our ResNet model is comparable to the proficiency of a radiologist with 5-10 years’ experience. In summary, this is the first report showing that the CAD system is a viable tool for diagnosing pituitary microadenoma. CAD system is applicable to radiology departments, especially in primary health care institutions.